入学者選抜研究に関する調査室報告書２

大学入試センターシンポジウム２０１４

大学入試の日本的風土は変えられるか平成 27 年（2015（2015 年）3）3 月
独立行政法人大学入試センター入学者選抜研究に関する調査室⊂❧⾜ᨻἲே኱Ꮫධヨࢭࣥࢱ࣮ධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊሗ࿌᭩㸰኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘ ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿

┠ḟ‫࣭࣒ࣛࢢࣟࣉۍ‬ሗ࿌⪅ࣉࣟࣇ࢕࣮ࣝ ͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐

1‫ۍ‬㛤఍ᣵᣜ

ᒣᮏᘅᇶ኱Ꮫධヨࢭࣥࢱ࣮⌮஦㛗 ͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐

3‫ۍ‬㊃᪨ㄝ᫂

Ⲩ஭ඞᘯ኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲⤫ᣓᐁ ͐͐͐͐͐͐͐͐͐͐͐͐

5ධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗‫ۍ‬ᇶㄪㅮ₇

₇㢟ࠕPISA㸸ᢤᮏⓗᨵ㠉࡬ࡢᣮᡓ ̿ఱࢆ࡝࠺
ࡢ࠿̿
ᒣᮏවኴᮁ

ᐃࡋ࡝࠺ホ౯ࡋ࡚࠸ࡃ

Major innovations in PISA 2015 and beyondࠖ ͐͐͐͐͐͐͐͐ 9
Deputy Director of the Center of Global Assessment, Principal Research
Scientist of ETS㸦Educational Testing Service, USA.㸧

‫ۍ‬ሗ࿌㸯

₇㢟ࠕヨ㦂ࡢ᪥ᮏⓗ㢼ᅵࠖ ͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐ 51๓ᕝ┾୍‫ۍ‬ሗ࿌㸰

ᮾிᕤᴗ኱Ꮫ኱Ꮫ㝔♫఍⌮ᕤᏛ◊✲⛉ᩍᤵ₇㢟ࠕධヨ㑅ᢤࡢ༡㢼ཎᮅ࿴‫ۍ‬ሗ࿌㸱

ᮾி኱Ꮫ኱Ꮫ㝔ᩍ⫱Ꮫ◊✲⛉㛗

₇㢟ࠕࢭࣥࢱ࣮ヨ㦂࡛ఱࡀ኱ὠ㉳ኵ‫ۍ‬ሗ࿌㸲

ᐃၥ㢟ࠖ ͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐ 61ࡽࢀࡿࡢ࠿㸽ࠖ ͐͐͐͐͐͐͐͐͐͐͐͐͐ 75

኱Ꮫධヨࢭࣥࢱ࣮◊✲㛤Ⓨ㒊㛗

₇㢟ࠕ㧗኱᥋⥆ࡢࡇࢀ࠿ࡽࠖ ͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐ 87ᮧୖ㝯୰ி኱Ꮫ⌧௦♫఍Ꮫ㒊㛗‫ ۍ‬CBT ࡢࢧࣥࣉ࣭ࣝࢹࣔࣥࢫࢺ࣮ࣞࢩࣙࣥ ͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐ 99኱ஂಖᬛဢ኱Ꮫධヨࢭࣥࢱ࣮◊✲㛤Ⓨ㒊ຓᩍ‫ࣥࣙࢩࢵ࢝ࢫ࢕ࢹࣝࢿࣃۍ‬ࢥࣥࣆ࣮ࣗࢱ࣭ᩍ⫱ᐃ࣭኱Ꮫධヨ ͐͐͐͐͐͐͐͐͐ 107

Kentaro Yamamoto Ặ ࣭ ๓ᕝ┾୍ Ặ ࣭ ༡㢼ཎᮅ࿴ Ặ ࣭኱ὠ㉳ኵ Ặ ࣭ ᮧୖ

㝯 Ặ

⥲ྜྖ఍

኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲๪⤫ᣓᐁ኱㷁㞝సධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗⿵బ‫ۍ‬኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘  ᚋグ ͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐͐ 117኱㷁㞝స኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲๪⤫ᣓᐁධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗⿵బ‫ۍ‬ཧ⪃㈨ᩱ

⊂❧⾜ᨻἲே኱Ꮫධヨࢭࣥࢱ࣮ධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊ୺ദ
ᚋ᥼㸸ᩥ㒊⛉Ꮫ┬࣭᪥ᮏࢸࢫࢺᏛ఍

኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘ 2014኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿
ࣉࣟࢢ࣒ࣛ‫۔‬㛤ദ᪥᫬

㸸

2014 ᖺ 11 ᭶ 29 ᪥㸦᪥㸧

‫۔‬఍

㸸

ᮾிᕤᴗ኱Ꮫ኱Ꮫ㝔♫఍⌮ᕤᏛ◊✲⛉ࢹࢪࢱࣝከ┠ⓗ࣮࣍ࣝሙ㛤ሙ 12 ᫬༙࣭㛤ጞ 13 ᫬‫ۍ‬㛤఍ᣵᣜ

ᒣᮏᘅᇶ኱Ꮫධヨࢭࣥࢱ࣮⌮஦㛗‫ۍ‬㊃᪨ㄝ᫂

Ⲩ஭ඞᘯ኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲⤫ᣓᐁධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗‫ ۍ‬ᇶ ㄪ ㅮ₇₇ 㢟 ࠕPISA㸸 ᢤ ᮏⓗ ᨵ 㠉 ࡬ ࡢᣮ ᡓ ̿ ఱ ࢆ ࡝ ࠺࠿̿ᐃࡋ࡝ ࠺ホ ౯ࡋ ࡚࠸ࡃ ࡢ

Major innovations in PISA 2015 and beyondࠖᒣᮏවኴᮁ

Deputy Director of the Center of Global Assessment, Principal Research
Scientist of ETS㸦Educational Testing Service, USA.㸧

‫ۍ‬ሗ࿌㸯

₇㢟ࠕヨ㦂ࡢ᪥ᮏⓗ㢼ᅵࠖ๓ᕝ┾୍‫ۍ‬ሗ࿌㸰

ᮾிᕤᴗ኱Ꮫ኱Ꮫ㝔♫఍⌮ᕤᏛ◊✲⛉ᩍᤵ₇㢟ࠕධヨ㑅ᢤࡢ༡㢼ཎᮅ࿴‫ۍ‬ሗ࿌㸱

ᮾி኱Ꮫ኱Ꮫ㝔ᩍ⫱Ꮫ◊✲⛉㛗

₇㢟ࠕࢭࣥࢱ࣮ヨ㦂࡛ఱࡀ኱ὠ㉳ኵ‫ۍ‬ሗ࿌㸲

ᐃၥ㢟ࠖࡽࢀࡿࡢ࠿㸽ࠖ኱Ꮫධヨࢭࣥࢱ࣮◊✲㛤Ⓨ㒊㛗

₇㢟ࠕ㧗኱᥋⥆ࡢࡇࢀ࠿ࡽࠖ
ᮧୖ㝯୰ி኱Ꮫ⌧௦♫఍Ꮫ㒊㛗‫ ۍ‬CBT ࢧࣥࣉࣝࡢࢹࣔ
኱ஂಖᬛဢ኱Ꮫධヨࢭࣥࢱ࣮◊✲㛤Ⓨ㒊ຓᩍ‫ࣥࣙࢩࢵ࢝ࢫ࢕ࢹࣝࢿࣃۍ‬ࢥࣥࣆ࣮ࣗࢱ࣭ᩍ⫱ᐃ࣭኱ᏛධヨKentaro Yamamoto Ặ ࣭ ๓ᕝ┾୍ Ặ ࣭ ༡㢼ཎᮅ࿴ Ặ ࣭኱ὠ㉳ኵ Ặ ࣭ ᮧୖ

㝯 Ặ

⥲ྜྖ఍

኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲๪⤫ᣓᐁ኱㷁㞝సධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗⿵బ－1－‫ڦ‬኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘ ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘ᴫせ኱Ꮫධヨࡢᢤ ᮏ ⓗ ࡞ ኚ 㠉 ࢆ ࡵࡊ ࡋ ࡚ ࠊ ᩍ ⫱ ෌ ⏕ ᐇ⾜఍ ㆟ࠊ ୰ኸᩍ ⫱ᑂ ㆟఍㧗 ኱᥋ ⥆≉ู 㒊఍ ➼ࢆ ୰ᚰ࡟㆟ㄽࡀ✚ࡳ㔜 ࡡ ࡽ ࢀ ࡚ ࡁ ࡚ ࠸ࡲ ࡍ ࠋ ࡑ ࡇ ࡛ ࡣ ࠊ 」ᩘᅇ ཷ㦂 ࠊ▱㆑ ά⏝ ຊ➼ࢆ 㔜ど ࡋࡓከ 㠃ⓗ ホ౯ ࠊྜᩍ ⛉ ࣭ ⛉ ┠ ᆺ ヨ 㦂 ࡢ 㛤 Ⓨ ࠊ 100 Ⅼ ‶ Ⅼ ࡢ ⣲ Ⅼ ࡟ ௦ ࢃ ࡿ ẁ 㝵 ู ホ ᐃ ࡢ ฼ ⏝ ࠊ ࢥ ࣥ ࣆ ࣗ ࣮ ࢱ ฼ ⏝ 㸦 CBT㸸
Computer Based Testing㸧➼ࠎࡢ᪂ࡓ࡞ධヨࡢᥦ᱌ࡀ࡞ࡉࢀ࡚ࡁ࡚࠸ࡲࡍࠋ࠸ࡎࢀࡶ㔜せ࡞ᥦ᱌ࠊㄽⅬ࡛ࡍࡀࠊࡑࡇ࡟ࡣ ᐃ ⌮ ㄽ ⓗ ࡟ ከ ࡃࡢ ㄢ 㢟 ࡀ ௜ 㝶 ࡋ ࡚ ࠸ࡲࡍ ࠋࡲ ࡓࠊ࡜ ࡾࢃ ࡅࠊࢃ ࡀᅜ ࡟࠾࠸ ࡚ࡣ ࠊࡑ ࢀࡽࡢᨵኚࡀᚲࡎࡋ ࡶ ෇ ⁥ ࡟ 㐍 ࡲ ࡞࠸ ࡜ ᛮ ࢃ ࢀ ࡿ ࢸ ࢫ ࢺᩥ໬ ࡀ᰿ ᙉࡃ₯ ࢇ࡛ ࠸ࡲࡍ ࠋࡑ ࡇ࡛ࠊ ᩍ⫱ ᐃ ࡢᑓ
㛛ⓗどⅬ࠿ࡽࠊ ኱ Ꮫ ධ ヨ ࡢ ࡼ ࡾࡼ ࠸ ᨵ 㠉 ࡢ ࡓ ࡵ ࡟ ࡝ࡢࡼ ࠺࡞ ၥ㢟ࡀ ලయ ⓗ࡟ṧ ࡉࢀ ࡚࠸ࡿ ࠿ࢆ ᩚ⌮ ࡍࡿ࡜ඹ࡟ࠊࡑࢀࡽ ࡢ ඞ ᭹ ࡢ ࡓ ࡵ ࡟࡝ ࡢ ࡼ ࠺ ࡞ ᑐ ฎ ࡀ ᚲせ࡜ ࡉࢀ ࡿࡢ࠿ ࡟ࡘ ࠸࡚ࠊ ࡇࡢ ࢩ࣏ࣥ ࢪ࢘ ࣒ࢆ ㏻ࡌ࡚ᾋࡁ᙮ࡾ࡟ࡋ࡚࠸ࡁࡲࡍࠋ‫ ڦ‬ㅮ₇⪅ࣉࣟࣇ࢕࣮ࣝ
ᇶㄪㅮ₇㸸ᒣᮏවኴᮁ 㸦Kentaro Yamamoto㸧
Deputy Director of the Center of Global Assessment, Principal Research Scientist of ETS㸦Educational Testing Service㸧ࠋ1987 ᖺ࢖ࣜࣀ࢖኱Ꮫ኱Ꮫ㝔ᩍ⫱ᚰ⌮Ꮫ◊✲⛉࡛ Ph.D.ྲྀᚓࠋྠᖺࠊETS ࡢᚰ⌮ᐃᏛ࣭⤫ィᏛ㒊㛛࡟◊✲ဨ࡜ࡋ࡚㉱௵ࠊ2004ᖺࡼࡾ⌧⫋ࠋ2006 ᖺࡼࡾࠊOECD ࡢ PISAࠊPIAAC ➼࡟ࡑࡢᑓ㛛ⓗ❧ሙ࠿ࡽ㛵ࢃ
ࡾࠊ2015 ᖺࠊ2018 ᖺࡢ PISA ࡢᚰ⌮ᐃᏛศ㔝࠿ࡽࡢᢸᙜ㈐௵⪅ࢆົࡵ࡚࠸ࡿࠋ

ሗ࿌㸯㸸๓ᕝ┾୍ 㸦ࡲ࠼࠿ࢃ࣭ࡋࢇ࠸ࡕ㸧ᮾ ி ᕤ ᴗ ኱ Ꮫ ኱ Ꮫ 㝔 ♫ ఍ ⌮ ᕤ Ꮫ ◊ ✲ ⛉ ᩍ ᤵ ࠋ 1984 ᖺ ᮾ ி ኱ Ꮫ ኱ Ꮫ 㝔 ே ᩥ ⛉Ꮫ ◊ ✲ ⛉ ᚰ ⌮ Ꮫᑓ 㛛 ㄢ ⛬ ༤ ኈ ㄢ⛬ ༢ ఩ ྲྀ ᚓ ‶ ᮇ㏥ Ꮫ ࠊ ࢔ ࢖ ࢜ ࣡኱ Ꮫ ༤ ኈ ㄢ⛬ಟ஢ࠊ Ph.D. 1985 ᖺ࢔࢖࢜࣡኱Ꮫ⤫ィᏛ⛉ᐈဨຓᩍᤵ 1986 ᖺࡼࡾ኱Ꮫධ
ヨ ࢭ ࣥ ࢱ ࣮ ◊ ✲ 㛤 Ⓨ 㒊 ࡢ ຓ ᩍ ᤵ ࠊ ᩍ ᤵ ࢆ ⤒ ࡚ ࠊ 2001 ᖺ ࡼ ࡾ ⌧ ⫋ ࠋ ᑓ 㛛 ࡣ ⤫ィ⛉Ꮫࠊࢸࢫࢺ⌮ㄽࠋ᪥ᮏࢸࢫࢺᏛ఍⌮஦ࠋሗ࿌㸰㸸༡㢼ཎᮅ࿴ 㸦ࡣ࠼ࡤࡽ࣭࡜ࡶ࠿ࡎ㸧ᮾ ி ኱ Ꮫ ኱ Ꮫ 㝔 ᩍ ⫱ Ꮫ ◊ ✲ ⛉ ᩍ ᤵ ࠋ 2013 ᖺ ᗘ ࡼ ࡾ ᩍ ⫱ Ꮫ ◊ ✲ ⛉ 㛗 ࣭ ᩍ ⫱ Ꮫ㒊㛗ࠋ࢔࢖࢜࣡኱Ꮫ኱Ꮫ㝔༤ኈㄢ⛬ಟ஢ࠊPh.D. 1982 ᖺࡼࡾ᪂₲኱Ꮫᩍ⫱Ꮫ㒊ㅮ ᖌࠊ ຓᩍᤵ ࠊ1993 ᖺᮾி ኱Ꮫ ᩍ⫱Ꮫ㒊ຓᩍᤵࠊ2002 ᖺࡼࡾྠᩍᤵࠋᑓ
㛛ࡣᚰ⌮⤫ィᏛࠊᚰ⌮ᐃᏛࠋ᪥ᮏࢸࢫࢺᏛ఍๪⌮஦㛗ࠋሗ࿌㸱㸸኱ὠ㉳ኵ 㸦࠾࠾ࡘ࣭ࡓࡘ࠾㸧኱Ꮫධヨࢭࣥࢱ࣮◊✲㛤Ⓨ㒊㛗ࠋ1983 ᖺ໭ᾏ㐨኱ᏛᩥᏛ◊✲⛉ಟኈㄢ⛬㸦ᚰ⌮Ꮫᑓᨷ㸧ಟ஢ࠋẸ㛫௻ᴗ໅ົࠊ໭ᾏ㐨኱ᏛᩥᏛ◊✲⛉ᩍᤵࢆ࡬࡚ 2003 ᖺᗘ ࡼ ࡾ ኱ Ꮫ ධ ヨ ࢭ ࣥ ࢱ ࣮ ◊ ✲ 㛤 Ⓨ 㒊 ᩍ ᤵ ࠋ 2014 ᖺ ᗘ ࡼ ࡾ ⌧ ⫋ ࠋ ᑓ 㛛 ࡣ ᚰ ⌮⤫ィᏛࠋሗ࿌㸲㸸ᮧୖ㝯 㸦ࡴࡽ࠿ࡳ࣭ࡓ࠿ࡋ㸧୰ி኱Ꮫ⌧௦♫఍Ꮫ㒊㛗ࠋᮾிᩍ⫱኱Ꮫ኱Ꮫ㝔ᩍ⫱Ꮫ◊✲⛉༤ኈㄢ⛬୰㏥ࠋ༤ ኈ 㸦 ᚰ ⌮Ꮫ ࣭ ⟃ Ἴ ኱ Ꮫ㸧ࠋ ྡ ྂ ᒇ኱ Ꮫ ᩍ ⫱ Ꮫ 㒊 ᩍ⫱ᚰ ⌮Ꮫ ⛉ຓᡭ ࣭ྠຓ ᩍᤵ࣭ྠ኱Ꮫ㝔ᩍ⫱Ⓨ㐩⛉Ꮫ◊✲⛉ᩍᤵࢆ⤒࡚ࠊ2007 ᖺᗘࡼࡾ୰ி኱Ꮫᩍᤵࠋ
2014 ᖺᗘࡼࡾ⌧⫋ࠋࡑࡢ㛫ࠊ2002 㹼 06 ᖺᗘ඲ᅜ኱ᏛධᏛ⪅㑅ᢤ◊✲㐃⤡༠㆟఍఍㛗ࠊ᪥ᮏ␃Ꮫヨ㦂ᐇ᪋ጤဨ࡞࡝ࢆṔ௵ࠋᑓ㛛ࡣィ㔞ᚰ⌮Ꮫࠋ

－2－

㛤఍ᣵᣜ
⊂❧⾜ᨻἲே኱Ꮫධヨࢭࣥࢱ࣮⌮஦㛗ᒣᮏᘅᇶ‫ྖۑ‬఍㸦኱ሯ㸧኱ኚ࠾ᚅࡓࡏ࠸ࡓࡋࡲࡋࡓࠋࡓࡔ࠸ࡲࡼࡾ኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࢘

࣒2014ࠕ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿ࠖࢆ㛤఍ࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋᮏ᪥ࡣ࠶࠸
࡟ࡃ㞵ᶍᵝ࡟࡞ࡾࡲࡋ࡚ࠊ࠾㊊ඖࡢᝏ࠸୰ࠊከࡃࡢⓙᵝ᪉࡟࠾㞟ࡲࡾ࠸ࡓࡔࡁࡲࡋ࡚ᮏᙜ࡟ ࠶ ࡾ ࡀ ࡜ ࠺ࡈ ࡊ ࠸ ࡲ ࡍࠋ 5᫬ ༙ࡲ ࡛ ࡢ 㛗 ୎ሙ ࡛ ࠶ ࡾ ࡲࡍ ࡅ ࢀ ࡝ ࡶ ࠊ࡝ ࠺ ࡒ ࡼ ࢁࡋ ࡃ ࠾ ௜ࡁྜ࠸ࡢ࡯࡝࠾㢪࠸࠸ࡓࡋࡲࡍࠋ⚾ࡣࠊᮏ᪥㐍⾜ࢆᢸᙜ࠸ࡓࡋࡲࡍ኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲๪⤫ᣓᐁࡢ኱ሯ㞝స࡜⏦ࡋࡲࡍࠋᮍ⇍࡛ࡣ࠶ࡾࡲࡍࡀࠊ㐍⾜ࢆົࡵࡉࡏ࡚࠸ࡓࡔࡁ
ࡲࡍࡢ࡛ࠊ᭱ᚋࡲ࡛ఱ࡜ࡒࡼࢁࡋࡃࡈ༠ຊࡢ࡯࡝࠾㢪࠸࠸ࡓࡋࡲࡍࠋ
ࡑࢀ࡛ࡣ᪩㏿ࠊ኱Ꮫධヨࢭࣥࢱ࣮⌮஦㛗ࠊᒣᮏᘅᇶࡼࡾࡈᣵᣜࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
‫ۑ‬ᒣᮏ⌮஦㛗ᮏ᪥ࡣࠊᖌ㉮ࢆ┠๓࡟ࡋࡓఱ࠿࡜ࡈከ

ᛁࡢ࡞࠿ࠊᅵ᭙᪥ࡢ༗ᚋ࡟඲ᅜࡼࡾከࡃࡢ᪉ࠎࡀࠊ኱
Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘࡟ࡈཧຍୗࡉ࠸ࡲࡋࡓࡇ
࡜ࢆࠊᚰࡼࡾᚚ♩⏦ࡋୖࡆࡲࡍࠋඛ 㐌 ࠊ 11᭶ 20᪥ ࡢ ୰ ኸ ᩍ ⫱ ᑂ ㆟ ఍ ࡢ ⥲ ఍ ࡟ ࠾ ࡁ ࡲ ࡋ࡚ ࠊࠕ ᪂ ࡋ ࠸ ᫬ ௦ ࡟ ࡩ ࡉ ࢃ ࡋ ࠸ 㧗 ኱ ᥋ ⥆ ࡢ ᐇ ⌧ ࡟ ྥ ࡅࡓ㧗➼Ꮫᰯᩍ⫱ࠊ኱Ꮫᩍ⫱ࠊ኱ᏛධᏛ⪅㑅ᢤࡢ୍యⓗᨵ㠉࡟ࡘ࠸࡚ࠖ࡜࠸࠺㛗࠸ᶆ㢟ࡢ⟅⏦᱌ࡀᢎㄆࡉࢀࠊ
᮶᭶ୗ᪪ࠊᩥ㒊⛉Ꮫ኱⮧࡟⟅⏦ࡉࢀࡿணᐃ࡜࡞ࡗ࡚࠸
ࡲࡍࠋⓙࡉࢇ᪉ࡣ༑ศࡈᏑࡌࡢࡇ࡜࡜ࡣᛮ࠸ࡲࡍࡀࠊ
ᖹᡂ24ᖺ8᭶࡟ᩥ㒊⛉Ꮫ኱⮧࠿ࡽࠕ኱ᏛධᏛ⪅㑅ᢤࡢᨵၿࢆࡣࡌࡵ࡜ࡍࡿ㧗➼Ꮫᰯᩍ⫱࡜኱Ꮫᩍ⫱ࡢ෇⁥࡞᥋⥆࡜㐃ᦠࡢᙉ໬ࡢࡓࡵࡢ᪉⟇࡟ࡘ࠸࡚ࠖࡀㅎၥࡉࢀࠊ⥲఍┤ᒓࡢ㧗኱᥋⥆≉ู㒊఍ࡀタ⨨ࡉࢀࡲࡋࡓࠋࡇࡢ㛫ࠊ᫖ᖺࡢ10᭶࡟ࡣ ᨻ ᗓ ࡢ ᩍ ⫱ ෌ ⏕ ᐇ ⾜ ఍ ㆟ ࡢ ➨ 4ḟ ᥦ ゝ ࡀ ࠶ ࡾ ࠊ ྜ ィ 21ᅇ ࡢ ≉ ู 㒊 ఍ࡀ 㛤 ദ ࡉ ࢀ࡚ ࠊ ࡇ ࡢ⟅⏦᱌࡟⮳ࡗࡓ࡜ࡇࢁ࡛ࡍࠋ㧗➼Ꮫᰯᩍ⫱ࠊ኱Ꮫᩍ⫱ࡢ࠶ࡾ᪉࡜୪ࢇ࡛ࠊ኱ᏛධᏛ⪅㑅ᢤࡢ࠶ࡾ᪉ࢆ୍యⓗ࡟ᨵ㠉ࡍࡿࡇ࡜ࡀồࡵࡽࢀ࡚࠸ࡲࡍࠋ≉ู㒊఍࡟࠾ࡁࡲࡋ࡚ࡣࠊ࡜ࡾࢃ
ࡅධヨᨵ㠉࡟㆟ㄽࡀ㞟୰ࡋࡓࡼ࠺࡛ࠊᮏ᪥ࡶࡇ࠺ࡋ࡚ከࡃࡢ᪉ࠎ࡟࠾㞟ࡲࡾ࠸ࡓࡔࡃ࡞࡝ࠊ
ධヨᨵ㠉ࡣᙜ஦⪅ࡢࡳ࡞ࡽࡎࠊ♫఍ⓗ࡟ࡶࡁࢃࡵ࡚㛵ᚰࡢ㧗࠸ࡇ࡜ࡀఛ࠼ࡲࡍࠋ
⟅⏦᱌࡟┒ࡾ㎸ࡲࢀ࡚࠸ࡿධヨᨵ㠉᱌ࡣከᒱ࡟ࢃࡓࡗ࡚࠸ࡲࡍࠋ᪂ࢸࢫࢺ࡜ࡋ࡚ࠊ㧗➼Ꮫᰯᇶ♏Ꮫຊࢸࢫࢺࠊ኱ᏛධᏛᕼᮃ⪅Ꮫຊホ౯ࢸࢫࢺ࡜࠸࠺ྡ๓ࡢ஧ࡘࡢ኱つᶍ࡞ࢸࢫࢺ
ࡢᐇ᪋ࠊࡑࡋ࡚ධᏛ⪅㑅ᢤ࡟㛵ࢃࡿᨭ᥼ࡸࡑࡢㄪᰝ◊✲࣭ேᮦ㣴ᡂ࡞࡝ࡀ࠶ࡆࡽࢀࠊࡇࢀ
ࡽࢆ඲࡚ࠊ኱Ꮫධヨࢭࣥࢱ࣮ࢆᢤᮏⓗ࡟ᨵ⤌ࡋࡓ᪂ࢭࣥࢱ࣮࡛⾜࠺ࡇ࡜࡜ࡉࢀ࡚࠸ࡲࡍࠋ
ࡑࢀࡔࡅ࡟⚾࡝ࡶ࡜࠸ࡓࡋࡲࡋ࡚ࡶࠊ㌟ࡢᘬࡁ⥾ࡲࡿᛮ࠸࡛ࡇࡢධヨᨵ㠉ࡢືྥࢆぢᏲࡗ࡚ࡁ࡚࠸ࡿ࡜ࡇࢁ࡛ࡍࠋ
ゝ࠺ࡲ࡛ࡶ࡞ࡃࠊධᏛ⪅㑅ᢤࡣࠊ኱Ꮫᩍ⫱࡬ࡢ㐺ᛂຊ࡛࠶ࡿ࡜࠿ࠊࡑࢀ࡟ᚲせ࡞Ꮫຊ࡞
࡝࡟ࡘ࠸࡚

ᐃࡋࠊࡑࡢ

ᐃ⤖ᯝ࡟ᇶ࡙࠸࡚㑅ᢤࡀ⾜ࢃࢀࡿ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࡀࠊධ

－3－ヨᨵ㠉ࢆ㆟ㄽࡍࡿ࡟࠶ࡓࡗ࡚ࡣࠊᩍ⫱ᐃㄽࡢᑓ㛛ⓗぢᆅ࠿ࡽࡢ༑ศ࡞㆟ㄽࡶᚲせ࡛࠶ࡿ࡜⪃࠼࡚࠸ࡲࡍࠋࡑࡇ࡛ࠊᨵࡵ࡚ࠊᮏ᪥ࡣࠊᩍ⫱ࡲࡾ࠸ࡓࡔࡁࠊ௒ᅇࡢධヨᨵ㠉࡟㛵ࢃࡿᐃࢆᑓ㛛࡜ࡋ࡚࠾ࡽࢀࡿඛ⏕᪉࡟࠾㞟ᐃㄽⓗㄢ㢟࡟ࡘ࠸࡚ྲྀࡾୖࡆ࡚࠸ࡓࡔࡃࡇ࡜࡟

࠸ࡓࡋࡲࡋࡓࠋ
࡜ࡾࢃࡅࠊᇶㄪㅮ₇ࢆ࠾㢪࠸ࡋ࡚࠸ࡲࡍᒣᮏවኴᮁඛ⏕ࡣࠊ࢔࣓ࣜ࢝ࡢࣉࣜࣥࢫࢺࣥ࡟
࠶ࡾࡲࡍETS㸦Educational Testing Service㸧ࡼࡾࡣࡿࡤࡿ࠾㉺ࡋ࠸ࡓࡔࡁࡲࡋࡓࠋ⌧ᅾ୰ᚰ࡜ ࡞ ࡗ ࡚ ᢸ ᙜ ࡉ ࢀ ࡚ ࠾ ࡽ ࢀ ࡿ PISAࡢ ື ྥ ࡟ ࡘ ࠸ ࡚ ㈗ 㔜 ࡞ ࠾ ヰ ࢆ ఛ ࠺ ᶵ ఍ ࢆ ᚓ ࡿ ࡇ ࡜ ࡀ ࡛ࡁ ࡲ ࡍ ࡇ ࡜ ࢆᚰ ࡼ ࡾ ឤ ㅰ࠸ ࡓ ࡋ ࡲ ࡍ ࠋPISAࡶ 3ᖺ ࡈ ࡜ ࡢᐇ ᪋ ࡜ ࡞ ࡗ ࡚࠸ ࡿ ࡜ ࠸ ࠺ࡇ ࡜ ࡛ ࠊ࠸ࢁ࠸ࢁ࡞ᨵ㠉࡟ྲྀࡾ⤌ࡲࢀ࡚࠸ࡿࡼ࠺࡛ࡋ࡚ࠊࡑࢀࢆᐇ㝿࡟ඛᑟࡉࢀ࡚࠸ࡿඛ⏕ࡢ࠾ヰࡣ࡜࡚ࡶཧ⪃࡟࡞ࡿࡇ࡜࡜ᴦࡋࡳ࡟ࡋ࡚࠾ࡾࡲࡍࠋ
⟅⏦࡟┒ࡾ㎸ࡲࢀ࡚࠸ࡿධヨᨵ㠉ࡢᥦ᱌ࡢ᪉ྥᛶࡣከᩘࡢඹឤࢆᚓࡿෆᐜ࡛ࡣ࠶ࢁ࠺࠿
࡜ᛮ࠸ࡲࡍࡀࠊᑓ㛛ⓗぢᆅ࠿ࡽᐃㄽⓗ࡞ㄢ㢟ࡀ✺ࡁࡘࡅࡽࢀࡲࡍ࡜ࠊၥ㢟Ⅼࡶከࠎᾋࡁ

᙮ࡾ࡟ࡉࢀ࡚ࡲ࠸ࡾࡲࡍࠋᡃࡀᅜࡢධヨࡢᅾࡾ᪉࡟ࡘ࠸࡚ࡢࡼࡾලయⓗ࡞㆟ㄽ࡟⤖ࡧ௜ࡅ࡚࠸ࡃࡓࡵ࡟ࡶࠊࡑ࠺࠸ࡗࡓㄢ㢟ࢆඹ᭷ࡍࡿࡇ࡜ࡀᚲせ࡛ࡍࠋᮏ᪥ࠊሗ࿌ࢆ࠾㢪࠸࠸ࡓࡋࡲࡋࡓ๓ᕝ┾୍ඛ⏕ࠊ༡㢼ཎᮅ࿴ඛ⏕ࠊᮧୖ㝯ඛ⏕ࡣࠊࡑࢀࡒࢀ
࡛ࡍࠋᐃㄽࡢ➨୍⥺ࡢඛ⏕᪉ᐃ⌮ㄽࡢ᭱ඛ➃ࡢどⅬ࠿ࡽࡢㄢ㢟ࢆ࠾ఏ࠼࠸ࡓࡔࡅࡿࡶࡢ࡜ᛮ࠸ࡲࡍࠋ≉࡟ࠊ⟅⏦᱌ ࡛ࡣCBT㸦Computer-Based

Testing㸧 ࡢᑟධ ࡞࡝ࡶồࡵࡽࢀ࡚࠸ࡲࡍࡀࠊࡑࢀࢆᐇ㝿࡟⾜࠺ࡓࡵ࡟ࡣࠊ௒ࡲ࡛࡜ࡣ඲ࡃ␗࡞ࡿヨ㦂࡟ᑐࡍࡿぢ᪉ࢆ⌮ゎࡋࠊ♫఍඲య࡛ඹ᭷ࡍࡿࡇ
࡜ࡶồࡵࡽࢀࡲࡍࠋࡍ࡞ࢃࡕࠊࡇࢀࡲ࡛㑅ᢤヨ㦂࡟ᑐࡋ࡚⚾ࡓࡕࡀᣢࡗ࡚࠸ࡿ㢼ᅵࢆኚ࠼࡚࠸ࡃࡇ࡜ࡀࠊධヨᨵ㠉ࡢ୍ࡘࡢせ௳࡟࡞ࡿ࡜࠸࠺ࡇ࡜ࢆࠊࢩ࣏ࣥࢪ࣒࢘ࢆ㏻ࡌ࡚ⓙࡉࡲ᪉࡜ඹ᭷ࡋ࡚࠸ࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ
⟅⏦࡛ㅻࢃࢀ࡚࠸ࡿࡇ࡜ࢆᐇ⌧࡟ྥࡅ࡚㐍ࡵ࡚࠸ࡃࡓࡵ࡟ࡣࠊ௒᪥ࡢࢩ࣏ࣥࢪ࣒࡛࢘㆟ㄽࡉࢀࡿ௨እ࡟ࡶከࡃࡢᅔ㞴࡞ㄢ㢟ࡀ࠶ࢁ࠺࠿࡜ᛮ࠸ࡲࡍࠋࡓࡔࠊ⚾ࡓࡕࡢཿᬛࢆࡶࡗ࡚ࠊ
ࡇࢀࡽࢆ୍ࡘࡦ࡜ࡘ஌ࡾ㉺࠼ࠊ᪂ࡓ࡞᫬௦ࢆぢᤣ࠼ࡓᩍ⫱ᨵ㠉ࠊධヨᨵ㠉ࢆ㐍ࡵ࡚࠸࠿࡞
ࡵࢀࡤ࡞ࡽ࡞࠸࡜ᙉࡃ⪃࠼࡚࠸ࡿ࡜ࡇࢁ࡛ࡍࠋ
༗ᚋ࠸ࡗࡥ࠸ࡢ㛗࠸ࢩ࣏ࣥࢪ࣒࢘࡟࡞ࡾࡲࡍࡀࠊࡈሗ࿌ࡢඛ⏕᪉ඹࠎࠊᐇࡾ࠶ࡿࢩ࣏ࣥ
ࢪ࣒࢘࡟࡞ࡾࡲࡍࡼ࠺ࠊⓙࡉࡲࡢࡈ༠ຊࢆ࠾㢪࠸ࡋ࡚ᣵᣜ࡜࠸ࡓࡋࡲࡍࠋ‫ྖۑ‬఍ᒣᮏ⌮஦㛗ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

－4－

㊃᪨ㄝ᫂
⊂❧⾜ᨻἲே኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲⤫ᣓᐁ㸭ධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗

Ⲩ஭ඞᘯ
‫ྖۑ‬఍

⥆ࡁࡲࡋ࡚ࠊࢩ࣏ࣥࢪ࣒࢘ࢆጞࡵࡿ࡟ᙜࡓࡾ

ࡲࡋ࡚ࠊ኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲⤫ᣓᐁࠊⲨ஭ඞ
ᘯࡼࡾ㊃᪨ㄝ᫂ࢆ࠸ࡓࡋࡲࡍࠋ
‫Ⲩۑ‬஭ヨ㦂࣭◊✲⤫ᣓᐁ௒ࠊࡈ⤂௓࠸ࡓࡔࡁࡲࡋࡓ

኱Ꮫධヨࢭࣥࢱ࣮ࡢⲨ஭࡛ࡈࡊ࠸ࡲࡍࠋ
⚾࠿ࡽ㊃᪨ㄝ᫂ࢆࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
ᡭ▷࡟࡜࠸࠺ࡇ࡜࡛ࠊ4 ᯛࢫࣛ࢖ࢻࢆ⏝ព࠸ࡓࡋࡲࡋ ࡓ ࡀ ࠊ3 ᯛ ┠ ࡜ 4 ᯛ┠ ࢆ୰ᚰ ࡟࠾ヰ ࡋࡉࡏ ࡚࠸ࡓ ࡔࡁࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
ࡲࡎࠕ኱Ꮫ࡜ࡣ㸽̿ᩍ⫱㑅ᢤࡢព࿡ࠖ࡜᭩࠸࡚࠶ࡿ
ࢫࣛ࢖ࢻ࡛ࡈࡊ࠸ࡲࡍࠋ⌧ᅾࡢ኱ᏛධᏛ⪅㑅ᢤࡢㄢ㢟࡜ࡣఱ࠿ࠊࡇࡇ࡛ⓙࡉࡲ࡜ㄆ㆑ࢆඹ᭷ࡋ࡚࠾ࡁࡓ࠸࡜⪃࠼ࡲࡋࡓࠋ
ࡈᏑࡌࡢࡼ࠺࡟኱Ꮫ㐍Ꮫ⪅ࡣቑ࠼࡚࠾ࡾࡲࡍࠋ኱ᏛධᏛ⪅㑅ᢤࡢᐇែࡣࠊᏛຊ࢚࣮ࣜࢺ
ࡢ㑅ᢤ࡜࠸࠺ࡼࡾࡣࠊࡇࢀ࠿ࡽࡢ▱㆑♫఍ࢆᨭ࠼ࡿᑐ㇟ᒙࢆ㑅ᢤࡋࠊᩍ⫱ࡋࠊ♫఍ⓗ࡟㓄ศࡋ࡚࠸ࡃ࡜࠸࠺ᙺ๭࡟ኚࢃࡗ࡚ࡁࡲࡋࡓࠋࡑ࠺⪃࠼࡚࠾ࡾࡲࡍࠋࡑ࠺࠸ࡓࡋࡲࡍ࡜ࠊ㧗ᰯ࠿ࡽ኱Ꮫ࡬ࡢ㐍Ꮫࡶࠊࡑࡢࣉࣟࢭࢫࡣࠕ㑅ᢤࠖ࡜࠸࠺ࡼࡾࡶᩍ⫱ࣉࣟࢭࢫ࡛࠶ࡾࠊᩍ⫱ㄢ⛬ࡢ✚ࡳୖࡆࢆព࿡ࡍࡿࠋࡑࡢࡓࡵ࡟ࠊࡘ࡞ࡂ┠࡜ࡋ࡚ࠊ኱Ꮫධヨࡣྍ⬟࡞㝈ࡾጇᙜ࡞ࡑࡋ࡚ಙ㢗ᛶࡢ㧗࠸ᩍ⫱ᐃ࡟㔜Ⅼࡀ⨨࠿ࢀ࡞ࡅࢀࡤ࡞ࡽ࡞࠸ࠋࡇࢀࡀ⚾ࡢ⌮ゎࡍࡿ࡜ࡇࢁ࡛ࡈࡊ࠸ࡲࡍࠋࡇ ࡇ ࡛ ࠕ ኱ Ꮫ ᩍ ⫱ ࡟ ᚲ せ ࡞ Ꮫ ຊ ࡢ ☜ ಖ ࠖࠕ ᩍ ⫱ ࣭ Ꮫ ⩦ ࡢ ᨭ ᥼ ࠖࠕ ᩍ ⫱ デ ᩿ ࡢ ಙ 㢗 ᛶ ࡜ ጇᙜᛶࡢྥୖࠖ࡜᭩ࡁࡲࡋࡓࡀࠊ➨ 2 ㇟㝈࠿ࡽ➨ 4 ㇟㝈࡟ྥ࠿ࡗ࡚኱ᏛධᏛ⪅㑅ᢤࡢ┠ⓗࡣኚࢃࡗ࡚ࡁࡓࠊ࡜࠸࠺ࡢࡀࠊࡇࡢᅗࡢពᅗ࡛ࡈࡊ࠸ࡲࡍࠋ
4 ᯛ┠ࡢࢫࣛ࢖ࢻ࡛ࡍࡀࠊ኱Ꮫධヨᨵ㠉࡜࠸ࡗ࡚࣐ࢫࢥ࣑ࡸୡ㛫ࡀ኱㦁ࡂࡍࡿࡀࠊ࠸ࡘ
ࡶྠࡌࡇ࡜ࢆ⧞ࡾ㏉ࡋ㆟ㄽࡋ࡚࠸ࡿࡔࡅ࡛ࡣ࡞࠸࠿ࠊࡑࡢࡼ࠺࡟࠾⪃࠼࡟࡞ࡿ᪉ࡶ࠾ࡽࢀ
ࡲࡍࠋࡋ࠿ࡋࠊ኱ᏛධヨࡢṔྐࢆࣞࣅ࣮ࣗࡋ࡚ࡳࡲࡍ࡜ࠊ୍ᗘࡓࡾ࡜ࡶධヨᨵ㠉ࡀྠࡌ࣏ࢪࢩࣙࣥ࡟఩⨨ࡋ࡚࠸ࡓࡇ࡜ࡣ࠶ࡾࡲࡏࢇࠋᡓᚋࡢ 70 ᖺ㛫ࢆぢࡲࡋ࡚ࡶࠊࡑࡢࡇ࡜࡟࠸
ࡉࡉ࠿ࡢ␲࠸ࡶ⏕ࡌ࡞࠸࡜☜ಙ࠸ࡓࡋࡲࡍࠋ
᪥ᮏ࡛ࡣࠊᡓᚋ࡟ 4 ࡘࡢධᏛඹ㏻ࢸࢫࢺࡀධࢀ᭰ࢃࡾᑟධࡉࢀ࡚ࡁࡲࡋࡓࠋ㐍Ꮫ㐺ᛶ᳨ᰝࠊ⬟◊ࢸࢫࢺࠊඹ㏻㸯ḟヨ㦂ࠊࢭࣥࢱ࣮ヨ㦂࡛ࡍࠋࡉࡽ࡟௒ᚋࠊࡲࡓ᪂ࡓ࡞ඹ㏻ࢸࢫࢺ
ࡢ㆟ㄽࡀᾋୖࡋ࡚࠾ࡾࡲࡍࠋᑡ࡞ࡃ࡜ࡶࠊࡇࢀࡲ࡛ࡢ 4 ࡘࡢヨ㦂࡟ࡘ࠸࡚ࡣࡑࢀࡒࢀࡢ᫬
௦ࡢㄢ㢟࡟ᛂࡌ࡚ᨵ㠉ດຊࡀ㐍ࡵࡽࢀ࡚ࡁࡓ࡜࠸࠺⤒⦋ࡀ࠶ࡾࡲࡍࠋ
㐍Ꮫ㐺ᛶ᳨ᰝ࡟ࡘ࠸࡚ࡣࠊᩋᡓ┤ᚋࡢᩍ⫱ΰ஘ᮇ࡟࠸࠿࡟ࡋ࡚኱ᏛධᏛ⪅ࢆ㑅ᢤࡍࡿ࠿ࡀㄢ㢟࡛ࡋࡓࠋ⬟◊ࢸࢫࢺࡣࠊ㧗ᗘ⤒῭ᡂ㛗ᮇࡢ࣐ࣥࣃ࣮࣡ィ⏬ࡢ୍⎔࡜ࡋ࡚ᵓ᝿ࡀ⦎ࡽ
ࢀࡲࡋࡓࠋඹ㏻㸯ḟヨ㦂ࡣᏛṔ♫఍ࡢᘢᐖࡸཷ㦂➇தࡢ⃭໬ࡢゎᾘࢆࡵࡊࡋࠊᩍ⫱ࡢṇᖖ－5－໬࡟ᐤ୚ࡍࡿ࡜࠸࠺ㄢ㢟ࡀ࠶ࡾࡲࡋࡓࠋࡑࢀࢆ┠ⓗ࡟ᐇ᪋ࡉࢀࡲࡋࡓࡀࠊ⤖ᯝ࡜ࡋ࡚ࠊඹ
㏻㸯ḟヨ㦂ࡣ኱Ꮫࡢᗎิ໬ࠊᚿ㢪⪅ࡢ㍯ษࡾࢆ㢧ᅾ໬ࡉࡏࡓ࡜࠸࠺ᢈุࢆᾎࡧ࡚ᗫṆ࡟࡞ࡾࡲࡋࡓࠋࢭࣥࢱ࣮ヨ㦂ࡣࠊἛ㦐ࡋࡓᅜẸⓗ࡞ධヨᢈุࢆ࡝ࡢࡼ࠺࡟࠿ࢃࡋ㙠㟼໬ࡍࡿ࠿ࠊ
ࡑࡢㄢ㢟ࢆゎỴࡍࡿࡓࡵ࡟Ⓩሙࡋࡓ࡜࠸࠺⤒⦋ࡀ࠶ࡾࡲࡍࠋ
ࡑࢀࡒࢀࡢ᫬௦ࠊධヨᨵ㠉ࢆ࡝ࡢࡼ࠺࡟㐍ࡵ࡚࠸ࡃ࠿ࠊࡑࡢゎỴࢆ୺ᑟࡍࡿᑓ㛛ᐙࡢ᪉ࠎࡶ኱ࡁࡃኚࢃࡗ࡚ࡁࡲࡋࡓࠋ౛࠼ࡤࠊ㐍Ꮫ㐺ᛶ᳨ᰝࡢ᫬௦࡟ࡣᚰ⌮ᐃࡢᑓ㛛ᐙࡀ୰ᚰ

ⓗ࡞ᙺ๭࡟ᢸࡗ࡚࠸ࡲࡋࡓࠋࡑࢀ࠿ࡽࠊ⬟◊ࢸࢫࢺࠊඹ㏻ 1 ḟヨ㦂࡟࡞ࡾࡲࡍ࡜ࠊᏛṔ♫఍ࠊ㑅ᢤ♫఍ࠊࡑࡋ࡚ᩍ⫱ィ⏬ࡀၥ㢟ࡢ୰ᚰ࡛ࡍ࠿ࡽࠊᩍ⫱♫఍Ꮫࠊᩍ⫱⤒῭Ꮫࠊ♫఍Ꮫࡢᑓ㛛ᐙࡀ๓㠃࡟ฟ࡚ࡃࡿ᫬௦࡟࡞ࡾࡲࡋࡓࠋ
ࢭࣥࢱ࣮ヨ㦂ࡢḟୡ௦ࡢࡇ࡜ࢆ⪃࠼ࡲࡍ࡜ࠊᩍ⫱ࡿࠊ࡜ඛ࡯࡝⏦ࡋୖࡆࡲࡋࡓࠋᩍ⫱ᐃࡢᢏ⾡ㄽࡀ௨๓࡟ࡲࡋ࡚㔜せ࡟࡞ᐃࡢ㐺ษࡉࠊⓗ☜ࡉ࡛ࡍࠋࢸࢫࢺ⏝ㄒ࡛ゝ࠼ࡤಙ㢗ᛶࠊጇᙜᛶࡢ㧗࠸ヨ㦂ࡢᐇ⌧ࡀ୙ྍḞࡔࢁ࠺࡜ᛮ࠸ࡲࡍࠋ௒ᚋࡣࠊࡇࡢᩍ⫱ᐃㄽࡢᑓ㛛ᐙࡢ᪉ࡀࡓࢆ୰ᚰ࡟ࠊࡉࡲࡊࡲ࡞ࡇ࡜ࢆ᳨ウࡋࠊᐇ᪋ࡋ࡞ࡅࢀࡤ࡞ࡽ࡞࠸᫬௦ࡀࡃࡿࠊ࡜
ᛮ࠸ࡲࡍࠋࡑࡇ࡛ࠊඛ࡯࡝⌮஦㛗࠿ࡽࡈ⤂௓ࡀ࠶ࡾࡲࡋࡓࡼ࠺࡟ࠊ௒ᅇࡢࢩ࣏ࣥࢪ࣒࡛࢘ࡣᇶㄪㅮ
₇ࢆ࠾㢪࠸ࡋࡲࡋࡓᒣᮏවኴᮁඛ⏕ࢆึࡵ࡜ࡋ࡚ࠊྛሗ࿌ࢆࡈᢸᙜ࠸ࡓࡔࡃࣃࢿࣜࢫࢺࡢ
᪉ࡀࡓࡣࠊ๓ᕝ┾୍ඛ⏕ࠊ༡㢼ཎᮅ࿴ඛ⏕ࠊ኱ὠ㉳ኵඛ⏕ࠊᮧୖ㝯ඛ⏕ࡢ㸳ே࡜ࡶ࡟ࠊࡍ
࡭࡚ᐃㄽࡢ➨୍⥺࡟❧ࡗ࡚࠾ࡽࢀࡿ◊✲⪅ࡢ᪉ࠎ࡛ࡍࠋࡉࡽ࡟ࢥࣥࣆ࣮ࣗࢱ࣮ࢸࢫࢺࡢ

ࢹࣔࢆࡸࡗ࡚ࡃࢀࡿ኱ஂಖᬛဢඛ⏕ࠊࡑࢀ࠿ࡽ௒᪥ࡢ⥲ྜྖ఍࡜㐍⾜ࠊࣃࢿࣝࢹ࢕ࢫ࢝ࢵ
ࢩࣙࣥࡢࢥ࣮ࢹ࢕ࢿ࣮ࢱ࣮ࢆົࡵ࡚ࡃࡔࡉࡿ኱ሯ㞝సඛ⏕ࡶࡼࡶ࠺⚾࡞࡝ࡀ௜ࡅධࡿ㝽ࡶ࡞࠸࡯࡝࡟ࠊᩍ⫱ᐃㄽࡢᑓ㛛ᐙ࡛ࡍࠋ࠸ࡼ࠸

ᐃࡢᑓ㛛ⓗ࡞▱ぢࢆ࣮࣋ࢫ࡟ࡋ࡞ࡅࢀࡤ

㆟ㄽࡀᡂࡾ❧ࡓ࡞࠸ࠊࡑ࠺࠸࠺᫬௦࡟࡞ࡗ࡚ࡁࡓ࡜࠸࠺ᐇឤࡀ࠸ࡓࡋࡲࡍࠋ
௨ୖࡀ௒ᅇࠊࡇࡢࢩ࣏ࣥࢪ࣒࢘ࢆ௻⏬ࡋࡓ㊃᪨࡛ࡈࡊ࠸ࡲࡍࠋ᭱ᚋࡲ࡛ࡼࢁࡋࡃ࠾㢪࠸࠸ࡓࡋࡲࡍࠋ
࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧‫ྖۑ‬఍Ⲩ஭ඛ⏕ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

－6－

－7－

࠙ᇶㄪㅮ₇ࠚ

PISA㸸ᢤᮏⓗᨵ㠉࡬ࡢᣮᡓ̿ఱࢆ࡝࠺ᐃࡋ࡝࠺ホ౯ࡋ࡚࠸ࡃࡢ࠿̿

Major innovations in PISA 2015 and beyond
Deputy Director of the Center of Global Assessment, Principal Research Scientist of ETS

ᒣᮏවኴᮁ
‫ྖۑ‬఍

⥆ࡁࡲࡋ࡚ࠊ࢔࣓ࣜ࢝ࡢධヨࢭࣥࢱ࣮࡜࠾⪃࠼࠸ࡓࡔࡅࢀࡤࡼ࠸࠿࡜ᛮ࠸ࡲࡍࡀࠊ

ETS㸦Educational Testing Service㸧ࡢ◊✲㒊㛛ࢆ≌ᘬࡋ࡚࠾ࡽࢀࡲࡍᒣᮏවኴᮁඛ⏕࡟ᇶㄪ
ㅮ₇ࢆ࠾㢪࠸࠸ࡓࡋࡲࡍࠋ
ᶆ㢟࡟࠶ࡾࡲࡍ PISA ࡟ࡘࡁࡲࡋ࡚ࡣࠊ≉࡟ᩍ⫱ᐃࡢᑓ㛛ⓗ࡞❧ሙ࠿ࡽࡢ㈐௵⪅࡜ࡋ࡚ࠊ௒᭶ࡶୡ⏺୰ࢆ㣕ࡧᅇࡗ࡚࠾ࡽࢀࠊࡑࡢ࠾ᛁࡋ࠸୰ࠊ඘ᐇࡋࡓࢫࣛ࢖ࢻࢆࡈ‽ഛ࠸ࡓࡔࡁࡲࡋࡓࠋᒣᮏඛ⏕ࡣ࢔࣓ࣜ࢝࠿ࡽ᮶ࡽࢀࡿ࡜ࡁ࡟ࠊᮾᾏᓊࡣ㞷࡛㣕⾜ᶵࡀ㐜ࢀ୍࡚᪥㐜ࢀ࡚᫖᪥ኪࡢࡈ฿╔࡟࡞ࡗࡓࡾࠊࡲࡓࠊ౽ࡀኚ᭦࡟࡞ࡗࡓࡇ࡜࡛ࢩ࢝ࢦࡢࢺࣛࣥࢪࢵࢩ
ࣙࣥࡢ㝿ࡢᡭ㐪࠸࡛Ⲵ≀ࡀ᪥ᮏ࡟ᒆ࠿࡞࠿ࡗࡓࡾࠊ࠸ࢁ࠸ࢁ࡜኱ኚࡔࡗࡓࡼ࠺࡛ࡍࠋࡑࢇ
࡞ࡇ࡜࡛࠾⑂ࢀࡢࡇ࡜࡜ᛮ࠸ࡲࡍࡀࠊ௒ᮅࡣ࠾ඖẼ࡟఍ሙ࡟᮶࡚ୗࡉ࠸ࡲࡋࡓࠋࡑࢀ࡛ࡣࠊ
ᒣᮏඛ⏕ࠊࡼࢁࡋࡃ࠾㢪࠸࠸ࡓࡋࡲࡍࠋ
‫ۑ‬ᒣᮏẶ

ึࡵࡲࡋ࡚ࠊᒣᮏවኴᮁ࡛ࡍࠋ

ࡶ࠺ 40 ᖺ㏆ࡃ࢔࣓ࣜ࢝࡟ఫࢇ࡛࠸࡚ࠊ᪥ᮏㄒ࡛ㅮ₇ࡍࡿࡇ࡜ࡣࡲࡎ࡞࠸ࡢ࡛ࠊࡍࡈࡃ⥭ᙇࡋ࡚࠸ࡲࡍࡼࢁ
ࡋࡃ࠾㢪࠸ࡋࡲࡍࠋ௒ᅇ࠾ヰࡋࡋࡼ࠺࡜ᛮࡗ࡚࠸ࡿࡢ
ࡣࠊ࣮ࣛࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺ㸦large-scale assessment㸧࡜ࡣఱ࠿ࠊ࡞ࡐ PISA ࡢ 2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࢆࡶ࠺
୍ᗘࡩࡾ㏉ࡗ࡚ศᯒࡋ࡞ࡅࢀࡤ࡞ࡽ࡞࠿ࡗࡓࡢ࠿ࠊࡑࢀ ࡜ ࠊ PISA2015 ࡢ ࣇ ࢕ ࣮ࣝ ࢻ ࢸ ࢫ ࢺࢆ ௒ ᖺ ⾜ ࠸ ࡲࡋ ࡓࡢ࡛ࡑࡢ⤖ᯝࢆᑡࡋヰࡋࡲࡍࠋ≉࡟ࡇࢀࡣࠊඛࠎ㐌ࡲ
࡛ 4 㐌㛫࠿ࡅ࡚ PISA ࡢ఍㆟ࡀ࠶ࡾࡲࡋࡓࡀࠊPISA ࡢࣇ࢕࣮ࣝࢻࢸࢫࢺ࡟㛵ࡋ࡚ࡣࠊPISA࡟┤᥋㛵୚ࡋ࡚࠸ࡿ᪉௨እࡣㄡࡶ▱ࡽ࡞࠸ࡇ࡜࡛ࡋࡓࡢ࡛ࠊ௒ᅇึࡵ࡚࠾ヰࡋࡍࡿࡇ࡜࡛
ࡍࠋࡑࢀ࠿ࡽࠊPISA2015 ࡢࢹࢨ࢖ࣥࡢ኱ࡲ࠿࡞ᴫせࠊࡑࢀ࡜ PISA ࡢࢥࣥࣆ࣮ࣗࢱ࣮࣭࣋
࣮ࢫࢻ࣭࢔ࢭࢫ࣓ࣥࢺ㸦computer based assessment㸧ࡢࢧࣥࣉࣝࢆぢࡏࡲࡍࠋ᪥ ᮏ ࡢ ኱ Ꮫ ධ ヨ ࡟ 㛵 ࡋ ࡚ ࡣ ࠊ ⚾ ࡣ ࡇ ࢀ ࡲ ࡛ 㛵 ࢃ ࡗ ࡚ ࡁ ࡚ ࠾ ࡾ ࡲ ࡏ ࢇ ࡢ ࡛ ࠊ PISA ࡢ ᪂ ࡓ࡞㛤Ⓨ㐣⛬࠿ࡽධヨ࡟♧၀ࢆཬࡰࡍࡶࡢࡣఱ࡞ࡢ࠿࡜⪃࠼࡚ࡳࡲࡋࡓࡀࠊࡲࡎࠊ⚾ࡓࡕࡀࡸࡗ࡚࠸ࡿ PISA ࡟㛵ࡋ࡚ࡣࠊఱࢆ⬟ຊ࡜ࡋ࡚ࢁ࠺࡜ࡋ࡚࠸ࡿࡢ࠿ࢆ᫂ࡽ࠿࡟ࡍࡿࡇ࡜ࠊ

ࡑࡋ࡚ࠊࢥࣥࣆ࣮ࣗࢱ࣮࣭࣮࣋ࢫࢻ࣭࢔ࢭࢫ࣓ࣥࢺ࡟ࡼࡗ࡚඲࡚

ᐃࡋࡼ࠺࡜ࡋ࡚࠸ࡿ࡜

࠸࠺ࡇ࡜ࠊࡑࢀ࡜ྠ᫬࡟ࠊཷ㦂⪅㞟ᅋ㸦population㸧ࡢࢺࣞࣥࢻ㸦trend㸧ࢆࣔࢽࢱ࣮ࡋࡼ࠺
࡜ࡋ࡚࠸ࡿࡇ࡜ࠊࡑࡢࡓࡵ࡟ࠊ2000 ᖺ࠿ࡽ 2015 ᖺࡲ࡛ྠࡌᑻᗘࢆ⏝࠸࡚ࡁ࡚࠸ࡿ࡜࠸࠺
ࡇ࡜࡞࡝ࡢⅬ࡛ࠊ኱Ꮫධヨ࡟ࡶཧ⪃࡟࡞ࡿࡶࡢࡀ࠶ࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࠋ

－9－࣮ࣛࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺ㸦large-scale assessment㸸LSA㸧࡜ࡣ࣮ࣛࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺ࡜࠸࠺ࡢࡣࠊ኱つᶍ࡞⬟ຊㄪᰝ࡟ࡼࡿホ౯࡜࠾⪃࠼࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࡀࠊࡑࢀࢆᐇ᪋ࡍࡿ୺࡞⌮⏤ࡣࠊᨻ⟇Ỵᐃࡢࡓࡵࡢࢹ࣮ࢱ࡟ᇶ࡙࠸ࡓゎ㔘ࢆᥦ౪ࡍࡿࡇ࡜࡛ࡍࠋᨻ⟇◊✲ࡢ㐺ษ࡞ᙺ๭ࡣᨻ⟇ࢆᐃ⩏ࡍࡿࡇ࡜࡛ࡣ࡞ࡃ࡚ࠊࡴࡋࢁ
ࡑࢀࡣࢹ࣮ࢱ࡟ᇶ࡙࠸ࡓࠊุ᩿ࡀྍ⬟࡜࡞ࡿドᣐࢆ☜❧ࡍࡿࡇ࡜࡛ࡍࠋࡑࡋ࡚๓ᥦ࡟ࡋ࡞
ࡅࢀࡤ࡞ࡽ࡞࠸ࡇ࡜ࡣࠊ࣮ࣛࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺࡣಶேࡢ⬟ຊࡢᐃࡀ┠ⓗ࡛ࡣ࡞࠸

࡜࠸࠺ࡇ࡜࡛ࡍࠋ
ࡋࡓࡀࡗ࡚ࠊ࣮ࣛࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺ࡜࠸࠺࡜ࡁ࡟ࠊࡲࡎ᭱ึ࡟⪃࠼ࡿࡇ࡜ࡣࠊಶ
ேⓗࠊࡲࡓ♫఍୍⯡ࡢᡂຌࢆᨭ࠼ࡿࢥࣥࣆࢸࣥࢩ࣮࡜࠸ࡗࡓࡶࡢ࡟ᑐᛂࡍࡿ࢔࢘ࢺ࣒࢝ࢆ
㆑ูࡋࠊィࡍࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋࡇࡢᅗ㸦ࢫࣛ࢖ࢻ 3㸧ࡢ࢔࢘ࢺ࣒࢝࡟ࡣࠊ

ᐃࡉࢀࡓ್ࡀධࡿࡇ࡜࡟࡞ࡾࡲࡍࠋࡑࡋ࡚ࠊఱࡀࢥࣥࣆࢸࣥࢩ࣮ࡢྥୖ࡟㈉⊩ࡋ࡚࠸ࡿࡢ࠿ࠊࡲࡓ
ጉࡆ࡚࠸ࡿࡢ࠿ࠊࢥࣥࣆࢸࣥࢩ࣮ࡣ࡝ࡢࡼ࠺࡟♫఍ⓗࡲࡓࡣ⤒῭ⓗ࡞ഴྥ࡜ࡋ࡚࠶ࡽࢃࢀ
ࡿ࠿ࢆㄪᰝࡍࡿࡇ࡜࡟࡞ࡾࡲࡍࠋࡑࢀ࡟ࡼࡗ࡚ࠊࢥࣥࣆࢸࣥࢩ࣮ࢆࡼࡾ㧗ᗘ࡟ᡂ❧ࡉࡏࡿ࡭ࡁᨻ⟇ࡢᡭẁࢆ㆑ูࡍࡿᡭຓࡅ࡜࡞ࡿࢃࡅ࡛ࡍࠋ
኱Ꮫධヨ࡟㛵ࡋ࡚⪃࠼ࢀࡤࠊࡇࡢᅗࡢࣜࢸࣛࢩ࣮ࡢ࡜ࡇࢁ࡟ࠊ௦ࢃࡾ࡟኱Ꮫධヨ࡛ᐃࡋࡼ࠺࡜ࡋ࡚࠸ࡿ⬟ຊࡀධࡾࡲࡍࡀࠊධヨࡢሙྜࡣࡑࢀࡔࡅࢆ࢔ࢭࢫ࣓ࣥࢺࡍࡿࡇ࡜࡟࡞
ࡾࡲࡍࠋࡑࢀ௨እࡢࣂࢵࢡࢢࣛ࢘ࣥࢻ࡜࡞ࡿኚᩘ࡟ࡘ࠸࡚ࡣ࡯࡜ࢇ࡝࢔ࢭࢫ࣓ࣥࢺࡍࡿࡇ
࡜ࡣ࠶ࡾࡲࡏࢇࠋࡑࢀࡣ࠶ࡿព࿡࡛ᙜ↛࡞ࡇ࡜࡛ࠊಶேࡢ⬟ຊࢆ࢔ࢭࢫ࣓ࣥࢺࡍࡿࡇ࡜ࡀ
ධヨࡢሙྜ࡟ࡣ୍␒㔜せ࡞ࡇ࡜࡛ࡍ࠿ࡽࠋ
ࡋ࠿ࡋࠊᨻ⟇࡟㛵ࡋ࡚⪃࠼ࡿ࡜ࡁ࡟ࡣࠊ࡝ࢇ࡞ࣂࢵࢡࢢࣛ࢘ࣥࢻࡀ࠶ࡿࡢ࠿ࠊ౛࠼ࡤ࡝
ࢇ࡞♫఍⤒῭ⓗᆅ఩㸦SES㸸socioeconomic status㸧࡟࠶ࡿࡢ࠿ࠊ࡝ࢇ࡞ᩍ⫱ࢆཷࡅࡓ࠿ࠊ➨୍ ゝ ㄒ 㸦Native language㸧ࡣ ఱ ࠿ ࠊ ࡝ ࢇ࡞ 㞀ᐖ㸦 disability㸧 ࢆࡶࡗ ࡚࠸ࡿ ࡢ࠿ࠊ ࡑࢀ࠿ ࡽᖺࢆ࡜ࡿࡇ࡜࡟ࡼࡗ࡚࡝ࢇ࡞ᙳ㡪ࡀ࠶ࡿࡢ࠿࡜࠸ࡗࡓࡇ࡜ࡀࠊ㏻ᖖ⪃࠼ࡽࢀ࡚࠸ࡿኚᩘ࡜
ゝ࠼ࡲࡍࠋࡑࢀࡀࣜࢸࣛࢩ࣮࡟࡝ࡢࡼ࠺࡞ᙳ㡪ࢆᣢࡕࠊࡑࡋ࡚ࠊࡑࡢ⬟ຊ࡟ࡼࡗ࡚ࠊ♫఍ⓗࠊ⤒῭ⓗ࡞⤖ᯝ࡜ࡋ࡚ࠊ౛࠼ࡤࠊᑵ⫋࡟࡝࠺⤖ࡧ௜ࡃࡢ࠿ࠊ೺ᗣ࡜࠿ᐙ᪘ᵓᡂࠊゝㄒᏛ⩦㸦Language Learning㸧ࠊࡑࢀ࠿ࡽࠊ࡝ࡢࡼ࠺࡟ᆅᇦࡢ≧ἣ࡟㈉⊩ࡍࡿ࠿࡜࠸ࡗࡓࡇ࡜ࡀ⪃࠼ࡽࢀ࡚࠸ࡲࡍࠋ࡛ࡍ࠿ࡽࠊ࣮ࣛࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺ࡛ࡣࠊ⬟ຊ࡜ࡑࢀ࡟㛵ࢃࡿ࡜⪃࠼ࡽࢀࡿࡉࡲࡊࡲ࡞ࣂ࢚ࣛࢸ࢕ࡢࡍ࡭࡚ࢆィࡍࡿࡇ࡜ࡀヨࡳࡽࢀࡿࡇ࡜࡟࡞ࡾࡲࡍࠋPISA ࡜ࡣPISA ࡣࠊᅜ㝿ⓗ࡟୍␒ࡼࡃ▱ࡽࢀ࡚࠸ࡿ኱つᶍ࡞⬟ຊㄪᰝ࡛ࡍࠋPISA ࡣ 15 ṓࡢᏛᰯ࡟㏻ࡗ࡚࠸ࡿ⏕ᚐࢆᑐ㇟࡟ࡋ࡚ࠊᩘᏛⓗࣜࢸࣛࢩ࣮ࠊㄞゎຊࠊ⛉Ꮫⓗࣜࢸࣛࢩ࣮ཬࡧᒓᛶ࡟
㛵ࡋ࡚ࡢタၥ࡞࡝࡛ᵓᡂࡉࢀ࡚࠸ࡲࡍࠋ2000 ᖺ࠿ࡽ 3 ᖺࡈ࡜࡟ 2012 ᖺࡲ࡛⾜ࢃࢀ࡚࠾ࡾࠊ
ࡇࢀ࠿ࡽࡶ 3 ᖺࡈ࡜࡟ࠊḟࡣ 2015 ᖺࠊࡑࡋ࡚ 2018 ᖺ࡜⥆ࡁࡲࡍࠋPISA ࡣࠊ15 ṓࡢᣢࡗ࡚࠸ࡿ▱㆑ࡸᢏ⬟ࢆᐇ⏕άࡢ࠸ࢁ࠸ࢁ࡞ሙ㠃࡛࡝ࢀࡔࡅά⏝࡛ࡁࡿ࠿ࢆぢࡿࡶࡢ࡛࠶ࡗ࡚ࠊ≉ᐃࡢᏛᰯ࣒࢝ࣜ࢟ࣗࣛࢆ࡝ࢀࡔࡅ⩦ᚓࡋ࡚࠸ࡿ࠿ࢆぢࡿࡶࡢ࡛ࡣ࠶ࡾࡲࡏࢇࠋྛᅜ㛫࡟ඹ㏻ࡋ࡚࠸ࡿࡶࡢࡣࠊࡑ࠺࠸࠺⏕ά࡟ᐇ⏝ࡉࢀ࡚࠸ࡿ㒊ศ࡟࡞ࡾࡲࡍࠋᛮ⪃ࣉࣟࢭࢫࡢ⩦ᚓࠊᴫᛕࡢ⌮ゎࠊཬࡧࠊྛศ㔝ࡢࡉࡲࡊࡲ࡞≧ἣࡢ୰࡛ࡑࢀࡽ
ࢆ⏕࠿ࡍຊࢆ㔜どࡋ࡚࠸ࡲࡍࠋ♫఍ࡣ௒ࠊᣢࡗ࡚࠸ࡿ▱㆑௨ୖ࡟ࠊᣢࡗ࡚࠸ࡿ▱㆑ࡸᢏ⬟ࢆ౑ࡗ࡚ఱࡀ࡛ࡁࡿ࠿ࢆホ౯ࡋ࡚࠸ࡿࠊࡑ࠺࠸࠺⪃࠼᪉࡟ᇶ࡙࠸࡚ PISA ࡣసࡽࢀ࡚࠸ࡲ
ࡍࠋ

－10－

PISA ࡣᴫࡡࡇࡢࢫࣛ࢖ࢻ㸦ࢫࣛ࢖ࢻ 6㸧࡟࠶ࡾࡲࡍࡼ࠺࡞ࢹࢨ࢖࡛ࣥᵓᡂࡉࢀ࡚࠸ࡲࡍࠋ
ࢡࣛࢫࢱ࣮㸦cluster㸧࡜࠸࠺ࡢࡣ 30 ศ࡯࡝࡛ᅇ⟅࡛ࡁࡿ⛬ᗘࡢ㉁ၥࡢࢭࢵࢺ࡛ࡍࠋࡑࡢࢭ
ࢵࢺࡣ⛉Ꮫࣜࢸࣛࢩ࣮࡜࠿ㄞゎຊ࡜࠿ࠊࡑࡢ 1 ศ㔝ࡢࢻ࣓࢖ࣥࡢ୰ࡢ 1 ࡘࡢࡶࡢࡔࡅ࡛ࡍࠋ
ࢡࣛࢫࢱ࣮ 1ࠊࢡࣛࢫࢱ࣮ 2 ࡜ 60 ศ࠿ࡅ࡚ᅇ⟅ࡋࠊఇ᠁ࡀ࠶ࡾࡲࡋ࡚ࠊࡑࡢᚋࡲࡓࠊ60ศ࠿ࡅ࡚ࢡࣛࢫࢱ࣮ 3ࠊࢡࣛࢫࢱ࣮ 4 ࡜㐍ࡳࡲࡍࠋࡑࡋ࡚ࠊࣂࢵࢡࢢࣛ࢘ࣥࢻࠊᒓᛶࡢ㉁ၥ㸦Background Question㸸BQ㸧ࡀ࠶ࡾࡲࡋ࡚ࠊࡑࡢᚋఇࢇ࡛ࠊ௚ࡢࢻ࣓࢖ࣥࡢ࢔ࢭࢫ࣓ࣥ
ࢺࡀ⾜ࢃࢀࡲࡍࠋ௚ࡢࢻ࣓࢖ࣥ࡜ࡣࠊ౛࠼ࡤࣇ࢓࢖ࢼࣥࢩࣕࣝࣜࢸࣛࢩ࣮㸦financial literacy㸧࡜࠸ࡗࡓ≉ู࡞ࣜࢸࣛࢩ࣮࡛ࡍࠋ
2015 ᖺࡢ PISA ࡀ 2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛࡜ẚ࡭࡚㝶ศኚࢃࡿ࡜࠸࠺Ⅼࡣࠊ඲㠃ⓗ࡟ࢥࣥࣆ࣮ࣗࢱ໬ࡍࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋࡑࢀ࠿ࡽࠊಙ㢗ᛶࡢ࠶ࡿᑻᗘࢆ౑ࡗ࡚๓ᅇࡢࢧ࢖ࢡࣝ
࠿ࡽࡢᕪࢆࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋࢺࣞࣥࢻ㸦trend㸧ࡢᐃ࡛ࡍࡡࠋࡲࡓࠊ᪂ࡋ࠸⛉Ꮫࣜࢸࣛࢩ࣮ࡀᐃ⩏ࡉࢀࠊࡑࢀ࡟ຍ࠼࡚᪂ࡋ࠸ࢸࢫࢺศ㔝ࡀ฼⏝ࡉࢀࡿࡇ࡜࡟࡞ࡾࡲࡍࠋࡑࡢ࡞
࠿࡛ࠊ୍␒኱ษ࡞ⅬࡣࠊᏳᐃࡋࡓ⤒ᖺ᥎⛣㸦stable trend㸧ࢆぢ࡚࠸ࡃ࡜࠸࠺ࡇ࡜࡛ࡍࠋ௒
ࡲ࡛ PISA ࡛⾜ࡗ࡚ࡁࡓ 2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࡢࢹࢨ࢖ࣥ࡜࢔ࢼࣜࢩࢫࡢ᪉ἲ࡛ࡣࠊࢺ
ࣞࣥࢻ࡟ࡘ࠸࡚ࡣㄗᕪ㸦error㸧ࡀΰධࡋ࡚ࡁ࡚࠸ࡿ࡜࠸࠺ࡇ࡜ࡀ࠶ࡿ࠿ࡽ࡛ࡍࠋ
㐣ཤࡢ PISA ࡢㄢ㢟ࡲࡎࠊࡇࡢࢢࣛࣇࢆぢ࡚ࡳࡲࡍ㸦ࢫࣛ࢖ࢻ 9㸧ࠋࡇࢀࡣࠊ2000 ᖺ࠿ࡽ 2006 ᖺ࡟࠿ࡅ࡚ㄞ
ゎຊࡢࢫࢥ࢔ࡀ࡝ࡢࡃࡽ࠸ኚࢃࡗࡓ࠿࡜࠸࠺ࡇ࡜ࢆᅜู࡟♧ࡋࡓࡶࡢ࡛ࡍࡀࠊᅜ࡟ࡼࡗ࡚
ࡣ 30 Ⅼ௨ୖኚ໬ࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡾࡲࡍࠋPISA ࡢⅬᩘࡣᖹᆒࡀ 500 Ⅼ࡛ᶆ‽೫ᕪࡀ 100
Ⅼ࡜࠸࠺༢఩࡛ࡍ࠿ࡽࠊ6 ᖺ㛫࡛ 30 Ⅼኚࢃࡿ࡜࠸࠺ࡢࡣࠊࡶࡢࡍࡈࡃ኱ࡁ࡞ኚࢃࡾࡼ࠺
࡜࠸࠺ࡇ࡜ࡀゝ࠼ࡲࡍࠋࡇࡢࢢࣛࣇ㸦ࢫࣛ࢖ࢻ 10㸧ࡣࠊࡶ࠺Ⓨ⾲ࡉࢀ࡚࠸ࡿ⤖ᯝ࡛ࡍࡅࢀ࡝ࡶࠊ2000 ᖺ࡜ 2009ᖺࡢㄞゎຊࡢࢺࣞࣥࢻࢆぢ࡚ࡳࡓࡶࡢ࡛ࡍࠋࢫࣞࢵࢩ࣮ࣗ࣍ࣝࢻ㸦threshold㸧࡜࠸࠺ࡢࡣࠊ
ྛ㡯┠࡟ṇ⟅ࡍࡿ᮲௳☜⋡ࡀ 0.62 ࡜࡞ࡿ PISA ࡢⅬᩘ࡟ᙜࡓࡿࡶࡢ࡛ࡍࠋࡇࡢࢢࣛࣇࡣࠊ
2000 ᖺ࡜ 2009 ᖺ࡟ඹ㏻ࡋࡓ㡯┠࡟ࡘ࠸࡚ࢫࣞࢵࢩ࣮ࣗ࣍ࣝࢻࢆࣉࣟࢵࢺࡋࡓࡶࡢ࡛ࡍࠋ
2000 ᖺ࠿ࡽ 2009 ᖺࡢᑻᗘࢆỴࡵࡿ࡟ࡣࡇࢀࡽࡢ࢔࢖ࢸ࣒ࡔࡅࡀ㛵୚ࡋ࡚࠸ࡿࡇ࡜࡟࡞ࡾ
ࡲࡍࠋࡇࡢࢫࣞࢵࢩ࣮ࣗ࣍ࣝࢻࡀ࠿࡞ࡾኚ໬ࡋ࡚࠸ࡿ㡯┠ࡀ࠶ࡾࡲࡍࡀࠊࡇࢀࡣ࠿࡞ࡾ኱
ࡁ࠸ࡇ࡜࡞ࢇ࡛ࡍࠋ࡞ࡐኚࢃࡗࡓ࠿࡜࠸࠺ࡇ࡜࡟ࡘ࠸࡚ࡣࠊᚋ࡯࡝ゐࢀࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
ḟࡢࢢࣛࣇࢆぢ࡚࠸ࡓࡔࡁࡲࡍࠋ⩦⇍ᗘࡢࣞ࣋ࣝ࡜࠸࠺ࡢࡀ࠶ࡾࡲࡍࡀࠊࡑࢀࡣ 407 Ⅼࠊ
480 Ⅼࠊ553 Ⅼࠊ626 Ⅼ࡛༊ษࡽࢀ࡚࠸࡚ࠊࡑࢀ௨ୖࠊ௨ୗ࡜࠸࠺ࡇ࡜࡛ࠊ⩦⇍ᗘࡢࣞ࣋ࣝ
ࡣࠊ1 ௨ୗࠊ1ࠊ2ࠊ3 ࡜࠿ 4 ࡜࠸ࡗࡓලྜ࡟Ỵࡵࡽࢀ࡚࠸ࡲࡍࠋࢫࣞࢵࢩ࣮ࣗ࣍ࣝࢻࡢ್
࡟ࡼࡗ࡚࢔࢖ࢸ࣒ࡢࢭࢵࢺࢆసࡗ࡚ࠊࡑࡢ࢔࢖ࢸ࣒ࡢࢭࢵࢺࢆ౑ࡗ࡚⩦⇍ᗘࡢࣞ࣋ࣝࡀ࡝
࠺࠸࠺ࡶࡢ࠿ࡢࢹ࢕ࢫࢡࣜࣉࢩࣙࣥࢆసࡗ࡚࠸ࡲࡍࠋࡑࢀ࡛ࡇࡢࢢࣛࣇ࠿ࡽ❚࠼ࡿࡇ࡜ࡣࠊ
25 㸣ࡢ࢔࢖ࢸ࣒ࡀࠊ2000 ᖺ࠿ࡽ 2009 ᖺ࡟࠿ࡅ࡚ࠊ⩦⇍ᗘࡢࣞ࣋ࣝࡀኚࢃࡗ࡚࠸ࡿ࡜࠸࠺
ࡇ࡜࡛ࡍࡡࠋࡑࢀ࡛ࡶ࡞࠾ࠊ2000 ᖺ࠿ࡽ 2009 ᖺ࡟࠿ࡅ࡚ࠊ⩦⇍ᗘࡢࣞ࣋ࣝࡢࢹ࢕ࢫࢡࣜ
ࣉࢩࣙࣥࡣኚࢃࡗ࡚࠸࡞࠿ࡗࡓࢇ࡛ࡍࠋ࡛ࡍࡅࢀ࡝ࡶࠊࣞ࣋ࣝࡢࢹ࢕ࢫࢡࣜࣉࢩࣙࣥ⮬యࡣࡑࢀࢆ౑ࡗ࡚ゎ㔘ࡋ࡚࠸ࡿࢃࡅ࡛ࡍ࠿ࡽࠊࡑࡢ࢔࢖ࢸ࣒ࡢࢭࢵࢺࡣ኱ษ࡞ࡶࡢ࡛ࡍࠋ
ࡇࡢࡼ࠺࡞ኚ໬ࡢ⌮⏤ࡣࠊ2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࡣࠊ➼໬㸦equating㸧ࡢࣔࢹࣝࢆ౑ࡗ
࡚࠸ࡿࢃࡅ࡛ࡍࡀࠊࢧ࢖ࢡࣝࡈ࡜࡟඲ࡃูࡢࢫࢣ࣮ࣜࣥࢢࢆࡋࡲࡋ࡚ࠊࡑࢀ࡛࢖ࢡ࢚࢖ࢸ
࢕ࣥࢢࡣࡑࡢ࢔࢖ࢸ࣒ࡢࣃ࣓࣮ࣛࢱࡢࢹ࢕ࢫࢺࣜࣅ࣮ࣗࢩࣙࣥࢆ࣐ࢵࢳࡋ࡚సࡗ࡚࠸ࡲ

－11－ࡍࠋࡑࡢࡓࡵ࡟ࠊ࢔࢖ࢸ࣒ࡢ್ࡀ 2000 ᖺ࠿ࡽ 2009 ᖺ࡟࠿ࡅ࡚ኚࢃࡿࡇ࡜࡟࡞ࡾࡲࡍࠋ➼໬ࡢ᪉ἲ࡜ࡋ࡚ࡣࠊᇶᮏⓗ࡟ࡣࠊཷ᳨⪅ࡢ⬟ຊࡢศᕸ࡟ᇶ࡙ࡃ࠿ࠊࡑࢀ࡜ࡶ࢔࢖ࢸ࣒ࣃࣛ
࣓࣮ࢱ࡟ᇶ࡙ࡃ࠿ࠊ࡝ࡕࡽ࠿࡟ᇶ࡙࠸࡚⾜ࢃࢀࡿࡇ࡜࡟࡞ࡾࡲࡍࡀࠊ≉࡟ࣛࢵࢩࣗࣔࢹࣝ
ࢆ౑࠺࡜ࡁ࡟ࡣࠊ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ౑࠺᪉ἲࡀẚ㍑ⓗᐜ᫆࡜ゝ࠺ࡇ࡜ࡶ࠶ࡾࠊࡑࢀࢆ
౑ࡗ࡚ࡸࡗ࡚࠸ࡲࡋࡓࠋࡑ࠺࠸࠺ࡇ࡜࡛࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡢ್ࡀኚࢃࡗ࡚࠸ࡿࢇ࡛ࡍࡡࠋ
ࡑࢀࢆࡶ࠺ࡕࡻࡗ࡜ู࡞᪉ἲ࡛ぢ࡚ࡳࡲࡍࠋࡇ ࢀ 㸦 ࢫ ࣛ ࢖ ࢻ 12㸧 ࡣ ࢻ ࣓ ࢖ ࣥ ࡈ ࡜ ࡟ ࠊ ౛ ࠼ ࡤ ᩘ Ꮫ 㸦 Math㸧 ࡜ ࠿ ⛉ Ꮫ 㸦 Science㸧 ࡜ ࠿ㄞゎຊ㸦Reading㸧࡟㛵ࡋ࡚ࠊ࢔࢖ࢸ࣒ࡢᩘࡀ࡝ࡢࡃࡽ࠸ኚ㑄ࡋ࡚࠸ࡿ࠿ࢆぢࡓࡶࡢ࡛ࡍࠋPISA ࡛ࡣࠊࢻ࣓࢖ࣥࡀ࣓࢖ࣥ࡜࣐࢖ࢼ࣮࡟ศࡅࡽࢀ࡚࠸࡚ࠊ2000 ᖺ࠿ࡽ 2012 ᖺࠊࢢ࣮ࣜࣥ ࡢ ࣂ ࢵ ࢡ ࢢ ࣛ ࢘ ࣥ ࢻ ࡀ ධ ࡗ ࡚ ࠸ ࡿ ࡢ ࡣ ࣓ ࢖ ࣥ ࡢ ࢔ ࢭ ࢫ ࣓ ࣥ ࢺ ࢆ ♧ ࡋ ࡓ ࡶ ࡢ ࡛ ࡍ ࠋ 2000ᖺࡣㄞゎຊࡀ࣓࢖࡛ࣥࠊᩘᏛ࡜⛉Ꮫࡣ࣐࢖ࢼ࣮ࡢࢻ࣓࢖࡛ࣥࡋࡓࠋ࡛ࡍ࠿ࡽࠊㄞゎຊࡣ 129ၥ࠶ࡾࡲࡋࡓࠋḟࡢ 2003 ᖺ࡟ࡣ࣐࢖ࢼ࣮࡟ᅇࡗ࡚ࠊࡑࡢ࠺ࡕࡢ 28 ၥࡀṧࡾࠊ2006 ᖺࡶ 28ၥ࡛ࠊࡑࢀࡽࢆࡶ࡜࡟ࡋ࡚ࢺࣞࣥࢻࡀ᳨ウࡉࢀࡿࡇ࡜࡟࡞ࡾࡲࡍࠋࡑࡋ࡚ 2009 ᖺ࡟ࡣ 131
ၥ࡛ࡍࠋ2000 ᖺ࠿ࡽ 2009 ᖺࡲ࡛࡛ࠊ28 ၥ௨ୖࠊ30 ၥࡄࡽ࠸ṧࡗࡓ࡜ᛮ࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ
ࡑࢀ࡟ຍ࠼࡚᪂ࡋ࠸࢔࢖ࢸ࣒ࡀධࢀࡽࢀ࡚ 131 ၥ࡟࡞ࡗ࡚࠸ࡲࡍࠋ2012 ᖺ࡟ࡣࡑࡢ 131 ၥࡢ࠺ࡕࡢ 44 ၥࡀṧࡗ࡚ࠊࡑࢀࡀ࣐࢖ࢼ࣮ࢻ࣓࢖ࣥ࡜ࡋ࡚౑ࢃࢀ࡚࠸ࡲࡍࠋ
ࡑ࠺ࡋࡲࡍ࡜ࠊ౛࠼ࡤ 129 ၥ࠿ࡽ 28 ၥ࡟⛣ࡗࡓ࡜ࡁࠊࡍ࡭࡚ࡢㄞゎຊࡢ୰ࡢ࢔࢖ࢸ࣒ࠊ
ࢧࣈࢫࢣ࣮ࣝ㸦౛࠼ࡤࠊࢸ࢟ࢫࢺࡢ⎔ቃࠊᙧᘧࠊࢱ࢖ࣉࠊഃ㠃ࠊ⏝㏵ࠊ≧ἣ ࡀᚲࡎࡋࡶ
ྠ୍ࡢࢫࢣ࣮࡛ࣝࡣ࠶ࡾࡲࡏࢇ࠿ࡽࠊࡑࢀࡀከḟඖ࡜࠸࠺ᙧ࡛ᑡࡋධࡗ࡚࠸ࡿࢃࡅ࡛ࡍࡡࠋ
ࡑࡢ࢔࢖ࢸ࣒ࡢࢭࣞࢡࢩࣙࣥࡢࡉࢀ᪉࡟ࡼࡗ࡚ࠊᅜ࡟ࡼࡗ࡚ࡣࡍࡈࡃ࢖ࣥࣃࢡࢺࡀ኱ࡁࡃ
࡞ࡿࢃࡅ࡛ࡍࠋ࡜࠸࠺ࡇ࡜࡛ࠊ౛࠼ࡤഅ↛ㄗᕪ㸦random error㸧ࡔࡅ࡛ࡣ࡞ࡃ࡚ࠊయ⣔ⓗㄗᕪ㸦systematic error㸧ࡀࢺࣞࣥࢻࡢ୰࡟ධࡗ࡚ࡁ࡚ࡋࡲ࠺ࡇ࡜࡟࡞ࡿࢃࡅ࡛ࡍࠋ
ࡇࢀ㸦ࢫࣛ࢖ࢻ 13㸧ࡣ 2015 ᖺࡢࣇ࢕࣮ࣝࢻࢸࢫࢺ࡟౑ࡗࡓ࢔࢖ࢸ࣒ࡢᩘ࡛ࡍࠋ83 ၥࠊ98
ၥ ࠊ 85 ၥ ࡜ ࠸ ࠺ ࡢ ࡣ ࠊ ᩘ Ꮫ ࡜ ㄞ ゎ ຊ ࡜ ⛉ Ꮫ ࡢ ࢺ ࣞ ࣥ ࢻ ࢆ ぢ ࡿ ࡓ ࡵ ࡢ ࢔ ࢖ ࢸ ࣒ ᩘ ࡛ ࡍ ࡅ ࢀ࡝ࡶࠊ᪂ࡋ࠸⛉Ꮫࡢၥ㢟ᩘࡣ 213 ၥ࡛ࡋࡓࠋࡇࢀࢆࢢࣛࣇ࡛ぢ࡚ࡳࡲࡍࠋࡇࡢࡃࡽ࠸ኚࢃ
ࡗ࡚࠸ࡿࢇ࡛ࡍࡡࠋ3 ศࡢ 1 ࠿ࡽ 4 ศࡢ 1 ࡄࡽ࠸ࡢ࢔࢖ࢸ࣒ࡋ࠿࣐࢖ࢼ࣮ࢻ࣓࢖ࣥࡢ࡜ࡁ
࡟ࡣṧࡗ࡚࠸࡞࠸ࢃࡅ࡛ࡍࠋ
ࡑ࠺࠸࠺ࡇ࡜࡛ࠊࢺࣞࣥࢻࢆ᳨ウࡍࡿࡇ࡜ࡀࠊ࢔ࢭࢫ࣓ࣥࢺࡢ㔜せ࡞㊃᪨࡟ࡶ࠿࠿ࢃࡽ
ࡎࠊయ⣔ⓗ࡞ㄗᕪ㸦systematic error㸧ࡀ PISA ࡢ୰࡟ࡣධࡗ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࠋࡑࢀࢆࡲࡎ
ኚ࠼ࡿ࡜࠸࠺ࡇ࡜ࢆ 2015 ᖺࡢࣉ࣏࣮ࣟࢨࣝ࡟⚾ࡓࡕࡣ᭩ࡁࡲࡋࡓࠋ
ࡇࡢࡼ࠺࡞⪃࠼᪉࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ௨๓ࡣࠊ౛࠼ࡤ 2006 ᖺ࠿ࡽ 2012 ᖺ࡟࠿ࡅ࡚ࠊࢢࣛ ࣇ ࡢ ࣂ ࣮ ࡢ 㧗 ࡉ ࡣ ᵓ ᡂ ᴫ ᛕ 㸦 construct㸧 ࡢ ࢝ ࣂ ࣞ ࣮ ࢪ ࠊ ࡍ ࡞ ࢃ ࡕ ࠊᐃࡢᑐ㇟࡜࡞ࡿᵓ

ᡂᴫᛕࡀࢻ࣓࢖ࣥ࡟࠾࠸࡚࡝ࢀࡔࡅ⥙⨶ࡉࢀ࡚࠸ࡿ࠿࡜࠸࠺ࡇ࡜ࢆ⾲ࡋ࡚࠾ࡾࡲࡋ࡚ࠊ࢔
࢖ࢸ࣒ࡢᩘ࡟┤᥋㛵ಀࡉࡏ࡚ᥥ࠸࡚࠶ࡾࡲࡍࠋࡑࢀࡀ 3 ศࡢ 1 ࠿ࡽ 4 ศࡢ 1 ࡄࡽ࠸࡟࡞ࡗ
࡚࠸ࡲࡍࡡࠋ2009 ᖺ࡜ 2012 ᖺࠋࡇࢀࡣᩘᏛ࡛ࡍࡅࢀ࡝ࡶࠋ
᪂ࡋ࠸ࢹࢨ࢖ࣥࡣࠊ2015 ᖺࠊ౛࠼ࡤ࣓ࢪ࣮ࣕࢻ࣓࢖ࣥࠊࡇࢀࡣ⛉Ꮫ࡛ࡍࡅࢀ࡝ࡶࠊ2018ᖺ࡜ 2021 ᖺ࡟ࡣ࡯࡜ࢇ࡝࢔࢖ࢸ࣒ࢆ඲㒊౑ࡗ࡚ࠊࡔࡅࢀ࡝ࡶ㡯┠ࡈ࡜ࡢ⿕㦂⪅ࡢᩘࢆῶࡽࡍࢃࡅ࡛ࡍࠋ࡜࠸࠺ࡇ࡜ࡣࠊࢹ࣮ࢱࡢᩘࡣྠࡌࡔࡅࢀ࡝ࡶ⿕㦂⪅ࡢᩘࢆῶࡽࡋ࡚ࠊఱࢆࡋ ࡞ ࡅ ࢀ ࡤ ࡞ ࡽ ࡞ ࠸ ࠿ ࡜ ࠸ ࠺ ࡜ ࠊ 2015 ᖺ ࠿ ࡽ ࢹ ࣮ ࢱ ࢆ ೉ ࡾ ࡞ ࡅ ࢀ ࡤ ࡞ ࡽ ࡞ ࠸ ࢇ ࡛ ࡍ ࡡ ࠋࡘࡲࡾࠊ2015 ᖺࡢࢹ࣮ࢱ࡜ 2018 ᖺࠊ2021 ᖺࡢࢹ࣮ࢱࢆྠ᫬࡟ゎᯒࡍࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋ

－12－࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡣࠊ2000 ᖺ࠿ࡽ 2012 ᖺࡢ PISAࠊࡲࡓ TIMMS ࡜࠿ PIAAC ࡜࠿ࠊ⚾ࡓࡕࡣࣜࢸࣛࢩ࣮ࢧ࣮࣋࢖㸦literacy survey㸧ࢆࡸࡗ࡚࠸ࡓࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢ㝿ࡢ⤒㦂࠿ࡽࡍࡿ࡜ࠊ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱ࡜࠸࠺ࡢࡣࡍࡈࡃࢫࢸ࣮ࣈ࡛ࣝࡍࠋ
ࡑ࠺࠸࠺ࡇ࡜࡛ࠊ඲ࡃูࠎࠊ௨๓ࡢࢹ࣮ࢱࢆࡲࡿ࡛౑ࢃࡎ࡟᪂ࡋ࠸ࢧ࢖ࢡࣝࡈ࡜࡟࢔ࢼ
ࣜࢩࢫࡍࡿࡢࡣࠊ㠀ᖖ࡟ࢹ࣮ࢱࢆ↓㥏㐵࠸ࡍࡿࡇ࡜࡟࡞ࡾࡲࡍࠋࡑ࠺࠸࠺ࡇ࡜࡛ࠊࢺࣞࣥ
ࢻ࡟㛵ࡋ࡚ࡣࠊ୸ࡁࡾ௨๓ࡢࡑࡢࡼ࠺࡞⪃࠼᪉ࢆኚ࠼ࡓࢃࡅ࡛ࡍࡡࠋࡍ࡞ࢃࡕࠊ㐣ཤࡢࡍ࡭࡚ࡢࢹ࣮ࢱࡢ᝟ሗࢆඹ᭷ࡍࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋࡑࢀ࡟ࡼࡗ࡚ࠊࡍ࡭࡚ࡢࢻ࣓࢖ࣥࡈ࡜࡟ 1ࡘࡢᑻᗘ࡟஌ࡏࡿࡇ࡜ࡀ࡛ࡁࠊྛ㡯┠ࡢࢫࣞࢵࢩ࣮ࣗ࣍ࣝࢻࡢ್ࡣࢧ࢖ࢡࣝࡈ࡜࡟ኚࢃࡽ
࡞ࡃ࡞ࡿ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋࡲࡓࠊ㐣ཤࡢࢹ࣮ࢱࢆ⿵ṇࡍࡿࡇ࡜ࡶ࡛ࡁࡲࡍࡢ࡛ࠊ㛗ᮇ㛫࡟ࢃࡓࡗ࡚ࢺࣞࣥࢻࢆࣔࢽࢱ࣮ࡍࡿࡇ࡜ࡀ࡛ࡁࡿࡇ࡜࡟࡞ࡾࡲࡍࠋࡲ ࡓ ࠊ ྛ 㡯 ┠ ㉁ ၥ ࡜ ྛ ᅜ ࡢ 㛵 㐃 ᛶ ࢆ ࣔ ࢽ ࢱ ࣮ ࡍ ࡿ ࡇ ࡜ ࡶ ࡛ ࡁ ࡲ ࡍ ࠋ 2012 ᖺ ࡲ ࡛ ࡣ ࢖ ࣥࢱ࣮ࢼࢩࣙࢼࣝ࡞࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡢࢭࢵࢺࡀ࠶ࡾࡲࡋ࡚ࠊྛᅜ㛫࡜ࡢ┦㛵ᛶࢆࡲࡿ࡛
↓どࡋ࡚ྠࡌ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ౑ࡗ࡚඲㒊ࡢ࢔ࢼࣜࢩࢫࢆࡋ࡚࠸ࡓࢇ࡛ࡍࡡࠋ౛࠼ࡤ
࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡀࡓࡲࡓࡲࡑࡢᅜࡢࢹ࣮ࢱ࡜ྜࡗ࡚࠸࡞ࡃ࡚ࡶࠊࡑࡢࡲࡲ࢖ࣥࢱ࣮ࢼ
ࢩࣙࢼࣝ࡞࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ౑ࡗ࡚ࡸࡗ࡚࠸ࡲࡋࡓࠋࡑ࠺ࡋ࡚ࡳࡲࡍ࡜ࠊᩘࢧ࢖ࢡࣝ
ྠࡌ㛫㐪ࡗࡓࣃ࣓࣮ࣛࢱࡢ㡯┠࡛ᐇ᪋ࡋ࡚࠸࡚ࠊ௒ᗘࠊ᪂ࡋ࠸ࢧ࢖ࢡ࡛ࣝᨵṇࡉࢀࡓ࡜࠸
࠺ࡇ࡜ࡀࢹ࣮ࢱ࡛ぢࡿ࡜ㄞࡵࡿࢃࡅ࡛ࡍࠋࡑ ࢀ ࡜ ྠ ᫬ ࡟ ࠊ ࡇ ࡢ ࢹ ࢨ ࢖ ࣥ ࢆ ぢ ࡚ ࢃ ࠿ ࡿ ࡜ ᛮ ࠸ ࡲ ࡍ ࡀ ࠊ 2015 ᖺ ࡢ ࣐ ࢖ ࢼ ࣮ ࢻ ࣓ ࢖ ࣥ࡜ ࠸ ࠺ ࡢ ࡣ ࠊ ࢹ ࣮ ࢱ ࡢ ᩘ ࡣ ㊊ ࡾ ࡞ ࠸ ࢃ ࡅ ࡛ ࡍ ࡡ ࠋ ࡛ ࡍ ࠿ ࡽ ࠊ 2015 ᖺ ࡢ ࣐ ࢖ ࢼ ࣮ ࢻ ࣓ ࢖ ࣥ࡟㛵ࡋ࡚ࡣࠊ࠶ࡿᅜ࡟࠾࠸࡚ࡣ㡯┠ࡢᩘ࡟ᑐࡋ࡚⿕㦂⪅ࡢᩘࡀᑡ࡞㐣ࡂࡿࡇ࡜࡟࡞ࡾࡲࡍ
࠿ࡽࠊ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ᥎

ࡍࡿࡇ࡜ࡀ࡛ࡁ࡞࠸࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋࡑࡇ࡛࡝࠺

ࡍࡿ࠿࡜࠸࠺࡜ࠊ2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛࡟౑ࡗࡓࢹ࣮ࢱࢆࡶ࡜࡟ゎᯒࡋ࡚ࠊ᪂ࡋ࠸ࣃࣛ
࣓࣮ࢱࢆసࡗ࡚ศᯒࢆ㐍ࡵ࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜࠸࠺ࡇ࡜࡛ࡍࠋ
㐣ཤࡢ PISA ࢹ࣮ࢱࡢ෌ศᯒ
ࡑ࠺࠸࠺ࡇ࡜࡛ࠊ2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࡢࢹ࣮ࢱࢆࡶ࠺୍ᗘศᯒࡍࡿࡇ࡜࡟ࡋࡲࡋࡓࠋ
⚾ࡣྂ࠸ࢹ࣮ࢱࢆ౑࠺ࡇ࡜ࡀ㢖⦾࡟࠶ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊࢹ࣮ࢱ࣮࣋ࢫࢆసࡿ࡜ࡁ࡟ࡣࠊ
୍ࡘ୍ࡘࡢᑠࡉ࡞ၥ㢟ࢆゎỴࡋ࡚࠸࠿࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜ゝ࠺ࡇ࡜ࡀ࠶ࡗ࡚ࠊ࡞࠿࡞࠿኱
ኚ࡛ࡍࠋ2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࡢྂ࠸ࢹ࣮ࢱࢆᢅ࠺ࡇ࡜࡟࡞ࡾࡲࡍࡀࠊ࢔࢖ࢸ࣒ࠊࢫࢥ
࢔ࣜࣥࢢ࡟㛵ࡋ࡚ࡢಶࠎࡢỴᐃ஦㡯ࡣグ㘓࡜ࡋ࡚ṧࡗ࡚࠸ࡲࡏࢇࡢ࡛ࠊ࠸ࢁ࠸ࢁ࡜ⱞປࡀ
࠶ࡿࢃࡅ࡛ࡍࠋࡍ࡭࡚ྜࢃࡏࡲࡍ࡜ 193 ୓ேࡢࢹ࣮ࢱࡀṧࡗ࡚࠸ࡲࡍ࠿ࡽࠊࡑࢀࢆࡶ࡜࡟
ࡋ࡚๓ࡢࢹ࣮ࢱࢆࡶ࠺୍ᗘᣢࡗ࡚ࡁ࡚ࠊ௒ᗘࡣ඲ࡃู࡟඲㒊⥙⨶ࡋ࡚࠸ࡿࢹ࣮ࢱ࣮࣋ࢫࢆ
సࡗ࡚ศᯒࡋࡲࡋࡓࠋ
ࡇࢀ㸦ࢫࣛ࢖ࢻ 17㸧ࡣᩘᏛࡢ౛࡛ࡍࡅࢀ࡝ࡶࠊ௨๓Ⓨ⾲ࡉࢀࡓ PISA ࡢ⤖ᯝ࡜࡯ࡰྠࡌ⤖ ᯝ ࡀ ࠊ ࣛ ࢵ ࢩ ࣗ ࣔ ࢹ ࣝ ࢆ ౑ ࡗ ࡚ ෌ ⌧ ࡛ ࡁ ࡚ ࠸ ࡲ ࡍ ࠋ 2000 ᖺ ࡢ ⤖ ᯝ ࡣ ≉ ␗ 㸦 anomaly㸧 ࡛࠶ࡗࡓ࡜ぢࡽࢀ࡚࠸ࡿࡢ࡛ࡍࡀࠊࡇࡇ࡛ࡶࡑࡢ≉ᚩࡀฟ࡚ࡁ࡚࠸ࡲࡍࠋࡇࡢࡼ࠺࡞⤖ᯝࡀᚓࡽࢀࡓ࡜࠸࠺ࡇ࡜ࡣࠊࢹ࣮ࢱ࣮࣋ࢫࡀṇ☜࡟෌⏕࡛ࡁࡓ࡜࠸࠺ࡇ࡜࠿࡜ᛮ࠸ࡲࡍࠋ
ࡑࢀࢆࡶ࡜࡟ࡋ࡚ࠊࢫࣛ࢖ࢻ 18 ࡟࠶ࡿࡼ࠺࡟ࠊ࠸ࢁ࠸ࢁ࡞ࣔࢹ࡛ࣝศᯒࡋ࡚ࡳࡲࡋࡓࠋ
ࣛࢵࢩࣗࣔࢹࣝ࡜࠸࠺ࡢࡣࠊࡶ࡜ࡶ࡜ࣔࢹࣝࡀࣇ࢕ࢵࢺࡋ࡚࠸࡞࠸࡜ࡁ࡟ࡣࡼࡃぢ࠼ࡲࡏࢇ࠿ࡽࠊ2 ࣃ࣓࣮ࣛࢱࣔࢹࣝ㸦2PL㸧ࠊࡑࢀ࠿ࡽ 2 ࣃ࣓࣮ࣛࢱࣔࢹࣝ࡜ࣛࢵࢩࣗࣔࢹࣝࢆࣁ ࢖ ࣈ ࣜ ࢵ ࢻ ࡋ ࡓ ࣔ ࢹ ࣝ ࠊ ࡲ ࡓ ࠊ 㡯 ┠ ࡜ ᅜ ࡜ ࡢ ࢖ ࣥ ࢱ ࣛ ࢡ ࢩ ࣙ ࣥ 㸦 IBCI㸸 item-by-country－13－interaction㸧 ࢆ ⪃ ៖ ࡋ ࡓ ࣛ ࢵ ࢩ ࣗ ࣔ ࢹ ࣝ ࡜ 2PL ࢆ ࣁ ࢖ ࣈ ࣜ ࢵ ࢻ ࡋ ࡓ ࣔ ࢹ ࣝ ࡜ ࡢ 㐺 ྜ ᗘ ࢆ ᳨ウࡋ࡚ࡳ࡚ࠊ᭱⤊ⓗ࡟ࡣࠊIBCI ࡢࣔࢹࣝࢆ᥇⏝ࡋࡲࡋࡓࠋ
ࣛࢵࢩࣗࣔࢹࣝ࡜࠸࠺ࡢࡣྠࡌഴࡁࢆࡶࡘ㡯┠≉ᛶࢆ௬ᐃࡋ࡚࠸࡚ࠊ㡯┠ࡢ≉ᛶࡢ㐪࠸ࡣഴࡁࡢࣃ࣓࣮ࣛࢱࢆ୚࠼ࡿ≉ᛶ᭤⥺ࡢ఩⨨ࡢ㐪࠸ࠊࡋࡤࡋࡤ b ࡜⾲ࡉࢀࡿ㞴᫆ᗘࣃ࣓ࣛ
࣮ࢱࡢࡳ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋ2PL ࡣഴࡁࡢࣃ࣓࣮ࣛࢱ࡜఩⨨ࡢࣃ࣓࣮ࣛࢱࡢ஧ࡘࡢࣃ
࣓࣮ࣛࢱࢆྵࡴࣔࢹࣝ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋIBCI ࡢ౛ࢆࠊࢫࣛ࢖ࢻ 20 ࡟♧ࡋࡲࡋࡓࠋࡇࢀࡣ PISA ࡢ౛࡛ࡣ࠶ࡾࡲࡏࢇࡅࢀ࡝ࡶࠊ
ࡓࡲࡓࡲࣇࣞࣥࢳ࢝ࢼࢲࡢࢹ࣮ࢱࡀ㝶ศ㐪ࡗ࡚ฟ࡚ࡁ࡚࠸ࡲࡋ࡚ࠊࡶࡋ௨๓ࡢࡼ࠺࡟࢖ࣥ
ࢱࣛࢡࢩࣙࣥࢆぢ࡞࠸࡛ྠࡌࣃ࣓࣮ࣛࢱࢆ౑ࡗ࡚࠸ࡲࡍ࡜ࠊࣇࣞࣥࢳ࢝ࢼࢲࡢ⤖ᯝࡣࣂ࢖࢔ࢫࢆࡶࡘࡇ࡜࡟࡞ࡾࡲࡍࠋ
ࣔࢹࣝࣇ࢕ࢵࢺࡢᣦᶆ࡜ࡋ࡚ࡣࠊᇶᮏⓗ࡟ࡣ AIC㸦㉥ụ᝟ሗ㔞つ‽㸧࡜ BIC㸦࣋࢖ࢬ᝟ሗ㔞つ‽㸧ࢆ౑࠸ࡲࡋ࡚ࠊ୺࡟ BIC ࢆ฼⏝ࡋࡲࡋࡓࠋࡑࢀ࡜ྠ᫬࡟ᖹᆒ೫ᕪ㸦MD㸸Mean
Deviation㸧ࠊࡇࢀࡣࠊᖹᆒ࠿ࡽࡢ೫ᕪ࡜ศᕸࡢ☜⋡ᐦᗘ㸦density㸧ࢆ᥃ࡅ࡚✚ศࡋࡓࡶࡢ࡛ࡍࠋRMSD㸦Root Mean Squared deviation㸧ࡣࠊࡑࢀࢆ஧஌ࡋࡓࡶࡢࢆ✚ศࡋ࡚ࠊࡑࡢᖹ᪉᰿
ࢆồࡵࡓࡶࡢ࡛ࡍࠋࡇࢀࡽࢆぢࡲࡍ㸦ࢫࣛ࢖ࢻ 22㸧࡜ࠊࣔࢹࣝࣇ࢕ࢵࢺࡣ᫂ࡽ࠿࡟ࠊࣛࢵࢩࣗࣔࢹࣝ࡜ 2PLࣔࢹࣝࡢࣁ࢖ࣈࣜࢵࢻࡋࡓࡶࡢ࡛࢔࢖ࢸ࣒࣭ࣂ࢖࣭࢝ࣥࢺ࣮࣭ࣜ࢖ࣥࢱࣛࢡࢩࣙࣥࢆ࡜ࡾ
࠸ࢀࡓࣔࢹ࡛ࣝ᭱ࡶ㐺ྜᗘࡀ㧗ࡃฟ࡚࠸ࡲࡍࠋ౛࠼ࡤࠊ↓⟅᱌ࡀ࡝ࢇ࡞↓⟅᱌࡞ࡢ࠿࡜࠸
ࡗࡓࡇ࡜ࢆゎ㔘ࡍࡿ㐪࠸࡟ࡼࡗ࡚ࡶูࠎ࡟ฟ࡚ࡁࡲࡍࠋ
ࢫࣛ࢖ࢻ 24 ࡣ RMSD ࢆ౑ࡗ࡚ࠊࡑࢀࡀ 0.15 ௨ୖࡢ࢔࢖ࢸ࣒࣭ࣂ࢖࣭࢝ࣥࢺ࣮࣭ࣜࣂ࢖
࣭ࢧ࢖ࢡࣝࡢ⤌ࡳྜࢃࡏ࡛ࠊᩘᏛࢆぢࡲࡍ࡜ 1,500 ௨ୖࡢ⤌ࡳྜࢃࡏࡢྍ⬟ᛶࡀ࠶ࡿࢇ࡛
ࡍࡅࢀ࡝ࡶࠊࡑࢀࢆぢࡿ࡜ 549ࠊ0.15 ௨ୖ࠶ࡗࡓࢣ࣮ࢫࡀ 4 ࡟ୗࡀࡗ࡚࠾ࡾࡲࡋ࡚ࠊ᫂ࡽ
࠿࡟ࣔࢹࣝࣇ࢕ࢵࢺࡔࡅ࡛ࡣ࡞ࡃ࡚ࠊࢹ࢕ࣅ࢚࣮ࢩࣙࣥࡢ኱ࡁࡉ⮬యࡶ࠿࡞ࡾῶࡗ࡚࠸ࡲ
ࡍࠋࢫࣛ࢖ࢻ 25 ࢆぢࡿ࡜ࠊ2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࡣࣛࢵࢩࣗࣔࢹࣝࢆ౑ࡗ࡚࠸ࡲࡋࡓࡀࠊ
࡝ࢀࡔࡅࣛࢵࢩࣗࣔࢹࣝ࡟ࡼࡿ㡯┠ࡀṧࡗ࡚࠸ࡿ࠿ࢆぢࡲࡍ࡜ࠊ43 㸣ࠊ19 㸣ࠊ14 㸣ࡄࡽ࠸࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋ
᭱⤊ⓗ࡟ࡣࠊPISA ࡢ 5 ᅇࡢㄪᰝࢆࡶ࡜࡟⥲ᣓⓗ࡞ࢹ࣮ࢱ࣮࣋ࢫࢆసࡾࡲࡋࡓࠋࣛࢵࢩࣗ ࣔ ࢹ ࣝ ࡜ ẚ ࡭ ࡚ 2PL ࡜ ࡢ ࣁ ࢖ ࣈ ࣜ ࢵ ࢻ ࡛ ࢔ ࢖ ࢸ ࣒ ࣭ ࣂ ࢖ ࣭ ࢝ ࣥ ࢺ ࣜ ࣮ ࣭ ࢖ ࣥ ࢱ ࣛ ࢡ ࢩࣙࣥࣔࢹࣝࡀ୍␒ࡼࡃࣇ࢕ࢵࢺࡋࡲࡋࡓࠋ3 ࡘࡢᇶᮏࣜࢸࣛࢩ࣮ࡢ⣼✚ࢹ࣮ࢱࡣࠊࡍ࡭࡚
㡯┠཯ᛂ⌮ㄽ㸦IRT㸧࡟ᇶ࡙࠸ࡓࣃ࣓࣮ࣛࢱ࡛᥎ᐃࡋࡲࡋࡓࠋ
ࣇ࢕࣮ࣝࢻࢸࢫࢺࡢศᯒ⤖ᯝḟ ࡟ ࠊ 2015 ᖺ ࡢ ࣇ ࢕ ࣮ ࣝ ࢻ ࢸ ࢫ ࢺ ࡢ ࢔ ࢼ ࣜ ࢩ ࢫ ࡢ ⤖ ᯝ ࢆ ぢ ࡚ ࡳ ࡲ ࡍ ࠋ ࡶ ࠺ ୍ ᗘ ゝ ࠸ ࡲࡍ ࡡ ࠋ ࡇ ࡢ ࢹ ࢨ ࢖ ࣥ ࢆ ᐇ ⌧ ࡍ ࡿ ࡓ ࡵ ࡟ ࠊ ࡲ ࡓ PBA㸦 paper-based

assessment㸧 ࠿ ࡽ CBA

㸦computer-based assessment㸧࡜࣮ࣔࢻࢆኚ࠼ࡿࡓࡵ࡟ࠊࣇ࢕࣮ࣝࢻࢸࢫࢺࡀࠊᑠつᶍ࡞ PISAࡢ࣓࢖ࣥࢧ࣮࣋࢖࡛ࡣ࡞ࡃ࡚ࠊࣜࣥ࢟ࣥࢢࢫࢱࢹ࢕㸦linking study㸧࡟࡞ࡗ࡚࠸ࡿࢇ࡛ࡍࡡࠋ
ࡔ࠿ࡽࠊ௒ࡲ࡛ࡢ PISA ࡢࣇ࢕࣮ࣝࢻࢸࢫࢺࡢᛶ᱁࡜ࡣ୸ࡁࡾู࡞ᛶ᱁ࢆᣢࡗ࡚࠸ࡲࡍࠋ
ࣇ࢕࣮ࣝࢻࢸࢫࢺ࣭࢔ࢭࢫ࣓ࣥࢺ࣭ࢹࢨ࢖ࣥࡣࡔ࠸ࡓ࠸ࡇࡢࡼ࠺࡟࡞ࡗ࡚࠸ࡿࢇ࡛ࡍࡅ
ࢀ࡝ࡶࠊ↓సⅭ࡟ 3 ࡘࡢࢢ࣮ࣝࣉ࡟ศࡅ࡚ࠊࢢ࣮ࣝࣉ 1 ࡜࠸࠺ࡢࡣ PBA ࡢࢺࣞࣥࢻ࢔࢖
ࢸ࣒ࡔࡅࢆ౑ࡗ࡚ࠊ᭱ึࡢࣇ࢛࣮࣒ࡣࠊ1 ࠿ࡽ 6 ࡲ࡛ࡢࣇ࢛࣮࣒ࡣ⛉Ꮫ࡜ᩘᏛࢆ 1 ᫬㛫ࡈ－14－࡜࡟౑ࡗ࡚࠸ࡲࡍࠋࡑࢀ࡜ྠࡌၥ㢟ࡀࣇ࢛࣮࣒ 31 ࠿ࡽ 36 ࡲ࡛ࠊࢥࣥࣆ࣮ࣗࢱ࣮࣋ࢫ࡛ࢢ
࣮ࣝࣉ 2 ࡣ౑ࡗ࡚࠸ࡲࡍࠋࢢ࣮ࣝࣉ 3 ࡣࡍ࡭࡚᪂ࡋ࠸࢔࢖ࢸ࣒࡛ࡍࠋ
⤖ᯝ࡜ࡋ࡚ぢ࠼࡚ࡁࡓࡢࡣࠊᩘᏛࣜࢸࣛࢩ࣮࡛ࡣࠊࢡࣛࢫࢱ࣮ࡀ 4 ␒┠࡟ฟ࡚ࡁࡓ࡜ࡁ
࡟ࡣ 16 ศࡄࡽ࠸࡛࡛ࡁࡓࡶࡢࡀࠊ᭱ึ࡟ฟ࡚ࡁࡓ࣏ࢪࢩࣙࣥࡢሙྜ࡟ࡣ 19.76 ศࠊ20 ศࡄࡽ࠸࠿࠿ࡗ࡚࠸ࡿࢇ࡛ࡍࡡࠋࡇࢀࡣ᪤࡟ ICT ࡢࢺ࣮ࣞࢽࣥࢢࢆ 5 ศࡄࡽ࠸ࡋࡓᚋࠊࡲࡓࡇࢀ ఩࠿࠿ ࡗ࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊ⤖ᒁࡇࢀࡣຠ⋡㸦efficiency㸧ࡀୖࡀࡗ࡚࠸ࡿࡢ࡛ࡣ
࡞࠸࠿࡜⪃࠼࡚࠸ࡲࡍࠋ
ࡋ࠿ࡋࠊࣞࢫ࣏ࣥࢫࡀ᪩ࡃ࡞ࡗ࡚ࡶṇ⟅⋡ࡀୗࡀࡗࡓࡽᅔࡿࢃࡅ࡛ࡍࡡࠋࡑ࠺࠸࠺ࡇ࡜࡛ ṇ ⟅ ⋡ ࢆ ぢ ࡲ ࡍ ࡜ ࠊ ᐇ 㝿 ᑡ ࡋ ୗ ࡀ ࡗ ࡚ ࠸ ࡿ ࢃ ࡅ ࡛ ࡍ ࠋ 2015 ᖺ ࡢ ࣇ ࢕ ࣮ ࣝ ࢻ ࢸ ࢫ ࢺ ࢆ ぢࡲࡍ㸦ࢫࣛ࢖ࢻ 31㸧࡜ᩘᏛࣜࢸࣛࢩ࣮ࡢሙྜ࡟ࡣ 0.44 ࠿ࡽ 0.43 ࡲ࡛ࠊ1 㸣࡜࠿ 3 㸣ࡄࡽ࠸ṇ⟅⋡ࡀୗࡀࡗ࡚࠸ࡲࡍࠋ࡛ࡶࠊࡇࢀࡣ 2015 ᖺࡔࡅ࡛ࡍ࠿ࡽࠊ๓ࡢ 2009 ᖺ࡟࡝ࢀࡔࡅୗ ࡀ ࡗ ࡚ ࠸ ࡓ ࠿ ࢆ ぢ ࡞ ࠸ ࡜ ẚ ㍑ ࡛ ࡁ ࡞ ࠸ ࢇ ࡛ ࡍ ࡡ ࠋ ࡑ ࢀ ࢆ ぢ ࡿ ࡜ ࠊ PBA ࡞ ࢇ ࡛ ࡍ ࡅ ࢀ ࡝ࡶࠊ2009 ᖺ࡟ࡣ 4 㸣࠿ࡽ 8 㸣ࡄࡽ࠸ࡲ࡛ୗࡀࡗ࡚࠸ࡿࢃࡅ࡛ࡍࡡࠋࡔ࠿ࡽࢥࣥࣆ࣮ࣗࢱ
࣮࣋ࢫ࡟ࡋࡓ࡜ࡁ࡟ࠊㄞゎຊ࡟ࡘ࠸࡚ࠊ2015 ᖺ࡛ 3 㸣ⴠࡕ࡚࠸ࡓࡢࡀ 2009 ᖺ࡛ࡣ 8 㸣ⴠࡕ࡚࠸ࡲࡍࠋせࡍࡿ࡟ࠊCBA ࡛ࡣ఩⨨ຠᯝ࡜࠸࠺ࡢࡀῶࡗ࡚࠸ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋ
ࣞࢫ࣏ࣥࢫࢱ࢖࣒࡟㛵ࡋ࡚ࠊ⬟ຊู࡟ぢ࡚ࡳࡲࡋࡓࠋ⬟ຊู࡟ぢࡲࡍ࡜ࠊࡇࢀࡣ⬟ຊࢆ୍␒ୗ࡜୰㛫࡜ୖ 3 ศࡢ 1 ࡢ 3 ࡘ࡟ศࡅ࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑ࠺ࡋ࡚ࡳࡲࡍ࡜ࠊ⬟ຊࡀ࠶ࡿேࡢ࡯࠺ࡀ᫬㛫ࢆ࠿ࡅ࡚ࡸࡗ࡚࠸ࡲࡍࡡࠋ⬟ຊࡢ࡞࠸ேࡢ࡯࠺ࡀ᪩ࡃࡸࡗ࡚࠸ࡿࠋ
࢔࢖ࢸ࣒࡟ࡼࡗ࡚ࠊࡇࡢࢢࣛࣇࡣ⛉Ꮫࡢࢺࣞࣥࢻࡢ㉁ၥࡢᅜูࡢࣞࢫ࣏ࣥࢫࢱ࢖࣒ࡢᖹᆒ
್࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡇࡇ࡛ࡣࡗࡁࡾฟ࡚ࡃࡿࡢࡣࠊࡍ࡭࡚ࡢᅜ࡛㉁ၥ⮬యࡢ≉ᛶ࡜ࡋ࡚ぢࡽࢀࡿࡇ࡜ࠋ㯮࠸ࣛ࢖ࣥࡣᶆ‽೫ᕪ࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀࡶ㉁ၥࡢᖹᆒ್࡜ྠᵝ࡟ື࠸࡚࠸ࡲࡍࠋ㉁ၥࡢࢡࣛࢫࢱ࣮ෆ࡛ࡢ఩⨨࡜ࡣవࡾ㛵ಀ࠶ࡾࡲࡏࢇࠋࢫࣆ࣮ࢻࢸࢫࢺ࡛ࡣ
࡞࠸࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋ
௚ࡢࢸࢫࢺࠊ≉࡟グ᠈ຊࡀከ኱࡟ᙳ㡪ࡋ࡚࠸ࡿࢸࢫࢺࡣࠊ⬟ຊࡢ㐪࠸ࡀࢫࣆ࣮ࢻ࡟࡚ࡁ
ࡵࢇ࡟ฟ࡚ࡃࡿࡇ࡜ࡀࡼࡃ࠶ࡾࡲࡍࠋ⬟ຊࡣࢫࣆ࣮ࢻ࡛ࡶ࠶ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋࡋ࠿ࡋࠊPIAAC ࡢࢹ ࣮ࢱࡶ ࡑ࠺࡞ ࢇ࡛ࡍ ࡅࢀ࡝ ࡶࠊPISA ࡢ ࢹ࣮ࢱ ࡣ⬟ຊ ࡜ࢫࣆ ࣮ࢻ࡜཯㠃ࡋ࡚࠸ࡲࡍࠋࡇ ࢀ 㸦 ࢫ ࣛ ࢖ ࢻ 34㸧 ࡣ ᩘ Ꮫ ࡢ ࢺ ࣞ ࣥ ࢻ ࡛ ࡍ ࠋ ㉁ ၥ ࡢ ࣞ ࢫ ࣏ ࣥ ࢫ ࢱ ࢖ ࣒ ࡢ ᖹ ᆒ ್ ࢆ ⬟ ຊ
ࡢୖࡢ 3 ศࡢ 1 ࡜ୗࡢ 3 ศࡢ 1 ࡜ẚ࡭ࡓࡶࡢ࡛ࡍࠋ᫬㛫ࡢ࠿࠿ࡿ㉁ၥࡣ≉࡟㞴ࡋ࠸㉁ၥࡀ
ከ࠸ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ⬟ຊࡢ࡞࠸ேࡣၥ㢟࡟㛵ಀ࡞ࡃࠊ࡯࡜ࢇ࡝ྠࡌࡼ࠺࡞᫬㛫ࡀࣞࢫ࣏ࣥࢫࡋ࡚ࠊ⬟ຊࡢ࠶ࡿேࡣࠊ᫬㛫ࡢ࠿࠿ࡿၥ㢟࡟㛵ࡋ࡚ࡣ㛗ࡃ᫬㛫ࢆ࠿ࡅ࡚ࡸࡗ࡚࠸ࡲࡍࠋ
↓సⅭࡢ 3 ࢢ࣮ࣝࣉࡣ࡯ࡰ࠺ࡲࡃ࡛ࡁࡲࡋࡓࡀࠊᑠࡉ࠸Ꮫᰯࡢሙྜࠊᅜ࡟ࡼࡗ࡚ࡣྠ➼໬ࡣ᏶඲࡛ࡣ࠶ࡾࡲࡏࢇ࡛ࡋࡓࠋ
᥇Ⅼ⪅ಙ㢗ᛶ㸦corderreliability㸧ࡢⅬ࡛ࡍࡀࠊPISA ࡢ 3 ศࡢ 1 ࡄࡽ࠸ࡣ࣮࢜ࣉ࣭ࣥࢡ࢚

ࢫࢳ࡛ࣙࣥࡍ࠿ࡽࠊࡑࡢᅇ⟅࡟ࡘ࠸࡚ࡣ᥇Ⅼ⪅࡟ࡼࡗ࡚᥇Ⅼࡉࢀࡲࡍࠋࡑࡢಙ㢗ᛶ࡟ࡘ࠸࡚ ࡶ ࣔ ࢽ ࢱ ࣮ ࡋ ࡚ ࠸ ࡲ ࡍ ࡅ ࢀ ࡝ ࡶ ࠊ ᥇ Ⅼ ⪅ ಙ 㢗 ᛶࡣ ࠿ ࡞ ࡾ ࡼ࠿ ࡗ ࡓ ࡛ ࡍ ࡡࠋ ᖹ ᆒ ࡛ ࡶ 94
㹼 95 㸣ࡄࡽ࠸࡜㧗࠸ࢇ࡛ࡍࠋࡋ࠿ࡋࠊᅜ࡟ࡼࡗ࡚ࡣࡎࡗ࡜ప࠸ᅜࡶ࠶ࡾࡲࡍࡋࠊ㡯┠࡟ࡼࡗ࡚ࡣ࠿࡞ࡾప࠸ࡶࡢࡶ࠶ࡾࡲࡍࠋࡔ࠿ࡽࠊ࣓࢖ࣥࢧ࣮࣋࢖ࡢ࡜ࡁ࡟ࡣಙ㢗ᛶࡢప࠸ၥ
㢟ࡣࡶ࠺୍ᗘࢺ࣮ࣞࢽࣥࢢࡋ┤ࡍࡇ࡜࡟࡞ࡾࡲࡍࠋ
ࡑࢀ࠿ࡽ↓ᅇ⟅ࡢ⋡࡛ࡍࡀࠊࡇࢀࡣ CBA ࡢ᪉ࡀ PBA ࡼࡾࡶపࡃ࡞ࡗ࡚࠸ࡲࡋࡓࠋᚋ࡟

－15－⥆ࡃࢡࣛࢫࢱ࣮ࡢ᪉ࡀṇ⟅⋡ࡀపୗࡋࡲࡍࡢ࡛ࠊࣞࢫ࣏ࣥࢫࢱ࢖࣒ࡀࢹ࣮ࢱࡢ㉁ࡢྥୖ࡟
㈉⊩ࡋ࡚࠸ࡿ࡜࠸࠺ࡇ࡜࠿࡜ᛮ࠸ࡲࡍࠋࣞࢫ࣏ࣥࢫࢱ࢖࣒ࡣ㡯┠ࡢ఩⨨ࡸᅜࡢ㐪࠸࡛ࡣ࡞ࡃࠊ㡯┠ࡢ≉Ṧᛶ࡜Ꮫ⏕ࡢ⬟ຊ࡟ᙉࡃᙳ㡪ࡉࢀ࡚࠸ࡲࡋࡓࠋ
CBT ࡜ PBT ࡢᕪ␗ḟ࡟ࠊPBA ࡜ CBA ࡢ࣮ࣔࢻ࢚ࣇ࢙ࢡࢺ࡞࡝࡟㛵ࡋ࡚ࡈ⤂௓ࡋ࡚ࡳࡲࡍࠋPISA ࡣ ホ ౯ ᑻ ᗘ ࡢ ྛ ࢧ ࢖ ࢡ ࣝ ࡢ ஫ ᥮ ᛶ ࡀ 㔜 せ ࡞ ఩ ⨨ ࢆ ༨ ࡵ ࡲ ࡍ ࠋ ࡇ ࢀ ࡣ ᩘ Ꮠ ⓗ ࡞ ➼ ໬ࡔࡅ࡛ࡣ࡞ࡃ࡚ࠊホ౯ᑻᗘࡢゎ㔘ࡢ஫᥮ᛶ࡜࠸࠺ࡇ࡜ࡶྵࡳࡲࡍࠋ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡀ
ྠࡌ࡟࡞ࡿ࡜࠸࠺ࡇ࡜ࡣࠊホ౯ᑻᗘࡢゎ㔘࡟஫᥮ᛶࡀ࠶ࡿ࡜࠸࠺ࡇ࡜ࢆព࿡ࡋࡲࡍࠋࡑࢀ
ࡣ➼໬࡜࠸࠺ࡼࡾࡶࠊඹ㏻ࡢࢫࢣ࣮ࣝࢆసࡿ࡜࠸࠺ព࿡ྜ࠸ࡀ࠶ࡾࡲࡍࠋࡍ࡞ࢃࡕࠊࢧ࢖
ࢡࣝ㛫ࡢ㡯┠ࣃ࣓࣮ࣛࢱࢆ➼໬ࡍࡿࡇ࡜࡛ࡍࠋඹ㏻㡯┠࡟㛵ࡋ࡚ࠊPISA2000 ࠿ࡽ 2012 ࡢࣃ࣓࣮ࣛࢱ࡜ 2015 ᖺࡢࢺࣞࣥࢻࢆぢࡿ㡯┠ࡢࣃ࣓࣮ࣛࢱࡀྠࡌ࡛࠶ࡿࡇ࡜ࡀࡲࡎ᭱ึ࡛ࠊ
ࡑࡢᚋࠊྛᅜ㛫ࡢࣃ࣓࣮ࣛࢱࡀྠࡌ࡛࠶ࡿ࡜࠸࠺ࡇ࡜ࠊࡑࡢᚋࠊPBA ࡜ CBA ࡢඹ㏻㡯┠ࡀྠࡌ࡛࠶ࡿ࡜࠸࠺ࡇ࡜࡞࡝ࢆ☜ㄆࡋ࡚࠸ࡃࡇ࡜࡟࡞ࡾࡲࡍࠋࡑࢀࡽ࡟㛵ࡋ࡚ IRT ࢆ౑ࡗ࡚ࡸࡗ࡚ࡳࡲࡋࡓࠋࡲ ࡎ ᭱ ึ 㸦 ࢫ ࣛ ࢖ ࢻ 38㸧 ࡣ ࠊ ᩘ Ꮫ ࣜ ࢸ ࣛ ࢩ ࣮ ࡟ ࡘ ࠸ ࡚ ࠊ ⾲ ഃ ࡣ ࢔ ࢖ ࢸ ࣒ ࠊ ⾲ 㢌 ࡣ ᅜ ࢆ࡜ࡗࡓࡶࡢ࡛ࡍࠋ⥳ࡀ MD㸦mean deviation㸧ࡀ 0.2 ࡼࡾᑠࡉ࠸࡜ࡇࢁ࡛ࡍࠋ㉥ࡣ MD ࡀ 0.2
ࡼ ࡾ ኱ ࡁ ࠸ ࡜ࡇ ࢁ ࠋ ⛉ Ꮫࣜ ࢸ ࣛ ࢩ ࣮ࡣ 95.1 㸣 ࢢ ࣜ ࣮ ࡛ࣥ ࡋ ࡓ ࠋ ࠸ ࡎࢀ ࡶ ᑻ ᗘ ࡢ஫ ᥮ ᛶ ࡀࡣࡗࡁࡾ࡜♧ࡉࢀࡲࡋࡓࠋ
ࢫࣛ࢖ࢻ 39 ࡣࠊ2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࡢ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ౑ࡗ࡚ࠊ2015 ᖺࡢ PISAࡢࣇ࢕࣮ࣝࢻࢸࢫࢺࡢࢹ࣮ࢱࢆぢ࡚ࡳࡓࡶࡢ࡛ࡍࠋ⛉Ꮫࡢ᪂ࡋ࠸࢔࢖ࢸ࣒࡟ࡘ࠸࡚ࡣ࢔࢖
ࢸ࣒ࣃ࣓࣮ࣛࢱࡀ᪤▱࡛ࡣ࠶ࡾࡲࡏࢇ࠿ࡽࠊࡇࡢ᪂ࡋ࠸࢔࢖ࢸ࣒ࡔࡅࢆ౑ࡗ࡚࢔࢖ࢸ࣒ࣃ
࣓࣮ࣛࢱࢆ᥎ᐃࡋ࡚ࠊࡑࢀࢆࡍ࡭࡚ࡢᅜ࡛࡝ࢀࡔࡅ MD ࡀ࠶ࡿ࠿ࢆぢࡓࡶࡢ࡛ࠊ98.1 㸣ࡀ
0.2 ௨ୗࡢ MD ࡜࡞ࡗ࡚࠸ࡲࡋࡓࠋ
PBA ࡜ CBA ࡢ஫᥮ᛶࡣࡶ࠺ࡕࡻࡗ࡜」㞧࡛ࡋ࡚ࠊ3 ࡘࡢࢢ࣮ࣝࣉࡀ↓సⅭ࡟ᢳฟࡉࢀ࡚ ࠸ ࡿ ࡇ ࡜ ࡀ ẚ ㍑ ࡢ ᇶ ᮏ ࡛ ࡍ ࡅ ࢀ ࡝ ࡶ ࠊ ࡇ ࢀ 㸦 ࢫ ࣛ ࢖ ࢻ 41㸧 ࢆ ぢ ࡿ ࡜ ࢃ ࠿ ࡿ ࡜ ᛮ ࠸ ࡲ ࡍࡀࠊ୍␒ୖࡢ‫ ࡢی‬CMC㸦complex multiple choice㸧ࡣࠊከ⫥㑅ᢥࡢ㡯┠࡛ࡍࡀࠊ2 ẁ㝵ࡢ㑅ᢥࡀྵࡲࢀࡿࡇ࡜࠿ࡽࠊ㏻ᖖࡢከ⫥㑅ᢥ㡯┠࡜㐪࠸ࠊၥ㢟ࡢࢱ࢖ࣉ࡟࠿࠿ࢃࡽࡎࠊഴࡁࣃ
࣓࣮ࣛࢱ a ࡟㛵ࡋ࡚ࡣࠊ࠿࡞ࡾ㧗࠸┦㛵ᛶࡀ࠶ࡾࡲࡍࠋ
ࡇࢀ㸦ࢫࣛ࢖ࢻ 42㸧ࡣࠊ఩⨨ࣃ࣓࣮ࣛࢱ b ࡟㛵ࡍࡿࡶࡢ࡛ࡍࠋࢺࣞࣥࢻࢆぢࡿࡓࡵࡢ࢔ ࢖ ࢸ࣒࡛ ࡍࡅࢀ ࡝ࡶࠊ PBA ࡜ CBAࠊ ᩘᏛࣜ ࢸࣛࢩ ࣮ࠊㄞ ゎຊࠊ ⛉Ꮫࣜ ࢸࣛࢩ ࣮ࠊࡍ ࡭࡚ධࢀ࡚ࣉࣟࢵࢺࡋࡓࡶࡢ࡛ࡍࠋࡇࢀࡶ࠿࡞ࡾ㧗࠸┦㛵ᛶࢆࡶࡗ࡚࠸ࡲࡍࠋ㧗࠸┦㛵ᛶࡣࠊഴࡁࣃ࣓࣮ࣛࢱ aࠊ఩⨨ࣃ࣓࣮ࣛࢱ b ࡢ୧ࣃ࣓࣮ࣛࢱ࡛ぢࡽࢀࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋࡇࡢ⤖ᯝࢆぢࡲࡍ࡜ࠊ࣮ࣔࢻ࢚ࣇ࢙ࢡࢺࡣࠊ࡯࡜ࢇ࡝࡞࠸࡜⪃࠼࡚ࡼ࠸࡜ᛮ࠸ࡲࡍࠋ࡛ࡍ࠿ࡽࠊ75
㹼 85 㸣ࡄࡽ࠸ࡢ࢔࢖ࢸ࣒ࡣࠊPBA ࡜ CBA ࡛ࠊྠࡌ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ౑ࡗ࡚ᕪࡋ㞀ࡾ࡞࠸࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋPISA2015 ࡢㄪᰝタィ
PISA2000 ᖺ࠿ࡽ 2012 ᖺ࡟࠿ࡅ࡚ࢡࣛࢫࢱ࣮ࡢᩘࡣࠊࡲࡎࠊ࣐࢖ࢼ࣮ࢻ࣓࢖ࣥࡢ࡜ࡁ࡟
ࡣ 2 ࠿ 3 ࡄࡽ࠸࡟࡞ࡿࢇ࡛ࡍࡡࠋ௒ᗘࠊ᪂ࡋ࠸ࢹࢨ࢖࡛ࣥࡣ 6 ࡛ࡍ࠿ࡽࠊࡑࡢ࠺ࡕ 4 ࠿ࡽ 5ࡄࡽ࠸ࡢࢡࣛࢫࢱ࣮ࡢศࡣྠࡌ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ౑࠼ࡿࠋࡇࢀࡣࡶ࠺ 2 ಸࡢᩘࡢ࢔࢖
ࢸ࣒࡛ࠊྠ୍ࡢࣃ࣓࣮ࣛࢱࢆ౑࠼ࡿࢃࡅ࡛ࡍࡡࠋ

－16－᪂ࡋ࠸ࢹࢨ࢖ࣥ࡟㛵ࡋ࡚ࡣࠊㄞゎຊ࡜ᩘᏛࡣࠊ୧᪉࣐࢖ࢼ࣮ࢻ࣓࢖࡛ࣥࡍࡅࢀ࡝ࡶࠊࡑ
ࢀࡒࢀ 6 ࢡࣛࢫࢱ࣮ࢆ౑ࡗ࡚࠸ࡲࡍࠋࣇ࢓࢖ࢼࣥࢩࣕࣝࣜࢸࣛࢩ࣮ࡣ≉ู࡞ࣜࢸࣛࢩ࣮࡛
ࢡࣛࢫࢱ࣮ࡀ 2 ࡘ࡛ࡍࠋ༠ㄪⓗၥ㢟ゎỴ㸦CPS㸸collaborative problem solving㸧ࡣ 3 ࢡࣛࢫࢱ࣮ࢆ౑࠸ࡲࡍࠋ⛉Ꮫࡣ 6 㸩 6 ࡛ࠊࢺࣞࣥࢻࡢ 6 ࢡࣛࢫࢱ࣮࡜᪂ࡋ࠸ 6 ࢡࣛࢫࢱ࣮࡛ࡍࠋࡇ
ࢀࡀ᪂ࡋ࠸ࢹࢨ࢖࡛ࣥࡍࠋ

ᇶᮏⓗ࡟ࡣ 6,300 ேࠊ150 ࡢᏛᰯ࡛ 42 ேࡎࡘ 66 ࣇ࢛࣮࣒ࢆ౑ࡗ࡚ࠊ௨๓ࡣ 13 ࣇ࢛࣮࣒࡛ࡋࡓ࠿ࡽࠊ࣮࣌ࣃ࣮࣮࣋ࢫࡢ࡜ࡁ࡟ࡣࣇ࢛࣮࣒ࡢᩘࡀቑ࠼ࡿ࡜࡜࡚ࡶ⟶⌮࡛ࡁ࡞࠿ࡗࡓ
ࢇ࡛ࡍࡀࠊࡇࢀࡣࢥࣥࣆ࣮ࣗࢱ࣮࣋ࢫ࡛ᐇ᪋ࡉࢀࡲࡍ࠿ࡽࠊࡍ࡭࡚ࡢ࢔ࢻ࣑ࢫࢺ࣮ࣞࢩࣙ
ࣥࡢࢹࢨ࢖ࣥࡣࢥࣥࣆ࣮ࣗࢱෆ㒊࡛ࡸࡗ࡚࠸ࡲࡍ࠿ࡽࠊࣇ࢛࣮࣒ࡢᩘࡀቑ࠼࡚ࡶ୸ࡁࡾၥ
㢟࡞࠸࡛ࡍࡡࠋ
⥳ࡣ⛉Ꮫ࡛ࢢ࣮ࣞࡀㄞゎຊ࡛ࡍࠋ⛉Ꮫࡣ 12 ࢡࣛࢫࢱ࣮࠶ࡾࡲࡍࠋࡑࡢ 12 ࢡࣛࢫࢱ࣮ࡢ
ࢥࣥࣅࢿ࣮ࢩࣙࣥࡣࡍࡈࡃ㞴ࡋ࠸ࢇ࡛ࡍࡼࠋࡑࢀࢆ᏶඲࡟࣏ࢪࢩ࡛ࣙࣥࣂࣛࣥࢫࡋ࡚ࠊࡑ
ࢀ࡜௚ࡢࢻ࣓࢖ࣥ࡜ࡢවࡡྜ࠸ࡶࣂࣛࣥࢫࡋ࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡛ࡍ࠿ࡽࠊࡇࢀࢆ౑ࡗ࡚ࠊ
ࡇࡢࢹࢨ࢖ࣥࢆࡶ࡜࡟ࡋ࡚ࠊࡑ࠺ࡍࡿ࡜᪂ࡋ࠸ࢡࣛࢫࢱ࣮ࡣ 8 ᅇࡎࡘࠊྂ࠸ࢡࣛࢫࢱ࣮ࡣ 4ᅇࡎࡘฟࡋࡲࡍࠋ
ࡇࢀࡣྠࡌࡇ࡜࡛ࡍࡅࢀ࡝ࡶࠊࡇࢀࢆ஘ᩘࢆ฼⏝ࡋ࡚ྜࢃࡏࡲࡍ࡜ࠊ66  6 ࡢ 396 ࣇ࢛࣮࣒ࢆసࡿࡇ࡜࡟࡞ࡿࢇ࡛ࡍࡡࠋࡑࢀࢆࡶ࡜࡟ࡋ࡚ࠊࡶ࡜ࡶ࡜⛉Ꮫ࡜ᩘᏛ࡜ㄞゎຊࡢ 2࢙࢘࢖ࡢඹศᩓ㸦covariance㸧࡜ࠊࡑࢀ࡜඲㒊౑ࡗ࡚࠸ࡿ 3 ࢙࢘࢖ࡢඹศᩓࢆ౑ࡗ࡚⤖ࡧࡘࡅ࡚࠸ࡲࡍࠋ
⛉Ꮫ㡿ᇦࡢࢧࣥࣉࣝ㡯┠⛉Ꮫࡢࢧࣥࣉࣝ࢔࢖ࢸ࣒㸦ࢫࣛ࢖ࢻ 52 㹼 59㸧ࡣࠊࡇࡢࡼ࠺࡞ࡶࡢࢆ౑ࡗ࡚࠸ࡲࡍࠋࡇ
ࢀࡣ PISA ࡛సࡽࢀࡓ᪂ࡋ࠸࢔࢖ࢸ࣒࡛ࡍࡅࢀ࡝ࡶࠊࡇࡢ࢔࢖ࢸ࣒ࡣᐇ㝿࡟ࡣ౑࠸ࡲࡏࢇࠋ
2015 ᖺ ࡢ ࡓ ࡵ ࡟ స ࡽ ࢀ ࡲ ࡋ ࡓ ࡅ ࢀ ࡝ ࡶ ࠊ ࡇ ࢀ ࡣ ⤖ᒁ ࠊ ᝏ ࠸࢔ ࢖ ࢸ ࣒࡛ ࡣ ࡞ ࠸ࢇ ࡛ ࡍ ࡼࠋ࠸࠸࢔࢖ࢸ࣒࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡇࢀ࡜ྠࡌࡼ࠺࡞ࢥࣥࢫࢺࣛࢡࢺࡢ௚ࡢ࢔࢖ࢸ࣒ࡀ࠶ࡾ
ࡲࡋࡓ࠿ࡽࠊࡑࢀࢆ౑ࡗ࡚ࠊࡇࢀࡣ౑ࢃ࡞࠸ࡇ࡜࡟Ỵࡵࡲࡋࡓࠋ࡛ࡶࠊࡇࢀࡣ࡜࡚ࡶ࠸࠸
౛ࡔ࡜ᛮ࠸ࡲࡍࠋࢩ࣑࣮ࣗࣞࢩࣙࣥࡢ࢔࢖ࢸ࣒࡛ࠊࢫࣛ࢖ࢲ࣮ࢆኚ࠼ࡿࡇ࡜ࡀ࡛ࡁࡓࡾࠊ
ࢩ࣑࣮ࣗࣞࢩࣙࣥࢆᐇ⾜ࡍࡿࡇ࡜ࡀ࡛ࡁࡓࡾࡋࡲࡍࠋ
ከ⫥㑅ᢥᘧࡢၥ㢟ࡶྵࡲࢀ࡚࠸ࡲࡍࡀࠊྑࡢ 2 ࡘࡢࢫࣛ࢖ࢲ࣮ࢆ౑ࡗ࡚ᐇ㝿࡟ࢹ࣮ࢱࢆ
సࡿ࡜࠸ࡗࡓၥ㢟ࡶ࠶ࡾࡲࡍࠋࡲࡓࠊከ⫥㑅ᢥ࡛ࡣ࡞ࡃࠊ⌮⏤ࢆグ㏙ࡍࡿ࣮࢜ࣉ࢚ࣥࣥࢻ
ࡢၥ㢟ࡶྵࡲࢀ࡚࠸࡚ࠊࡇࢀࡽࡣᐇ㝿࡟ࡣࢥ࣮ࢲ࣮ࡀ᥇Ⅼࡋࡲࡍࠋࡇࢀࡣࢥࣥࣆ࣮ࣗࢱ࠿
ࡽᢳฟࡋ࡚ࠊࡑࢀࢆࡶ࡜࡟᏶඲࡟࣮࢜࢞ࢼ࢖ࢬࡋ࡚ࢥ࣮ࢲ࣮࡟᣺ࡾศࡅࡲࡍࠋࢥ࣮ࢲ࣮ࡢ
ࣜࣛ࢖࢔ࣅࣜࢸ࢕ࡶྠ᫬࡟ぢࡲࡍࡋࠊࢥ࣮ࢲ࣮ࡢࣞࢥ࣮ࢻ࡜࠿ࡶࡍ࡭࡚グ㘓ࡋࡲࡍࠋ௨ୖ࡛ࡍࠋ㸦ᢿᡭ㸧

－17－

㉁␲ᛂ⟅
‫ྖۑ‬఍

࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

ࡕࡻࡗ࡜᫬㛫ࡀ࠶ࡾࡲࡍࡢ࡛ࠊఱ࠿஦ᐇ☜ㄆⓗ࡞ࡈ㉁ၥ࡞࡝࠶ࡾࡲࡋࡓࡽ࡝࠺ࡒ࠾ᡭࢆ
࠾ᣲࡆ࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ࣐࢖ࢡࢆ࠾ᣢࡕࡋࡲࡍࠋ
࡞࠸࡛ࡋࡻ࠺࠿ࠋ࡛ࡣࠊ⚾࠿ࡽ 1 ࡘࠋࢫࣞࢵࢩ࣮ࣗ࣍ࣝࢻࡢࢢࣛࣇࡀ࠶ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊࢫࣞࢵࢩ࣮ࣗ࣍ࣝࢻ࡜࠸࠺⪃࠼᪉ࡣ ᪥ ᮏ ࡛ ࡣ వࡾ ౑ ࢃ ࢀ ࡚࠸ ࡞ ࠸ ࡜ ᛮ࠸ ࡲ ࡍ ࡀ ࠊ 㡯┠ ࡢ ṇ ⟅ ⋡ࡀ 0.62 ࡜ ࡞ ࡿ Ⅼ ᩘ࡜ ࠸ ࠺ ࡇ࡜࡛ࡍࡼࡡࠋ
‫ۑ‬ᒣᮏẶ

ࡑ࠺࡛ࡍࠋࡔ࠿ࡽᮏ᮶ࠊ࣐ࢫࢱࣜࣥࢢ࡜࠸࠺ࡇ࡜ࢆ⪃࠼ࡿ࡜ 0.8 ࡜࠸࠺ࡢࢆࡼ

ࡃ౑ࡗ࡚࠸ࡲࡋࡓࠋ㛗ࡃ࢔ࢲࣝࢺࣜࢸࣛࢩ࣮ࢆసࡗ࡚࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊࡑࡢ࡜ࡁ࡟ࡣ 0.8
ࢆ౑ࡗ࡚࠸ࡲࡋࡓࠋࡑࢀࡣᴫᛕࢆ࣐ࢫࢱ࣮ࡋࡓ࡜ࡁ࡟ࡣ 0.8 ࡀ࠸࠸ࢇࡌࡷ࡞࠸࠿࡜࠸࠺ࡇ
࡜࡛ࡍࡡࠋ0.62 ࡜࠸࠺ࡢࡣࠊ⚾࡜ࡋ࡚ࡣప㐣ࡂࡿ࡜ᛮ࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀࡣ OECD ࡛Ỵࡵࡓࡇ࡜࡛ࡍ࠿ࡽࠋ
‫ྖۑ‬఍

0.62 ࡜࠸࠺ࡢࡣᡃࠎ࠿ࡽࡍࡿ࡜ࡕࡻࡗ࡜୰㏵༙➃࡞Ẽࡀࡍࡿࡢ࡛ࠊ࡝࠺ࡸࡗ࡚Ỵࡲࡗࡓࡢ࠿࡞࡜ࠋ
‫ۑ‬ᒣᮏ

0.62 ࡜࠸࠺ࡢࡣࠊ୍␒ୗࡢࣞࣥࢪࡀ࠶ࡾࡲࡍࡡࠋࡑࡇࡢ࡜ࡇࢁ࡛ 0.5 ࡟࡞ࡿ࢔࢖

ࢸ࣒ࢆ᥈ࡋ࡚࠸ࡿࢇ࡛ࡍࡼࠋࣞ࣋ࣝࡢ୰ࡢࡍ࡭࡚ࡢ࢔࢖ࢸ࣒ࢆࠊࢸࢫࢺ≉ᛶ᭤⥺ࢆࡘࡃࡗࡓ࡜ࡁ࡟ 0.5 ࡟࡞ࡿࡼ࠺࡟ࠋࡑࢀ࡜ࣞࣥࢪ⮬యࡶኚ࠼࡚࠸ࡲࡍ࠿ࡽࠋ
ࡑࡢ࡜ࡁ࡟ࠊࡑࢀࢆ 2000 ᖺ࡟ࡘࡃࡗࡓࡢ࡛ࠊࡑࢀࡀࡑࡢࡲࡲࡘ࡞ࡀࡗ࡚࠸ࡿࢇ࡛ࡍࡡࠋ
ࡔ࠿ࡽᮏ᮶ࡣࠊࡶࡋ⩦ᚓࣞ࣋ࣝࡢ࢔࢖ࢸ࣒ࡢࢭࢵࢺࡀኚࢃࡗࡓ࡞ࡽࡤ⩦ᚓࣞ࣋ࣝࡢࢹ࢕ࢫ
ࢡࣜࣉࢩࣙࣥ⮬యࡶኚ࠼ࡿ࡭ࡁ࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ௒ᅇࠊ2000 ᖺ࠿ࡽ 2012 ᖺࡲ࡛ࡢࣜࢫ
ࢣ࣮ࣜࣥࢢࢆࡋ࡚࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱ࣮ࡀỴࡲࡾࡲࡋࡓ࠿ࡽࠊ௒ᅇࡣࡑࡢ⩦ᚓࣞ࣋ࣝࡢࢹ
࢕ࢫࢡࣜࣉࢩࣙࣥࡣኚ࠼࡞ࡃ࡚῭ࡴࢇ࡛ࡍࠋ
‫ྖۑ‬఍⣽࠿࠸㒊ศࡣ⚾ࡶࡼࡃ࢟ࣕࢵࢳ࢔ࢵࣉ࡛ࡁ࡚࠸࡞࠸ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ᪂ࡋ࠸ࢸࢫ

ࢺ㛤Ⓨࢆࡍࡿ࡜ࡁ࡟ࠊ㠀ᖖ࡟⏝ព࿘฿࡜࠸࠸ࡲࡍ࠿ࠊࣇ࢕࣮ࣝࢻࢸࢫࢺࡶྵࡵ࡚ࢡࣛࢫࢱ
࣮ࡢ࣏ࢪࢩࣙࣥ࡟ࡼࡗ࡚࡝࠺㐪࠺࠿࡜࠸࠺ศᯒࡲ࡛ࡉࢀ࡚࠸ࡿ࡜࠸࠺ࡢࡣࠊࡇࢀ࠿ࡽ᪂ࡋ
࠸ࢸࢫࢺࢆ㛤Ⓨࡍࡿ࡜ࡁ࡟ぢ⩦ࢃ࡞ࡃ࡚ࡣ࠸ࡅ࡞࠸Ⅼࡔ࡞࡜ᛮ࠸ࡲࡋࡓࠋ
࠸࠿ࡀ࡛ࡋࡻ࠺࠿ࠋⓙࡉࢇ࠿ࡽఱ࠿ࡈࡊ࠸ࡲࡍ࡛ࡋࡻ࠺࠿ࠋ
ࡈᡤᒓ࡜࠾ྡ๓ࢆ࠾㢪࠸ࡋࡲࡍࠋ
‫ۑ‬Ᏻ㔝Ặ

ᅜ❧ᩍ⫱ᨻ⟇◊✲ᡤࡢᏳ㔝࡜⏦ࡋࡲࡍࠋࡕࡻࡗ࡜ᩍ࠼࡚࠸ࡓࡔࡁࡓ࠸ࡢࡣࠊ௒ᖺࡢࣇ࢕࣮ࣝࢻࢸࢫࢺࡢ࡜ࡁ࡟࣮࣌ࣃ࣮࣮࣋ࢫࡢ
ࡶࡢ࡜ࢥࣥࣆ࣮ࣗࢱࡢࡶࡢࢆẚ㍑ࡋࡲࡋࡓࡀࠊࡑࡢ࡜ࡁ࡟ 1 ࡘࠊᐇ᪋ࡋ࡚࠸ࡿ࡜ࡁ࡟ࡕࡻ
ࡗ࡜࡝࠺࠿࡞࡜ᛮ࠸ࡲࡋࡓࡢࡣࠊࢥࣥࣆ࣮ࣗࢱᆺࡢ࡯࠺࡛⏬㠃࡛ぢ࡚ࡸࡿࡢ࡜࣮࣌ࣃ࣮࡛
๭࡜ᕪ␗ࡀ࠶ࡿࡢࡣࠊグ᠈࡛ࡣ࡞ࡃ࡚ᛮ⪃ຊ࡟㛵ࢃࡿၥ㢟࡛࣓ࣔࡀᚲせ࡛࠶ࡗࡓࡾ࡜࠿ࠊ
ࡑ࠺࠸ࡗࡓၥ㢟ࡣୖ఩ᒙ࡛ᕪࡀฟࡿࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺ᠱᛕࡀࡕࡻࡗ࡜࠶ࡗࡓࢇ࡛ࡍࡅࢀ
࡝ࡶࠊᐇ㝿࡟⤫ィⓗ࡟ࡣࠊࡑࡢࡼ࠺࡞࢔࢖ࢸ࣒ࡣฟ࡚ࡁࡲࡋࡓ࡛ࡋࡻ࠺࠿ࠋ
‫ۑ‬ᒣᮏẶ

ฟ࡚ࡁࡲࡍࡼࠋ౛࠼ࡤᩘᏛࡢ࢔࢖ࢸ࣒࡛≉࡟ฟ࡚ࡁ࡚࠸ࡿࡢࡣࠊ౛࠼ࡤ᪉⛬ᘧࢆ᭩ࡃ࡜ࡁ࡟ࠊ▱ࡗ࡚࠸ࡿேࡣ᪉⛬ᘧࢆᡭ࡛᭩ࡃࡢࡣẚ㍑ⓗ࡛ࡁࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊ᪉⛬ᘧࢆ࢚ࢹ࢕ࢱ࣮࡛ࡘࡃࡿࡢࡣ㞴ࡋ࠿ࡗࡓࡾࡋࡲࡍࡡࠋࡑࢇ࡞࡜ࡇࢁࡀࡼࡃ࡛ࡁ࡚࠸࡞࠿ࡗ
ࡓ࡜࠸࠺ࡇ࡜ࡣ࠶ࡗࡓ࠿࡜ᛮ࠸ࡲࡍࠋ

－18－‫ۑ‬Ᏻ㔝Ặ௒ᅇࡸࡣࡾ᪥ᮏࡣ᪥ᮏㄒ࡜ᩘᘧࡢධຊࡀ㠀ᖖ࡟㞴ࡋ࠿ࡗࡓࢇ࡛ࡍࡡࠋࡑࡇࡽ㎶ࡶᅜ࡟ࡼࡗ࡚ࠊゝㄒ࡜ධຊ࡟ࡼࡿ㐪࠸࡜࠸࠺ࡢࡣฟ࡚ࡁ࡚࠸ࡲࡍ࠿ࠋ
‫ۑ‬ᒣᮏẶ

ฟ࡚ࡁ࡚࠸ࡲࡍࡡࠋ≉࡟ࣞࢫ࣏ࣥࢫࡢࢫࣆ࣮ࢻ࡜࠿ࠊ࡜࡟࠿ࡃᅜ࡟ࡼࡗ࡚ࡣࢫ

ࣆ࣮ࢻࡀ㝶ศ㐪ࡗ࡚࠸ࡲࡍࠋᖹᆒࡢࢫࣆ࣮ࢻ࡜ࡋ࡚ࡔ࠸ࡓ࠸ 40 㸣ࡄࡽ࠸ࠊ30 㸣ࡃࡽ࠸࠿࡞ࠊ᪩࠸ᅜࡀ࠶ࡿࢇ࡛ࡍࡡࠋ
ࡔࡅࢀ࡝ࡶࠊ᪥ᮏࡢ PISA ࡢࣇ࢕࣮ࣝࢻࢸࢫࢺࡣ࢔ࢲࣉࢸ࣮ࢩࣙࣥ࡟㛵ࡋ࡚ࡕࡻࡗ࡜ၥ
㢟ࡀ࠶ࡗࡓࢇ࡛ࡍࠋ௚ࡢᅜ࡜ẚ࡭࡚ࠋせࡍࡿ࡟ࠊ༙ゅ࡜඲ゅࡢ͐͐‫ۑ‬Ᏻ㔝ẶᩘᏐ࡟඲ゅ࡜༙ゅࡀ࠶ࡗࡓࡾ࡜࠿࡛ࡍࡡࠋ

‫ۑ‬ᒣᮏẶ

ࡑ࠺ࡑ࠺ࠋࡑࢀࡀࡕࡻࡗ࡜ၥ㢟ࡀ࠶ࡗࡓࡾࡋ࡚ࠊࡑࢀࡶ඲㒊ᨵⰋࡉࢀࡲࡋࡓࡅ

ࢀ࡝ࡶࠊࡑࢇ࡞ࡇ࡜ࡶከศ࢚࣮ࣛࡢ୰࡟ධࡗ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࠋ
‫ۑ‬Ᏻ㔝Ặ

ᛮ⪃ຊࢆせࡍࡿၥ㢟ࡀࠊࢥࣥࣆ࣮ࣗࢱ࣮࣋ࢫ࡟⛣ࡋࡓ࡜ࡁ࡟ᮏᙜ࡟࣮࣌ࣃ࣮ࡼ

ࡾṇ☜࡟ࢀࡿࡢ࠿࡞࡜ࠊࡕࡻࡗ࡜␲ၥ࡟ឤࡌࡓࢇ࡛ࡍࡡࠋ

࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ
‫ۑ‬༡㢼ཎẶᮾி኱Ꮫࡢ༡㢼ཎ࡛ࡍࠋඛ࡯࡝㡯┠ࡢࣃ࣓࣮ࣛࢱࡀᅜ࡟ࡼࡗ࡚㐪࠺㒊ศࡀ࠶ࡿ࡜ࠋ࡜࠸࠺ࡇ࡜ࡣ㡯┠ࡢാࡁ᪉ࡀࠊ
࡝ࡢᅜ࡛ᐃࡍࡿ࠿࡟ࡼࡗ࡚㐪࠸ࡀ࠶ࡿ࡜࠸࠺ࡇ࡜ࡔ࡜ᛮ࠺ࢇ࡛ࡍࡀࠊࡑࢀࢆㄆࡵࡓୖ࡛

ྠࡌࢫࢣ࣮࡛ࣝᅜࡢẚ㍑ࡀ࡛ࡁࡿ࡜࠸࠺ࡢࡣࠊ࡝࠺࠸࠺ࣟࢪࢵࢡ࡟࡞ࡿࢇ࡛ࡋࡻ࠺࠿ࠋ
‫ۑ‬ᒣᮏẶ

౛࠼ࡤࠊ௒ࡲ࡛ࠊ࢔ࢲࣝࢺࣜࢸࣛࢩ࣮㒊ศ࡞࡝ࡶྵࡵ࡚ࠊࡍ࡭࡚ྠࡌࡼ࠺࡟ࡸ

ࡗ࡚ࡁࡲࡋࡓࡅࢀ࡝ࡶࠊ౛࠼ࡤࠊ኱ᮌࡀ࠶ࡗ࡚ࠊ1 ࡘࡢ኱ࡁ࡞ᖿࡀ࠶ࡿ࡜᝿ᐃࡋ࡚࠸ࡓࡔ
࠸࡚ࠊࡑࢀ࡛ࠊ࢔࢖ࢸ࣒࡟ࡼࡗ࡚ࡣᯞࡀฟ࡚ࡃࡿ࡜࠸࠺ឤࡌ࡛ࡋࡻ࠺࠿ࠋᅜ࡟ࡼࡗ࡚㐪࠺ᯞ ࡀ ฟ ࡚ ࡃ ࡿ ࡜ ࠸ ࠺ ࡇ ࡜ ࡛ ࡍ ࡡ ࠋ ௒ ࡲ ࡛ ࢔ ࢲ ࣝ ࢺ ࣜ ࢸ ࣛ ࢩ ࣮ ࡟ 㛵 ࡋ ࡚ ぢ ࡓ ࡜ ࡁ ࡟ ࠊ 95 㸣ࡄࡽ࠸ࡣ࢖ࣥࢱ࣮ࢼࢩࣙࢼࣝ࡞࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱ࡛ࡼࡃࣇ࢕ࢵࢺࡋ࡚࠸࡚ࠊ5 㸣࡜࠿ࡑࡢࡃࡽ࠸ࣘࢽ࣮ࢡ࡞࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡀ࠶ࡾࡲࡋࡓࠋ࡛ࡍ࠿ࡽẚ㍑࡛ࡁࡿ㒊ศࡣ኱ࡁ࡞
ᖿࡢ࡜ࡇࢁ࡛࠶ࡗ࡚ࠊࡑࢀ࡛ࣘࢽ࣮ࢡ࡞Ⅼ࡜ࡋ࡚ฟ࡚ࡃࡿࡢࡣ 5 㸣ࡄࡽ࠸ࡎࡘฟ࡚ࡁ࡚࠸
ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋẚ㍑ࡍࡿࡢࡣࡑࡇࡢ 95 㸣ࠊ࢖ࣥࢱ࣮ࢼࢩࣙࢼࣝࡢࣜࣇ࢓ࣞࣥࢫ࡟㛵
ࡋ࡚ࠊ95 㸣ࡢ࢔࢖ࢸ࣒࡟㛵ࡋ࡚ẚ㍑࡛ࡁ࡚࠸ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋ
ṧࡾࡢ 5 㸣ࡢ㒊ศࡣࠊᚲࡎࡋࡶᅜࡢ㐪࠸࡜࠸࠺ࡇ࡜ࡔࡅࡀ⌮⏤࡛ࡣ࡞ࡃ࡚ࠊ࢔࢖ࢸ࣒ࡢ
ࢺࣛࣥࢫ࣮ࣞࢩࣙࣥࡀᝏ࠿ࡗࡓࡾࠊᅇ⟅᪉ᘧ࡬ࡢ࢔ࢲࣉࢸ࣮ࢩࣙࣥࡀᝏ࠿ࡗࡓࡾࠊࡑ࠺࠸
࠺ࡇ࡜ࡶ࠶ࡿ࡜ᛮ࠸ࡲࡍࠋ౛࠼ࡤࠊ๓࡟࠶ࡗࡓࢇ࡛ࡍࡅࢀ࡝ࡶࠊ࢜ࣛࣥࢲ࡛సࡽࢀࡓ࢔࢖
ࢸ࣒࡛ࠊ⮬㌿㌴ࡢࢧ࢖ࢬࢆ㑅ࡪၥ㢟ࡀ࠶ࡾࡲࡋࡓࠋࢼࣥࣂ࣮ 5 ࡀࡍࡈࡃ኱ษ࡞ࢼࣥࣂ࣮ࡔ
ࡗࡓࢇ࡛ࠊᐇ㝿࢔࢖ࢸ࣒ࢆసࡗࡓ࢜ࣛࣥࢲ࡛ࡣࠊࢼࣥࣂ࣮ 5 ࢆᩘᏐࡢ 5 ࡛ࡣ࡞ࡃ࡚᭩ࡁฟ
ࡋࡓࢇ࡛ࡍࡼࠋࡑ࠺ࡍࡿ࡜ࠊ࢜ࣛࣥࢲ࡛ࡣࡑࡢ࢔࢖ࢸ࣒ࡀᴟ➃࡟㞴ࡋࡃ࡞ࡗ࡚ࡋࡲࡗࡓ࡜
࠸࠺ࡇ࡜ࡀ࠶ࡾࡲࡍࠋ
‫ۑ‬༡㢼ཎẶ‫ۑ‬ᒣᮏẶ

ᝏ࠸㡯┠ࡣࠊ౑࠺ࢇ࡛ࡍ࠿ࠊ౑ࢃ࡞࠸ࢇ࡛ࡍ࠿ࠋ
౑࠸ࡲࡍࠋ౑࠸ࡲࡍࡀࠊูࡢ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࢆ࢔ࢭࢫ࣓ࣥࢺࡍࡿࡇ࡜࡛ࠋ

࢔࢖ࢸ࣒࣭ࣂ࢖࣭࢝ࣥࢺ࣮ࣜࡢ࢖ࣥࢱࣛࢡࢩࣙࣥࡀ࠶ࡿ࡜࠸࠺ࡇ࡜ࡣࠊᚲࡎࡋࡶᝏ࠸࢔࢖
ࢸ࣒࡜ࡣ㝈ࡽ࡞࠸ࢇ࡛ࡍࡡࠋ௚ࡢู࡞⌮⏤࡛ࠊ౛࠼ࡤࡶࡗ࡜ࣇ࢓࣑ࣜ࢔࡞ࠊࡶࡗ࡜㢖⦾࡟౑ࢃࢀ࡚࠸ࡿㄒᙡࡀฟ࡚࠸ࡓ࡜࠿ࠊࡑ࠺࠸࠺ࡇ࡜࡟㛵ࡋ࡚ࡶ࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡀᑡࡋኚ
ࢃࡿሙྜࡀ࠶ࡾࡲࡍ࠿ࡽࠋ
‫ྖۑ‬఍ࡲࡔࡲࡔ௚࡟࠶ࡿ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡲࡓࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥࡢ࡜ࡁ࡟ࡈ

－19－

㉁ၥࠊࡈពぢ➼࠾ᐤࡏ࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ
ᒣᮏඛ⏕ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧ࡑࢀ࡛ࡣࠊࢥࣥࣆ࣮ࣗࢱࡢධࢀ᥮࠼➼ࡶ࠶ࡾࡲࡍࡢ࡛ࠊሗ࿌࡟ධࡿ๓࡟ఇ᠁࡜ࡉࡏ࡚࠸
ࡓࡔࡁࡲࡍࠋ
ࠑ༗ᚋ 2 ᫬ 15 ศ

ఇ᠁ࠒ

－20－

Purposes of LSA

Major Innovations in PISA
2015 and Beyond• The primary reason for developing andconducting large-scale assessment is to provide
empirically grounded interpretations upon which
to inform policy decisions. The appropriate rolefor policy research is not to define policy; rather
it is to establish a body of evidence from which
informed judgments can be made. (Messick)

Kentaro Yamamoto
ETS
11/29/2014

2

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Rationale of LSA
Causal variables

Outcomes
Causal variables

SES

HealthLiteracy
Familydisability

LL learning

Aging

3

5

• The OECD’s Program for International Student
Assessment (PISA) is one of the most widely known
large scale surveys.
• PISA assesses sampled in-school 15-year-old students inthree core domains of Reading, Mathematics andScience, and 30 min. of background questions (BQ).
• Assessment every three years since 2000.
• Participation has grown from 228,784 students from 43
countries in 2000 to 514,531 students from 32 countries
in 2012, and more in 2015

Outcomes
Employment

Education
N-Language

What is PISA

Civic Participation

4

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What PISA measures

General PISA Design• PISA measures what students have learned and been taught
and how well they can extend what they have learned and applythat knowledge in new or unfamiliar settings, both in and outsideof school.
• The modern societies tend to reward individuals not for what
they know, but for what they can do with what they know.
• This focus on applications of skills instead of measuring theacquired knowledge has been implemented and gaining
strength as seen in National Adult Liteacy Survey, NALS (1992),
International Adult Literacy Surveys, IALS (1994), Adult Literacy
and Life Skills Survey, ALL (2004), and most recently in
Programme for the International Assessment of Adult
Competences, PIAAC (2012).

• A cluster is a set of items from one domain and
expected to take less than 30 minutes to respond by
most students. A set of items in a cluster is unique.

Cluster Cluster
1
2
䠏䠌 ศ

6

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－21－

䠏䠌 ศ

Cluster Cluster
3
4
ఇ

䠏䠌 ศ

䠏䠌 ศ

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Other
Domain

BQ

䠏䠌 ศ

ఇ

䠒䠌ศ

Goals of PISA 2015
• Fully computer delivered• Reliable trend information on Reading,Mathematics, and Science• Expanded construct of Science• New domain: Collaborative Problem Solving• Stable trend

7

Major Issues of Past PISA

8

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Other Significant Changes in PISA
Reading Literacy Scores

Thresholds of Trend Reading Items
Between 2000 and 2009 PISA
650

2009 reading item threshold

600

550

500

450

400
400

450

500

550

600

650

2000 reading item threshold

9

10

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Thresholds of PISA Reading Trend Items

Number of Items Used Before

407
650
480

Math
Reading

2000
45
129

2003
84
28

2006
48
28

2009
35
131

2012
109
44

Science

45

34

103

53

53

553
626
600

R067Q01
R101Q02
R220Q05
R102Q07550

R083Q02

2015FT
83
98
85 (T)
213(N)

Reading Scale

R055Q01

Note: Major domains in green

R104Q01

500

R227Q02T

Number of Items Used in Past PISA by Domain

R083Q03
R101Q04450

200

R219Q02
R227Q06

150

R055Q05
400

items

R219Q01T
R245Q01

Math

100

Reading

R067Q04350

Science50

R083Q04
R220Q06

0

R083Q01

11

2000

R220Q02B

300
2000 Testing Service. All rights reserved.
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2009

12

－22－

2003

2006

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2009

2012

2015

What do we gain?

Conceptual Designs of Trend
Old PISA Design Concept

2006

2009

• large linked databases– Math, Reading, Science with 1.9 million students– Financial Literacy with a set of 2012 countries

New Design Concept

2012• Integrated (5-cycle) evidence on:– For an item: Item functioning over time
– For a country: Item by country interactions over time
– Study improvement of model-data fit with all data
– Non-Rasch items are not (necessarily) bad items

2015 2018 2021

• Width of bars represents the number of respondents per item.

13

14

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Creating the Database

What do we gain, again?

The analysis used all available (published data)
Funded under ETS research allocation
Used existing public documentation & data

• Linking of cycles 2015, 2018,…
– ‘historical’ databases can be amended– Gives raise to a tool for studying longer term trends– Allows country by item (rather than by cycle)
treatment– Detect changes in item difficulty, by curriculum effectsof PISA, or ‘leakage’ of items into instruction• Forward looking:– Basis for comparisons of PBA and CBA assessment– Stable parameters for concurrent linking of 2015 cycle
15

2000

2003

2006

2009

2012

Student

228,784

276,165

398,750

515,958

514,531

Country

43

41

57

74

68

Total N: 1,934,188
16

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Year

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Creating the Database:More General IRT Models

Comparison to Published Data
Mathematics 2000 - 2012
Estimated Country Mean from Rescaling

2• The data for math, reading, science, and financial literacy
were concurrently calibrated using more general IRT models

1.5
1
0.5
0

– Rasch/PCM Model (baseline), (2000-2012)– 2PL/GPCM Model– Rasch/2PL (PCM/GPCM) Hybrid• (item slopes released for some items)– Rasch/2PL (PCM/GPCM) IBCI• (accounting for item-by-country interactions)

2000
-0.5

2003
2006

-1

2009
2012

-1.5
-2
-2.5
-3
250

300

350

400

450

500

550

600

650

700

Published Country Mean

17

18

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Example of Item by Country Interaction
Rasch Model

19

2PL Model

20

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Model Fit and Item Fit Evaluation
•
•
•

•

Model Fit
Scoring

Overall Model fit was evaluated using AIC and BIC

Model

Science

Fin Lit

26400730 30968125 29908518

3857879

2PL

26118134 30675531 29585732

3814562

Hybrid26175012 30691983 29591677

3818645

Hybrid IBCI 25946516 30472304 29302806

3804268

Rasch

Item fit was evaluated using the Mean Deviation (MD) and the Root Mean
Squared Deviation (RMSD)

Omit is Wrong

MD and RMSD were calculated for all items in each country-language
group

ReadingRasch24170443 28198612 27918199

36198552PL

23952026 27966585 27676158

3584239

Hybrid23999181 27984532 27681378

3590496

Hybrid IBCI 23787968 27720962 27372668

3564699

Omit is notreached

RMSD values greater than 0.2, and MD values greater than 0.2 or smaller
than -0.2, respecting the small sample size per country, were considered
as deviations

Math

BIC (Schwarz, 1978) reported in the table

21

22

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Improved Model Fit

Improved Model Fit
1400

Reading(N=18,603)
Science(N=16,223)
Fin Lit(N=718)

Rasch

2PL

HybridHybrid
IBCI

N

549

397

415

4

Percent

3.48%

2.51%

2.63%

0.03%

N

1,233

960

962

250

Percent

6.63%

5.16%

5.17%

1.34%

N

921

717

708

8

Percent

5.68%

4.42%

4.36%

0.05%

N

29

23

23

5

Percent

4.04%

3.20%

3.20%

0.70%

1200
Number of RMSD Deviations > 0.15

Math
(N=15,795)

Large
Deviations

1000

800

600

400

200

0
Rasch

23

2PL
Math #

Note: Deviations defined as item*country*cycle RMSD greater than 0.15
24

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－24－

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HybridScience #Fin Lit #Hybrid IBCI

Retained Rasch Items

MathReading
ScienceFinancial Lit.

Summary

Total Numberof Items

Rasch #
Retained

Rasch %
Retained

179

77

43%

223

42

19%

133

19

14%

40

15

38%

• Created a comprehensive database using all available
PISA items across the 5 cycles• Compared to the Rasch model Hybrid IBCI model fits thedata best for all content domains– A number of items were still retained as Rasch items– Most items exhibit some item-by-country-by-cycle
interactions that can be accounted for by the Hybrid
IBCI model• Created a database using Hybrid IBCI model containing
common item parameters across each of the threedomains that can be used to estimate trends with PISA
2015

Note: Rasch items (slope=1.0) retained under the Hybrid and Hybrid
IBCI models
25

26

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Conceptual Designs of Trend
Field Test Analysis

Old PISA Design Concept

2006

2009

New Design Concept

20122015 2018 2021

• Width of bars represents the number of respondents per item.

27

28

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FT Assessment Design (CBA)

Response Time Decreases
by Cluster Position (min)Sample=1950(25 schools,
78 students)
P=0.23
N= 45029

P=0.35
N= 700Group 3 – CBA New
Sci/CPS

Group 1 - PBA Trend

Group 2 - CBA Trend(Forms 01-18)(Forms 31-48)

01-06

31-36

49-60

(S & M)(S & M)(New S)(Forms 49-72)

07-12

37-42

61-72

(M &R)(M & R)(New S & CPS)

13-18

43-48

(R & S)(R & S)• Students spent fair amount of time.
• Considering response accuracy was not affected by cluster
position, reduced response time may indicate response efficiency.

P=0.42
N= 800

Position 1 Position 2 Position 3 Position 4

Position 4Position 1*

Mathematics

19.76

18.84

17.26

16.03

-3.72

Reading

24.11

21.47

20.75

18.44

-5.67

Science

22.32

20.61

19.21

17.11

-5.21

CPS

22.67

21.26

20.96

18.79

-3.88

FL

19.49

14.49

-5.00

Note: Cluster response time is a sum of time spent on responding to every item.
The two FL clusters alternated positions.
30

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－25－

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CBA Reduces Cluster Position
EffectCluster Level Response Timeby Proxy Skill Level (min)P+: Proportion correct averaged across countries
Position
1

2015

31

Position3

Position
4

Position 4Position 1

Mathematics

0.439

0.454

0.432

0.427

-0.012

Reading

0.575

0.580

0.566

0.544

-0.031

Science2009

Position
2

0.409

0.418

0.401

0.385

-0.024

Position1

Position2

Position3

Position
4

Position 4Position 1

Mathematics

0.411

0.401

0.384

0.371

-0.040

Reading

0.581

0.557

0.532

0.499

-0.083

Science

0.490

0.478

0.457

0.435

-0.05532

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Low Ability

Moderate
AbilityHigh Ability

Difference(High-Low)

Mathematics

15.17

18.97

20.92

5.65

Reading

18.97

22.60

23.64

4.67

Science

17.45

21.31

22.86

5.41

CPS

19.69

22.23

22.77

3.08

Financial Literacy

15.76

19.97

22.11

6.35

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Mean Response Time by Item: Science Trend
50

33

Top 1/3 Proxy34

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Bottom 1/3 Proxy

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Data Quality: Summary• Random student assignment worked well, but…
• High coder reliability overall, but…
• Advantage of CBA over PBA– Fewer omitted responses– Reduced cluster position effect on P+
decline– Response time improves data quality• Item response time is driven by item demands
and students’ ability not by country nor byposition
35

Establishing Comparability
Through IRT Scaling

36

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－26－

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CM474Q01

CM909Q01

CM953Q03

CM411Q02

CM496Q02

CM564Q01

CM943Q01

CM919Q01

CM155Q01

CM564Q02

CM909Q02

CM949Q01

CM915Q02

CM408Q01

CM305Q01

CM828Q03

CM192Q01

CM571Q01

CM496Q01

CM998Q02

CM00KQ02

CM954Q01

CM406Q02

CM273Q01

0.0
CM982Q04

CS133Q04T

CS327Q02T

CS252Q02T

CS421Q02T

CS458Q01T

CS437Q06T

CS437Q01T

CS304Q03aT

CS524Q07T

CS268Q02T

CS456Q01T

CS495Q01T

CS476Q02T

CS415Q02T

CS438Q02T

CS514Q03T

CS428Q03T

CS131Q02T

CS465Q01T

CS425Q05T

CS498Q03T

CS413Q04T

CS478Q02T

CS326Q04T

CS326Q01T

CS466Q01T

CS527Q01T

CS519Q01T

CS408Q05T

CS408Q01T

CS269Q01T

0

CM982Q030

1.0

CM446Q02

5

2.0CM828Q02

10
50

3.0

CM442Q02

15

100

4.0

CM446Q01

20

5.0

CM953Q02

25
150

6.0

CM955Q03

30
200

7.0

CM954Q04

Mean response Time in Second

35

CM949Q03

40

250

Average Response Time of
Mathematics Items by Proxy Level

ARE
BFL
BFR
BGR
CAN
CHE
CHN
COL
CZE
DEU
DNK
DOM
EST
FIN
FRA
GRC
HKG
HRV
HUN
IRL
ISL
ISR
ITA
KOR
LTU
LUX
LVA
MAC
MEX
NLD
NOR
POL
PRT
QCY
QSC
QUK
RUS
SGP
SVK

item response time (min)45300

CM155Q03

350

Math Scale – PBA
Deviation Against 2000-12 Parameters

Establishing Comparability ThroughIRT Scaling - 1

37

PISA 2015 needs to establish comparability across cycles, assessment
modes, and countries for trend items in the domains Science, Math,
Reading, and Financial Literacy; and comparability across countries for
new Science items and CPS•

The best way to achieve this is through building common scales•This was done through a multi-step process requiring:1)

establishing comparability between historical PISA data (cycles from
2000-2012) and the PISA 2015 Field Trial in each domain for PBA

2)

establishing comparability across countries in each domain for CBA

3)establishing comparability between modes of assessment (PBA,
CBA) in each domain

Mathematics items•

countries

38

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Science-new Scale – CBA
Deviation Across Countries

97.8%

Examining CBA/PBA consistency
with Field Test data

countries• The 3 student groups in the FT establish a basis forcomparing CBA and PBA using randomly equivalentsamples from the same student population(s)• Initial IRT modeling targeted the equivalency of item
functions in PBA and CBA modes• Evaluation of item parameters based on international
comparability + graphical model checks

Science-new items
39

Deviation >0.2
Deviation<0.2
N is too small

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Deviation >0.2
Deviation<0.2
N is too small

䠕䠔䠊䠍䠂
40

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Comparison of Slope Parameter
Estimates Across PBA and CBAComparison of Difficulty Parameter
Estimates Across PBA and CBA

2.5
CMC

3
CMC

ComputerComputer

Human

2

2

Human

SMC
SMC

⥺ᙧ (CMC)⥺ᙧ (CMC)1

1.5⥺ᙧ (Computer)

CBA b value

CBA a value⥺ᙧ (Computer)⥺ᙧ (Human)⥺ᙧ (SMC)1⥺ᙧ (Human)⥺ᙧ (SMC)0y = 0.9011x + 0.1145
R² = 0.882-1

y = 0.9812x - 0.0394
R² = 0.7945

y = 1.0686x + 0.2097
R² = 0.9139

0.5
y = 1.0185x - 0.0435
R² = 0.801

-2.5

-2

-1.5

-1

-0.5

0

0.5

-2

y = 0.9415x + 0.0822
R² = 0.822

0
0

0.5

1

1.5

2

2.5

1.5

2

2.5

y = 0.8744x + 0.126
R² = 0.8905
y = 0.9103x + 0.1429
R² = 0.8569

y = 0.9777x - 0.0003
R² = 0.8815-3

PBA a value

41

1

42

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－27－

PBA b value

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Summary: IRT Modeling for linking
PBA and CBA scales• A series of extended IRT models was estimated to
examine the high level of agreement seen in graphicalmodel checks• The majority of items show strong measurement
invariance, while the remainder of items still shows aweaker form of invariance• Between PBA and CBA, we expect to see 75%-85%
common parameters across domains• This corresponds to roughly 4-5 of the 6 trend clusters, astronger link than in past cycles
43

Cognitive Assessment Design2015 PISA Main Study

44

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Many Clusters for Each Domain

Benefits and Features of Design

A cluster is a set of items from one domain and
expected to take less than 30 minutes to respond by
most students. A set of items in a cluster is unique.

• Reduced systematic errors due to incomplete
coverage of the constructs by expanded constructcoverage across M-m-m cycles• Three assessments (M-m-m) over 9 years can bethought as a package instead of 3 independent trend
points• Allow to introduce sizeable changes through renewedmeasurement constructs every 9 years

45

•
•
•
•
•

46

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MS CBA Design DetailFirst hourS

P=0.33

P=0.336300
150 schools
42 students

P=0.04

P=0.0466 Forms

P=0.04

P=0.2247

12 Forms
Science
Reading
12 Forms
Science
Mathematics
12 Forms
Science
Reading
Mathematics
12 Forms
Science
Mathematics
CPS
12 Forms
Science
Reading
CPS

6 Forms
Science
CPS

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Reading has 6 clusters of trend items,
Mathematics has 6 clusters of trend items,
Financial Literacy has 2 clusters of trend items,
CPS has 3 clusters of new items,
Science has 6 clusters of trend items and 6 clusters of
new items.

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MS CBA: 2 Domain Forms
Forms 31-42
2 Domains

Second hour

S

R1

R2

S
S
S
S
S

S
S
S
S
S

R2
R3
R4
R5
R6ab

R3
R4
R5
R6ab
R1

R1
R2
R3
R4
R5
R6ab

R3
R4
R5
R6ab
R1
R2

S
S
S
S
S
S

S
S
S
S
S
S

S
S
S
S
S
S

S
S
S
S
S
S

M1
M2
M3
M4
M5
M6ab

M2
M3
M4
M5
M6ab
M1

M1
M2
M3
M4
M5
M6ab

M3
M4
M5
M6ab
M1
M2

S
S
S
S
S
S

S
S
S
S
S
S

S
S
S
S
S

S
S
S
S
S

M1
R2
M3
R4
M5

R1
M2
R3
M4
R5

S
R1
M2
R3
M4
R5

S
M1
R2
M3
R4
M5

R6ab
S
S
S
S
S

M6ab
S
S
S
S
S

M6ab
S
S
S
S
S

R6ab
S
S
S
S
S

S
C1
M2
C3
M4
C2

S
M1
C2
M3
C3
M5

S
M1
C3
M3
C1
M5

S
C2
M2
C1
M4
C3

M6ab
S
S
S
S
S

C1
S
S
S
S
S

C2
S
S
S
S

M6ab
S
S
S
S

S
R1
C2
R3
C3

S
C1
R2
C3
R4

S
S
C2
R2
C1
R4

S
S
R1
C3
R3
C1

R5
C1
S
S
S
S

C2
R6ab
S
S
S
S

C3
R6ab
S
S
S
C2

R5
C2
S
S
S
C1

S
S
C1
C2
C3
S

S
S
C2
C3
C1
S

C3
C1

C2
C3

S
S

S
S

Science
Reading
P=0.33
N=2080

48

－28－

Form

Cluster 1

Cluster 2

Cluster 3

Cluster 4

31
32
33
34
35
36
37
38
39
40
41
42

S
S
S
S
S
S
R1
R2
R3
R4
R5
R6ab

S
S
S
S
S
S
R3
R4
R5
R6ab
R1
R2

R1
R2
R3
R4
R5
R6ab
S
S
S
S
S
S

R2
R3
R4
R5
R6ab
R1
S
S
S
S
S
S

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Science Clusters Design2

Science Clusters Design1

Trend (S1-S6) & New (S7-S12)

SCIENCE CBA cluster pairs
Second Cluster
S1

49

S3

S4

S5

S6

S7
1

34

S8

S9 S10 S11 S12
2

3
4
6

first cluster

S1
S2
S3
S4
S5
35
S6
S7
S8
11
S9
S10
S11
S12
st
1 cluster 2nd
2 cluster 2
total
4

S2

5
7
8

36

9
10

19

20

12
13
15

2
2
4

24
26

16

17
2
2
4

2
2
4

2
2
4

31
4
4
8

23
25

27
29

18
2
2
4

4
4
8

New Clusters:
S7-S1221
22

14

Trend Clusters:
S1-S6

28
30

32
4
4
8

4
4
8

33
4
4
8

4
4
8
50

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Both New (S7-S12)

Both Trend (S1-S6)

Pairs

1st

2nd

Pairs

1st

2nd

Pairs

1st

2nd

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

S1
S1
S2
S3
S3
S4
S4
S5
S6
S7
S8
S8
S9
S9
S10
S11
S11
S12

S7
S10
S8
S9
S12
S7
S10
S11
S12
S6
S1
S5
S2
S6
S3
S2
S4
S5

19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

S7
S7
S7
S8
S8
S9
S9
S10
S10
S10
S11
S11
S12
S12
S12

S8
S9
S11
S10
S12
S8
S11
S7
S9
S12
S8
S10
S7
S9
S11

34
35
36

S2
S5
S6

S4
S1
S3

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Science Clusters Selection
Form
31
32
33
34
35
36
37
38
39
40
41
42

1
1
2
11
35
34
36
35
34
36
1
2
11

2
13
16
5
4
15
3
4
15
3
13
16
5

Random number
3
4
6
9
12
10
17
14
7
19
8
20
18
21
7
19
8
20
18
21
6
9
12
10
17
14

5
22
31
26
23
24
27
23
24
27
22
31
26

6
25
32
29
30
28
33
30
28
33
25
32
29

Sample Science Items

1-18: Trend & New Clusters
19-33: Both New Clusters
34-36: Both Trend Clusters
51

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52

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53

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54

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－29－

55

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56

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57

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58

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Questions and Discussion

59

60

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－30－

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Major innovations in PISA 2015 and beyond
Kentaro Yamamoto
ETS
November, 2014

Introduction
The OECD Program for International Student Assessment (PISA) is one of the most widely
known large scale surveys on three of the main academic subject domain skills and background questionsof secondary school students across many countries in the world. PISA assesses statistically sampled 15year-old students who attend schools in participating countries in three core domains of Reading,Mathematics and Science every three years since 2000. Participation has grown from 228,784 students
from 43 countries in 2000 to 514,531 students from 68 countries in 2012.
Over the years, important innovations in terms of both assessment domains and mode of delivery
have been introduced. Through the inclusion of innovative assessment domains, PISA has sought tobroaden the range of knowledge and skills measured in this international assessment of 15-year-old
students. These innovative domains have included assessments of problem-solving competences in 2003
and 2012, and of non-cognitive dispositions such as self-assessments of learning strategies in 2000 andattitudes toward science in 2006. Beginning in 2006, in recognition of the expanding role of technology in
educational systems, the workplace, and everyday life, innovative computer-based modules have been
included in each cycle of PISA. These included the Computer-Based Assessment of Student Skills in
Science in 2006 and the PISA 2009 Electronic Reading Assessment. In 2012, computer-based
assessments were administered for Reading, Mathematics, and Problem Solving. In each cycle, computerbased modules were optional, but the number of countries participating in these options increased steadily
over the years.
PISA examines not only what they have learned and been taught but also examines how well theycan extrapolate from what they have learned and apply that knowledge in new or unfamiliar settings, bothin and outside of school. This approach reflects the fact that modern societies tend to reward individuals
not for what they know, but for what they can do with what they know. This notion of assessment of
successful applications of skills instead of measuring the acquired knowledge has been implemented andgaining strength as seen in National Adult Liteacy Survey (NALS) in 1992, International Adult Literacy
Surveys (IALS) in 1994, Adult Literacy and Life Skills Survey (ALL) in 2004, and most recently in
Programme for the International Assessment of Adult Competences (PIAAC) in 2012.

Historical PISA (2000-2012)
In each cycle, one domain is identified as the major domain while the other two are treated asminor domains. This distinction is important because the amount of time allotted for the measurement ofeach domain is standardized for each participating school and student, as is the number of students whoare sampled. This means that the total volume of data that is collected in each cycle is determined by the
number of assessment items and the number of students who respond to each item.
PISA emphasized the major domain in each cycle at the expense of the volume of data collectedin each of the minor domains. This was done primarily by reducing the number of items or the
representation of constructs in each of the minor domains. Often, the number of items for the minor
domains was reduced by a factor of 3 or 4, while maintaining a sufficient sample size per item. This
choice was, among other things, based on the operational consideration of limiting the number of booklets
in a paper based assessment. In addition, it enabled the estimation of item parameters without－31－

the need of borrowing or carrying over item parameter information from previous cycles.
The reduction of the number of items from nearly 130 for a major domain to about 30 or 40 forminor domains brings with it the potential for introducing systematic errors because items are neither
deleted nor selected at random and the presence of item by country interactions is more pronounced insmaller item samples. In addition, one can argue that even if the “best possible” items are selected, areduction in the number of items used in a given cycle by a factor of 3 or 4 will reduce the content
coverage of practically every assessment of student skills. This item reduction approach may haveintroduced unintended bias due to poorer representation of the construct compared to the major domain
assessment. While such effects can be ignored if there are no item by country interactions within adomain in terms of bias, a recent article by Gebhardt & Adams (2007) as well as recent chapters by
Urbach (2012) and Carstensen (2012), point out that these effects are more salient in some countries.
PISA, like many large scale surveys, used a BIB spiral design in order to reduce respondent’s
burden while using relatively large number of items to represent the measurement construct as thoroughly
as possible, i.e., a student received a subset of items instead of all items in the item pool. Following tablebelow shows the booklet design for 2003 PISA when Mathematics was the major domain, Reading andscience were the minor domains and problem solving was the innovative domain. Every cluster wasbalanced in terms of position distributed among 4 out of 13 unique booklets.
Table 1: 2003 PISA Booklet Design
Booklet
1
2
3
4
5
6
7
8
9
10
11
12
13

Cluster1
M1
M2
M3
M4
M5
M6
M7
S1
S2
R1
R2
PS1
PS2

Cluster2
M2
M3
M4
M5
M6
M7
S1
S2
R1
R2
PS1
PS2
M1

Cluster3
M4
M5
M6
M7
S1
S2
R1
R2
PS1
PS2
M1
M2
M3

Cluster4
R1
R2
PS1
PS2
M1
M2
M3
M4
M5
M6
M7
S1
S2

IRT model has been used for all past PISA surveys using random samples of 500 students fromeach of participating countries, i.e., about 150 students per item per country, thus it is too small to detect
item by country interaction. Item by country interaction was assumed non-existent and a single model
item calibration was carried out.
Every cycle, similar item calibration was carried out based on the data collected in one cycle
without using data from other previous cycles. In order to evaluate trend on the comparable scale, scaleequating was carried out by matching the distributions of item parameters. Since Rasch models was usedin the past, i.e., an additive constant was used to match means of location parameters from multiple cycles.
This resulted in the trend items administered in multiple cycles having different item parameters for eachcycle.

－32－

2015 PISA conceptual design: Major minor distinction reduced
Any assessment must contend with two types of errors — random and systematic. Random errors
do not result in bias but do increase uncertainty and, therefore, the precision of results. Systematic errors,on the other hand, introduce bias especially in the measurement of trends, and are less desirable because
their direction is unknown and not easily quantified or controlled for by statistical means. All large-scale
surveys, including PISA, struggle with these two sources of error and aim to control them by optimising
the assessment design, as well as sample size, sampling methods, and other contributing factors. An
increase in random errors will reduce the ability to detect differences among groups of interest and cantypically be offset by increasing sample size. However, an increase in systematic errors not only reduces
the ability to detect differences, but also may lead to the attribution of false differences; i.e., differencesthat are considered significant, even though the true differences are negligible, or even zero. Because of
the possibility of introducing bias, a reduction in systematic errors is generally preferable over a reductionof random error components.
Figure 1 below provides a graphic representation of the relative difference in construct coverage
between the major and minor domains as implemented from 2000–2012. The vertical height of each bar
represents the proportion of items measured in each assessment cycle by domain, while the width conveys
the relative number of students who respond to each item within each domain. The reduced height of thebars for the minor domains is intended to represent the reduction of items in that domain and therefore thedegree to which construct coverage has been reduced.
Figure 1. Comparison of Construct Coverage in the 2000–2012 PISA Design by Major and Minor
Domains

ETS proposed a new approach to measuring trends for PISA 2015 and believes it should becontinued for 2018 and beyond. The design stabilizes the trend through reducing potential bias byincluding all (or almost all) of the items in each minor domain while reducing the number of students
responding to each item. This strategy also keeps the volume of data the same for each cycle andincreases the construct coverage for the minor domains, while reducing the number of students
responding to each item. The result is that the construct representation for each minor domain is at a levelcomparable to the major domain cycle. More importantly, this approach reduces the potential for bias
introduced due to item by country interactions and the switch from major to minor domains in thecurrent design. The result both stabilizes and improves the measurement of trend.
The approach adopted in 2015 is represented graphically in Figure 2 below. As represented by thevertical bars, the construct coverage for the minor domain is comparable to the major domain design, atthe expense of reducing the number of students who respond to each of the minor domain trend items.

－33－

This reduction of student responses per minor trend item is represented in the figure by the narrowing of
the bars for the two minor domains.
Figure 2. Approach Used to Balance Major/Minor Domains in 2015 and Beyond

Under this approach for measuring trends, each domain goes through a “domain rotation” or anine year period that begins with a new or revised framework and continues with the two subsequent
cycles in which it is a minor domain. As an example, for Reading Literacy, one domain rotation would be
2009, 2012, and 2015 and another will become 2018, 2021, and 2024 moving forward. Thinking about
the assessment design in terms of this domain rotation clarifies the specific function of each cycle withinthat nine year period and the importance of the construct. Over a domain rotation, each major and minor
cycle serves a specific function in terms of its contribution to the measurement of trend. Information
about item functioning is carried across each domain rotation, with the choice of which items to carryforward being based on the most accurate item parameter estimation (occurring when a construct ismeasured as a major domain). The set of items that are carried forward in the rotation represents theconstruct. In this way, the notion of trend is defined both by the coverage of the construct and by thestatistical methodology employed.

Main Study Assessment Design
The Main Study assessment design for PISA 2015 covers the domains of reading, mathematicaland scientific literacy as computer-based and paper-based designs with the computer-based design adding
the fourth domain of collaborative problem solving. A computer-based design for countries opting out of
the assessment of Collaborative Problem Solving is described as part of the next section. These designs
require participating countries to sample a minimum of 150 schools representing their national populationof 15-year-old students. Countries taking the computer-based assessment will need to sample 42 students
from each of 150 schools for a total sample of 6,300 students while countries taking the paper-based
assessment will need to sample 35 students from each of 150 schools for a total sample of 5,250. It is
important to understand that 88% to 92% of students will receive a form that consists of four 30-minuteclusters (or sets of tasks) assembled from two domains, resulting in one hour of assessment time perdomain, with a total of two hours of testing time per student. An additional 8% to 12% of students will
receive forms that consist of four 30-minute clusters covering three of the four core domains. Scientific
Literacy is included in each of these forms.

－34－

Main Study CBA Assessment Design
This design includes six intact clusters from each of the trend domains of Scientific, Reading andMathematical Literacy based to the extent possible on the assessment cycle when each was the major
domain – 2006 for Scientific Literacy, 2009 for Reading Literacy and 2012 for Mathematical Literacy.
Additionally, it also includes six clusters of new Scientific Literacy and 3 clusters of Collaborative
Problem Solving materials based on the new 2015 Frameworks. The six intact clusters will provide trend
information for Mathematical and Reading Literacy. The six intact clusters of Scientific Literacy tasks
will carry not only the trend information but also link to the new items developed to reflect the 2015
framework – this was done as part of the Field Trial analysis, including the mode study. In addition, threeclusters of CPS items will be assembled for the Main Study. These materials will be organized according
to the scheme shown in Figure 3 below (Forms 31-96).
Figure 3. Main Study Computer-Based Assessment Design

Computerα͸͵ͲͲȋͳͷͲ ǡͶʹ Ȍ
P=0.33͵ͳǦͶʹȋ   Ȍ

P=0.33Ͷ͵ǦͷͶȋ    Ȍ

P=0.04ͷͷǦ͸͸ȋ   ǡ ǡ
   Ȍ

P=0.04͸͹Ǧ͹ͺȋ    ǡ Ȍ

P=0.04͹ͻǦͻͲȋ   ǡ ǡ Ȍ

P=0.22ͻͳǦͻ͸ȋ   Ȍ－35－

As reflected in Figure 3, there are 30 different test forms that combine two of the four domains –88 percent of students receive one of these forms. These combinations include: i) Scientific and Reading
Literacy (Forms 31-42), ii) Scientific and Mathematical Literacy (Forms 43-54), and iii) Scientific
Literacy and Collaborative Problem Solving (Forms 91-96). In these test forms, students take one hour of
Scientific Literacy (one cluster each of trend and new science) plus one hour of a another domain –Reading, Mathematical Literacy or Collaborative Problem Solving. These 30 test forms provide strong
pairwise covariance information between Scientific Literacy and each of the three other domains.
In addition, there are 36 additional forms providing covariance information about three of thefour domains. 12 percent of students receive one of these forms. In these forms, students receive onehour of Scientific Literacy plus two 30-minute clusters of items from each of the other three domains.
These combinations are: iv) Scientific, Reading and Mathematical Literacy (Forms 55-66); v) Scientificand Mathematical Literacy and Collaborative Problem Solving (Forms 67-78), vi) Scientific and Reading
Literacy and Collaborative Problem Solving (Forms 79-90). It is important to note that these threedomain test forms will ensure that all covariance estimates among the four domains are indeed based on
the joint assessment of the domains.
As Scientific Literacy is the major domain for 2015, it is paired with one or two of the other threedomains, and each of the different combinations of domains is balanced in terms of position to provide
important covariance information. The design also reflects the fact that the random assignment of a form
within a school follows a specific probability. According to this design, 33% of students within eachschool will be assigned to one of 12 Scientific and Reading Literacy test forms. Another 33% will beassigned to one of 12 Scientific and Mathematical Literacy test forms. In addition, some 22% of thesampled students within each school will be assigned to one of the 12 Scientific Literacy and CPS test
forms. To provide additional covariance information, 4% percent of students will be assigned to one of
12 Scientific Literacy, Mathematical Literacy and CPS test forms; 4% to one of 12 Scientific Literacy,
Reading Literacy and CPS test forms; and 4% to one of 12 Reading, Mathematical, and Scientific
Literacy test forms1.
The rotation of clusters – which identifies the form to be received by the respondent – will occur
in a multi-step process that will take place when students are sampled.
STEP 1: Assignment of the base test form
The first step will be the assignment of base test forms. This assignment will be based on the 2digit random number identified as “CC”. This number will range from 31-96 and is directly linked to a
specific base test form that is shown in Figure 4. These base test forms are useful in identifying the actual
location and clusters for Math and Reading but only identify the location of Science clusters (i.e., which
Science clusters are not yet assigned; these are only identified as “S”). The probability of assignment ofeach form type is also shown in column “Probability” and varies from 33% to 4% according to the
combination of domains.

1

These percentages are based on random assignment of test forms to students across schools. Each student in each classroom has a real probability ofreceiving any of the forms.

－36－

Figure 4. Main Study Computer-Based Assessment Base Test Forms
Probability Base Cluster Cluster Cluster Cluster
of
Test
1
2
3
4
assignment Forms
S
S
R1
R2
31
32
S
S
R2
R3
33
S
S
R3
R4
34
S
S
R4
R5
35
S
S
R5
R6ab
33%
36
S
S
R6ab
R1
37
R1
R3
S
S
38
R2
R4
S
S
39
R3
R5
S
S
40
R4
R6ab
S
S
41
R5
R1
S
S
42
R6ab
R2
S
S
S
S
M1
M2
43
44
S
S
M2
M3
45
S
S
M3
M4
46
S
S
M4
M5
47
S
S
M5
M6ab
33%
48
S
S
M6ab
M1
49
M1
M3
S
S
50
M2
M4
S
S
51
M3
M5
S
S
52
M4
M6ab
S
S
53
M5
M1
S
S
54
M6ab
M2
S
S
S
S
M1
R1
55
56
S
S
R2
M2
57
S
S
M3
R3
58
S
S
R4
M4
59
S
S
M5
R5
4%
60
S
S
R6ab M6ab
61
R1
M1
S
S
62
M2
R2
S
S
63
R3
M3
S
S
64
M4
R4
S
S
65
R5
M5
S
S
66
M6ab
R6ab
S
S

Probability Baseof
Test
assignment Forms
67
68
69
70
71
4%
72
73
74
75
76
77
78
79
80
81
82
83
4%
84
85
86
87
88
89
90
91
92
22%
93
94
95
96

Cluster Cluster Cluster Cluster
1
2
3
4
S
S
S
S
S
S
M1
C3
M3
C1
M5
C2
S
S
S
S
S
S
C2
R2
C1
R4
C3
R6ab
S
S
S
C2
C3
C1

Where:xxxx
x

R1-R6 represent Reading clusters in computer (Trend)
M1-M6 represent Math clusters in computer (Trend)
S represents Science clusters in computer (Trend and New)
C1-C3 represent Collaborative Problem Solving clusters in computer (New)a represents standard clusters and b represents easier clusters

－37－

S
S
S
S
S
S
C2
M2
C1
M4
C3
M6ab
S
S
S
S
S
S
R1
C3
R3
C1
R5
C2
S
S
S
C1
C2
C3

C1
M2
C3
M4
C2
M6ab
S
S
S
S
S
S
R1
C2
R3
C3
R5
C1
S
S
S
S
S
S
C1
C2
C3
S
S
S

M1
C2
M3
C3
M5
C1
S
S
S
S
S
S
C1
R2
C3
R4
C2
R6ab
S
S
S
S
S
S
C2
C3
C1
S
S
S

STEP 2: Assignment of science cluster pairs
The second step will be the assignment of science clusters. This assignment will be based on the
1-digit random number , “S”. This number will range from 1-6, will be uniformly distributed, and will be
used in combination with the base form (e.g., selected by the first 2-digit random number) to identify
which combination of Science clusters a student will receive. Figure 5 shows the lookup table where the31-96 base forms are identified by the rows and the 1-6 lookup numbers are identified by the columns.
The combination of these two numbers will identify which of the 36 possible combinations of scienceclusters will be inserted into the assigned base test form.
Figure 5. Lookup Table for Random Number “S”
Base
Form
(CC)
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63

Random number (S)
1
1
2
11
35
34
3
35
34
3
1
2
11
1
2
11
35
34
3
35
34
3
1
2
11
1
2
11
35
34
3
35
34
3

2
13
16
5
4
15
36
4
15
36
13
16
5
13
16
5
4
15
36
4
15
36
13
16
5
13
16
5
4
15
36
4
15
36

3
6
12
17
7
8
18
7
8
18
6
12
17
6
12
17
7
8
18
7
8
18
6
12
17
6
12
17
7
8
18
7
8
18

4
9
10
14
19
20
21
19
20
21
9
10
14
9
10
14
19
20
21
19
20
21
9
10
14
9
10
14
19
20
21
19
20
21

5
22
31
26
23
24
27
23
24
27
22
31
26
22
31
26
23
24
27
23
24
27
22
31
26
22
31
26
23
24
27
23
24
27

Base
Form
(CC)
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96

6
25
32
29
30
28
33
30
28
33
25
32
29
25
32
29
30
28
33
30
28
33
25
32
29
25
32
29
30
28
33
30
28
33

Random number (S)
1
1
2
11
1
2
11
35
34
3
35
34
3
1
2
11
1
2
11
35
34
3
35
34
3
1
2
11
1
2
11
35
34
3

2
13
16
5
13
16
5
4
15
36
4
15
36
13
16
5
13
16
5
4
15
36
4
15
36
13
16
5
13
16
5
4
15
36

3
6
12
17
6
12
17
7
8
18
7
8
18
6
12
17
6
12
17
7
8
18
7
8
18
6
12
17
6
12
17
7
8
18

4
9
10
14
9
10
14
19
20
21
19
20
21
9
10
14
9
10
14
19
20
21
19
20
21
9
10
14
9
10
14
19
20
21

5
22
31
26
22
31
26
23
24
27
23
24
27
22
31
26
22
31
26
23
24
27
23
24
27
22
31
26
22
31
26
23
24
27

Using the two random numbers, base test form identified by the 2-digit number as rows and the 1digit random number as columns, Figure 6 shows the 36 possible Science Clusters combination (e.g.,
rotation of Science clusters S1 to S12) that will be inserted into the selected base test form.

－38－

6
25
32
29
25
32
29
30
28
33
30
28
33
25
32
29
25
32
29
30
28
33
30
28
33
25
32
29
25
32
29
30
28
33

Figure 6. Main Study Computer-Based Assessment Assignment of Science Cluster Pairs
Science Cluster
Combination
N
S
S
1
S1
S7
2
S1
S10
3
S2
S8
4
S3
S9
5
S3
S12
6
S4
S7
7
S4
S10
8
S5
S11
9
S6
S12
10
S7
S6
11
S8
S1
12
S8
S5
13
S9
S2
14
S9
S6
15
S10
S3
16
S11
S2
17
S11
S4
18
S12
S5

Science Cluster Combination
N
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

S
S7
S7
S7
S8
S8
S9
S9
S10
S10
S10
S11
S11
S12
S12
S12
S2
S5
S6

S
S8
S9
S11
S10
S12
S8
S11
S7
S9
S12
S8
S10
S7
S9
S11
S4
S1
S3

Coding of open ended questions
Throughout all large-scale assessments, the essential activities associated with maintaining
scoring consistency are the same. Typically, the required procedure to monitor scoring reliability is tohave a portion of the items double scored (i.e., scored independently by two different scorers), and thencompare the resulting scores to measure agreement.
The goals of such procedures are to ensure the accuracy and reliability of scoring within countries
and identify scoring inconsistencies or problems early in the process so that interventions can be appliedand problems resolved as soon as possible. In general, inconsistencies or problems arise because scorers
misunderstand general scoring guidelines and/or a specific rule relating to a particular item. Double
scoring yields a reliability measure that represents the extent to which two scorers agree on how aparticular response should be scored, and thus demonstrates how comparably the scoring guideline isbeing interpreted and applied. The goal in PISA is to reach a within-country inter-rater reliability of 0.92
(92% agreement) across all items, with at least 85% agreement for each item.
When discrepancies among scorers occur, experience has shown that they fall into two distinct
classes.
• _The first type of discrepancy reveals a consistent bias on the part of one scorer — for example,
Scorer 1 may be consistently scoring more leniently than other scorers. To detect and address
such discrepancies, countries are required to monitor reliability at key points during the scoringprocess so that problematic scorers can be retrained or, if necessary, dismissed.
• _The second type of scoring problem that can be revealed through analysis of the rescoringprocess is more challenging to address. This occurs when the scoring results reveal generalinconsistencies between the scorers, with no pattern that can be attributed to one scorer or theother. This is relatively rare, but when it occurs it is generally the result of a problem with an itemor an error in the scoring guides. One procedure for addressing this situation involves conducting
a review of all inconsistently scored responses to determine if there is a systematic pattern and, if
one is found, having those items rescored. Additionally, the scoring guides for such items can be－39－revised to clarify any issue identified as causing inconsistent scoring. When a specific problem
cannot be determined, unique item parameters may be required for one or more countries toreflect this ambiguity in scoring.
For PISA 2015, the Open-Ended Coding System (OECS) tool was used to support the scoring of
open-ended computer-based items. Once scoring is complete for each item, data can be imported into the
OECS to be integrated across scorers and the OECS will produce summary reports that countries use to
examine scoring reliability.
Consistent scoring within countries does not guarantee comparable scoring across countries.
Therefore, in addition to examining the accuracy and reliability of scoring within countries, it is also
important to check that scorers across countries are consistently applying the same criteria. This aspect isparticularly important with the increased number of countries and languages participating in international
surveys such as PISA. The approach used in PISA 2015, based on that used in the Adult Literacy andLifeskills Survey (ALL) and PIAAC, involves the use of anchor responses. Anchor responses are acommon set of items and associated responses prepared by test developers. For PISA 2015, anchor
responses were provided for New Science items. Since it is the open-ended items that are the focus of thisprocess, anchor responses for PISA are created using only those item types and it is these responses thatare double scored. Because anchor responses are provided in English, scoring teams in each country are
asked to designate two bilingual scorers responsible for the double-scoring process.
As was the case in 2015, the scoring design will dictate when in the scoring process the anchor
responses should be scored as well as which of the two scorers should be the first scorer and which shouldbe the second. The OECS simplifies this process for countries by organising responses according to thisscoring design. Scorers receive a PDF file for each item
A part of the PISA 2015 CBA open-ended responses required human coding, a process that took
place with the aid of the Open-Ended Coding System (OECS) – a computer tool that supported coders incoding the computer-captured responses according. During coding, the responses were organized anddistributed to coders following a pre-determined coding design and the specific coding guides. Coding
reliability of all computer based open-ended questions that required human coding was evaluated on asubset of responses. This tool reduced the coding administrator’s burden of managing coding activities
and ensured that the coding design requirements were met. Coders received a PDF file that contained all
the responses assigned to the coder, including anchor responses (in English), if appropriate, with one
response per page. Each page displayed a part of the question, the individual responses, and theacceptable coding categories for each question. The coder clicked the circle next to the selected code,
which was then saved within the file.
During coding activities, OECS generate three types of reliability reports: i) proportion agreement,ii) coding category distributions, and iii) deferred and missing responses. The “proportion agreement”
report shows the average agreement per item across all coders as well as average agreement per coder.
The “coding category distributions” report is used to compare coding distributions across coders for each
item. That is, it compares tallies or counts of the response categories for each item across all coders andidentifies whether the distribution of response categories given by any single coder differs from theaverage distribution across coders. The “deferred and uncoded responses” report provides counts of
responses that have not been coded during the coding process, being flagged as either deferred or uncoded
to be scored later.
The human coding was conducted in the time frame allocated, and rare technical problems wereaddressed immediately so that a complete set of human coded responses was available for the analyses
described below. A report was provided separately to each country summarising the overall level ofagreement reached for each scale. Countries also are able to review level of agreement reached on each
item.

－40－

Psychometric modeling
The reanalysis of PISA data from prior cycles (2000-2012) aimed to stabilize the trend measure
and to ensure its quality. With PISA 2015 introducing CBA as the main mode of assessment, the concern
was that it might influence the item parameter estimates for the linking items. Moreover, some linking
items might not work equally well for all of the populations assessed in PISA 2015. Utilizing linking
items that do not work equally well across subgroups (i.e., are not measuring the same construct in all
populations) reduces the comparability of the trend measure. These linking items needed to be identified
and excluded from the Main Study item pool. However, given the new scaling approach for PISA 2015
(the combined Rasch and 2PL model), it might still be possible to retain a larger share of these items.
Results from prior analyses (PISA 2000-2012) were replicated and then reexamined using the
combined Rasch and 2PL model. The reanalysis produced a common parameter for each of the previously
used items that is contained in the databases from PISA 2000 to 2012 and that can be considered as astarting point of scaling the PISA 2015 data. This ensures a solid database of item characteristics on acommon international scale based on the past frameworks of each domain.
PISA has collected data in representative samples of 15-year old-students around the world every
three years since 2000. In each of these five cycles (2000, 2003, 2006, 2009, 2012), both OECD- andpartner- countries participated, resulting in almost 300 cohorts defined by assessment year and country.
Many of the OECD countries as well as a substantial number of partner countries participated in each of
the five PISA cycles so far.
In an effort to utilize the complete evidence on item functioning and scale coverage of the task
material used in PISA, we compiled a database that merged all five cycles and all countries. This yieldeda file that contains roughly 2 million student records assigned to each of the cycles by country/jurisdiction
combinations. We utilized state-of-the-art multiple group IRT (Bock & Zimowski, 1997, Yamamoto &Mazzeo, 1992, von Davier & Yamamoto, 2004; von Davier & von Davier, 2007, Mazzeo & von Davier,2008, 2013, Weeks, von Davier & Yamamoto, 2013) to specify a model that allows linking all items
across all PISA cycles by country combinations.
Following four distinct models were considered; Rasch/PCM, 2PL/GPCM, Rasch/PCM and 2PL/GPCM ,RaschPCM and 2PL/GPCM with item by cohort interactions.
Table 2 summarizes the improvement in item fit for the domains of mathematics, reading, andscience. The table shows the results for the Rasch/PCM model, the 2PL/GPCM and the “hybrid”
Rasch/2PL/GPCM model, with one set of item parameters for all countries, and a model that accounts for
item-by-country (IBCI) interactions by releasing some country-specific parameters. These results are
based on all cycles from 2000-2012 combined for the three main domains. In each domain, the IBCI
model fits best (as characterized by the BIC), followed by the 2PL/GPCM, hybrid, and Rasch/PCM
models. This can also be seen in the concomitant decrease in the number of items-by-country-by-cyclewith RMSD values greater than 0.15. Approximately 3% of the items in math, 7% of the items in reading,and 6% of the items in science did not fit the Rasch model in one or more countries. On the other hand,around 1% of the items exhibit misfit in reading for the IBCI model and less than 0.1% of the items
exhibit misfit in math and science under the IBCI model. For all subsequent analyses, the item parameter
estimates from the IBCI model were used.

－41－

Table 2: Changes in Model Fit Summary
Rasch/PCM 2PL/GPCM
Hybrid# of item-country-cycle deviations
549
397
415
Math
Bayesian Information Criterion (BIC)
26400730
26118134
26175012# of item-country-cycle deviations
1233
960
962
Reading
Bayesian Information Criterion (BIC)
3096812530675531
30691983# of item-country-cycle deviations
921
717
708
Science
Bayesian Information Criterion (BIC)29908518
29585732
29591677
Total item-country-cycle values: Math = 15,795, Reading = 18,603, Science = 16,223
Deviations defined as RMSE values > 0.15

IBCI
425946516
250
30472304
8
29302806

IRT Scaling of FT data
The new science items developed for 2015 are based on a revised science assessment framework.
These new items exist in the CBA mode only because PISA 2015 represents a shift from a paper-based to
a computer-based survey. The IRT scaling of the new science items was straightforward, but somechanges to the scoring of two of the CPS units were necessary before the data could be used for IRT
scaling. The CPS scale consists of seven units, which in turn comprise 165 items that can be used for the
IRT scaling. The CPS units are based on simulated conversations with one or more computer-based
agents that are designed to provide a virtual collaborative conversation. Test takers have to choose an
optimal sentence from a multiple-choice list to go through the conversation with agents, or choose one ormore actions programmed in the unit. It was found that data from two units had dependencies in the
responses due to different paths that could be taken by students through the simulated chat. Therefore, theCPS chat items that showed this kind of dependency were combined into “composite items” by summing
the responses for the different paths respondents could take. With this approach it was determined thateach path-based response string could be scored to provide valid data and introduced into the IRT analysis.
Table 3 gives an overview of the combination for composite items. The table shows how chat-based items
in two CPS units (101 and 105) were combined into composite items in order to generate polytomous
items for the purpose of reducing issues with local dependencies. Details about the items going into thisrescoring can be found in the databases containing country-specific data as well as variable and valuelabels.

－42－

Table 3: Combination of CPS Items to Achieve Fair Scoring
NewItemIDfor CombinationsofCPSItems
CompositeItems
CC101201C
CC101201+CC101202
CC101203C
CC101203+CC101204+CC101205
CC101206C
CC101206+CC101207
CC101301C
CC101301+CC101302+CC101303
CC101304C
CC101304+CC101305
CC101307C
CC101307+CC101308+CC101309A+CC101309B+ CC101310+CC101311+
CC101312A
CC101312BC
CC101312B+CC101313
CC101317C
CC101317+CC101318+CC101319
CC105103C
CC105103+CC105104
CC105105C
CC105105+CC105106+CC105107
CC105201C
CC105201+CC105202
CC105208C
CC105208+CC105209+CC105210
CC105212C
CC105212+CC105213
CC105304C
CC105304+CC105305

Across the seven units and 165 chat/selection items, 10 had to be dropped during the analysis due
either to no response variance or the presence of technical issues. Table 4 provides a list of these items.
Table 4: CPS Items Excluded from the IRT Analyses based on Classical Item Analyses
DomainCPS (10 items)

Item
CC100403
CC102202
CC102208
CC102212
CC104303
CC105108
CC105303
CC105403
CC105405
CC106306

Mode of
Administration
CBA
CBA
CBA
CBA
CBA
CBA
CBA
CBA
CBA
CBA

The new science items were scaled together with the trend science items. In addition to 91 trend
items, the science domain consists of 213 new items resulting in a total of 304 items. For science, five
new items had to be excluded from the IRT analyses due to either a lack of response variance, or very low
or even negative item total correlations. Table 5 gives an overview of these items. Most items showed no
obvious defects, and the initial IRT scaling was conducted on 208 new science items.

－43－

Table 5: New Science Items Excluded from the IRT Analyses Based on Classical Item Analyses
DomainScience (5 items)

Item
CS644Q02S
CS638Q03S
CS656Q06S
CS661Q07S
CS650Q03S

Mode of
Administration
CBA
CBA
CBA
CBA
CBA

For science, data from 53 countries were received in time to be included in the IRT analyses, andfor CPS data from 42 countries were received. For the IRT analyses, the sample was divided by country
and language, resulting in 70 country/language groups for science, and 55 country/language groups for
CPS.
While the item parameters of trend items were fixed to those obtained from the reanalyses ofprevious PISA cycles (historical data), the new science and CPS items had to be scaled based solely on
the Field Trial data. For the CPS scale, both a multigroup Rasch model/PCM was estimated as well as amultigroup 2PL model/GPCM. For the new science items and CPS items a concurrent calibration wasused to evaluate whether items are working equally across country/language groups or whether there areitem-by-country/language interactions. Both model approaches were compared to each other. Item
parameters for new science and CPS items that are provided for the countries and used to identify items
for the Main Study are based on the 2PL model due to the improved model-data fit and because more
information (with regard to slope parameters) about each single item is provided. These item parameters
were also used for generating a standardized proxy (EAP) estimate standardized within countries that isavailable in the data delivery to countries).
Table 6: Comparison of Rasch model/PCM and 2PL model/GPCM for new items
Likelihood:A-penalty
AIC
B-penalty
BIC
CPS
RM/PCM-985477.57
686
1971641.15
3877.09
1974832.24
2PL/GPCM-971208.69
994
1943411.38
5617.83
1948035.21
Science
RM/PCM-2215483.30 1266
4432232.60
7406.464438373.06
2PL/GPCM-2192778.99 1698
4387255.97
9933.784395491.75

The item fit of the new science items and the CPS items was evaluated with regard to the
concurrent calibration. Table 7 gives an overview of the percentage of RMSD and MD that wasconsidered to be deviant using a rather strict criterion of RMSD > 0.20 and, MD > 0.20 and <í0.20.
Table 7: RMSD and MD Deviations of Trend Items and New Items overall Countries/Languages

Percent of
RMSD > 0.20
MD > 0.20 and < í0.20

Science-new
new items
CBA
0.93%
0.49%

CPS
new items
CBA
0.94%
0.51%

Table 7 shows that item deviations for new science and CPS items are generally small. The
deviation frequencies were not found to be substantially higher for any one particular country or language－44－

group. The results illustrate that the items show a good fit when using the same item parameters acrossdifferent countries and languages. Moreover, both scales show sufficient IRT-based (marginal)
reliabilities (Sireci, Thissen, & Wainer, 1991; Wainer, Bradlow, & Wang, 2007) with 0.80 for science(based on trend and new items) and 0.88 for CPS.

What we found from 2015 PISA FT and from 2012 PIAAC on CBAThe Field Trial analyses are based on data from 53 countries received by 31 July 2014. Of these53 countries, 45 were considered computer-based assessment (CBA) countries, meaning that they
implemented the survey using both paper and computer, while eight countries implemented the survey asa paper-based assessment (PBA). Altogether 120,303 cases including all multiple languages were
included.
Almost all countries met the sample size requirements for the major assessment language. Morespecifically, 50 out of 53 countries produced more than 95% of required sample sizes: 1,950 for CBA
countries, 900 for PBA countries, and 1,750 for CBA without CPS countries.
The CBA design specified that 1,950 students from multiple schools were randomly assigned to
one of three groups according to prescribed probabilities: 1) 23% of the sampled students would be
administered trend items through paper administration, referred to as Group 1-PBA Trend, 2) 35% would
be administered trend items in computer administration, referred to as Group 2-CBA Trend, and 3) 42%
would be administered new items through computer administration, referred to as Group 3-CBA and New
Science/CPS. The successful implementation of the design above rested on strict random assignment ofstudents to one of the three groups identified in Figure below.
In order to examine the transition to computer delivery, a set of 18 paper-based forms covering
the domains of reading, mathematics, and science were constructed for Group 1 PBA-Trend from items
administered in the past PISA surveys. A set of tasks “identical” to those contained in the 18 paper-based
forms were then adapted and authored for computer administration, yielding 18 equivalent computerbased test forms. In addition, there were 12 computer-based test forms consisting of the new 2015 sciencetasks and 12 new test forms combining those 2015 science tasks with the new CPS tasks. The schematicfor the CBA design is shown in Figure below.

Information yield by the CBA platform
Two key features of the computer delivery platform, not available in paper-based assessments,
are the availability of detailed timing and process sequence information. Response times are recorded foreach item in milliseconds; hence, they allow for precise, timing-related analyses. For instance, these data
can be used to identify rapid responders (Wise & DeMars, 2005) and/or potential administration issues(e.g., groups of examinees who take substantively longer to complete the test than expected). Timing
information can also be used to address issues of speededness and fatigue, between-country differences inallocated time, position effects, and interaction effects with variables such as examinee performance.
Sequence information, on the other hand, can provide insights into how examinees progress through a setof items, including the number of times that an item is revisited, item sets that are skipped, and items that－45－

are truly not reached. This information can be used in conjunction with the timing data to identify
potentially problematic items, units, and/or clusters.
The Field Trial data indicate that timing data as well as process data have been successfullyrecorded for all data collections in the CBA countries. The available timing data was instrumental inevaluating the level of engagement and effort students spent over the course of the four 30-minute clusters.
Analyses were conducted and results of these indicate that the CBA assessment provides valid
information that can be used to evaluate response processes within and across countries.

Reduced position effects and improved omission rates in the CBA
Item position effects are a common issue in large assessment programs because substantial
position effects can increase measurement error and introduce bias. The PISA 2015 Field Trial designbalanced cluster position in order to monitor its impact on various measures. We examined the cluster
position effects in terms of: 1) proportions correct, 2) response time, and 3) interactions by domain as
well as by country.
In order to have a reference point to examine the magnitude of position effects, PISA 2009 data
was examined in terms of proportions correct (P+) at the cluster level (PISA 2009 was the last time thatscience was the major domain). The values for PISA 2009 are shown in Table 8 below. In all content
domains there is a decrease of 4 to 8 percentage points in the P+ values between positions 1 and 4. For the
PISA 2015 Field Trial data (see Table 9), a smaller decrease of 1 to 3 percentage points in P+ values,
compared to the 2009 values, is seen between positions 1 and 4.
Table 8: PISA 2009 PBA Proportions Correct Across Clusters and Across Countries

Mathematics
Reading
Science

Position 1

Position 2

Position 3

Position 4

Position 4Position 1

0.411
0.581
0.490

0.401
0.557
0.478

0.384
0.532
0.457

0.371
0.499
0.435

-0.040-0.083-0.055

Table 9: PISA 2015 CBA/PBA Proportions Correct Across Clusters and Across Countries

Mathematics
Reading
Science

Position 1

Position 2

Position 3

Position 4

Position 4Position 1

0.439
0.575
0.409

0.454
0.580
0.418

0.432
0.566
0.401

0.427
0.544
0.385

-0.012-0.031-0.024

Table 10: PISA 2015 CBA Cluster Timing Averaged Across Countries (in Minutes)

Mathematics
Reading
Science
CPS
FL

Position 1

Position 2

Position 3

Position 4

Position 4Position 1*

19.76
24.11
22.32
22.67
19.49

18.84
21.47
20.61
21.26
14.49

17.26
20.75
19.21
20.96
NA

16.03
18.44
17.11
18.79
NA

-3.72
-5.67
-5.21
-3.88
-5.00

－46－

Note: * For financial literacy, the difference is taken between positions 1 and 2 because these instruments
only had two clusters.
While the P+ values remain quite stable across positions for the 2015 Field Trial data, there is anotable decrease in the mean response times (around four to six minutes, i.e., nearly 20% reduction) for
clusters administered in each of the four positions (see Table 10). These finding suggest that the decrease
in response times over positions has little association with P+ values.
To further address the relationship between response time and examinee performance, we
considered mean times grouped using a preliminary ability estimate or what we are calling a proxy. Thisability estimate was derived based on the IRT scaling by domain that was carried out to evaluate the fit oftrend item parameters. The IRT scaling in the three domains of math, reading, and science was used togenerate an expected a posteriori (EAP) score that was averaged across domains to provide thispreliminary ability estimate. This preliminary estimate is referred to as the proxy score. This proxy score
cannot be used for country comparisons since it is based on FT instrumentation which is preliminary, andsample sizes used in the FT do not allow sufficiently reliable estimates of country-level statistics. Table
11 below reports mean response times at three proxy score levels (low, moderate and high ability) basedon an equal split into three groups (33.3% each). It is evident that low-ability students take several
minutes less on average to complete a cluster. In some cases, the average differences between the lowand high-ability groups exceed five minutes.
Table 11: PISA 2015 Cluster Level Response Time by Proxy Level (in Minutes)

Mathematics
Reading
Science
CPS
Financial Literacy

Low Ability
15.17
18.97
17.45
19.69
15.76

Moderate
Ability
18.97
22.60
21.31
22.23
19.97

High Ability
20.92
23.64
22.86
22.77
22.11

The analysis of missing responses looks at the Field Trial data comparing the omission (student
nonresponse) rate differences between CBA and PBA with the purpose to further examine the quality ofdata in the two modes. Items that were presented only in PBA or only in CBA mode as well as a verysmall number of items that exhibited technical issues were not included in this analysis. In total, 318
items were compared in terms of omission rates across CBA and PBA modes.
We defined the omission rate as the number of omitted responses divided by the total number oftest takers who received the item in each country. A high omission rate is defined by omission rates larger
than 10%. The effect of omissions was investigated by checking the differences between two approaches
of calculating percent correct (P+) item score. These two approaches are referred to as the W rule
(omitted responses treated as “wrong”) and X rule (omitted responses excluded), which in effect differedby the inclusion or exclusion of omissions into the sample size incorporated in the calculation of P+
values. An omission effect is present when the P+ calculated under the W rule and the X rule,
respectively, differs by more than 5%.
The correlation of omission rate and omission effect between CBA and PBA was 0.88 and 0.80,
respectively. It was found that 13 countries had high omission effects in PBA, while only three countries
were detected to have a high omission effect in CBA. Further, 23 countries were found to have more than
50 items that were labeled as high-omission-effect items in PBA, that is, the difference between W ruleand X rule was over 5% and across at least 20 countries, while only nine countries were found under thesame conditions in CBA. On average across countries, high omission effect for the CBA mode was 32.5
items, which is lower than the 44.4 items with high omission effect in the PBA mode.

－47－

Open response items accounted for the largest proportion of items with a high omission rate andimpacted the P+ value calculation the most. When counting the number of items that have omission rates
higher than 10% across at least 20 countries, there were 47 CBA items, among which 98% were openresponse items. In comparison, 54 items were labeled as having a high omission rate in PBA, all of whichwere open-ended items. The omission rate in all content domains was higher for PBA than CBA, except
financial literacy, which had a 1.1% higher omission rate in CBA than PBA. Mathematics items took thehighest proportion (approximate 35%) in both of the modes and were almost the same in CBA and PBA.
Reading items were slightly higher in omission, by 2%, for PBA. Bigger differences were found for itemsin the domains of science (5% increase) and financial literacy (6% decrease) in PBA.
In conclusion, reductions in omission rate and omission effect were observed in CBA over PBA.
In addition, the findings of higher omission rate and omission effect in PBA items compared to CBA,
especially in open-response items, provides motivation to study the processing behavior of the students
when transitioning to CBA. Process data may provide new insights into test-taker behavior during the test.
It would be helpful to analyze process data stored in log files to further understand which student
behaviors are related to correct, incorrect, and omitted responses and explore the patterns between
omissions and item types as well.

Future directions of PISAThe future design and functionality of PISA is solely in the hands of OECD and PISA Governing
Board and proposals consortium have made, so it is not certain about specific features or their timeline.
However, as a contractor for both 2015 and 2018 contractors, I have a vantage point of view. It is clearthat CBA is here to stay and measurement constructs in any domain will expand beyond PBA constructs.
The future constructs may incorporate how students use skills to learn deeper and further in the domain.
Such activities will include measuring what they have learned already as well as how they might be able
to use in more realistic settings. Although, depth of knowledge being measured and realism of questions
would introduce contextual factors that may interfere generalizability of measured skills. PISA items
tend not to require special factual knowledge but higher organizational knowledge to utilize necessary andextra information to solve problems. CBA may require awareness of what he/she knows and does notknow in order to pursue which direction to solve problems as information explorations take place.
Historical item construction criteria of strict adherence to over one third to be hand scored issomewhat arbitrary and ignores the advanced capability of computer scoring. It is expected that
proportion of computer based scoring would increase as more computer based scoring algorithm mature.
It has been believed among those who have been involved in the large scale assessment with emphasis on
trend assessment that “to measure trend don’t change the measure”, meaning keep the intact clusters aslarge as possible across cycles. However, since 1990 we have been seeing the data in adult literacy
surveys, cluster size can be fairly small to maintain consistent item parameters across cycles, certainly not
as large as 30 minutes worth of sequence of items. If intact clusters can be as small as a set of items
based on a single stem, more flexible item combinations can be possible and open a door to manydifferent kind of adaptive testing.
Regularity of response time, number of actions and sequence of actions among students, language
and countries are remarkable. Future analysis model would include additional information unique to
CBA for estimating skill distribution of students population.

－48－

References
Bock, R. D., & Zimowski, M. F. (1997). Multiple group IRT. In W. J. van der Linden & R. K. Hambleton(Eds.), Handbook of modern item response theory (pp. 433-448). New York, NY: SpringerVerlag.
Carstensen, C. H. (2012). Linking PISA competencies over three cycles – Results from Germany? In:
Gebhardt, E. & Adams, R.J. (2007). The influence of equating methodology on reported trends in PISA.
Journal of Applied Measurement 8(3), 305̺322.
Mazzeo, J., & Davier, M. von (2008). Review of the Programme for International Student Assessment(PISA) Test Design: Recommendations for Fostering Stability in Assessment Results. doc.ref.
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Mazzeo, J. & von Davier, M. (2013). Linking Scales in International Large-Scale Assessments. In
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Assessment: Background, Technical Issues, and Methods of Data Analysis. CRC Press (Chapman& Hall).
Prenzel, M., Kobarg, M., Schöps, K., & Rönnebeck, S. (Eds.) Research in the context of the Programme
for International Student Assessment. Berlin: Springer.
Sireci, S. G., Thissen, D., & Wainer, H. (1991). On the reliability of testlet-based tests. Journal of
Educational Measurement, 28, 237-247.
Urbach, D. (2012). An investigation of Australian OECD Pisa Trend results. In: Prenzel, M., Kobarg,
M.,Schöps, K., & Rönnebeck, S. (Eds.) Research in the context of the Programme forInternationalStudent Assessment. Berlin: Springer.
von Davier, M., & Yamamoto, K. (2004). Partially observed mixtures of IRT models: an extension of thegeneralized partial credit model. Applied Psychological Measurement, 28(6), 389-406.
von Davier, M. & von Davier, A. (2007). A Unified Approach to IRT Scale Linkage and Scale
Transformations. Methodology, 3, 3, 115-124.
Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet response theory and its applications. New York,
NY: Cambridge University Press.
Weeks, J., Yamamoto, K., & von Davier, M. (2013). Design considerations for the program forinternational student assessment. In L. Rutkowski, M. von Davier, & D. Rutkowski (Eds.),
Handbook of international large-scale assessment: Background, technical issues, and methods ofdata analysis. Boca Raton, FL: CRC Press.
Wise, S. L. & DeMars, C. E. (2005). Low examinee effort in low-stakes assessment: Problems andpotential solutions. Educational Assessment, 10, 1-17.
Yamamoto, K., & Mazzeo, J. (1992). Item response theory scale linkage in NAEP. Journal of Educational
Statistics, 17(2), 155-173.

－49－

࠙ሗ࿌㸯ࠚ

ヨ㦂ࡢ᪥ᮏⓗ㢼ᅵᮾிᕤᴗ኱Ꮫ኱Ꮫ㝔♫఍⌮ᕤᏛ◊✲⛉ᩍᤵ

๓ᕝ┾୍
‫ྖۑ‬఍

ࡑࢁࡑࢁ෌㛤ࡋࡓ࠸࡜ᛮ࠸ࡲࡍࠋ௒᪥ࡣࢩ࣏ࣥ

ࢪ࣒࢘ࡢࢸ࣮࣐࡟࠿࠿ࢃࡾࡲࡋ࡚ࠊ4 ࡘࡢሗ࿌ࢆ‽ഛࡉࡏ࡚࠸ࡓࡔ࠸࡚࠾ࡾࡲࡍࠋࡲ ࡎᮾி ᕤᴗ኱ Ꮫࡢ๓ ᕝ┾ ୍ඛ⏕ ࡼࡾࠊࠕヨ㦂ࡢ᪥ᮏⓗ㢼ᅵࠖ࡜࠸࠺ࡇ࡜࡛ㅮ₇ࢆ࠾㢪࠸ࡋࡲࡍࠋ
๓ᕝඛ⏕ࠊࡼࢁࡋࡃ࠾㢪࠸ࡋࡲࡍࠋ
‫ۑ‬๓ᕝẶ

ึࡵࡲࡋ࡚ࠋᮾிᕤᴗ኱Ꮫࡢ๓ᕝ࡛ࡍࠋᮏ᪥

ࡣࠕヨ㦂ࡢ᪥ᮏⓗ㢼ᅵࠖ࡜࠸࠺㢟࡛࠾ヰࡋࡉࡏ࡚࠸ࡓࡔ
ࡁࡲࡍࠋࡇࡢヰࠊࡇࡇ 10 ᖺࡄࡽ࠸࠸ࢁ࠸ࢁ࡞Ꮫ఍࡛ヰࡋࡓࡾࡋ࡚࠸ࡿࡢ࡛ࠊ࠾⪺ࡁ࡟࡞ࡗࡓ᪉
ࡶఱே࠿࠾ࡽࢀࡿ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡶ࠺୍ᗘ࠾ヰࡋࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
᪥ᮏࡢヨ㦂ⓗ㢼ᅵෆᐜ࡛ࡍࡅࢀ࡝ࡶࠊ᪥ᮏࡢヨ㦂ⓗ㢼ᅵ࡜࿧ࡤࢀ࡚࠸ࡿࡶࡢࠊ᪥ᮏ࡛⾜ࢃࢀ࡚࠸ࡿヨ㦂ࠊࢸࢫࢺࡢ≉ᚩࠊ᪥ᮏⓗヨ㦂ᩥ໬࡜࿧ࢇࡔࡾ᪥ᮏⓗࢸࢫࢺᩥ໬࡜࿧ࢇࡔࡾࡋ࡚࠸ࡿࡶࡢ࡛ࡍ
ࡀࠊࡑࢀࡀ࡝࠺࠸࠺ࡶࡢ࡞ࡢ࠿ࠊࡶ࠺୍ᅇᨵࡵ࡚᭩ࡁฟࡋ࡚ㄆ㆑ࡋࡼ࠺ࠊࡑ࠺࠸࠺ヰ࡛ࡍࠋࠕ ヨ㦂ࠖ ࡜ゝࢃ ࢀ࡚࠸ ࡿࡶ ࡢࡣ᪥ ᮏㄒ࡛ ࡣࠕ᳨ ᰝࠖࠕヨ 㦂ࠖࠕ⪃ᰝࠖࠕࢸࢫࢺࠖࡑࢀ࠿ࡽ
⤫ィⓗ᳨ᐃ࡜࠿₎Ꮠ᳨ᐃ࡜࠿࢘ࣝࢺ᳨࣐ࣛࣥᐃ࡜࠿ゝࢃࢀ࡚࠸ࡿࠊ࠶ࢀࡣࡳࢇ࡞ྠࡌࡶࡢ࡛ ࡍ ࠋ ⱥ ㄒ ࡛ ゝ ࠺ ࡜ ࠕ ࢡ ࢖ ࢬ ࠖࠕ ࢸ ࢫ ࢺ ࠖࠕ ࢖ ࢢ ࢨ ࣑ ࢿ࣮ ࢩ ࣙ ࣥ ࠖ ࡢ 3 ࡘ ࡀ ࡑ ࢀ࡟ ᙜ ࡓ ࡿ࡜࠸࠺ࡇ࡜࡛ࠊ౛࠼ࡤ࢖ࣥࢱ࣮ࢿࢵࢺ࡛ࠕࢸࢫࢺࠖ࡜ࠕ࢖ࢢࢨ࣑ࢿ࣮ࢩࣙࣥࠖ࡜࡝࠺㐪࠺ࡢ࠿ࢆᘬ࠸࡚ࡳࡿ࡜ࠊ࠶ࡿ኱Ꮫࡢ࣮࣒࣮࣍࣌ࢪࡀฟ࡚ࡁ࡚ࠕ࠺ࡕࡢ኱Ꮫ࡛ࡣࡇ࠺౑ࡗ࡚࠸
ࡲࡍࠖ࡜࠸࠺ࡼ࠺࡞ㄝ᫂ࡀぢࡘ࠿ࡿ࡜ᛮ࠸ࡲࡍࠋ࢖ࢢࢨ࣑ࢿ࣮ࢩࣙࣥࡀ࡝ࡕࡽ࠿࡜࠸࠺࡜
㞴ࡋ࠸᪉ࠊࢸࢫࢺࡀ⡆༢࡞᪉ࠊࢡ࢖ࢬ࡜࠸࠺ࡢࡣࡶ࠺୍ࡘ⡆༢࡞ࡶࡢࠊࡑ࠺࠸࠺ឤࡌ࡛ࡍࠋ
᪥ᮏㄒࡣ࡝࠺౑࠸ศࡅ࡚࠸ࡿ࠿ࠊࡕࡻࡗ࡜ࡼࡃࢃ࠿ࡾࡲࡏࢇࡅࢀ࡝ࡶࠊᏛຊ᳨ᰝࠊᏛຊ
ヨ㦂ࠊᏛຊ⪃ᰝࠊᏛຊࢸࢫࢺࠊࡣ౑࠸ࡲࡍࡼࡡࠋࡋ࠿ࡋᛶ᱁ࢸࢫࢺࠊᛶ᱁᳨ᰝ࡜ࡣゝ࠺ࡅࢀ࡝ࡶࠊᛶ᱁ヨ㦂࡜ࡣవࡾゝࢃ࡞࠸ࠋఱ࠿ᚤጁ࡞౑࠸᪉ࡀ࠶ࡿࡢࡔ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊ
࠸ࡎࢀ࡟ࡋ࡚ࡶࠊࢸࢫࢺࠊヨ㦂࡜࠸࠺ࡶࡢ࡛ࡍࠋ
᪥ᮏⓗࢸࢫࢺᩥ໬࡟ࡣ࡝࠺࠸࠺ࡶࡢࡀ࠶ࡿࡢ࠿ࠊ⟠᮲᭩ࡁ࡟ࡋ࡚ࡳࡲࡋࡓࠋ
ࡇࡇ࡛ゝࡗ࡚࠸ࡿ᪥ᮏࡢࢸࢫࢺ࡜࠸࠺ࡢࡣධヨࢆྵࡴ኱つᶍ࡞ࢸࢫࢺࡢヰ࡛ࡍࠋྖἲヨ㦂࡜࠿኱ᏛࡢධᏛヨ㦂࡜࠿ࠊࡑ࠺࠸࠺ࡶࡢࡢࡇ࡜ࢆ⪃࠼࡚࠸ࡲࡍࠋ
ᖺ࡟ 1 ᗘࠊྠ୍ၥ㢟࡛ヨ㦂ࢆ୍ᩧ࡟ᐇ᪋ࡍࡿࠋ
᪂ࡋ࠸ၥ㢟ࡀẖᖺฟ࡚ࡃࡿࠋඛ࡯࡝ᒣᮏඛ⏕࠿ࡽࣇ࢕࣮ࣝࢻࢸࢫࢺ࡜࠸࠺ヰࡀ࠶ࡾࡲࡋ
ࡓࡅࢀ࡝ࡶࠊ᪥ᮏࡢࢸࢫࢺࡣࣇ࢕࣮ࣝࢻࢸࢫࢺࢆ୍ษࡸࡾࡲࡏࢇࠋࡑࢀࡀ≉ᚩࡢ 1 ࡘ࡛ࡍࠋ－51－ࡑࢀ࠿ࡽࠊヨ㦂ၥ㢟ࢆබ㛤ࡋࡲࡍࠋࡉࡗࡁࡢ PISA ࡢၥ㢟࡜࠿ࠊⓙࡉࢇࡈᏑࡌࡢ TOEIC࡜࠿ TOEFL ࡜࠿࠶ࡢ㢮ࡢࢸࢫࢺࠊヨ㦂ၥ㢟ࡣබ㛤ࡉࢀࡲࡏࢇࡼࡡࠋ᪥ᮏࡣ㏫࡟ヨ㦂ၥ㢟ࢆබ㛤ࡋࡲࡍࠋ
ࡑࢀ࠿ࡽࠊᑠࡉ࡞ၥ㢟ࡀ࠸ࡗࡥ࠸ᐤࡏ㞟ࡲࡗ࡚ᗈ࠸ศ㔝ࢆࡿ࡜࠸࠺ឤࡌ࡛ࡣ࡞ࡃ࡚ࠊ

ၥ㢟ࡢ୍ࡲ࡜ࡲࡾࠊࢭࣥࢱ࣮ヨ㦂࡛ࡣࠕ኱ၥࠖ࡜࿧ࢇ࡛࠸ࡿࢇ࡛ࡍࡀࠊ኱ၥᙧᘧࡢၥ㢟ࡀᗄࡘ࠿ฟࡿ࡜࠸࠺ᙧࡀከࡃ౑ࢃࢀ࡚࠸ࡿࠋ
ࡑࢀ࠿ࡽࠊࡇࡇࡣࡕࡻࡗ࡜ఱࡢࡇ࡜ࢃ࠿ࡽ࡞࠸࠿ࡶࡋࢀࡲࡏࢇࡅࢀ࡝ࡶࠊၥ㢟ࢆసࡿே࡜ၥ㢟ࡢ⣲ᮦࢆ㞟ࡵ࡚ࢸࢫࢺࡢ෉Ꮚࢆ⦅㞟ࡍࡿேࡀྠࡌ࡛࠶ࡿࠋ
ࡑࢀ࠿ࡽࠊඛ࡯࡝࠿ࡽᚰ⌮ᐃ࡜࠿ࢧ࢖ࢥ࣓ࢺࣜࢩࣕࣥ࡜࠿࠸࠺ヰ࠿ฟ࡚࠸ࡲࡍࡅࢀ࡝

ࡶࠊࡑ࠺࠸࠺࠸ࢃࡺࡿ⤫ィⓗ࡞ࢸࢫࢺ࣭ᚰ⌮ᐃࢆࡸࡗ࡚࠸ࡿேࡣၥ㢟సᡂ࡟ཧຍࡋ࡞࠸

࡜࠸࠺≉ᚩࡀ࠶ࡾࡲࡍࠋ
᭱ᚋ࡟ฟ࡚ࡃࡿⅬᩘ࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ౛࠼ࡤ 20 㢟ࡢၥ㢟࠿ࡽᡂࡿヨ㦂ࢆཷࡅࡲࡋࡓࠋ
Ⅼᩘࡣ 1 㢟ࡢ㓄Ⅼࢆ 5 ࡜ࡋ࡚ 100 Ⅼ‶Ⅼ࡟ࡋࡲࡍࠋఱ㢟ṇ⟅ฟ᮶ࡓ࠿࡟㓄Ⅼࢆ᥃ࡅࡓᙧ࡛
Ⅼᩘࡀฟࡲࡍࠋࡑࢀࢆࡇࡇ࡛ࡣ⣲Ⅼ࡜࿧ࢇ࡛࠸ࡲࡍࠋ㓄Ⅼࢆ฼⏝ࡋࡓ⣲Ⅼࢆ౑ࡗ࡚ࠊࡔ࠿
ࡽ 20 㢟࡛㓄Ⅼࡀ 5 ࡔࡗࡓࡽ 100 Ⅼ‶Ⅼࠋ0 Ⅼ࡜‶ⅬࡀỴࡲࡗ࡚࠸ࡿࡼ࠺࡞ࠊࡑ࠺࠸࠺ᑻᗘ
࡛ᡂ⦼ࡀฟ࡚ࡃࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋࡔ࠿ࡽࠊྠࡌ 100 Ⅼ‶Ⅼࡢࢸࢫࢺ࡛ࡶ㞴ࡋ࠸ࢸࢫࢺࡢ
100 Ⅼ࡜ࡸࡉࡋ࠸ၥ㢟ࡢ 100 Ⅼࡣᮏᙜࡣ㐪࠺ࡣࡎ࡞ࡢ࡟ࠊ᪥ᮏࡢࢸࢫࢺࡣవࡾࡑࡇࡢ࡜ࡇ
ࢁࢆ⪃࠼࡚࠸࡞࠸ࠊࡑ࠺࠸࠺≉ᚩࡀ࠶ࡾࡲࡍࠋ
࡞ࡐࡇࢇ࡞ࡩ࠺࡟࡞ࡗ࡚࠸ࡿࡢ࠿࡜࠸࠺ࡇ࡜࡛ࡍࡀࠊ㉥࠸࡜ࡇࢁࡀࠊ࡞ࡐࡇ࠺࡞ࡗࡓ࠿
࡜࠸࠺⌮⏤࡛ࡍࠋ
ྠࡌヨ㦂ၥ㢟ࢆᖺ࡟ 1 ᅇࠊ୍ᩧ࡟ᐇ᪋ࡍࡿ࡜࠸࠺ࡇ࡜࡟㛵ࡋ࡚ࡣࠊၥ㢟ࡀ㐪ࡗࡓࡽ୙බ
ᖹ࡛ࡣ࡞࠸࠿ࠊ᮲௳ࡀ㐪ࡗࡓࡽ୙බᖹ࡛ࡣ࡞࠸࠿࡜࠸࠺ឤぬࡀ࠶ࡿࡢࡔ࡜ᛮ࠸ࡲࡍࠋ
ࡑࢀ࠿ࡽࠊ᪂సၥ㢟ࡢࡳ࡛ࡢヨ㦂ࡢᐇ᪋࡟࠿ࢇࡋ࡚ࡳࠊࣉࣜࢸࢫࢺ࡜࠿ࣇ࢕࣮ࣝࢻࢸࢫ
ࢺ࡜࠿ゝࢃࢀ࡚࠸ࡿࡶࡢࠊ๓ࡶࡗ࡚ヨ㦂ၥ㢟ࢆ࡝ࡇ࠿࡛ヨࡋ࡚Ⰻ࠸ၥ㢟ࡔࡅࢆᮏ␒࡛౑࠺
࡜࠸࠺సᴗࡣࠊ୙බᖹࡔ࡜ᛮࢃࢀ࡚࠸ࡿࠋ
ヨ㦂ၥ㢟ࢆබ㛤ࡍࡿࡇ࡜࡟㛵ࡋ࡚ࡣࠊ1 ࡘࡣᩍ⫱ⓗぢᆅ࠿ࡽࠊࡇ࠺࠸࠺ၥ㢟ࢆฟࡋࡓࢇ
ࡔࡼ࡜࠸࠺ࡢࢆぢࡏࡿ࡜࠸࠺ࡇ࡜࡛ࡍࡀࠊ௚࡟ࡶࠊ㠀බ㛤࡟ࡋࡓ࠸኱つᶍヨ㦂ࡣ࠸ࡗࡥ࠸
࠶ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊ᪥ᮏࡢ᝟ሗබ㛤ἲ࡜࠿つไ⦆࿴ࡢᙳ㡪࡛ࠊࡑࢀࡀ௒ࠊฟ᮶࡞࠸≧ἣ࡟࠶ࡿ࡜࠸࠺ࡇ࡜ࠋ
࠶࡜ࠊ኱ၥᙧᘧࡢ฼⏝࡜࠸࠺ࡢࡣࠊᑠࡉ࠸ၥ㢟࡛ἑᒣᖜᗈࡃ

ࡗ࡚࠸ࡃࡓࡵ࡟ࡣࠊヨ㦂ၥ㢟సᡂࡢࣈ࣮ࣝࣉࣜࣥࢻ࡜࠸࠺࠿ࢫ࣌ࢵࢡ࡜࠸࠺࠿ࠊࡑ࠺࠸࠺ࡶࡢࡀᚲせ࡞ࢇ࡛ࡍࡅࢀ
࡝ࡶࠊࡑࢀࢆཝᐦ࡟సࡗ࡚⣽࠿࠸ၥ㢟ࢆࡓࡃࡉࢇฟࡍࡼ࠺࡞ᙧ࡛ฟ᮶࡚࠸ࡿ࠿࡝࠺࠿࡜࠸
࠺␲ၥࡀ࠶ࡾࡲࡍࠋ࠶࡜ࠊ኱ၥᙧᘧࡔ࡜ከ⫥㑅ᢥᙧᘧ࡛ࡶᛮ⪃ຊࢆࡿၥ㢟ࡀࡘࡃࢀࡿࡢ

࡛ࡣ࡞࠸࠿ࠊࡑ࠺࠸࠺ࡇ࡜ࢆ⪃࠼࡚࠸ࡿ࡜ࡇࢁࡀ࠶ࡾࡲࡍࠋ
ヨ 㦂 ၥ㢟 ࠊ ≉ ࡟ ධヨ ၥ 㢟 ࡟ 㛵ࡋ ࡚ ࡣ ኱ Ꮫ ࡢඛ ⏕ࡀ సࡾࡲ ࡍ࠿ ࡽࠊࠕ࠾ࢀ ࡓࡕࡀ సࡗࡓ ၥ㢟ࡣ࠸࠸ၥ㢟ࡔࠋ௚ࡢࡸࡘࡀᩥྃࢆゝ࠺➽ྜ࠸ࡣ࡞࠸ࢇࡔࡼࠖࡳࡓ࠸࡞ᙧ࡛ࡘࡃࡗ࡚࠸ࡿ
࡜࠸࠺ࡢࡀᶒጾ୺⩏࡜࠸࠺࡜ࡇࢁࠋ
࠶࡜ࠊṇ⟅ᩘ࡟㔜ࡳࢆ࠿ࡅ࡚‶Ⅼ࡟ࡍࡿ࡜࠸࠺ࡢࡣࠊࡶ࠺ࡕࡻࡗ࡜ูࡢࡸࡾ᪉ࠊࡶࡗ࡜
࠸࠸ࡸࡾ᪉ࡀ࠶ࡿࢇࡔࡼ࡜࠸࠺ᚰ⌮ࡑ࠺ࡸࡗ࡚ᚰ⌮ᐃᏛࡀ୺ᙇࡍࡿࡇ࡜࡟ᑐࡍࡿ↓㛵ᚰ࡜࠿ࠊࡑࢀ࠿ࡽࠊ

ᐃᏛ⪅ࡀࡘࡃࡗࡓᑻᗘᚓⅬ࡟ᑐࡍࡿ୙ಙឤࡳࡓ࠸࡞ࡶࡢࡀ࠶ࡿࠋࡇ࠺࠸

－52－࠺ࡶࡢࡀ኱య⌮⏤ࡔ࡜ᛮࢃࢀࡲࡍࠋ
᪥ᮏࡢヨ㦂ⓗ㢼ᅵࡢ㏫㸻ୡ⏺ᶆ‽࡛ࡣࠊࡑࡢ㏫ࢆ⪃࠼࡚ࡳࡲࡋࡻ࠺࡜࠸࠺ࡇ࡜࡛ࡍࡀࠊ1 ᖺ㛫࡟ 1 ᗘࠊྠ୍ၥ㢟࡛ヨ㦂ࢆ୍ᩧ࡟ᐇ᪋ࡍࡿࡇ࡜ࡢ཯ᑐࡣࠊᖺ࡟」ᩘᅇࠊ␗࡞ࡿၥ㢟࡛ศᩓⓗ࡟ᐇ᪋ࡍࡿࠋ౛࠼ࡤ IRT
ࢆ౑ ࠺࡜࠿ CBT ࢆ౑࠺࡜࠿ࠊࡑ࠺࠸࠺ヰ࡛ࡍࡡࠋࡑࢀ࠿ࡽࠊ᪂సၥ㢟ࡢࡳ࡛ヨ㦂ࢆᐇ᪋ࡍࡿࡇ࡜ࡢ཯ᑐࡣࠊ⤫ィⓗᛶ㉁ࠊ౛࠼ࡤ㞴᫆ᗘࡳࡓ࠸࡞ࡶࡢࡢࢃ࠿ࡗ࡚࠸ࡿၥ㢟ࡢࡳ࡛ࠊⰋ࠸ၥ㢟ࡔࡅ࡛ࢸࢫࢺၥ㢟ࢆᵓᡂࡍࡿ࡜࠸࠺ヰࠋࡑࢀ࠿ࡽබ㛤࡟㛵ࡋ࡚ࡣࠊၥ㢟ࡣ㠀බ㛤ࠋࡓࡔࡋࠊಶே࡟ࡣᡂ⦼ࢆ㏦ࡾࡲࡍࡼ࡜࠸࠺ヰࠋ኱ၥᙧᘧࡢ฼⏝ࡢ཯ᑐࡣࠊ⣽࠿࠸ࢫ࣌ࢵࢡ࡟ᇶ࡙ࡃᗈ࠸ศ㔝ࢆᐃࡍࡿࠊ⊂❧㡯┠ࢆࡓࡃࡉࢇ฼⏝ࡍࡿࠋู࡟⊂❧㡯┠࡛࡞ࡃ࡚ࡶࠊ኱ၥᙧᘧ࡛ἑᒣࡸࡗ࡚ࡶၥ㢟ࡣ࡞࠸ࢇ࡛ࡍࡅ
ࢀ࡝ࡶࠊၥ㢟ᩘ࡜࠸࠺Ⅼ࠿ࡽ⊂❧㡯┠ࢆከ⏝ࡍࡿࠋࡑࢀ࠿ࡽࠊၥ㢟సᡂ࡜ࢸࢫࢺ⦅㞟ࡢ⼥ྜࡢ཯ᑐࡣࠊࢸࢫࢺ⌮ㄽࢆ⏝࠸ࡓ㞴᫆ᗘࡢࢥࣥࢺ
࣮ࣟࣝࠋ
⣲Ⅼࡢ฼⏝ࡢ཯ᑐࡣࠊࢸࢫࢺ⌮ㄽࢆ⏝࠸ࡓẚ㍑ྍ⬟࡞ᑻᗘᚓⅬࡢ฼⏝࡜࠸࠺ࡇ࡜࡟࡞ࡾ
ࡲࡍࠋࡇࡢ᪥ᮏⓗヨ㦂ᩥ໬ࡢ㏫࡜ࡇࡇ࡟᭩ࡁࡲࡋࡓࡶࡢࡀࠊᐇࡣࠊࡲ࠶ୡ⏺ᶆ‽࡛࠶ࢁ࠺࡜ᛮ
ࢃࢀ࡚࠸ࡲࡍࠋ
ࠕୡ⏺ᶆ‽ࠖ࡜ࡇࡇ࡟᭩ࡁࡲࡋࡓࡅࢀ࡝ࡶࠊ᪥ᮏ௨እࡢᅜࡀ඲㒊ࡇ࠺ࡸࡗ࡚࠸ࡿ࠿࡜࠸
࠺࡜ࠊᚲࡎࡋࡶࡑ࠺࡛ࡣ࡞ࡃ࡚ࠊ௒ࡣࡸࡾࡢᾏὒᅜᐙ࡜ゝࢃࢀ࡚࠸ࡿᅜࡀ࠶ࡾࡲࡍࡼࡡࠋ
࢔࣓ࣜ࢝࡜࠿࢜ࣛࣥࢲ࡜࠿࢖ࢠࣜࢫ࡜࠿ࠋ࠶ࡢ࠶ࡓࡾࡣ᫂ࡽ࠿࡟ࡇࡢ᪥ᮏᩥ໬ࡢ㏫ࢆࡸࡗ
࡚࠸ࡿࠋᾏὒᅜᐙ࡜࠸࠺ࡢࡣᡓத࡟㈇ࡅࡓࡇ࡜ࡀ࡞࠸ᅜࡽࡋ࠸ࡢ࡛ࠊ࡝࠺ࡶୡ⏺ᶆ‽࡜࠸࠺ࡇ࡜ࡽࡋ࠸ࢇ࡛ࡍࡀࠊ᪥ᮏⓗヨ㦂ᩥ໬ࡢ㏫ࡀୡ⏺ᶆ‽࡜࠸࠺ࡇ࡜࡛ࠊ࡛ࡣࠊ࡝࠺⪃࠼ࡿ࠿࡜࠸࠺ࡇ࡜࡛ࡍࡀࠊᖺ࡟」ᩘᅇࠊ␗࡞ࡿၥ㢟࡛ศᩓᐇ᪋ࢆࡍࡿࠋᖺ࡟」ᩘᅇࠊཷ㦂ᶵ఍ࡀ୚࠼ࡽࢀࡿࡇ࡜ࡣࠊཷ㦂⏕࡟࡜ࡗ࡚ࡣ㠀ᖖ࡟࠶ࡾࡀࡓ࠸ࡇ࡜࡛ࡣ࡞࠸࠿࡜⚾࡞࡝ࡣᛮ࠸
ࡲࡍࠋࡓࡔࠊసࡿ᪉࡟࡜ࡗ࡚ࡣ஦ົࡣ↹㞧໬ࡍࡿࡋࠊၥ㢟ࡀ㐪ࡗࡓࡽ௒࡜ྠࡌࡼ࠺࡞ᙧ࡛
సࡗ࡚࠸ࡓࡢ࡛ࡣබᖹᛶ࡜࠿ࢭ࢟ࣗࣜࢸ࢕ࡢၥ㢟ࡀ㉳ࡁࡿࡢ࡛ࡣ࡞࠸࠿ࠋ
ࢥࣥࣆ࣮ࣗࢱࢆ౑ࡗ࡚ࠊࡑࢀ࠿ࡽඛ࡯࡝࣮࢜ࣉ࢚ࣥࣥࢹ࢕ࢵࢻ࡜࠸࠺ゝⴥࢆᒣᮏඛ⏕ࡣ࠾౑࠸࡟࡞ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊ⮬⏤ᙧᘧグ㏙ࡢᙧ࡛ࢥࣥࣆ࣮ࣗࢱ࡟⟅࠼ࢆ᭩ࡃࡼ࠺࡞ࠊࡕ
ࡻࡗ࡜ゝⴥࡣྂ࠸࡛ࡍࡅࢀ࡝ࡶ࠸ࢃࡺࡿࣁ࢖ࢸࢡࣀࣟࢪ࣮ࢆ౑ࡗࡓࢸࢫࢺ࡞ࢇ࡛ࡍࡀࠊࡇ
ࢀࡣᒣᮏඛ⏕ࡢヰ࡟ࡶ࠶ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊᯝࡓࡋ࡚⣬࡜㖄➹ࡢࢸࢫࢺ࡜ྠࡌ⬟ຊࡀࢀ࡚࠸ࡿࡢ࠿࡜࠸࠺␲ၥࡀ࠶ࡾࡲࡍࡋࠊࡑࢀ௨๓࡟ࠊᮏᙜ࡟ࢥࣥࣆ࣮ࣗࢱࢆ౑ࡗ࡚ヨ㦂ࡀ࡛ࡁࡿࡢ࠿࡜࠸࠺⣲ᮔ࡞␲ၥࡀ࠶ࡿ࠿ࡶࡋࢀࡲࡏࢇࠋࡋ࠿ࡋࠊࡑࢀࡀฟ᮶ࡿࡢࡣ㛫㐪࠸࡞࠸ࢇ࡛ࡍࠋḟ࡟ࠊ⊂❧ᑠ㡯┠ࢆከ⏝ࡍࡿࠋᗈ࠸⠊ᅖࢆ࢝ࣂ࣮࡛ࡁࡿࡢ࡛ࡇࢀࡣ࠸࠸ࡇ࡜࡞ࢇ࡛ࡍࡅ
ࢀ࡝ࡶࠊ኱ၥᙧᘧ࡟ࡋ࡞࠸࡜ᛮ⪃ຊࡸά⏝ຊࢆࢀ࡞࠸ࢇࡔ࡜⪃࠼ࡓࡾࠊ኱ၥᙧᘧࡢ࡯࠺

ࡀᜍࡽࡃసࡿࡢࡣࠊ1 ࡘࡢࢸ࣮࣐ࡢࡶ࡜࡟ᗄࡘ࠿ࡢ㡯┠ࢆ㓄⨨ࡍࡿࢃࡅ࡛ࡍ࠿ࡽࠊ⡆༢࡟ฟ᮶ࡿ࡜࠸࠺஦᝟ࡀ࠶ࡿࡔࢁ࠺ࡋࠊᑠࡉ࠸㡯┠࡛ᗈ࠸⠊ᅖࢆ࢝ࣂ࣮ࡋࡼ࠺࡜ࡍࡿ࡜ࠊࡍࡈ
ࡃἑᒣࡢ㡯┠ࢆసࡽ࡞ࡅࢀࡤ࠸࠿ࢇࡢ࡛ࡍࡀࠊ࡞࠿࡞࠿ࡑ࠺࠸࠺స㢟యไࡀ௒ࡲ࡛ࡣ࡞࠸
࡜࠸࠺ࡇ࡜ࠋࡑ ࢀ ࡼࡾ ࡶ ࠊ ᪥ ᮏࡢ ࡇ ࠺ ࠸ ࠺኱ つ ᶍ ヨ 㦂 ࡢ㡯 ┠ࢆ సࡗ࡚ ࠸ࡿ ඛ⏕᪉ ࡣࠊࠕ⊂❧ ᑠ㡯┠ ࡣ－53－ရࡀ ࡞࠸ࠖࠊࠕ ࡇࢇ࡞ ࢸࢫࢺ ࡣసࡾ ࡓࡃ࡞ ࠸ࠖࠊ࡜ ࠾ࡗࡋ ࡷࡿඛ⏕ࡀࡍࡈࡃከ࠸ࢇ࡛ࡍࡡࠋ
ࡇࡇ࡟ࡣࠕస㢟⪅ࡢឤᛶࠖ࡜᭩ࡁࡲࡋࡓࡅࢀ࡝ࡶࠊࡇࢀࡣᮏᙜ࡟ࠊࡑ࠺࠸࠺ࡩ࠺࡟ᛮࡗ࡚
࠾ࡽࢀࡿ᪉ࡀከ࠸࡜࠸࠺ࡇ࡜ࡀ࠶ࡾࡲࡍࠋ
᭱ᚋࠊᑻᗘᚓⅬࢆཷ㦂⏕࡬ሗ࿌ࡍࡿࡇ࡜࡟㛵ࡋ࡚ࡣࠊ⣲Ⅼ࡛ࡣ࡞ࡃ࡚㞴᫆ᗘࢆㄪ⠇ࡋࡓ
ࡼ࠺࡞Ⅼᩘ࡛ࡍ࠿ࡽࠊ」ᩘᅇᐇ᪋ࡍࡿࠊᖺࢆࡲࡓ࠸ࡔᡂ⦼ࡀฟ࡚ࡃࡿ࡜࠸࠺࡜ࡁ࡟ࡑࡢᡂ
⦼ࡀẚ㍑ྍ⬟࡟࡞ࡿࡢ࡛ࠊཷ㦂⏕࡟࡜ࡗ࡚ࡶ฼⏝ࡍࡿഃ࡟࡜ࡗ࡚ࡶⰋ࠸࡜࠸࠺ࡇ࡜࡞ࢇ࡛
ࡍࡅࢀ࡝ࡶࠊᑻᗘᚓⅬࡣಙ⏝࡛ࡁ࡞࠸࡜࠿ࠊఱၥṇ⟅ࡋࡓ࠿࡜࠸࠺⣲Ⅼࢆᐇࡣ▱ࡾࡓ࠸࡜࠿ࠊࡑ࠺࠸࠺せᮃࡀ࠶ࡗ࡚ࠊ᪥ᮏⓗヨ㦂ᩥ໬ࡀ᪥ᮏ࡛ࡣ౑ࢃࢀ࡚࠸ࡿ࡜࠸࠺ࡇ࡜ࡔ࡜ᛮ࠸
ࡲࡍࠋ
ヨ㦂ࡢ᪥ᮏⓗ㢼ᅵ࡬ࡢ⤒⦋ఱᨾࡇࢇ࡞ࡩ࠺࡟࡞ࡗࡓࡢ࠿ࠋࡇࢀࡣᮏᙜ࡟ࡇ࠺࡞ࡢ࠿ࡼࡃࢃ࠿ࡾࡲࡏࢇࡅࢀ࡝ࡶࠊධ
ヨ࡟㛵ࡋ࡚ゝ࠸ࡲࡍ࡜ࠊಶู኱ᏛࡢධᏛヨ㦂࡟௦ࢃࡿࡶࡢ࡜ࡋ࡚ඹ㏻➨ 1 ḟᏛຊヨ㦂ࡸࢭࣥࢱ࣮ヨ㦂ࡀᑟධࡉࢀࡓࠋ1 ᖺ༢఩࡛ࡑࡢᖺࡢཷ㦂⏕ࡢࣛࣥ࢟ࣥࢢࡀ࡛ࡁࢀࡤ࠸࠸࡜࠸࠺
ᙧ࡛ࠊู࡟ᖺᗘࡈ࡜࡟㞴᫆ᗘࡀኚࢃࡗ࡚ࡶ 1 ᖺࡈ࡜࡟ࣛࣥ࢟ࣥࢢࡀ࡛ࡁࢀࡤࠊୖ࠿ࡽ㡰࡟᥇ࡗ࡚࠸ࡅࡤ࠸࠸ࡸ࡜࠸࠺ឤࡌ࡛ࠊవࡾᑻᗘᚓⅬࡳࡓ࠸࡞ࡇ࡜࡟ࡣࡇࡔࢃࡗ࡚࠸࡞࠸ࡢ࡛
ࡣ࡞࠸࠿࡜࠸࠺Ẽࡀࡋࡲࡍࠋ
ࡑࢀ࠿ࡽࠊᑗ᮶ࡢᒎᮃࡢḞዴ࡜࠸࠺ࡢࡣࠊ౛࠼ࡤᏛຊపୗၥ㢟࡞ࢇ࡚࠸࠺ࡢࡀヰ㢟࡟࡞ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊࢭࣥࢱ࣮ヨ㦂࡜࠿ඹ㏻୍ḟヨ㦂ࢆࢹࢨ࢖ࣥࡋࡓ࡜ࡁ࡟ࠊ᪥ᮏேࡢᏛຊ
ࡀపୗࡍࡿ࡞ࢇ࡚ㄡࡶ⪃࠼࡚࠸࡞࠿ࡗࡓ࡜࠸࠺ࡇ࡜࡛ࠊẖᖺࡢᏛຊኚ໬ࠊᏛຊࡢ⤒ᖺኚ໬ࢆࡿࡼ࠺࡞⿦⨨ࢆࢸࢫࢺࡢ୰࡟⤌ࡳ㎸ࢇ࡛࠸࡞࠿ࡗࡓ࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋ௒ࠊᛮ࠸ฟࡍࢇ ࡛ ࡍ ࡅ ࢀ ࡝ ࡶ ࠊ 90 ᖺ ௦ ࡟ ᒣ ᮏ ඛ ⏕ ࡀ ᪥ ᮏ ࡢ ㏻ ⏘ ┬ ࡟ ࢔ ࢲ ࣝ ࢺ ࣜ ࢸ ࣛ ࢩ ࣮ ࡢ ࡇ ࡜ ࡛ ヰ ࡟⾜࠿ࢀࡲࡋࡓࡼࡡࠋࡑࢀ࡛㏻⏘┬ࡢ࠾ᙺே࠿ࡽ෭ࡓࡃ࠶ࡋࡽࢃࢀ࡚㠀ᖖ࡟ᅔࡗࡓ࡜࠸࠺ヰࡀ࠶ࡾࡲࡋࡓࡼࡡࠋ᪥ᮏேࡣ໅ຮ࡞ࡶࡢ࡛ࡍ࠿ࡽࠊᏛຊపୗ࡜࠿ࠊ࡝ࢀࡔࡅᏐࡀㄞࡵࡿ࠿
࡜࠿ࠊࡑ࠺࠸࠺ࡇ࡜ࡣㄪ࡭࡞ࡃ࡚ࡶ㛫㐪࠸࡞ࡃඃࢀ࡚࠸ࡿࢇࡔࡼ࡜࠸࠺ព㆑ࡀ࠶ࡗࡓࢇࡔ
࡜ᛮ࠸ࡲࡍࠋ
࠶࡜ࠕᚰ⌮ᐃ࡬ࡢ↓⌮ゎࠊ᣸ᢡࠖ࡜᭩ࡁࡲࡋࡓࡅࢀ࡝ࡶࠊࡇࡢ᣸ᢡ࡜࠸࠺ࡢࡣࠊඛ࡯

࡝Ⲩ஭ඛ⏕࠿ࡽ࠾ヰࡀ࠶ࡾࡲࡋࡓࠋ㐍Ꮫ㐺ᛶࢸࢫࢺࠊࡑࢀ࠿ࡽ⬟◊ࢸࢫࢺ࡜࠸࠺ࡶࡢࡀ࠶
ࡗࡓࢇ࡛ࡍࡀࠊ࠶ࢀࡀవࡾཷࡅධࢀࡽࢀ࡞࠿ࡗࡓࠋ࠶ࡢ࡜ࡁ࡟ᚰ⌮ᐃࡢᑓ㛛ᐙࡣసᡂ࡟

ཧຍࡋࡓࢇ࡛ࡍࡅࢀ࡝ࡶࠊ࡝࠺ࡶࡑࢀࡀࢭࣥࢱ࣮ヨ㦂࣭ඹ㏻୍ḟࡢ᫬ࡣࠊ㐣ཤ࡟࠺ࡲࡃཷ
ࡅࡽࢀ࡞࠿ࡗࡓࡇ࡜ࢆ཯┬ࡋⴎ⦰ࡋ࡚࠸ࡓࡢ࡛ࡣ࡞࠸࡛ࡋࡻ࠺࠿ࠋ
᭱ᚋ࡟ࠊࡸࡣࡾసࡗ࡚࠸ࡿࡢࡀ࠾ᙺᡤ࡛ࡍ࠿ࡽࠊ᪥ᮏேࡣ᫇ࡣ࠾ᙺᡤࡀࡘࡃࡿࡶࡢࡣಙ
㢗ࡋ࡚࠸ࡲࡋࡓ࠿ࡽࠊᑡࠎ୙ഛࡀ࠶ࡗ࡚ࡶ᭷ࡾ㞴ࡀࡗ࡚౑ࡗ࡚࠸ࡓࡼ࠺࡞࡜ࡇࢁࡀ࠶ࡿࡋࠊ
㏫࡟ᙺᡤࡢ࡯࠺ࡣࠊᑡࠎḞⅬࡀ࠶ࡗ࡚ࡶㄝ᫂㈐௵ࡣ኱ࡋ࡚ྲྀࡽ࡞ࡃ࡚ࡶⰋ࠸ࡳࡓ࠸࡞ࠊࡑ
࠺࠸࠺ᙧ࡛ࢸࢫࢺࡀసࡽࢀ࡚ࡁࡓ࡜࠸࠺ࡇ࡜ࡀ࠶ࡿ࡜ᛮ࠸ࡲࡍࠋ
ヨ㦂ࡢ᪥ᮏⓗ㢼ᅵࡢၥ㢟Ⅼ࡜ᐃࡢᇶᮏࣔࢹࣝ

࡛ࡣࠊ࠸ࢁ࠸ࢁᣲࡆࡲࡋࡓࡅࢀ࡝ࡶࠊ୍␒ࡢၥ㢟Ⅼࡣఱ࠿࡜࠸࠺࡜ࠊࡇࢀࡣࡸࡣࡾ⚾ࡢಶேⓗ࡞ឤࡌ࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ᭱ᚋࡢ㓄Ⅼࢆ฼⏝ࡋࡓ⣲Ⅼࡢ฼⏝ࠊࡇࡇࡢ࡜ࡇࢁࡀ୍␒ࠋ
⣲Ⅼࢆ౑࠺ࢸࢫࢺၥ㢟ࡀฟ࡚ࠊࡑࡢ࠺ࡕఱ㢟ṇ⟅ࡋࡓࠊࡑࢀࢆ౑ࡗ࡚ᡂ⦼ࡢᑻᗘ࡜ࡋ࡚࠸
ࡿ࡜ࡇࢁࡀ୍␒Ẽ࡟࡞ࡿ࡜ࡇࢁ࡛ࡍࠋ
ࡇࡇ࠿ࡽࡣࡕࡻࡗ࡜ᩍ⫱ᐃ࡜࠸࠺ヰ࡟࡞ࡿࢇ࡛ࡍࡀࠊほ－54－ࡉࢀࡿࢸࢫࢺࡢᚓⅬࠊ౛࠼ࡤ⚾ࡀఱ࠿ࢸࢫࢺࢆཷࡅ࡚ 65 Ⅼྲྀࡾࡲࡋࡓࡼ࡜࠸࠺ࡢࡀ X ࡛ࡍࠋ⚾ࡢⅬᩘ X ࡣࠊᐇࡣ⚾ࡢ┿ࡢᚓⅬࠊ60 Ⅼ࡟⚾ࡢㄗᕪࠊ5 Ⅼࡀຍࢃࡗࡓࡶࡢ࠿ࡶࡋࢀ࡞࠸ࡋࠊ⚾ࡢ┿ࡢᚓⅬ 70 Ⅼ࡟ㄗᕪ࣐࢖ࢼࢫ 5 ࡀຍࢃࡗࡓࡶࡢ࠿ࡶࡋࢀ࡞࠸࡜࠸ࡗࡓᙧ࡛ࠊほࡉࢀࡓࢸࢫࢺᚓⅬࡑࡢࡶࡢࡀ┿ࡢ್ࢆ࠶ࡽࢃࡋ࡚࠸ࡿࡢ࡛ࡣ࡞ࡃ࡚ࠊࡇࡢࡼ࠺࡟ࠊ┿ࡢᚓⅬࣉࣛࢫㄗᕪࡳࡓ࠸࡞
ᙧ࡛ศゎࡋ࡚⪃࠼ࡿࡢࡀᩍ⫱ᐃࡢᇶᮏ࡛ࡍࠋ

᪥ᮏࡢヨ㦂ᩥ໬࡛ࡣࠊ࠶ࡓ࠿ࡶࡇࡢ㒊ศࠊㄗᕪࡢ㒊ศࡣᏑᅾࡋ࡞࠸ࡼࠊ࡜࠸࠺ࡼ࠺࡟⪃࠼࡚࠸ࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮࡗ࡚࠸ࡲࡍࠋ≉࡟⣲Ⅼࢆ฼⏝ࡍࡿ࡜࠸࠺ࡇ࡜࡟㛵ࡋ࡚ࡣࠋࡇࡢ X ࡜࠸࠺ࡢࡣ T ࡜ E ࡢ࿴ࡔࡼ࡜࠸࠺ࡢࢆㄆࡵࡿ࡜ࠊ౛࠼ࡤ X ࡢ୰࡟ T ࡢ㒊ศࡀ࡝ࢀࡔࡅ࠶ࡿࡢ࠿࡜࠸࠺ࡼ࠺࡞㔞࡛ࢸࢫࢺࡢಙ㢗ᛶࡳࡓ࠸࡞ࡶࡢࢆᐃ⩏ࡍࡿࡇ࡜ࡀ࡛ࡁࡿࠋ
ࡔ࠿ࡽࠊࢸࢫࢺࢆసࡿ࡜ࡁ࡟ࡣ࡞ࡿ࡭ࡃ E ࡢ㒊ศࢆᑠࡉࡃࡋ࡚ࠊT ࡢ㒊ศࡀ኱ࡁࡃ࡞ࡿࡼ
࠺࡟సࡾࡲࡋࡻ࠺࡜࠸࠺⪃࠼᪉ࡀฟ࡚ࡃࡿ࡜ᛮ࠸ࡲࡍࠋ
ᚰ⌮ᐃ࡜࠿ࢸࢫࢺ⌮ㄽ࡜࠿࡛ࢸࢫࢺࢆ࡝࠺⪃࠼࡚࠸ࡿ࠿࡜ゝ࠸ࡲࡍ࡜ࠊࢸࢫࢺ࡜࠸࠺

ࡢࡣ⬟ຊࡸᏛຊࠊᛶ᱁ࡸ㐺ᛶࠊࡑࡢ௚࠸ࢁ࠸ࢁ࡞ࡶࡢࠊࡇࡇ࡛ࡣࠕே㛫ࡢ⢭⚄ⓗ≉ᛶࠖ࡜᭩ࡁࡲࡋࡓࡅࢀ࡝ࡶࠊࡑࢀࢆ
ࡋ࡜࠿

ᗘࢆ

ࡿ

ᐃࡍࡿ㐨ල࡛࠶ࡿ࡜⪃࠼࡚࠸ࡲࡍࠋࡔ࠿ࡽ㛗ࡉࢆᗘィ࡜࠿య㔜ࢆࡿ≀ᕪࡿయ㔜ィ࡜࠿㟁ᅽィ࡜࠿ࠊࡑ࠺࠸࠺ࡶࡢ࡜඲ࡃྠࡌ

ࡶࡢ࡛ࡍࠋ≀ᕪࡋ࡜࠸࠺ࡢࡣࠊⰋ࠸≀ᕪࡋࠊᝏ࠸≀ᕪࡋࡀ࠶ࡾࡲࡍࡼࡡࠋఙࡧ⦰ࡳࡍࡿ≀ᕪࡋࡣవࡾⰋ࠸≀ᕪࡋ࡛ࡣ࡞࠸࡛ࡍࡼࡡࠋࡑࢀ࡜ྠࡌࡼ࠺࡟ࠊ㐨ල࡛ࡍ࠿ࡽၿࡋᝏࡋࡀᏑᅾࡍࡿࠋ
ඛ࡯࡝ࡢ X 㸻 T 㸩 E ࡜࠸࠺ᙧ࡛ၿࡋᝏࡋࢆ⾲⌧ࡍࡿ࡜ࠊE ࡢ㒊ศࡀ኱ࡁ࠸ࢸࢫࢺࡣᝏ࠸
ࢸࢫࢺ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋ࠶࡜ࠊ ᗘィࡸఱ࠿ࡶࡑ࠺࡛ࡍࡅࢀ࡝ࡶࠊᐊෆࡢ
Υ࡜࠿ 500 Υ࡜࠿ 1,000 Υࡄࡽ࠸ࢆ

ࡿ

ᗘࢆ

ࡿ

ᗘィ࡜ఱ࠿ᐇ㦂ࢆࡋ࡚ 200ᗘィ࡜ࠊᙜ↛㐪࠺ᗘィࢆ౑ࢃ࡞ࡅࢀࡤ࠸࠿ࢇ

ࢃࡅ࡛ࡍࡀࠊࢸࢫࢺࡶྠࡌ࡛ࠊୗࡢ࡯࠺ࡢ⬟ຊࡢேࢆࡿࢸࢫࢺࠊୖࡢ࡯࠺ࡢ⬟ຊࡢேࢆࡿࢸࢫࢺࠊᙜ↛࡛ࡍࡅࢀ࡝ࡶኚ࠼࡞࠸࡜⢭ᗘࡀࡼࡃ࡞࠸ࡳࡓ࠸࡞ࠊࡑ࠺࠸࠺ࡇ࡜ࡶ⪃࠼ࡽࢀࡿࢃࡅ࡛ࡍࠋ
ࢸࢫࢺࢆᢅ࠺⌮ㄽ࡜ࡋ࡚ࠊྂ඾ⓗࢸࢫࢺ⌮ㄽ࡜࠿㡯┠཯ᛂ⌮ㄽ࡜࠿࠸࠺ヰࡀฟ࡚ࡁࡲࡍࠋ
ඛ࡯࡝ࡢᒣᮏඛ⏕ࡢヰ࡟ࡣ IRT Item Response Theoryࡀฟ࡚࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊࡑࢀࡣ࠸

ࢃࡺࡿ㡯┠཯ᛂ⌮ㄽ࡛ࠊ㡯┠༢఩ࡈ࡜࡟ࡑࡢࢸࢫࢺࡢ≉ᛶࢆ࠶ࡽࢃࡍࣃ࣓ࣛࢱࡳࡓ࠸࡞ࡶ
ࡢࢆ᥎ᐃࡍࡿࠊࡑ࠺࠸࠺⌮ㄽ࡟࡞ࡾࡲࡍࠋ
᪥ᮏࡢ኱つᶍヨ㦂ࡢ⌧≧ࡇࢀࡣࡕࡻࡗ࡜ูࡢヰ࡟࡞ࡾࡲࡍࡅࢀ࡝ࡶࠊ௒ࠊ᪥ᮏ࡛⾜ࢃࢀ࡚࠸ࡿ኱つᶍヨ㦂ࡣ࡝ࢇ࡞ឤࡌ࠿࡜࠸࠸ࡲࡍ࡜ࠊୖ࡟᭩ࡁࡲࡋࡓྖἲヨ㦂ࠊබົဨ᥇⏝ヨ㦂ࠊࡑࢀ࠿ࡽ་ᖌࡢᅜᐙ
ヨ㦂ࠊࡇࡢ㎶ࡣవࡾࠊ࠸ࢃࡺࡿ

ᐃㄽⓗ࡟ࡣࡑࢀ࡯࡝ཝᐦ࡛ࡣ࡞ࡃࠊ᪥ᮏⓗヨ㦂ᩥ໬ⓗ࡟ࡘࡃࡽࢀ࡚࠸ࡿ࡜࠸࠺ឤࡌ࡛ࡍࠋୗ࡟᭩ࡁࡲࡋࡓ᝟ሗฎ⌮ᢏ⾡⪅ヨ㦂࡜࠿་Ꮫ㒊ࡢඹ⏝ヨ㦂࡜࠿ࣅࢪࢿࢫ᪥ᮏㄒヨ㦂࡜࠿ࠊ᪥ᮏ␃Ꮫヨ㦂ࠊ᪥ᮏㄒ⬟ຊヨ㦂ࡲ࡛ࡣࠊ୍ᛂࢸࢫࢺ⌮ㄽࢆ฼⏝ࡋ࡚సࡗࡓࡾࢥࣥࣆ࣮ࣗࢱ࣮࡛ᐇ᪋ࡋࡓࡾࡋ࡚࠸ࡿ኱つᶍヨ㦂࡛ࡍࠋ᝟ሗฎ⌮ᢏ⾡⪅ヨ㦂࡜࠸࠺ࡢࡣᅜᐙヨ㦂࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ᪤࡟ࢥࣥࣆ࣮ࣗࢱࢆ౑ࡗ࡚ࠊࢸࢫࢺ⌮ㄽࢆ౑ࡗ࡚ࡘࡃࡽࢀ࡚࠸ࡲࡍࠋ
᪥ᮏ␃Ꮫヨ㦂࡜࠸࠺ࡢࡀୗࡢ࡯࠺࡟࠶ࡾࡲࡍࡅࢀ࡝ࡶࠊࡇࢀࡣ␃Ꮫ⏕ࡢࡓࡵࡢධᏛヨ㦂࡛ࡑࡢཷ㦂⏕ࡣ᪥ᮏே࡛ࡣ࠶ࡾࡲࡏࢇࠋእᅜே࡛ࡍࠋࡔ࠿ࡽࡇࢀࡣ࠶ࡿព࿡ࠊୡ⏺ᶆ‽࡟

－55－ᇶ࡙࠸࡚ࡘࡃࡽࢀࡓヨ㦂࡜࠸࠺ࡇ࡜࡛ࠊࢥࣥࣆ࣮ࣗࢱ࡛ࡣᐇ᪋ࡋ࡚࠸ࡲࡏࢇࡅࢀ࡝ࡶࠊࢸ
ࢫࢺ⌮ㄽࢆ౑ࡗ࡚ᐇ᪋ࡋ࡚࠸ࡲࡍࠋᖺ㛫 30,000 ேࡄࡽ࠸࠿࡞ࠊཷ㦂⏕ࡀ࠸ࡲࡍࠋᖺ࡟ 2 ᅇࡸࡗ࡚࠸ࡲࡍࠋ
࠶࡜ࠊ୍␒ୗ࡟᭩ࡁࡲࡋࡓࡅࢀ࡝ࡶࠊ᪥ᮏࡢẸ㛫ࡢ఍♫࡛࠸ࢁ࠸ࢁ࡞ࢸࢫࢺࡀࡘࡃࡽࢀ
࡚࠸ࡲࡍࡅࢀ࡝ࡶࠊࡑࢀ࡟ࡣ IRT ࢆ౑ࡗ࡚࠸ࡿㄒᏛࢸࢫࢺࡀ⤖ᵓࡓࡃࡉࢇ࠶ࡿ≧ἣ࡛ࡍࠋࡑ࠺࠸࠺ࡇ࡜࡛ࠊ᪥ᮏⓗヨ㦂ᩥ໬࡜࠸࠺ࡶࡢ࡟㛵ࡋ࡚ゝཬࡋࡓㄽᩥࢆࠊᗄࡘ࠿ࡇࡇ࡟ཧ
⪃ᩥ⊩࡜ࡋ࡚ᣲࡆ࡚࠸ࡲࡍࡋࠊୗ࠿ࡽ 2 ␒┠ࠊ᭱㏆ࠊ᪂ࡋ࠸ᮏࡀฟ࡚࠸ࡲࡍ࡜࠸࠺ࡇ࡜࡛ࠊ
࡜ࡾ࠶࠼ࡎࠕ᪥ᮏⓗヨ㦂ᩥ໬ࠖ࡜࠸࠺ࡢࡣఱ࠿࡜࠸࠺᚟⩦ⓗ࡞࠾ヰࢆࡉࡏ࡚࠸ࡓࡔࡁࡲࡋ
ࡓࠋ௨ୖ࡛ࡍࠋ㸦ᢿᡭ㸧‫ྖۑ‬఍๓ᕝඛ⏕ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

ఱ࠿஦ᐇ☜ㄆⓗ࡞ࡈ㉁ၥࡀ࠶ࡾࡲࡋࡓࡽ࠾ཷࡅ࠸ࡓࡋࡲࡍࡅࢀ࡝ࡶࠊ࠸࠿ࡀ࡛ࡋࡻ࠺࠿ࠋ
኱ၥ࡜ᑠၥ࡛ྂ඾ⓗࢸࢫࢺ⌮ㄽ࡜ IRT ࡜ศࡅ࡚ᑐᛂ࡙ࡅࡉࢀ࡚࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊᒣᮏ
ඛ⏕ࡀࡈ⤂௓ୗࡉࡗࡓ᭱ᚋࡢࢧࣥࣉࣝၥ㢟࡞࡝ࡣࠊ࡝ࡕࡽ࠿࡜࠸࠼ࡤ኱ၥ࡛ࡍࡼࡡࠋ࠶ࢀ
ࡶ IRT ࡛ࣃ࣓࣮ࣛࢱࢆồࡵ࡚࠸ࡽࡗࡋࡷࡿࢇ࡛ࡍ࠿ࠋ
‫ۑ‬๓ᕝẶ

㡯┠཯ᛂ⌮ㄽؐ IRT ࡜࠸࠺ࡢࡣࠊ௒ࡲ࡛ࡣṇ⟅࠿ㄗ⟅࠿࡛᥇Ⅼࡉࢀࡿ㡯┠ࢆ୺࡟ྲྀࡾᢅࡗ࡚ࡁࡓࢇ࡛ࡍࡅࢀ࡝ࡶࠊ0 Ⅼࠊ1 Ⅼࠊ2 Ⅼࠊ3 Ⅼࠊ4 Ⅼࠊ5 Ⅼࡳࡓ࠸࡞ᙧ࡛኱ၥ
ⓗ࡞㡯┠ࡣࠊ㸰್㡯┠࡟ᑐࡋ࡚ከ್㡯┠࡜࿧ࡧ᪉ࢆࡍࡿࢇ࡛ࡍࡀࠊࡑࢀࡶ㏻ᖖࡢ㡯┠཯ᛂ⌮ㄽࡢᙧ࡛ྲྀࡾᢅ࠺ࡇ࡜ࡀฟ᮶ࡲࡍࠋࡕ ࡞ࡳ࡟ ࠊከ್ 㡯┠ࡢ IRT ࢆ᭱ ึ࡟ᥦ᱌ࡉࢀࡓࡢࡣ᪥ᮏேࡢඛ⏕࡛ࠊ1960 ᖺ௦ࡔ࡜ᛮ
࠸ࡲࡍࡅࢀ࡝ࡶࠊ㩪ᓥྐᏊඛ⏕࡛ࡍࠋ‫ྖۑ‬఍኱ၥ࡞࡝ࡣࠊࡑࡇ࡟ྵࡲࢀࡿ㡯┠ࡣ࠶ࡿព࿡࡛⊂❧࡛࡞࠸࡜ࡳ࡞ࡏࡿࡢ࡛ࡣ࡞࠸

࠿࡜ᛮ࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑ࠺࠸ࡗࡓᒁᡤ⊂❧ࡢ௬ᐃࡀከᑡࡎࢀ࡚࠸࡚ࡶవࡾၥ㢟࡞࠸࡜࠸࠺ࡇ࡜࡛ࡍ࠿ࠋ
‫ۑ‬๓ᕝẶ

5 ẁ㝵ࡢ್࡛᥇Ⅼࡉࢀࡿ 1 ࡘࡢ㡯┠࡜࠸࠺ᙧ࡛ࠊ኱ၥࡈ࡜࡟⊂❧࡜⪃࠼ࢀࡤⰋ

࠸ࡢ࡛ࡣࠋ
‫ྖۑ‬఍

኱ၥࡢ୰࡟ࢩ࣑࣮ࣗࣞࢩࣙࣥࡋࡓࡾ⌮⏤ࢆ᭩࠿ࡏࡓࡾ࡜࠸࠺ၥ㢟ࡀ࠶ࡿ࡛ࡣ࡞࠸

࡛ࡍ࠿ࠋࡑࢀࡣ࡝ࡢࡼ࠺࡟ᢅࢃࢀࡿࡇ࡜࡟࡞ࡾࡲࡍ࠿ࠋ
‫ۑ‬๓ᕝẶ

ࡔ࠿ࡽ኱ၥࡢྜィ࡛ఱ㢟࡜࠸࠺ࡇ࡜࡛ࡍࡼࡡࠋ

‫ۑ‬ᒣᮏẶ

๓ᕝࡉࢇࡢゝࡗ࡚࠸ࡿࡢࡣࠊ᭱ᚋࡢ࣮࢜ࣉ࢚ࣥࣥࢻࡢၥ㢟ࡀ࠶ࡗࡓ࡛ࡋࡻ࠺ࠋ

࠶ࢀࡣ࠶ࢀ⮬యࡀ኱ၥ࡟࡞ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋࡔ࠿ࡽ඲㒊ࡢࢩ࣑࣮ࣗࣞࢩࣙࣥࡢ࢔࢖ࢸ
࣒⮬యࡣṇ⟅ࠊㄗ⟅࡟㛵ࡋ࡚ࠊ౛࠼ࡤ඲㒊࡛ 5 ၥࡄࡽ࠸࠶ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊ4 ၥࡣṇ⟅ࠊㄗ⟅࡛࡛ࡁࡲࡍࡅࢀ࡝ࡶࠊ᭱ᚋࡢ࢔࢖ࢸ࣒ࡣከ್㡯┠ࡢၥ㢟࡛ࠊ࣏ࣜࢺ࣐ࢗࢫ࢔࢖ࢸ࣒
㸦Polytomous item㸧࡜ⱥㄒ࡛ゝ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡑࡢࡼ࠺࡞㡯┠࡟㐺ࡋࡓ IRT ࣔࢹࣝࢆ౑
࠸ࡲࡍࠋ
‫ྖۑ‬఍

ࡑࡢ 5 ࡘࡢ㡯┠ࡣ⊂❧࡜ࡋ࡚ࠊࣃ࣓࣮ࣛࢱࢆࡑࢀࡒࢀồࡵ࡚࠸ࡃࡇ࡜࡟࡞ࡾࡲࡍ

ࡡࠋ‫ۑ‬ᒣᮏẶ

ࡑ࠺࡛ࡍࠋᒁᡤⓗ࡞ᚑᒓᛶ㸦local dependency㸸࣮ࣟ࢝ࣝࢹ࢕࣌ࣥࢹࣥࢩ࣮㸧࡟㛵ࡋ࡚㉁ၥࡉࢀ࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢⅬ࡟ࡘ࠸࡚ࡣࠊETS ࡛ࡶࡍࡈࡃࡼࡃ◊✲ࡉࢀ

－56－

࡚࠸ࡿࢇ࡛ࡍࡡࠋ࣮ࣟ࢝ࣝࢹ࢕࣌ࣥࢹࣥࢩ࣮࡟ࡼࡗ࡚࡝ࢀࡔࡅࣂ࢖࢔ࢫࡀฟ࡚ࡃࡿ࠿࡜࠸
ࡗࡓほⅬ࡟࡞ࡾࡲࡍࡀࠊࡑࢀࢆ᥈ࡋ࡚ࡶぢࡘࡅࡽࢀࡓࡇ࡜ࡀ࡞࠸ࢇ࡛ࡍࡼࠋࣁ࣮࣡ࢻ࣭࢘
࢙࢖ࢼ࣮㸦Howard Weiner㸧࡜࠸࠺ࣜࢧ࣮ࢳ࣮ࣕࡀ ETS ࡟ࡋࡤࡽࡃ࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊᙼ
ࡣࡍࡈࡃດຊࡋ࡚᥈ࡋ࡚࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊぢࡘࡅࡽࢀ࡞࠸ࢇ࡛ࡍࡡࠋ⤖ᒁࠊ⤖ᯝ࡜ࡋ࡚࠶ࡿࡢࡣࠊ࣮ࣟ࢝ࣝࢹ࢕࣌ࣥࢹࣥࢩ࣮ࡀ࠶ࡿ࡜࢔࢖ࢸ࣒ࣃ࣓࣮ࣛࢱࡀࠊࢫ࣮ࣟࣉࡀࡶࡗ࡜ᛴ ࡟ ࡞ ࡗ ࡚ ࠊ ࡑ ࢀ ࡛ ⤖ ᒁ ࠊ ᝟ ሗ 㔞 㸦 information㸧 ࢆ 㐣 ኱ ࡟ ᥎ ᐃ 㸦 estimate㸧 ࡋ ࡚ ࡋ ࡲ ࠺ ࡇ ࡜࡟࡞ࡾࡲࡍࡅࢀ࡝ࡶࠊࡔࡅࢀ࡝ࡶࠊࣂ࢖࢔ࢫࡢⅬ࡛ࡣၥ㢟ࡀ࡞࠸࡜࠸࠺ࡇ࡜࡛ࡍࠋ
‫ྖۑ‬఍ࡕࡻࡗ࡜ᑓ㛛ⓗ࡞ヰ࡟࡞ࡗ࡚ࡋࡲࡗࡓ࠿ࡶࡋࢀࡲࡏࢇࡅࢀ࡝ࡶࠊIRT ࡜࠸࠺ࡢࡣࠊ

᪥ᮏㄒ࡛ゝ࠺࡜㡯┠ྠኈࡢᒁᡤ⊂❧࡜࠸࠺᮲௳ࡀ࠶ࡗ࡚ࠊࡑࡢ๓ᥦࡢୗ࡛ࡢ☜⋡ィ⟬࡟ᇶ࡙࠸࡚⌮ㄽࡀᡂࡾ❧ࡗ࡚࠸ࡲࡍࡢ࡛ࠊ1 ࡘࡢ኱ၥࡢ୰࡟࠶ࡿ㡯┠ྠኈࡣ࡝࠺ࡋ࡚ࡶ⊂❧࡜ゝ࠼ࡿࡢ࠿࡝࠺࡞ࡢ࠿ࡀၥ㢟࡟࡞ࡾࡲࡍࡢ࡛ࠊࡑࡢ࠶ࡓࡾࢆࡕࡻࡗ࡜㆟ㄽࡉࡏ࡚࠸ࡓࡔࡁ
ࡲࡋࡓࠋࡑࢀ࡜ࠊࡶ࡜ࡶ࡜ࡣ 2 ್ࢹ࣮ࢱࡢṇㄗ࡟㛵ࡍࡿṇ⟅☜⋡࡛⌮ㄽࡀᡂࡾ❧ࡗ࡚࠸ࡓࡢ࡛ࡍ
ࡀࠊ౛࠼ࡤ 5 Ⅼ‶Ⅼ࡛࠶ࢀࡤࡑ࠺࠸࠺ẁ㝵ⓗ࡞㒊ศ࡟ࡶ IRT ࡀⓎᒎⓗ࡟ᗈࡀࡗ࡚ࡁ࡚࠸ࡿ
࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋᢿᡭࢆ࠾㢪࠸࠸ࡓࡋࡲࡍࠋ㸦ᢿᡭ㸧

－57－

ƸơǊƴ

ᚾ᬴Ʒଐஜႎ᫘‫ם‬ᚾ᬴Ʒଐஜႎ᫘‫ם‬ଐஜưᘍǘǕƯƍǔᚾ᬴ȷȆǹȈƷཎࣉଐஜႎᚾ᬴૨҄ଐஜႎȆǹȈ૨҄ிʮ߻ಅ‫ܖٻ‬‫ܖٻ‬ᨈᅈ˟ྸ߻‫ܖ‬ᄂᆮᅹЭ߷჆ɟ౨௹Ŵᚾ᬴Ŵᎋ௹ŴȆǹȈŴᲢ౨‫ܭ‬Უ
SWK\VGUVGZCOKPCVKQP

1

2

ଐஜႎȆǹȈ૨҄ଐஜႎȆǹȈ૨҄ᲢྸဌᲣ࠰ƴɟࡇŴӷɟբ᫆ưƷᚾ᬴Ʒ૪ɟܱ଀࠰ƴɟࡇŴӷɟբ᫆ưƷᚾ᬴Ʒ૪ɟܱ଀ૼ˺բ᫆ƷǈưƷᚾ᬴Ʒܱ଀ᚾ᬴բ᫆Ʒπ᧏‫ٻ‬բ࢟ࡸƷМဇբ᫆˺঺ƱȆǹȈዻᨼƷᗡӳᲢӷɟբ᫆ưƳƍƱɧπ࠯Უૼ˺բ᫆ƷǈưƷᚾ᬴Ʒܱ଀ᲢȗȪȆǹȈƸɧπ࠯Უᚾ᬴բ᫆Ʒπ᧏Ტ૙Ꮛႎᙸ‫ע‬Ჹऴ‫إ‬π᧏ᲹᙹСዼԧᲹᲣ‫ٻ‬բ࢟ࡸƷМဇᲢ‫ٶ‬Ꮓᢠ৸ࡸư࣬ᎋщǛยǔѐщᲹ ኬƔƍǹȚȃǯƷഎ‫ڦ‬ᲹᲣբ᫆˺঺ƱȆǹȈዻᨼƷᗡӳȆǹȈȷ࣎ྸย‫ܭ‬Ʒ‫ܼᧉݦ‬Ʒɧ‫נ‬ȷɧМဇȆǹȈȷ࣎ྸย‫ܭ‬Ʒ‫ܼᧉݦ‬Ʒɧ‫נ‬ȷɧМဇᲢೌ‫ی‬ɼ፯Უ

እໜȷᣐໜƷМဇᲢᲪໜƱ฼ໜᲣእໜȷᣐໜƷМဇᲢᲪໜƱ฼ໜᲣ‫ׅ‬ǛǇƨƙƱൔ᠋ɧӧᏡ‫ׅ‬ǛǇƨƙƱൔ᠋ɧӧᏡᲢ࣎ྸย‫ܖܭ‬ǁƷ໯᧙࣎Ŵ‫ࢽࡇݿ‬ໜǁƷɧ̮ज़Უଐஜႎᚾ᬴૨҄Ʒᡞɭမ೅แƷཎࣉƱွբ࠰ƴᙐૠ‫ׅ‬ŴီƳǔբ᫆ưƷЎ૝ܱ଀Ӗ᬴ဃƴƱƬƯƸஊǓᩊƍƸƣ࠰ƴɟࡇŴӷɟբ᫆ưƷᚾ᬴Ʒ૪ɟܱ଀Ტ࠰ƴᙐૠ‫ׅ‬ŴီƳǔբ᫆ưЎ૝ႎƴܱ଀Ŵ+46%$6Უૼ˺բ᫆ƷǈưƷᚾ᬴Ʒܱ଀ʙѦƷጛᩃ҄π࠯ࣱȷǻǭȥȪȆǣƷբ᫆ᲢወᚘႎࣱឋᲢᩊତࡇሁᲣƷЎƔƬƯƍǔբ᫆ƷǈᲣᚾ᬴բ᫆Ʒπ᧏Ტᚾ᬴բ᫆Ʒ᩼π᧏ȷϐМဇŴ̾ʴǁƷ঺ጚᡫჷᲣ
‫ٻ‬բ࢟ࡸƷМဇǳȳȔȥȸǿ҄Ტ%%$6ᲣȷᐯဌᚡᡓƷМဇᲢኬƔƍǹȚȃǯȷ࠼ƍЎ᣼Ǜย‫ܭ‬Ŵ཯ᇌ᪮ႸƷ‫ٶ‬ဇᲣᢘࣖ‫׹‬ȆǹȈȷ**KIJ6GEJŴ +%6 ƷМဇբ᫆˺঺ƱȆǹȈዻᨼƷᗡӳӷơ‫ܖ‬щƕย‫ܭ‬ӧᏡƔᲹǳȳȔȥȸǿưᚾ᬴ᲛᲹȆǹȈȷ࣎ྸย‫ܭ‬Ʒ‫ܼᧉݦ‬Ʒɧ‫נ‬ȷɧМဇᲢȆǹȈྸᛯǛဇƍƨᩊତࡇƷǳȳȈȭȸȫᲣእໜȷᣐໜƷМဇᲢᲪໜƱ฼ໜᲣɭမ೅೅แ‫ׅ‬ǛǇƨƙƱൔ᠋ɧӧᏡᲢȆǹȈྸᛯǛဇƍƨൔ᠋ӧᏡƳ‫ࢽࡇݿ‬ໜƷМဇᲣ
6－58－

ഭӪႎᏑ୎

ɭမ೅แƷཎࣉƱွբ཯ᇌ‫ݱ‬᪮ႸƷ‫ٶ‬ဇ̾К‫ܖٻ‬Ʒλ‫ܖ‬ᚾ᬴ƴˊǘǔǋƷƱƠƯƷ
σᡫᇹᲫഏ‫ܖ‬щᚾ᬴Ǎǻȳǿȸᚾ᬴࠼ƍር‫׊‬ǛǫȐȸưƖǔ‫ٻ‬բ࢟ࡸƷ૾ƕ࣬ᎋщǍ෇ဇщǛǑǓᑣƘยǔ˺᫆Ʒʙऴ Ტ‫ٻ‬բ࢟ࡸƷ૾ƕܾତᲹᲣᐔ‫ٻ‬Ƴ᪮Ⴘૠƴ‫ࣖݣ‬Јஹǔ˺᫆˳С˺᫆ᎍሁƷज़ࣱ Ტ཯ᇌ᪮ႸƸԼƕƳƍᲛᲣɟ࠰ƝƱƴӖ᬴ဃƷ᪯ˮƕ˄ƖƞƑƢǕƹᑣƍ‫ݩ‬ஹƷ‫ޒ‬ஓƷഎ‫ڦ‬ѮѠƳଐஜʴƷ‫ܖ‬щ˯ɦƳǜƯƋǓࢽƳƍᲹ࣎ྸย‫ܭ‬ǁƷ໯ྸᚐŴਯ৵‫ࢽࡇݿ‬ໜǛӖ᬴ဃǁᡶᢘȷᏡᄂȆǹȈǁƷӒႾȷᓨ጑ࢽໜƕ଺஖ǛឭƑƯൔ᠋ӧᏡ‫ࢽࡇݿ‬ໜƸ̮ဇưƖƳƍ˴բദሉƠƨƔǍእໜǛჷǓƨƍ฼ໜƱ ໜƴॖԛƕƋǔ‫ܫ‬ᙌȆǹȈ
ƓɥǁƷ̮᫂ȷᛟଢᝧ˓Ʒഎ‫ڦ‬78

ଐஜႎȆǹȈ૨҄இ‫ٻ‬Ʒբ᫆ໜȆǹȈࢽໜƷࣱឋ࠰ƴɟࡇŴӷɟբ᫆ưƷᚾ᬴Ʒ૪ɟܱ଀ૼ˺բ᫆ƷǈưƷᚾ᬴Ʒܱ଀ᚾ᬴բ᫆Ʒπ᧏‫ٻ‬բ࢟ࡸƷМဇբ᫆˺঺ƱȆǹȈዻᨼƷᗡӳ:6 '
ほ 䛥䜜䛯
䝔䝇䝖ᚓⅬ

┿䛾ᚓⅬ

ȆǹȈȷ࣎ྸย‫ܭ‬Ʒ‫ܼᧉݦ‬Ʒɧ‫נ‬ȷɧМဇㄗᕪ᪥ᮏⓗヨ㦂ᩥ໬䛷䛿䛣䛾㒊ศ䛾Ꮡᅾ䜢ㄆ䜑䛺䛔䟿እໜȷᣐໜƷМဇᲢᲪໜƱ฼ໜᲣ‫ׅ‬ǛǇƨƙƱൔ᠋ɧӧᏡ䝔䝇䝖䛾ಙ㢗ᛶ = X 䛾୰䛾 T 䛾๭ྜ
10঻ƕ‫׎‬Ʒ‫ᙹٻ‬೉ᚾ᬴Ʒྵཞ࣎ྸย‫ܭ‬ȷȆǹȈྸᛯӮඥᚾ᬴ ᲢඥѦႾᲣ࠰᧓Ძ‫ׅ‬Ŵᙐૠ࠰МဇπѦՃ੔ဇᚾ᬴ Ტʴʙᨈȷଐஜʴʙᚾ᬴ᄂᆮǻȳǿȸᲣ࠰᧓Ძ‫ׅ‬Ŵ࠯‫ר‬ȷ೅แ͞ࠀሁƴǑǔ೅แ҄Ҕࠖ‫ܼ׎‬ᚾ᬴ Ტҽဃі΁ႾᲣȗȸȫСᲢ᪮Ⴘȗȸȫȷ᪮ႸȐȳǯᲣǁᆆᘍɶƩƬƨƕȷȷȷȆǹȈƱƸŴᏡщȷ‫ܖ‬щȷࣱ఍ȷᢘࣱሁƷʴ᧓ƷችᅕႎཎࣱǛย‫ܭ‬ƢǔᢊφưƋǔŵཋࠀŴภࡇᚘŴ˳᣻ᚘŴᩓ‫ן‬ᚘሁƱӷಮƴŴᢊφƷծƠफƠƕ‫נ܍‬Ƣǔŵऴ‫إ‬ϼྸ২ᘐᎍᚾ᬴ +6ȑǹȝȸȈᚾ᬴ ᲢኺฎငಅႾȷऴ‫إ‬ϼྸਖ਼ᡶೞನᲣ
%$6 +46 ưܱ଀ɶҔ‫ܖ‬ᢿσဇᚾ᬴ ᲢிʮҔᅹഫᅹ‫ܖٻ‬Ҕഫ‫ܖ‬૙ᏋǷǹȆȠᄂᆮǻȳǿȸᲣ%$6 +46 ưܱ଀ɶȓǸȍǹଐஜᛖᚾ᬴ $,6 ᲢኺฎငಅႾȷଐஜ᝵ତਰᐻ˟ ,'641 ๮౨Უ+46ưܱ଀ɶ
ଐஜသ‫ܖ‬ᚾ᬴ ',7 Ტଐஜ‫׎‬ᨥ૙Ꮛң˟ #+',Უ+46ưܱ଀ɶ ‫ࢽࡇݿ‬ໜƷМဇӞχႎȆǹȈྸᛯᲢ‫ٻ‬բᲣ%NCUUKECN6GUV6JGQT[᪮ႸӒࣖྸᛯᲢ᪮ႸࣖሉྸᛯᲣ
Ტ᪮ႸᲣ+VGO4GURQPUG6JGQT[ +46ଐஜᛖᏡщᚾ᬴ ,.26Ტ‫׎‬ᨥʩ්ؕ᣿ȷଐஜ‫׎‬ᨥ૙Ꮛૅੲң˟Უ +46 ưܱ଀ɶඥᅹ‫ܖٻ‬ᨈλ‫ܖ‬ᚾ᬴ Ტ ‫ܖٻ‬λᚾǻȳǿȸƱଐࡰᡲ ᲣஜᢰИƷᇤʗҾྸƷ‫ݰ‬λᲹൟ᧓Ʒ‫ٶ‬ƘƷᛖ‫ܖ‬ȆǹȈưƸ +46ƕМဇƞǕƯƍǔŵ
11

12

－59－

Ӌᎋ૨ྂ
஭ɥᨙ  ᇹᚕᛖƱƠƯƷଐஜᛖᏡщȆǹȈƷ᧏ႆệ ɟᑍႎƳբ᫆Ʊ‫׍‬ஊƷ‫׉‬ᩊ  ᚘยƱСࣂ ᇹ ࠇᒰʟฌ̀ȷЭ߷ ჆ɟ  ଐஜƷπႎƳ‫ᙹٻ‬೉ᚾ᬴ƴᙸǒǕǔཎࣉ  ೅แ҄ƷᚇໜƔǒ  ଐஜȆǹȈ‫˟ܖ‬ᛏங஭ਏՁ  ৆ࢸଐஜƴ૿ƍƯžȆǹȈƷ‫ܼᧉݦ‬ſƱƸ૙Ꮛऴ‫ܖإ‬ᄂᆮᛡưƋƬƨƷƔᲹ௷‫ޛ‬Ⴚ  ଐஜƷȆǹȈ૨҄ƴƭƍƯ ʴʙᚾ᬴ᄂᆮ
ጛటምဏዻ   ૼƠƍ଺ˊƷ‫ܖٻ‬λᚾ ᣿‫୿܇‬৐ᒉ௎ଡ‫܇‬Ŵனήɟ঺  ǘƕ‫׎‬Ʒ‫ܼ׎‬ᚾ᬴ƴƓƚǔᚾ᬴բ᫆π᧏ƷྵཞƱͼӼ ଐஜȆǹȈ‫˟ܖ‬ᛏƝฌᎮஊᩊƏƝƟƍǇƠƨŵ
13

－60－

࠙ሗ࿌㸰ࠚධヨ㑅ᢤࡢᐃၥ㢟ᮾி኱Ꮫ኱Ꮫ㝔ᩍ⫱Ꮫ◊✲⛉㛗

༡㢼ཎᮅ࿴
‫ྖۑ‬఍

⥆ ࠸ ࡚ ᮾ ி ኱ Ꮫ ࡢ ༡ 㢼 ཎ ᮅ ࿴ ඛ ⏕ ࠿ ࡽ ࠊࠕ ධヨ㑅ᢤࡢᐃၥ㢟ࠖ࡜࠸࠺ࡇ࡜࡛ࡈሗ࿌ࢆ࠾㢪࠸࠸ࡓࡋࡲࡍࠋ‫ۑ‬༡㢼ཎẶᮾி኱Ꮫࡢ༡㢼ཎ࡛ࡍࠋࡇ࠺࠸࠺ࢸ࣮࣐ࢆ୚࠼ࡽࢀࡲࡋࡓࡢ࡛ࠊධᏛヨ㦂࡜࠸࠺ࡢࡣᐃㄽࡔࡅ࡛ㄒࡿࡇ࡜ࡢ࡛ࡁࡿၥ㢟࡛ࡣ࠶ࡾࡲࡏࢇࡅࢀ࡝ࡶࠊࡑࡢ୍᪉࡛

ᐃㄽᢤࡁ࡟ㄒࡿࡇ࡜ࡶ

࡛ࡁ࡞࠸࡜࠸࠺ࡇ࡜࡛ࠊ⚾ࡢ࡯࠺࡛ࡣ

ᐃ࡜࠸࠺ࡇ࡜

࡟↔Ⅼࢆᙜ࡚࡚࠾ヰࡋࡋࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
ᐃࡢ┠ᶆࡣ༢⣧࡛ࠊࡾࡓ࠸≉ᛶࢆ࡛ࡁࡿࡔࡅ㧗࠸ጇᙜᛶؐ validity ࡜࠸࠸ࡲࡍࡀ̿

㧗࠸ጇᙜᛶ࡛

ࡿࡇ࡜࡟ᑾࡁࡲࡍࠋ࣮࣮࢟࣡ࢻࡣࠕ㔜せ࡞ࡇ࡜ࡣࠊ

ࡾࡓ࠸≉ᛶࡣఱ࡞ࡢ࠿ࠊఱࢆ

ࡾࡓ࠸≉ᛶࠖ࡜ࠕጇᙜᛶ࡛ࠖࡍࡀࠊ

ࡿ࡭ࡁ࡞ࡢ࠿࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋࡇࡢ㆟ㄽࡣ㠀ᖖ࡟኱஦࡞࡜ࡇࢁ࡛ࡍࠋ
ࡶ࠺୍ࡘࡣࠕጇᙜᛶࠖ࡜࠸࠺ゝⴥࠊఱᗘࡶฟ࡚ࡁࡲࡍࡅࢀ࡝ࡶࠊࡇࢀࢆ࡝࠺ゎ㔘ࡍࡿ࠿ࠋྠࡌᅵಥ࡛ヰࢆࡋ࡞ࡃ࡚ࡣ࠸ࡅ࡞࠸ࡢ࡛ࠊጇᙜᛶ࡜࠸࠺ᴫᛕࢆࡁࡕࢇ࡜⌮ゎࡋ࡞ࡃ࡚ࡣ࠸
ࡅ࡞࠸ࠋ౛࠼ࡤࠕಙ㢗ᛶࠖ࡜࠸࠺ゝⴥ࡜࡝࠺㐪࠺ࡢ࠿ࠊࡑࡢ࠶ࡓࡾࡢ༊ูࡶࡘࡅ࡚㆟ㄽࢆࡋ࡚࠸ࡃᚲせࡀ࠶ࡾࡲࡍࠋ
ඛ࡯࡝Ⲩ஭ඛ⏕࠿ࡽࡶ࠶ࡾࡲࡋࡓ୰ᩍᑂࡢ⟅⏦᱌࡛ࡣࠊࡇࡢ኱ᏛධᏛᕼᮃ⪅Ꮫຊホ౯ࢸࢫࢺ࡜࿧ࡤࢀࡿࡶࡢ࡟࠾࠸࡚ࠕ௨ୗࡢࡶࡢࢆ

ࡿࠖ࡜ᥦ᱌ࡉࢀ࡚࠸ࡲࡍࠋ▱㆑ࠊᢏ⬟࡛ࡣ࡞ࡃ࡚ࠊࠕࡑࢀࢆά⏝ࡋ࡚⮬ࡽㄢ㢟ࢆⓎぢࡋࠊࡑࡢゎỴ࡟ྥࡅ࡚᥈ồࡋࠊᡂᯝ➼ࢆ⾲⌧ࡍࡿࡓࡵ࡟ᚲせ࡞ᛮ⪃ຊࠊุ᩿ຊࠊ⾲⌧ຊ➼ࡢ⬟ຊࠖࠊࡇࢀࡀࡑࡢᥦ᱌࡟࠾ࡅࡿ

ࡾࡓ࠸ࡶ

ࡢࠊ

ࡿ࡭ࡁ࡞

ࡿ࡭ࡁࡶࡢ࡛ࡍࡡࠋࡲࡎࠊࡇࡇ࡛ࡢ㆟ㄽࡀ 1 ࡘ኱஦࡟࡞ࡾࡲࡍࠋࡇࢀࢆ

ࡢ࠿ࠊ▱㆑ࠊᢏ⬟ࡼࡾࡶ౛࠼ࡤ⾲⌧ຊ࡜࠸࠺ࡶࡢࢆ

ࡿ࡭ࡁ࡞ࡢ࠿ࠊࡇࢀࡣ㆟ㄽࡍ࡭ࡁ࡜

ࡇࢁ࡛ࡍࠋࡑࡢ㆟ㄽࡣ㠀ᖖ࡟㔜せ࡛ࡍࡅࢀ࡝ࡶࠊ࡜ࡾ࠶࠼ࡎࠊ௬࡟ࡇࢀࡀ
࠶࡜ࡣࡇࡢࡾࡓ࠸ࡶࡢࡔ࡜ࡍࡿ࡜ࠊ

ࡾࡓ࠸ࡇ࡜ࠊ⾲⌧ຊࡸᛮ⪃ຊ࡜࠸࠺ࡶࡢࢆࠊ࠸࠿࡟㧗࠸ጇᙜᛶ࡛

ࡿࡇ࡜ࡀ࡛ࡁࡿ࠿ࡀၥ㢟࡟࡞ࡾࡲࡍࠋ
ጇᙜᛶࡢ࡜ࡽ࠼᪉
ᑡࡋ኱Ꮫࡢᤵᴗࡢࡼ࠺࡟࡞ࡗ࡚ᜍ⦰࡛ࡍࡅࢀ࡝ࡶࠊጇᙜᛶ࡜࠸࠺ࡢࡣࢫࣛ࢖ࢻ 4 ࡢࡼ࠺࡟ࠊᴫᛕⓗ࡟ࡣᐃ⩏࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿ࠋጇᙜᛶ࡟㛵ࡍࡿ㆟ㄽࡔࡅ࡛ࡶ㠀ᖖ࡟㘒⥈ࡋ࡚࠸ࡿ㒊ศࡀ࠶ࡾࡲࡋ࡚ࠊ⚾ࡣࡇࢇ࡞ࡩ࠺࡟⪃࠼ࡿࡢࡀ⏕⏘ⓗ࡛ࠊ㆟ㄽࡍࡿୖ࡛ࡣᙺ࡟❧ࡘࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼࡚࠸ࡲࡍࠋ

－61－ඛ࡯࡝๓ᕝඛ⏕࠿ࡽࡶ࠶ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊࡲࡎ

ᐃ್ࠊᐇ㝿࡟ࢫࢥ࢔࡜ࡋ࡚࠶ࡽࢃࢀ

ࡿࡶࡢࡣࠊ࠸ࡃࡘ࠿ࡢᡂศ࠿ࡽᡂࡾ❧ࡗ࡚࠸ࡿ࡜⪃࠼ࡲࡍࠋࡲࡎ 1 ࡘ┠ࡀࠊඛ࡯࡝ࡢ

ࡾࡓ࠸≉ᛶࢆ཯ᫎࡍࡿ㒊ศࠋconstruct ࡜࠸࠺ゝⴥࡀ౑ࢃࢀࡿࡢ ࡛ ࠕ cࠖ ࡜ ࡋ ࡚ ࠸ ࡲ ࡍ ࠋ construct ؐ ᵓ ᡂ ᴫ ᛕ ࡜ ࠸ ࠺ ゝ ⴥ ࡀ ౑ ࢃ ࢀ ࡲ ࡍ ࡅ ࢀ ࡝ ࡶ ࠊ ࡜ ࡶ ࠿ࡃࡾࡓ࠸ࡶࡢࡀᑡ࡞ࡃ࡜ࡶ୍㒊཯ᫎࡉࢀ࡚࠸ࡿࡔࢁ࠺ࠋࡇࡇ࡛ࠊ

࠶ࢀࡤࠊࡘࡲࡾ

ᐃ್ࡀࠊᐃ್ x ࢖ࢥ࣮ࣝ c ࡛ࡾࡓ࠸ࡶࡢࡢࡳࢆ཯ᫎࡋ࡚࠸ࡿࡢ࡛࠶ࢀࡤࠊఱࡶ

㢟ࡣ࡞࠸ࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀࣉࣛࢫఱࠎ࡜࠸࠺ࡢࡀ࠶ࡿࡢ࡛ࠊ

ᐃㄽࡢၥᐃࡢၥ㢟ࡀ⏕ࡌࡿࢃࡅ࡛ࡍࡡࠋࡲࡎࠊࡇࡇ࡛

ᐃ್࡜࿧ࢇ࡛࠸ࡿࡢࡣࠊࢸࢫࢺࡢࡳ࡞ࡽࡎᑠㄽᩥࡢホᐃ࡛࠶ࡗࡓࡾ㠃᥋ࡢホ౯Ⅼ࡛࠶ࡗࡓࡾࠊ࠸ࢁ࠸ࢁ࡛ࡍࠋᩘ್໬ࡍࡿࡶࡢࡍ࡭࡚࡛ࡍࠋࡑࢀ࢖ࢥ࣮ࣝ c ࡛࠶ࢀࡤ࠸࠸ࢃࡅ࡛ࡍࡀࠊ㏻ᖖࡣఱࡽ࠿ࡢㄗᕪࡀຍࢃࡿࢃࡅ࡛ࡍࡡࠋㄗᕪࢆࡇࡇ࡛ࡣ 2 ✀㢮࡟ศࡅ࡚ࠊ⣔⤫ⓗ࡞ㄗᕪ࡜അ↛ⓗ࡞ㄗᕪࠊ๓⪅ࡣࢩࢫࢸ࣐ࢸ࢕ࢵࢡ࡜࠸࠺ࡇ࡜࡛ࠕsࠖࠊᚋ⪅ࡣࣛࣥ ࢲ࣒࡞ ࢚࣮ࣛ࡜ ࠸࠺ࡇ ࡜࡛ࠕeࠖ࡜ࡋ ࡚࠸ࡲ ࡍࠋ⣔⤫ㄗᕪ࡜࠸࠺ࡢࡣᏳᐃࡋ࡚࠸࡚ఱ
ᗘࡶฟ࡚ࡃࡿࠊࡶ࠺୍ᅇ

ࡗ࡚ࡶฟ࡚ࡃࡿࢇࡔࡅࢀ࡝ࡶ

ࡾࡓ࠸ࡶࡢ࡜ࡣ㐪࠺࡜࠸࠺ࠊ

irrelevant ࡞ࣇ࢓ࢡࢱ࣮ࡔ࡜࠸࠺ࡇ࡜࡛ࡍࠋẖᅇኚືࡍࡿࢃࡅ࡛ࡣ࡞࠸ࡅࢀ࡝ࡶࠊstable ࡔࡅࢀ࡝ࡶ irrelevant ࡜࠸࠺ࡢࡀ s ࡛ࡍࠋࡇࡇ࡟ࠕศᩓࠖ࡜࠸࠺ゝⴥࡀ࠶ࡾࡲࡍࠋࡇࢀࡣ⤫ィ⏝ㄒ࡛ࡍࡅࢀ࡝ࡶࠊ࡜ࡶ࠿ࡃᐃ್ x࡟ࡣಶேᕪࡀ࠶ࡿࢃࡅ࡛ࡍࡡࠋ㧗࠸ே࠿ࡽప࠸ேࡲ࡛ಶேᕪࡀ࠶ࡿࠋࡇࡢࡤࡽࡘࡁࡢ⛬ᗘࡀศᩓ࡛ࡍࠋ඲ဨࡀྠⅬ࡛࠶ࢀࡤఱࡢホ౯ࡶ࡛ࡁ࡞࠸ࡢ࡛ࠊࡲࡎศᩓࡀ࠶ࡿࡇ࡜ࡀ๓ᥦ࡛
ࡍࡅࢀ࡝ࡶࠊ㔜せ࡞ࡇ࡜ࡣࠊࡑࡢศᩓࡀ࡝ࢀࡔࡅᮏ≀ࡢศᩓ࠿࡜࠸࠺ࡇ࡜࡛ࡍࠋㄗᕪࡔࡅ
࡛ࡶศᩓࠊಶேᕪࡣ⏕ࡌࡿࢃࡅ࡛ࡍࠋࡇࡢࢩࢫࢸ࣐ࢸ࢕ࢵࢡ࡞ࠊirrelevant ࡞ㄗᕪࡔࡅ࡛ࡶ
ศᩓࡣ⏕ࡌࡿࢃࡅ࡛ࡍࠋࡔ࠿ࡽ඲యࡢศᩓࠊ┠࡟ぢ࠼ࡿ㧗ᚓⅬ࠿ࡽప࠸Ⅼࡲ࡛ࡢศᩓࡢ࠺ࡕఱ㸣ࡀぢࡓ࠸㒊ศࡢಶேᕪࢆ཯ᫎࡋ࡚࠸ࡿ࠿ࠊࡇࡢ๭ྜࡀጇᙜᛶ࡜࠸࠺ࡩ࠺࡟ᴫᛕⓗ࡟ࡣ࡜ࡽ࠼ࡿࡇ࡜ࡀ࡛ࡁࡲࡍࠋ
ࡋࡓࡀࡗ࡚ࠊࡇࢀ࠿ࡽᐃホ౯ࡢၥ㢟ࢆ⪃࠼ࡿ࡜ࡁ࡟ࡇࡢ 3 ศ๭ࢆ⪃࠼࡚ࠊᐃ್ࡀ࡝ࢀࡄࡽ࠸ c ࢆ཯ᫎࡋ࡚࠸ࡿ࠿ࠊࡲࡓࠊ࡝࠺ࡸࡗ࡚ࡑࡢ๭ྜࢆ㧗ࡵࡿ࠿࡜࠸࠺ࡇ࡜ࡀ኱஦࡛
ࡍࠋࡇࢀࡣࠊࢫࣛ࢖ࢻ 4 ࡢࡼ࠺࡟ศゎࡍࢀࡤ⡆༢࡛ࠊ㉥࠸࡜ࡇࢁ㸦c ࡢศᩓ㸧ࢆ኱ࡁࡃࡋ
࡚ࠊࡑࡢ௚ࡢ㯮࠸࡜ࡇࢁࢆᑠࡉࡃࡍࡿࡢࡀጇᙜᛶࢆ㧗ࡵࡿດຊ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋ
ࡑࢀࢆゝⴥ࡛᭩࠸ࡓࡢࡀࢫࣛ࢖ࢻ 5 ࡟࡞ࡾࡲࡍࡀࠊጇᙜᛶࢆ㧗ࡵࡿ࡟ࡣࠊࡲࡎࠊ

ࡾࡓ࠸≉ᛶࡢಶேᕪࢆ཯ᫎࡍࡿࡼ࠺࡞㡯┠ࡀᚲせ࡟࡞ࡾࡲࡍࠋࡑࢀࡣࠕ⾲⌧ຊ࡜࠿ุ᩿ຊ࡜࠿
ᛮ⪃ຊࡀ㧗࠸ேࡣࡇ࠺཯ᛂࡋࠊప࠸ேࡣࡇ࠺཯ᛂࡍࡿࠖ࡜࠸࠺ࠊࡑࡢぢࡓ࠸ࡶࡢࢆ཯ᫎࡍ
ࡿ㡯┠ࢆ࡝࠺㛤Ⓨࡍࡿ࠿ࡀࠊࡲࡎ୺せ࡞ດຊࡢ┠ᶆ࡜࡞ࡾࡲࡍࠋ౛࠼ࡤࡑࢀࢆࡍࡿࡢ࡟」ᩘࡢᩍ⛉ࢆྜࢃࡏࡓࡼ࠺࡞ྜᩍ⛉ᆺࠊྜ⛉┠ᆺࡀ᭷⏝࡞ࡢ࠿ࠊ࠶ࡿ࠸ࡣ PISA ᆺ࡜࠸ࢃࢀ
ࡿ㡯┠ࡀ᭷⏝࡞ࡢ࠿ࠋࡇࡇࡣ㛤Ⓨࡢၥ㢟࡟࡞ࡿࢃࡅ࡛ࡍࡡࠋ
୍᪉࡛ࡣࠊࡑࢀ௨እࡢせᅉࡢศᩓࢆῶࡽࡍࡇ࡜ࡀᚲせ࡟࡞ࡾࡲࡍࠋ2 ࡘ┠࡛ࡍࡡࠋࡾࡓ࠸≉ᛶ௨እࡢせᅉ࡟ࡼࡿ⣔⤫ㄗᕪࢆῶࡽࡍࠋ౛࠼ࡤ௒ࡢ୰ᩍᑂࡢ᱌ࡢࡼ࠺࡟▱㆑ࠊᢏ⬟࡛ࡣ࡞࠸࡜ゝ࠺࡞ࡽࡤࠊ▱㆑ࠊᢏ⬟ࡢಶேᕪࡀ཯ᫎࡉࢀࢀࡤࡇࢀࡣㄗᕪ࡟࡞ࡿࢃࡅ࡛ࡍࡡࠋ
༢⣧࡞▱㆑ࠊ༢⣧࡞ᢏ⬟ࡔࡅ࡛㧗࠸Ⅼࡀ࡜ࢀࡿࡢ࡛࠶ࢀࡤࠊࡇࢀࡣㄗᕪ࡟࡞ࡿࢃࡅ࡛ࡍࠋ
ࡲࡓࠊධヨᑐ⟇࡛㠃᥋ࡢᑐ⟇࡜࠸࠺ࡢࡀ࠶ࡿࡽࡋ࠸࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀ࡛ᚓⅬࡀୖୗࡍࡿ

－62－ࡼ࠺࡛࠶ࢀࡤࠊࡑࡢ㒊ศࡣㄗᕪ࡟࡞ࡿࢃࡅ࡛ࡍࡡࠋぢࡓ࠸࡜ࡇࢁ࡛ࡣ࡞࠸ࢃࡅ࡛ࡍ࠿ࡽࠋ
ࡑࢀ௨እ࡟അ↛ࡢせᅉࡀ࠶ࡾࡲࡋ࡚ࠊㄡࡀホ౯࡟ᙜࡓࡿࡢ࠿ࠊ㠃᥋ࡸᑠㄽᩥ࡛ఱࢆၥ࠺
ࡢ࠿ࠋ㠃᥋ࡸᑠㄽᩥࡢ㉁ၥࡣࣛࣥࢲ࣒࡛ࡣ࡞࠸࡜⪃࠼ࡽࢀࡿ࠿ࡶࡋࢀࡲࡏࢇࡅࢀ࡝ࡶࠊึ
ࡵ࠿ࡽ 1 ࡘ࡟Ỵࡲࡿࢃࡅ࡛ࡣ࡞ࡃࠊࡓࡃࡉࢇࡢྍ⬟࡞ᑠㄽᩥࡢ㡯┠ࠊ㠃᥋ࡢၥ࠸ࡢ࠺ࡕ 1ࡘࡀ࠶ࡿ✀ࠊࡓࡲࡓࡲ㑅ࡤࢀࡿࢃࡅ࡛ࡍࡼࡡࠋูࡢ㡯┠ࢆࡸࢀࡤูࡢಶேᕪࡀ⏕ࡌࡿࢃࡅ࡞ࡢ࡛ࠊࡇࡇࡣࣛࣥࢲ࣒࢚࣮ࣛ࡜ぢࡿࡇ࡜ࡀ࡛ࡁࡿࢃࡅ࡛ࡍࠋ
࡜ࡶ࠿ࡃᚋ⪅ 2 ࡘࡢせᅉࢆ࡝ࢀࡔࡅῶࡽࡍࡢ࠿ࠊ࠶ࡿ࠸ࡣ࡝ࢀࡔࡅࡑࢀࡀᙳ㡪ࡋ࡚࠸ࡿ࠿ࢆᰝᐃࡋ࡞ࡀࡽῶࡽࡋ࡚࠸ࡃࡇ࡜ࡀጇᙜᛶࢆ㧗ࡵࡿດຊ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋ
࡞ࡢ࡛ࠊ࠶ࡿ⬟ຊࢆࡿ࡜ࡁ࡟ࠊࡇࡢ 3 ศ๭ࡢほⅬ࠿ࡽぢ࡚࠸ࡃࡇ࡜ࡀ኱஦࡟࡞ࡿ࠿࡜

ᛮ࠸ࡲࡍࠋಙ㢗ᛶ࡜ࡢ㛵ಀࡑࢀ࠿ࡽࠊඛ࡯࡝ฟࡲࡋࡓಙ㢗ᛶ࡜࡝࠺㐪࠺࠿࡜࠸࠺ࡇ࡜࡛ࡍࡅࢀ࡝ࡶࠊ๓ᕝඛ⏕ࡢᘧࡢ୰࡟ X 㸻 T 㸩 E ࡜࠸࠺ࡢࡀ࠶ࡾࡲࡋࡓࠋࡑࢀ࡜ጇᙜᛶࡢᘧࡣ࡝࠺㐪࠺࠿࡜࠸࠺࡜ࠊT࡟ ࡣ ࢫࣛ ࢖ ࢻ 6 ࡟♧ࡋ ࡓࡼ࠺ ࡟ࡾࡓ ࡃ࡞࠸ ࡅࢀ࡝ࡶ stable ࡞ ࡶࡢࡶධ ࡗ࡚ࡃ ࡿࢃࡅ࡛ࡍࠋ࡞ࡢ࡛ࠊಙ㢗ᛶ࡜࠸࠺ࡢࡣጇᙜᛶࡑࡢࡶࡢ࡛ࡣ࡞ࡃࠊࣉࣛࢫࠊ࠸ࡲࡢ౛࡛ࡣ౛࠼ࡤ༢
⣧࡞▱㆑ࠊᢏ⬟ࡳࡓ࠸࡞ࡇ࡜࡟ࡼࡿศᩓࡶධࡗ࡚ಙ㢗ᛶ࡜࿧ࡤࢀࡲࡍࠋࡔ࠿ࡽࠊಙ㢗࡛ࡁࡿࡅࢀ࡝ࡶጇᙜ࡛࡞࠸࡜࠸࠺ྍ⬟ᛶࡀ࠶ࡿࢃࡅ࡛ࡍࡡࠋಙ㢗࡛ࡁࡿࡅࢀ࡝ࡶࠊ࡯࡜ࢇ࡝ sࡢศᩓࠊirrelevant ࡞ศᩓ࡟ࡼࡗ࡚ಙ㢗ᛶࡀ㧗ࡲࡗ࡚࠸ࡿྍ⬟ᛶࡶ࠶ࡿࢃࡅ࡛ࡍࠋ㏫࡟ࠊࡇࡢ㉥࠸࡜ࡇࢁ඲యࡀᑠࡉ࠸ࠊಙ㢗ᛶࡀప࠸࡜ࡋࡓࡽࠊጇᙜᛶࡣᚲࡎᑠࡉ࠸ࢃࡅ࡛ࡍࠋࡔ࠿
ࡽࠊಙ㢗ᛶࡀప࠸࡜ࡶ࠺ጇᙜ࡛ࡣ࠶ࡾᚓ࡞࠸࡜࠸࠺ࡇ࡜࡛ࠊࡑ࠺࠸࠺ព࿡࡛ࡣಙ㢗ᛶࡣᚲせ࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋ
ࡇࢀࢆゝⴥ࡛᭩࠸ࡓࡢࡀࢫࣛ࢖ࢻ 7 ࡟࡞ࡾࡲࡍࠋಙ㢗ᛶࡀప࠸࡜ጇᙜᛶࡣ㧗ࡃ࡞ࡽ࡞࠸ࠋ
ጇᙜᛶࡀ኱஦࡞ࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊ㧗࠸ጇᙜᛶࡢࡓࡵ࡟ࡣ㧗࠸ಙ㢗ᛶࡀᚲせ࡛ࡍࠋゝⴥࢆ᥮࠼ࡿ࡜ࠊಙ㢗ᛶࡢ㧗ࡉࡣ᝟ሗࡢ㔞࡛ࡍࠋࣛࣥࢲ࣒࡛࡞࠸ࠊstable ࡞᝟ሗࡢ㔞࡞ࢇ࡛ࡍࡡࠋ
᝟ሗ㔞ࡀᑡ࡞ࡅࢀࡤጇᙜ࡛ࡣ࠶ࡾᚓ࡞࠸ࢇࡔࡅࢀ࡝ࡶࠊ᝟ሗࡣࡓࡗ࡫ࡾ࠶ࡗ࡚ࡶࡑࢀࡀ▱ࡾࡓ࠸᝟ሗ࡛࡞࠸ࠊࡺࡀࢇࡔࡶࡢࠊ౛࠼ࡤධヨᑐ⟇ࡢฟ᮶࣭୙ฟ᮶ࠊᐙᗞࡢ⤒῭ⓗ࡞ࡶࡢࡢ㧗పࠊࡑ࠺࠸ࡗࡓࡶࡢࡀ཯ᫎࡉࢀ࡚࠸ࡿྍ⬟ᛶࡶ࠶ࡿ࡜࠸࠺ࡇ࡜࡛ࠊࡇࢀࡀጇᙜᛶࡢၥ
㢟࡜࡞ࡾࡲࡍࠋ
୰ᩍᑂࡢ⟅⏦᱌࠿ࡽ෌ᗘࠊ୰ᩍᑂࡢ⟅⏦᱌࡛ࠊ኱ᏛධᏛᕼᮃ⪅Ꮫຊホ౯ࢸࢫࢺ࡛ࡣ࠸ࡃࡘ࠿ࡢࡡࡽ࠸ࡀ᭩࠿
ࢀ࡚࠸ࡲࡍࡅࢀ࡝ࡶࠊࡇࡇ࡛ 2 ࡘྲྀࡾୖࡆ࡚ࠊࡇࡢ 2 ࡘ࡟↔Ⅼࢆᙜ࡚࡚ᐃㄽ࡜࠸࠺࠿ࠊ

᝟ሗ㔞ࡢほⅬ࠿ࡽᑡࡋ⪃ᐹࡋ࡚ࡳࡓ࠸࡜ᛮ࠸ࡲࡍࠋࡇࢀࡀ௒᪥ࡢヰ㢟ᥦ౪ࡢ࣓࢖ࣥࡢࢸ࣮
࣐࡜࡞ࡾࡲࡍࠋ
ࢫࣛ࢖ࢻ 8 ࡢࡲࡎ 1 ࡘ┠࡛ࡍࡀࠊ㑅ᢤᛶ࡜࠸࠺ࡢࡣ࠸ࢃࡺࡿ೫ᕪ್ࡢ㧗࠸኱Ꮫࠊప࠸኱
Ꮫ࡜࠸࠺ࡇ࡜ࢆព࿡ࡋ࡚࠸ࡿࡼ࠺࡛ࡍࡅࢀ࡝ࡶࠊ㑅ᢤᛶࡢ㧗࠸ప࠸࡟࠿࠿ࢃࡽࡎࠊከࡃࡢ
኱Ꮫ࡛ࡑࡢࢸࢫࢺࡀά⏝࡛ࡁࡿࡼ࠺࡟㞴᫆ᗘࡣᗈࡃ࡜ࡿࠋ㠀ᖖ࡟㞴ࡋ࠸ࡶࡢ࠿ࡽ᫆ࡋ࠸ࡶ
ࡢࡲ࡛ධࢀࡿࠋ≉࡟ࠊ㑅ᢤᛶࡢ㧗࠸኱ᏛࡀධᏛ⪅㑅ᢤࡢホ౯ࡢ୍㒊࡜ࡋ࡚༑ศά⏝࡛ࡁࡿỈ‽ࡢࠊ㧗㞴ᗘࡢฟ㢟ࢆྵࡴࡶࡢ࡜ࡍࡿ࡜࠸࠺ࡇ࡜ࡀࠊࡇࡢࢸࢫࢺࡢࢹࢨ࢖ࣥ࡜ࡋ࡚ᥦ᱌
ࡉࢀ࡚࠸ࡲࡍࠋ㞴ࡋ࠸ၥ㢟ࡶྵࢇ࡛ࠊ࠸ࢃࡺࡿ㞴㛵ࡢ኱Ꮫ࡛ࡶࡇࢀࢆධᏛヨ㦂࡛฼⏝࡛ࡁ－63－ࡿࡼ࠺࡟ࡍࡿ࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋ
ࡑ࠺࠸࠺ព࿡࡛ࡣࠊ୰ᩍᑂࡢ⟅⏦࡛ࡶᐃࡢ⢭ᗘࡸ᝟ሗ㔞࡜࠸࠺ࡶࡢࢆព㆑ࡋࡓ᱌࡜࡞

ࡗ࡚࠸ࡲࡍࠋ
2 ࡘ┠ࠋࡇࢀࡣ࣐ࢫࢥ࣑࡛ࡶࡼࡃྲྀࡾୖࡆࡽࢀ࡚࠸ࡲࡍࡀࠊ1 Ⅼ้ࡳࡢᐈほᛶ࡟ᅃࢃࢀࡓホ౯࠿ࡽ⬺ࡋ࡚ࠊྛ኱Ꮫࡢಶู㑅ᢤؐ஧ḟヨ㦂࡟࠾ࡅࡿከᵝ࡞ホ౯᪉ἲࡢᑟධࢆಁ㐍ࡍࡿほⅬ࠿ࡽࠊ኱Ꮫཬࡧ኱ᏛධᏛᕼᮃ⪅࡟ᑐࡋ࡚ࠊ1 Ⅼ้ࡳࡢⅬᩘ࡛ࡣ࡞ࡃ࡚ẁ㝵ู⾲♧ࠊ5
ẁ㝵ࡢ 5 ࡜࠿ 5 ẁ㝵ࡢ 4 ࡜࠸ࡗࡓẁ㝵ู⾲♧࡟ࡼࡿᡂ⦼ᥦ౪ࢆ⾜࠺ࠋࡇࡢ 2 Ⅼ࡟ࡘ࠸࡚ᗈ⠊ᅖࡢᐃࠊࡲࡓ᝟ሗ㔞࡜࠸࠺ほⅬ࠿ࡽᑡࡋ⪃ᐹࡋ࡚ࡳࡓ࠸࡜ᛮ࠸ࡲࡍࠋ

ᐃ࡜᝟ሗ㔞

ࡲࡎ 1 Ⅼ┠ࡣࠊ㞴᫆ᗘࢆᗈࡃ࡜ࡿ࡜࠸࠺ࡇ࡜࡛࠶ࡾࡲࡋࡓࠋࡇࢀࡀඛ࡯࡝࠿ࡽฟ࡚࠸ࡿ
㡯┠཯ᛂ⌮ㄽࠊItem Response Theory ؐ IRT ࡢ኱஦࡞࡜ࡇࢁ࡟࡞ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊ㡯┠཯ᛂ⌮ㄽࡢ୍ࡘࡢ኱஦࡞Ⅼࡣࠊ⬟ຊࡸ≉ᛶ࡜࠸ࡗࡓࡇ࡜࡜㡯┠ࡢฟ᮶࣭୙ฟ᮶࡜࠿ࢸࢫࢺ
ࡢⅬᩘࢆศࡅ࡚⪃࠼ࡿࠋࡔ࠿ࡽ⦪㍈࡟ࡣ㡯┠ࡢṇ⟅࣭ㄗ⟅ࠊ࠶ࡿ࠸ࡣࢸࢫࢺࢫࢥ࢔ࡀฟ࡚
ࡁࡲࡍࡀࠊࡑࢀ࡜ࡣู࡟ࠊࡑࢀࡽࢸࢫࢺ࡬ࡢゎ⟅ࢆࡍ࡭࡚㏻ࡋࡓ⬟ຊ࡜࠸࠺ࡶࡢࢆู㏵⪃
࠼ࡿࡇ࡜ࡀ 1 ࡘ㔜せ࡞࣏࢖ࣥࢺ࡟࡞ࡾࡲࡍࠋ
ࢫࣛ࢖ࢻ 9 ࡢ୰ኸࡢ㯮࠸᭤⥺ࡣ 1 ࡘࡢ㡯┠ࢆ࠶ࡽࢃࡋ࡚࠸ࡲࡍࡅࢀ࡝ࡶࠊ⬟ຊࡀ㧗ࡃ࡞ࡿ࡯࡝ࡇࡢ㡯┠࡟ṇ⟅ࡍࡿ☜⋡ࡣୖࡀࡗ࡚࠸ࡃࢃࡅ࡛ࡍࡡࠋࡑࢀࡀ㡯┠ࡢ≉ᚩࢆ࠶ࡽࢃࡋ
࡚࠸ࡿࢃࡅ࡛ࡍࠋඛ࡯࡝ᒣᮏඛ⏕ࡢヰ࡟࠶ࡗࡓ㡯┠ࡢࣃ࣓࣮ࣛࢱ࡜࠸࠺ࡢࡣࠊࡑࡢ㡯┠ࡀ࡝ࢀࡔࡅ㞴ࡋ࠸࠿ࠊ࡝ࢀࡔࡅ㆑ูຊࡀ࠶ࡿ࠿࡜࠸ࡗࡓࡇ࡜ࢆព࿡ࡋ࡚࠸ࡿࢃࡅ࡛ࡍࡀࠊࡇ
ࡢ㯮࠸㡯┠ࡣ୰⛬ᗘࡢ㞴ࡋࡉࢆ࠶ࡽࢃࡋ࡚࠸ࡲࡍࠋ౛࠼ࡤᕥഃࡢ㉥࠸ࡇࡢ㡯┠ࡣࠊ⬟ຊࡀపࡃ࡚ࡶ⤖ᵓṇ⟅࡛ࡁࡿࠋྑ➃ࡢࡶࡢࡣ⬟ຊࡀ㠀ᖖ࡟㧗ࡃ࡞࠸࡜ṇ⟅࡛ࡁ࡞࠸࡜࠸࠺ࡇ࡜
࡛ࠊྑഃ࡟࠶ࡿ㡯┠࡯࡝㞴ࡋ࠸㡯┠࡜࠸࠺ࡇ࡜࡛ࠊ㡯┠཯ᛂ⌮ㄽ࡛ࡣࠊࡇ࠺࠸࠺ᙧ࡛㡯┠ࡢ≉ᚩࢆ⾲⌧ࡋࡲࡍࠋඛ࡯࡝ࡢᗈ⠊ᅖࡢ㞴᫆ᗘࢆ࡜ࡿ࡜࠸࠺ࡇ࡜࡟ࡘ࠸࡚ࠊࡇࡇ࡛ࡕࡻࡗ࡜ࢩ࣑࣮ࣗࣞࢩࣙࣥ
ⓗ࡟⪃࠼࡚ࡳࡓࡢࡣࠊࡍ࡭࡚୰⛬ᗘࡢ㞴᫆ᗘࡢ㡯┠ࠊࡘࡲࡾ┿ࢇ୰ࡢ㯮࠸㡯┠ࡔࡅ࡛ࢸࢫ
ࢺࢆᵓᡂࡋࡓሙྜ࡜ࠊ୰ᩍᑂࡢゝ࠺ᗈ⠊࡞㞴᫆ᗘࢆ࡜ࡗࡓሙྜࠊࡘࡲࡾࢫࣛ࢖ࢻ 9 ࡢ୰ኸࡢ㯮ࡢ㡯┠ࡢࡳࡢࢸࢫࢺ࡜㉥ࡢ㡯┠ࢆྵࡵࡓ඲యࡢࢸࢫࢺ࡟ศࡅ࡚ࠊ᝟ሗࡢ㔞ࢆィ⟬ࡋ࡚ࡳࡲࡋࡓࠋࡇࡢ᝟ሗ㔞࡜࠸࠺ࡢࡣಙ㢗ᛶ࡟㠀ᖖ࡟㏆࠸ᴫᛕ࡛ࡍࡅࢀ࡝ࡶࠊಙ㢗ᛶ࡜㐪ࡗ࡚᝟ሗ㔞࡜࠸࠺ࡢࡣࠊࢫࣛ࢖ࢻ 10 ࡢࡼ࠺࡟ࠊ࠶ࡿࣞ࣋ࣝࡢ⏕ᚐ࡟ᑐࡍࡿ

ᐃࡢ᝟ሗ㔞࡜࠸࠺ࡩ࠺࡟ࠊ

⬟ຊࣞ࣋ࣝ࡟ᛂࡌ࡚᝟ሗࢆ㛵ᩘ࡜ࡋ࡚⾲⌧࡛ࡁࡿࠊࡇࢀࡀ㡯┠཯ᛂ⌮ㄽࡢࡶ࠺୍ࡘࡢ≉ᚩ࡞ࢇ࡛ࡍࡡࠋ౛࠼ࡤ୰⛬ᗘࡢ㞴᫆ᗘࡢ㡯┠ࡢࡳ࡛ࡸࡿ࡜ࠊ᝟ሗ㔞ࡣ㯮ࡢ᭤⥺ࡢࡼ࠺࡟࡞ࡾࠊ
ᙜ↛୰⛬ᗘࡢ⏕ᚐࡢ⬟ຊࢆ㆑ูࡍࡿୖ࡛ࡣ኱ኚ᝟ሗ㔞ࡀ㧗࠸ࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊ㧗࠸ࣞ࣋ࣝࡢ⏕ᚐ࡟ᑐࡋ࡚ࡣ࡯࡜ࢇ࡝᝟ሗࡀ࡞࠸ࢃࡅ࡛ࡍࠋࡇࡢᏊࡓࡕ࡟࡜ࡗ࡚ࡳࢀࡤ࡯࡜ࢇ࡝࡛
ࡁ࡚ࡋࡲ࠺ࡢ࡛ࠊࡇࡢࣞ࣋ࣝࡢ⏕ᚐࡓࡕࡢ⬟ຊࢆ㆑ูࡍࡿࡇ࡜ࡣ࡛ࡁ࡞࠸ࢃࡅ࡛ࡍࡡࠋ㏫
࡟ࠊప࠸ࣞ࣋ࣝࡢᏊ౪ࡓࡕ࡟࡜ࡗ࡚ࡣ࡜࡚ࡶ㞴ࡋ࠸㡯┠ࡤ࠿ࡾ࡞ࡢ࡛඲ဨ࡯࡜ࢇ࡝ 0 Ⅼ࡟࡞ࡗ࡚ࠊࡸࡣࡾ᝟ሗࡀ࡞࠸ࠋ
ࡑࢀ࡟ᑐࡋ࡚㞴᫆ᗘࢆᗈ⠊࡟࡜ࡗࡓࢸࢫࢺࡢ᝟ሗ㔞ࡣࠊ㉥ࡢ᭤⥺ࡢࡼ࠺࡟࡞ࡾࡲࡍࠋᙜ
↛୰ᚰ࡛ࣞ࣋ࣝࡣୗࡀࡾࡲࡍࡅࢀ࡝ࡶࠊࣞ࣋ࣝࡀ㧗࠸࡜ࡇࢁ࡛ࡶ๭ྜ᝟ሗࡀ࠶ࡿࠋప࠸࡜

－64－ࡇࢁ࡛ࡶ๭ྜ᝟ሗࡀ࠶ࡿ࡜࠸࠺ࡇ࡜࡛ࠊࡑ࠺࠸࠺ព࿡࡛ࡣࠊᗈ⠊ᅖ࡟࡜ࡿࡇ࡜࡟ࡼࡗ࡚㧗࠸Ỉ‽ࡢ኱Ꮫ࡟ࡶᑐᛂࡍࡿ࡜࠸࠺ࡢࡣࠊ࠶ࡿ⛬ᗘᐇ⌧࡛ࡁࡑ࠺࡟ぢ࠼ࡲࡍࠋࢫࣛ࢖ࢻ 11 ࡣྠࡌࡇ࡜࡛ࡍࡅࢀ࡝ࡶࠊඛ࡯࡝ࡢ㉥ࡢࢸࢫࢺ࡜㯮ࡢࢸࢫࢺࡢ᝟ሗ㔞ࡢẚࢆ࡜ࡾࡲࡋࡓࠋఱಸ᝟ሗ㔞ࡀ࠶ࡿ࠿࡜࠸࠺ẚࢆ࡜ࡗࡓࡶࡢ࡛ࡍࠋ1 ಸ࡛࠶ࢀࡤྠࡌ࡛ࡍࡡࠋ
ࡇࢀࡣᗈ⠊ᅖ࡟࡜ࡗࡓሙྜࠊ୰⛬ᗘࡢࡳࡢሙྜ࡟ẚ࡭࡚ఱಸࡢ᝟ሗ㔞ࡀ࠶ࡿ࠿ࠋ୰ᚰ௜㏆࡛ࡣࡸࡣࡾ 1 ࢆୗᅇࡾࡲࡍࡅࢀ࡝ࡶࠊ㧗࠸࡛ࣞ࣋ࣝࡣ 2 ಸࠊ3 ಸࡢ᝟ሗ㔞ࡀ࠶ࡿ࡜࠸࠺ࡇ
࡜࡞ࡢ࡛ࠊ௒ࠊゝ࠸ࡲࡋࡓࡼ࠺࡟ࠊࡇࡢࡼ࠺࡟ࡍࡿࡇ࡜࡟ࡼࡗ࡚㑅ᢤᛶࡢ㧗࠸㞴㛵኱Ꮫ࡛ࡶࠊࡇࡢࡼ࠺࡟⠊ᅖࢆᗈࡆࢀࡤᑐᛂ࡛ࡁࡑ࠺࡟ぢ࠼ࡲࡍࠋࡇࢀࡀ 1 Ⅼ┠࡟ࡘ࠸࡚ࡢᬻᐃⓗ࡞ホ౯࡟࡞ࡾࡲࡍࠋ
ᡂ⦼ࡢẁ㝵ู⾲♧࡜᝟ሗ㔞
2 Ⅼ┠ࡣࠊẁ㝵ู⾲♧࡛ࡍࡡࠋࢫࣛ࢖ࢻ 12 ࡀ௒᪥ࡢࢫࣛ࢖ࢻࡢ୰୍࡛␒኱஦࡞ࢫࣛ࢖ࢻ࡟࡞ࡿ࠿࡜ᛮ࠺ࢇ࡛ࡍࡅࢀ࡝
ࡶࠊ࡜ࡾ࠶࠼ࡎࢸࢫࢺ࡜ࡋ࡚ࡣࠊඛ࡯࡝ࡢ୰⛬ᗘࡢ㞴᫆ᗘࡢ㡯┠ࢆ 100 ಶ⏝ពࡋ࡚ࠊ0 Ⅼ
࠿ࡽ 100 Ⅼࡲ࡛ࡢࢸࢫࢺ࡟ࡋࡲࡋࡓࠋࡓࡔࡋࠊࡑࢀࢆ 20 Ⅼ้ࡳ࡟ࡋ࡚ࠊ20 Ⅼ௨ୗࡣ 1 Ⅼࠊ20
࠿ࡽ 40 Ⅼࡣ 2 Ⅼ࡜࠸࠺ࡩ࠺࡟ 5 ẁ㝵⾲♧ࢆ⾜࠸ࡲࡋࡓࠋࡑ࠺ࡍࡿ࡜ࠊࡇࡢ㯮ࡢୖ᪼ࡍࡿ
᭤⥺ࡣẁ㝵್ࡢᮇᚅ್࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ⬟ຊࡢప࠸࡜ࡇࢁ࡛ࡣࠊྑഃࡢ⦪㍈ࢆぢࡿ࡜ࠊ࡯ࡰ 1 Ⅼ࡟࡞ࡾࡲࡍࠋ⬟ຊࡀ㧗࠸࡜ࠊࡶ࠺ᕪࡀࡘ࠿࡞࠸ࡢ࡛ 5 Ⅼ࡟࡞ࡾࡲࡍࠋẁ㝵ࡢቃ⏺
Ⅼࡀ࠶ࡗ࡚ࠊࡑࡇࡲ࡛ࡸࡸࣇࣛࢵࢺ࡟࡞ࡾࠊቃ⏺Ⅼ௜㏆࡛ୖࡀࡗ࡚ࡲࡓࡸࡸࣇࣛࢵࢺ࡟࡞
ࡾࠊࡇࢀࡀẁ㝵ู⾲♧ࢆࡋࡓ࡜ࡁ࡟ࠊẁ㝵್ࡀ⬟ຊ࡟ࡼࡗ࡚࡝࠺ኚ໬ࡋ࡚࠸ࡃ࠿࡜࠸࠺᭤
⥺࡟࡞ࡿࢃࡅ࡛ࡍࡡࠋ

ࡶ࠺ࡇࡇ࠿ࡽࡣࢃ࠿ࡿ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊ࠶ࡿ⛬ᗘ௨ୖ⬟ຊࡀୖࡀࡗ࡚ࡶ 5 Ⅼࠊ5 Ⅼࠊ5Ⅼ࡞ࡢ࡛ᕪࡀࡘ࠿࡞࠸ࢃࡅ࡛ࡍࠋࡋࡓࡀࡗ࡚ࠊ㉥ࡢ᭤⥺࡛♧ࡋࡓ 5 ẁ㝵⾲♧ࡢ᝟ሗ㔞᭤⥺࡛ぢࡲࡍ࡜ࠊ≉ᛶ್ࡢ㧗࠸࡜ࡇࢁ࡛ࡣ࡯࡜ࢇ࡝᝟ሗ㔞ࡀ࠶ࡾࡲࡏࢇࠋ㏫࡟ప࠸࡜ࡇࢁ࡛ࡣ

－65－ࡇࡢ⠊ᅖ࡛ࡣ࡯࡜ࢇ࡝ 1 Ⅼࡋ࠿࡜ࡽ࡞࠸ࡢ࡛ࠊ᝟ሗ㔞ࡀ࡞࠸ࠊᕪࡀࡘ࠿࡞࠸ࢃࡅ࡛ࡍࡡࠋ
ẁ㝵್ࡢᮇᚅ್ࢆ♧ࡍ᭤⥺ࡢࢫ࣮ࣟࣉࡀᛴ࡟࡞ࡗ࡚࠸ࡿ࡜ࡇࢁ࡛᝟ሗ㔞ࡀࣆ࣮ࢡ࡟࡞ࡾࡲ
ࡍࠋ5 ẁ㝵ࡔ࡜ቃ⏺Ⅼࡀ 4 ࡘ࠶ࡾࡲࡍࡢ࡛ࠊ4 ࡘࡢᒣࡀ࡛ࡁࡿ᝟ሗ㔞᭤⥺࡟࡞ࡿࢃࡅ࡛ࡍ
ࡡࠋẁ㝵ู⾲♧ࡢ᝟ሗ㛵ᩘࡢ≉ᚩࢆ࠶ࡽࢃࡋ࡚࠸ࡲࡍࠋ
ࡓࡔࠊ኱஦࡞ࡇ࡜ࡣࠊ1 Ⅼ้ࡳࡢሙྜ࡟ẚ࡭࡚ẁ㝵ู⾲♧ࡢ᝟ሗ㔞ࡀ࡝࠺࡞ࡢ࠿࡜࠸࠺
ࡇ࡜࡛ࡍࡀࠊ1 Ⅼ้ࡳ࡟ࡍࡿ࡜ࠊࡇࡢୖ㒊࡟࠶ࡿ⥳ࡢ᭤⥺ࡀ᝟ሗ㔞᭤⥺࡜࡞ࡾࡲࡍࠋẁ㝵ู⾲♧࡟ࡍࡿࡇ࡜࡟ࡼࡗ࡚᫂ࡽ࠿࡟᝟ሗࡢࣟࢫࡀ⏕ࡌࡿࢃࡅ࡛ࡍࡡࠋ1 Ⅼ้ࡳ࡛ࡣࡇࢀࡔ
ࡅࡢ᝟ሗ㔞ࡀ࠶ࡗࡓࡢ࡟ࠊẁ㝵ู࡟ࡍࡿࡇ࡜࡟ࡼࡗ࡚ࠊ≉ᛶ್ࡢ㧗࠸㒊ศ࡛ࡣ࡯ࡰࢮࣟ࡟
࡞ࡗ࡚ࡋࡲ࠺࡜࠸࠺ࡇ࡜࡛ࠊ5 ẁ㝵⾲♧ࡢሙྜࡢ᝟ሗ㔞ࡣẁ㝵ࡢቃ⏺௜㏆࡛ࡣ┦ᑐⓗ࡟኱ࡁࡃؐ┦ᑐⓗ࡟࡜࠸࠺ࡢࡣࠊࡇࡢ୰࡛࡜࠸࠺ࡇ࡜࡛ࡍࡡؐ࡞ࡾࡲࡍࡀࠊ1 Ⅼ้ࡳࡢሙྜࡼ
ࡾࡣ᫂ࡽ࠿࡟ࠊ࠿࡞ࡾᑠࡉࡃ࡞ࡾࡲࡍࠋ
ࢫࣛ࢖ࢻ 13 ࡣ᝟ሗ㔞ࡢẚࢆ࡜ࡗࡓࡶࡢ࡛ࠊẁ㝵ู࡟ࡋ࡚ࡶࠊࡶࡋ⣲Ⅼ࡜ྠࡌࡔࡅࡢ᝟
ሗ㔞ࡀ࠶ࢀࡤࡇࡢ 1 Ⅼࡢࣛ࢖ࣥ࡟࡞ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡎࡗ࡜ୗࡢ࡯࠺࡟࡞ࡗ࡚࠸ࡲࡍ
ࡼࡡࠋ㧗࠸࡛ࣞ࣋ࣝࡣ࡯࡜ࢇ࡝᝟ሗࡀ࡞࠸࡜࠸࠺ࡇ࡜࡛ࠊ᝟ሗ㔞ࡀ኱ࡁ࠸࡜ࡇࢁ࡛ࡶ┦ᑐⓗ࡟ 0.6 ࡜࠸࠺ࡇ࡜ࡣࠊࡑࡢ㏫ᩘࡢಸࡢ㡯┠ᩘࡀᚲせࡔ࡜࠸࠺ࡇ࡜࡛ࡍࠋ᝟ሗ㔞ࡀ 0.5 ࡋ࠿࡞࠸࡜࠸࠺ࡇ࡜ࡣࠊྠࡌ᝟ሗ㔞ࢆᚓࡿࡓࡵ࡟ 2 ಸࡢᩘࡢ㡯┠ࡢࢸࢫࢺࢆࡋ࡞ࡃ࡚ࡣ࠸ࡅ
࡞࠸࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋࡇࡢᅗ࠿ࡽࡍࡿ࡜ 5 ẁ㝵⾲♧ࢆࡋ࡚ 1 Ⅼ้ࡳࡢሙྜ࡜ྠ➼ࡢ᝟ሗ㔞ࢆᚓࡿࡓࡵ࡟ࡣࠊ2 ಸ࠿ࡽ 5 ಸ㛗࠸ࢸࢫࢺࢆࡋ࡞ࡃ࡚ࡣ࠸ࡅ࡞࠸ࠊࡑࢀࡔࡅࡢࣟࢫࡀ⏕ࡌࡿ࡜࠸࠺ᐃ㔞ⓗ࡞ィ⟬࡟࡞ࡾࡲࡍࠋ୧➃ࡢ࡯࠺࡛ࡣࠊ┦ᑐຠ⋡̿᝟ሗ㔞ࡢẚࡣ࡯ࡰࢮࣟ
࡜࡞ࡿࢃࡅ࡛ࡍࠋ
ࢫࣛ࢖ࢻ 14 ࡣ㞴ࡋ࠸㡯┠࡛ࡸࡗࡓሙྜ࡛ࡍࡅࢀ࡝ࡶࠊഴྥ࡜ࡋ࡚ࡣྠࡌ࡛ࠊࡓࡔࠊ㧗࠸ࣞ࣋ࣝࡢ࡜ࡇࢁ࡛ྠࡌἼࢆᡴࡗࡓࡇ࡜ࡀ⏕ࡌࡿࡇ࡜࡟࡞ࡾࡲࡍࠋ
ඛ࡯࡝ᗈ⠊ᅖࡢ㞴᫆ᗘ࡟ࡍࡿࡇ࡜࡟ࡼࡗ࡚㧗࠸Ỉ‽ࡢ࡜ࡇࢁ࡟ࡶ᝟ሗ㔞ࢆṧࡍࡇ࡜ࡀ࡛
ࡁࡑ࠺ࡔ࡜࠸࠺ᬻᐃⓗ࡞ホ౯ࢆゝ࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊ5 ẁ㝵ホ౯࡟ࡍࡿࡇ࡜࡟ࡼࡗ࡚ࡑࢀ
ࡀྎ↓ࡋ࡟࡞ࡗ࡚ࠊ㧗࠸ࣞ࣋ࣝࡢ࡜ࡇࢁ࡟᝟ሗࡀ࠶ࡗ࡚ࡶ 5 ẁ㝵ࠊẁ㝵ู⾲♧࡟ࡍࡿࡇ࡜
࡟ࡼࡗ࡚᝟ሗࡣ᏶඲࡟ࢮࣟ࡟࡞ࡗ࡚ࡋࡲ࠺ࠊࡑ࠺࠸࠺௙⤌ࡳ࡟࡞ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋ
ࢫࣛ࢖ࢻ 15 ࡀࡑࡢ㒊ศࡢࡲ࡜ࡵ࡟࡞ࡾࡲࡍࡅࢀ࡝ࡶࠊ㑅ᢤ࡜࠸࠺ࡢࡣ࠸ࢁ࠸ࢁ࡞᝟ሗ
ࢆࡶ࡜࡟㑅ᢤࡍࡿࢃࡅ࡛ࡍࡀࠊࡑࡢ๓ࡢẁ㝵࡛ࡢࢫࢥ࢔ࡀ
࡚ᥦ౪ࡍࡿࡢࡀ

ᐃ࡟࡞ࡾࡲࡍࠋ᝟ሗࢆ཰㞟ࡋᐃ࡛ࡍࡀࠊ᭱⤊ⓗ࡟ࡣධᏛヨ㦂࡞ࡢ࡛ࠊ2 ẁ㝵⾲♧ࢆࡍࡿࢃࡅ࡛ࡍࡼࡡࠋ

ྜ࣭ྰࠋࡋ࠿ࡋࠊࡑࡢ๓࡛ẁ㝵ู࡟ࡉࢀ࡚ࡋࡲ࠺࡜᝟ሗࡀኻࢃࢀ࡚ࡋࡲ࠺ࠊྜྰࡢุ᩿ࡀ
≬ࡗ࡚ࡋࡲ࠺ࢃࡅ࡛ࡍࡡࠋ࡞ࡢ࡛ࠊ

ᐃࡢẁ㝵࡛ẁ㝵ู⾲♧ࢆ⾜࠺ࡇ࡜ࡢၥ㢟ࢆࡇࡇ࡟♧ࡋࡓ࡜ࡇࢁ࡛ࡍࠋ
ࡑ࠺࠸࠺ࡇ࡜࡛ࠊ1 Ⅼ้ࡳ࡟ᑐࡋ࡚࡜࠸࠺ࡇ࡜࡛ࡍࡅࢀ࡝ࡶࠊᮾி኱Ꮫ࡛ࡣ࡝࠺࡞ࡗ࡚
࠸ࡿ࠿࡜࠸࠺࡜ࠊࢫࣛ࢖ࢻ 16 ࡟࠶ࡿࡼ࠺࡟ࠊ1 Ⅼ้ࡳ࡝ࡇࢁ࠿ 0.0001 ้ࡳ࡛ホ౯ࡉࢀ࡚࠸ࡿ࡜ࡇࢁ࡛࠶ࡾࡲࡍࠋࡇࢀࡀࡼ࠸࡜࠸࠺ព࿡࡛ࡣ࠶ࡾࡲࡏࢇࡅࢀ࡝ࡶࠋ
ẁ㝵ู⾲♧ࡢࡑࡢ௚ࡢၥ㢟Ⅼ࣓࢖ࣥࡢヰࡣ࡯ࡰ௨ୖ࡛ࡍࡅࢀ࡝ࡶࠊẁ㝵ู⾲♧ࢆ⾜࠺ࡇ࡜ࡢၥ㢟Ⅼ࡟ࡘ࠸࡚ᑡࡋ㏣ຍ
࡛㆟ㄽࡋࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
ࢫࣛ࢖ࢻ 17 ࡢ㸦ϸ㸧ࡢ᝟ሗ㔞ࡢῶᑡ࡜࠸࠺ࡢࡣࠊ௒ࠊ᪤࡟㏙࡭ࡓࡇ࡜࡛ࡍࠋ㸦Ϲ㸧㸦Ϻ㸧

－66－㸦ϻ㸧࡟ࡘ࠸࡚⡆༢࡟࠾ヰࡋࡋࡲࡍࠋ
㸦Ϲ㸧ࡣẁ㝵࡟ศࡅࡿࠊඛ࡯࡝ࡣ 20 Ⅼࠊ40 Ⅼ࡜࠸࠺ࡩ࠺࡟ࡑࢀࡇࡑ᜛ពⓗ࡟ศࡅࡲࡋ
ࡓࡅࢀ࡝ࡶࠊࡑࡢศࡅ᪉ࡢ᜛ពᛶࡀၥ㢟࡟࡞ࡿࠋ௬࡟ศࡅࡿ࡜ࡋ࡚ࡶࠊ࡝ࡇ࡛ศࡅࡿࡢ࠿
ࡀၥ㢟࡟࡞ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࠋࡑࡇ࡛ཧ⪃࡟࡞ࡿ࡜ᛮࢃࢀࡿࡢࡀࠊ࢔࣓࡛ࣜ࢝ࡢࠊ≉࡟ࣁ࢖ࢫࢡ࣮ࣝࡢ༞ᴗࡢᇶ‽ࢆ‶ࡓࡋ࡚࠸ࡿ࠿࡝࠺࠿࡜࠸࠺࣑ࢽ࣐࣒ࢥࣥࣆࢸࣥࢩ࣮ࢸࢫࢺࠊ
ࡇࢀࡣ‶ࡓࡋ࡚࠸ࡿ࣭‶ࡓࡋ࡚࠸࡞࠸࡜࠸࠺ 2 ẁ㝵⾲♧࡟࡞ࡿࢃࡅ࡛ࡍࡀࠊࡑࡇ࡛ࡢ⤒㦂ࡀࠊụ⏣ኸඛ⏕ࡀ┘ヂࡉࢀࡓࠗᩍ⫱ᐃᏛ࠘ࡢ୰ࡢ 1 ࡘࡢ❶࠿ࡽࡢᘬ⏝࡛ࡍࡅࢀ࡝ࡶࠊࢫ

ࣛ࢖ࢻ 19 ࡢࡼ࠺࡞ᩥ❶ࡀ࠶ࡾࡲࡋࡓࠋࠕᇶ‽タᐃؐ࡝ࡇ࡛ẁ㝵࡟ศࡅࡿ࠿ؐ࡟㛵ࡍࡿᩥ⊩ࡀఱ࠿Ỵᐃⓗ࡞Ⅼࢆᣢࡗ࡚࠸ࡿ࡜ࡋ
ࡓࡽࠊࡑࢀࡣࠊࢥࣥࣆࢸࢩ࣮ࢸࢫࢺ࡛ࠊ᧦ㆤࡋᚓࡿᇶ‽ࢆタᐃࡍࡿࡇ࡜ࡢᅔ㞴ࡉ࡟ࡘ࠸࡚
࡛࠶ࡿࠖࠊẁ㝵ศࡅࡣ㞴ࡋ࠸࡜࠸࠺ࡇ࡜ࡔࡅ࡛ࡣྜពࡉࢀ࡚࠸ࡿ࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋ
ࠕGlass ࡶ Shepard ࡶࠊࢥࣥࣆࢸࣥࢫࡣᴫᛕୖ᏶඲࡞㐃⥆ኚᩘ࡛࠶ࡿ࡜ᣦ᦬ࡍࡿࠋࡑࢀ࡞
ࡽࠊࢥࣥࣆࢸࣥࢫࢆࡶࡘ࠿ྰ࠿࡛Ꮫ⏕ࢆ 2 ࡘࡢูಶࡢ࢝ࢸࢦࣜ࡟௬࡟ศࡅࡿศ๭Ⅼࢆタᐃࡍࡿࡇ࡜ࡣࠊ㠀⌧ᐇⓗ࠿ࡘ㠀ㄽ⌮ⓗ࡛࠶ࡿࠖࢫࣛ࢖ࢻ 20 ࡣ┬␎ࡋࡲࡍࡅࢀ࡝ࡶࠊᇶ‽タᐃ࡟⏝࠸ࡽࢀ࡚࠸ࡿ᪉ἲࡀ࠸ࡃࡘ࠿࠶ࡿࢇ
࡛ࡍࡅࢀ࡝ࡶࠊ࡝ࢀࡶࠕ࡞ࡿ࡯࡝ࠊࡇࢀ࡛ࡍࢀࡤ࠸࠸࡞ࠖ࡜࠸࠺Ỵᐃⓗ࡞ࡶࡢࡀ࠶ࡾࡲࡏ
ࢇࠋ1 ࡘࡔࡅ⤂௓ࡋࡲࡍ࡜ࠊAngoff ࡉࢇࡢ᪉ἲࡣࠊࡂࡾࡂࡾᇶ‽ࢆࢡࣜ࢔ࡋ࡚࠸ࡿ⏕ᚐࢆᑓ㛛ᐙࡀ᝿ᐃࡋࡲࡍࠋࡑࡢ⏕ᚐࡀࡇࡢၥ㢟࡟ṇ⟅ࡍࡿ☜⋡ࡣ࠸ࡃࡽ࠿ࠊḟࡢၥ㢟࡟ṇ⟅ࡍࡿ☜⋡ࡣ࠸ࡃࡽ࠿ࠊࡇࢀࢆ㊊ࡋྜࢃࡏ࡚ࡑࡢࡂࡾࡂࡾࡢ⏕ᚐࡢᮇᚅᚓⅬࢆ⟬ฟࡋࠊࡑࢀࢆ
ศ๭Ⅼ࡜ࡍࡿ࡜࠸࠺ࡇ࡜࡛ࠊ࠿࡞ࡾ୺ほⓗ࡞ุ᩿ࡀᚲせ࡟࡞ࡗ࡚ࡁࡲࡍࠋࡑ࠺࠸ࡗࡓ࠸ࡃ
ࡘ࠿ࡢ᪉ἲࡀ࠶ࡿࢃࡅ࡛ࡍࡀࠊ᪉ἲࢆ㐪࠼ࡿ࡜୙ྜ᱁⋡ࡀ 30 ಸ㐪࠺࡜࠸ࡗࡓ⤖ᯝࡶฟ࡚ࡁ࡚ࠊࡑ࠺࠸࠺ẁ㝵ࡢศࡅ᪉ࡢၥ㢟ࡀᣦ᦬ࡉࢀ࡚࠸ࡿ࡜ࡇࢁ࡛࠶ࡾࡲࡍࠋ
ࢫࣛ࢖ࢻ 23 ࡢ㸦Ϻ㸧ࡣࠊྠ୍ẁ㝵ࡢ୰࡛ࡢಶேᕪࡸኚ໬ࡀ↓どࡉࢀ࡚ࠊẁ㝵㛫࡛ࡢᕪࡸኚ໬ࡀ㄂ᙇࡉࢀ࡚ࡋࡲ࠺ࠋࡘࡲࡾẁ㝵ู⾲♧࡛ࡣࠊྠ୍ẁ㝵ࡢ୰࡛ࡣᇶᮏⓗ࡟➼㉁࡞ࡶ
ࡢࠊ4 ࡢேࠊ5 ࡢே࡜࠸࠺ࡩ࠺࡟➼㉁࡞ࡶࡢ࡜ࡉࢀࡲࡍࡅࢀ࡝ࡶࠊᐇែࡣྠࡌẁ㝵ࡢ୰࡟࠿࡞ࡾ኱ࡁ࡞ಶேᕪࡀ࠶ࡿࢃࡅ࡛ࠊ࠶ࡿẁ㝵ࡢ୰ᚰ௜㏆࡟఩⨨ࡍࡿ⪅ࡣࠊ࠿࡞ࡾࡢྥୖࡀ
࠶ࡗ࡚ࡶྠࡌẁ㝵࡟࡜࡝ࡲࡾࠊດຊࡀ⤖ᯝ࡟཯ᫎࡉࢀ࡞࠸ࠋ
ୗ࡟ࡕࡻࡗ࡜వィ࡞ࡇ࡜ࢆ᭩ࡁࡲࡋࡓࡅࢀ࡝ࡶࠊࢲ࢖࢚ࢵࢺ࡛ 3 ࢟ࣟ⑭ࡏࡓࡢ࡟ࠕࡸࡸ
⫧‶ࠖࠊ5 ࢟ࣟ⑭ࡏࡓࡢ࡟ࠕࡸࡸ⫧‶ࠖࠊኚࢃࡽ࡞ࡅࢀࡤࡸࡿẼࢆ࡞ࡃࡋࡲࡍࡼࡡࠋࡸࡣࡾ
ດຊࡀ཯ᫎࡉࢀࡿࡼ࠺࡞ධᏛヨ㦂ࡀᚲせ࡛ࡣ࡞࠸࠿࡜ᐇឤࡋࡓ࡜ࡇࢁࡀ࠶ࡾࡲࡋࡓࡢ࡛ࠊ
ࡕࡻࡗ࡜᭩࠸࡚࠾ࡁࡲࡋࡓࠋ
ࢫࣛ࢖ࢻ 24 ࡛ࡍࡀࠊᑡ࡞࠸ẁ㝵ᩘ࡛ࠊ౛࠼ࡤ 5 ẁ㝵࡛ 4 ࡜࠿ 3 ࡜࠿ูẁ㝵࡟ホᐃࡉࢀࡿ࡜ࠊᐇ㝿࡟ࡣࢃࡎ࠿࡞ᕪ␗࡛ࡶࡑࢀࡀᣑ኱ࡉࢀ࡚ྜྰุᐃ࡟Ỵᐃⓗ࡞ᙳ㡪ࢆ୚࠼࡚ࡋࡲ
࠺ࠋẁ㝵ࡢቃ⏺௜㏆࡟࠸ࡿ⪅ࡣࠊᏛຊࡢኚ໬ࡀ࡞ࡃ࡚ࡶㄗᕪኚືࡔࡅ࡛ࡶୖୗࡋ࡚ࡋࡲ࠺ࠋ
ࡑࢀࡀࡲࡓỴᐃⓗ࡞ᙳ㡪ࢆ୚࠼࡚ࡋࡲ࠺࡜࠸࠺ࡇ࡜࡛ࡍࡼࡡࠋ
ḟࡣࡕࡻࡗ࡜ᙉㄪࡋࡓ࠸࡜ࡇࢁ࡛ࡍࡅࢀ࡝ࡶࠊ1 Ⅼ้ࡳࡢᕪࡣࠊ୰ᩍᑂ⟅⏦࡟ࡶ࠶ࡿࡼ
࠺࡟ࠊᐇ㉁ⓗ࡞ព࿡ࡣ࠶ࡾࡲࡏࢇࡀࠊࡑࢀࡣⓙࡉࢇࢃ࠿ࡗ࡚࠸ࡿࢃࡅ࡛ࡍࠋᮾ኱ࡢ 550 Ⅼ‶Ⅼࡢ 0.0001 ࡟ᐇ㉁ⓗ࡞ព࿡ࡀ࠶ࡿ࡜ࡣᛮࡗ࡚࠸࡞࠸ࢃࡅ࡛ࡍࡼࡡࠋ1 Ⅼ࡜࠿ 0.0001 ࡔ࠿ࡽࡇࡑၥ㢟ࡣ࡞࠸࡜ᛮ࠺ࢃࡅ࡛ࡍࠋࡋ࠿ࡋࠊẁ㝵࡟࡞ࡿ࡜ࠊᐇ㉁ⓗ࡞ព࿡ࡢ࡞࠸ቃ⏺௜㏆－67－ࡢᕪࡀᐇ㉁ⓗ࡞ព࿡ࢆࡶࡘ࠿ࡢࡼ࠺࡟ࠊ3 ࡜ 4 ࡟ࡣࠕࡑࢀࡣᐇ㉁ⓗ࡞ព࿡ࡀ࠶ࡿ࡛ࡋࡻ࠺ࠖ
࡜ᛮࡗ࡚ࡋࡲ࠺ࠋࡋ࠿ࡋࠊᐇ㝿࡟ࡣ 550 Ⅼࡢ࠺ࡕࡢ 0.0001 ࡋ࠿㐪ࢃ࡞ࡃ࡚ࡶ 3 ࡟࡞ࡗࡓࡾ 4࡟࡞ࡗࡓࡾࡍࡿࢃࡅ࡛ࡍࡼࡡࠋࡑࢀࡀ୙ᙜ࡟኱ࡁ࡞ព࿡ࢆᣢࡗ࡚ࡋࡲ࠺ࠋ
ࡲࡓࠊ5 ẁ㝵ࡢホ౯࡞ࡢ࡛ࠊࡇࢀࡀ࠿࡞ࡾᅛᐃ໬ࡉࢀ࡚ࠊࡇࡢேࡣ 4 ࡢேࠊ3 ࡢே࡜࠸࠺ࡩ࠺࡟ᅛᐃ໬ࡉࢀ࡚ࠊ୙ᙜ࡟ᕪู໬ࡉࢀࡿࡇ࡜ࡶᠱᛕࡉࢀࡿࡢ࡛ࡣ࡞࠸࠿ࠋ
ࢫࣛ࢖ࢻ 25 ࡢẁ㝵ู⾲♧ࡢၥ㢟Ⅼࡢ㸦ϻ㸧࡛ࡍࠋ㑅ᢤࡣ኱Ꮫࡈ࡜࡟࠸ࢁ࠸ࢁᰂ㌾࡟ࡸࡗࡓࡽ࠸࠸࡜ᛮ࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠊᰂ㌾࡟ࡸࢁ࠺࡜ࡋ࡚ࢸࢫࢺࡢᚓⅬ࡟㔜ࡳࢆࡘࡅ࡚ࠊ௚
ࡢࠊ౛࠼ࡤ㠃᥋➼ࡢ᝟ሗࢆ⤌ࡳྜࢃࡏࡼ࠺࡜ࡋࡓሙྜࡸࠊ኱Ꮫ⊂⮬ࡢᇶ‽࡛ẁ㝵ศࡅࢆࡋࡓ࠸࡜࠸ࡗࡓሙྜ࡛ࡶࠊ᪤࡟ẁ㝵ศࡅࡉࢀ࡚࠸ࡿ࡜ࡑ࠺࠸ࡗࡓከᵝ࡞ᰂ㌾ᛶࢆ㜼ᐖࡋ࡚ࡋࡲ࠺࡜࠸࠺ࡢࡀ 4 ࡘ┠ࡢၥ㢟࡜ࡋ࡚ᣲࡆࡽࢀࡲࡍࠋ
౛እⓗ࡟ẁ㝵ู⾲♧ࡀᚲせ࡞ሙྜࠊ᥎ዡࡉࢀࡿྍ⬟ᛶࡀ࠶ࡿሙྜ࡜ࡋ࡚ࠊࡇࢀࡣᰘᒣࡉ
ࢇࡢㄽᩥ࠿ࡽ࡜ࡗࡓࡶࡢ࡛ࡍࡅࢀ࡝ࡶࠊ౛࠼ࡤᑠㄽᩥࡢ᥇Ⅼࡢሙྜࡣࠊ100 Ⅼ‶Ⅼࡢ⣽࠿࠸ホᐃࡣࠊࡲࡎホᐃࡢ⬟ຊ࠿ࡽࡋ࡚↓⌮ࡀ࠶ࡿࡔࢁ࠺࡜࠸࠺ࡇ࡜࡛ࠊࡼࡾಙ㢗ᛶࢆ㧗ࡵࡿࡓࡵ࡟ࡣࠊࡴࡋࢁ 5 ẁ㝵➼࡟ࡍࡿࡢࡀࡼ࠸ࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺ࡇ࡜࡛ࡍࠋࡑ࠺࠸࠺ẁ㝵ูホ౯ࡀ᭷ຠ࡞ሙ㠃ࡶ࠶ࡿ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡶ࡜ࡶ࡜᝟ሗ㔞㇏࠿࡟ࢫࢥ࢔ࣜࣥࢢࡉࢀ࡚
࠸ࡿࡶࡢࢆẁ㝵ู࡟ࡍࡿࡇ࡜࡟ࡣၥ㢟ࡀ࠶ࡿ࡜࠸࠺ࡇ࡜࡛࠶ࡾࡲࡋࡓࠋ
኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵ᭱ᚋ࡟ࠊᮏ᪥ࡢࢩ࣏ࣥࢪ࣒࢘ࡢ࣓࢖ࣥࢸ࣮࣐࡟ᣲࡆࡽࢀ࡚࠸ࡲࡍ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵ࡟ࡘ࠸࡚ࡣࠊ⚾ࡢ୰࡛ࡣࡇࡇࡀ࠿࡞ࡾ᪥ᮏⓗࡔ࡜ᛮࡗ࡚࠸ࡿࡢࡀࠊࡑࢀࡒࢀࡢ኱Ꮫࡀ⊂⮬࡟⭾኱࡞᫬㛫ࠊேࠊࢥࢫࢺࢆ࠿ࡅ࡚ಶูᏛຊヨ㦂ࢆసᡂࡋࠊ᥇Ⅼࡍࡿࠋࡑࡋ࡚᭩ᗑ࡟⾜ࡃ࡜኱Ꮫࡈ࡜ࡢ㉥ᮏࡀᮾி኱Ꮫࠊி㒔኱Ꮫ࡜࠸࠺ࡩ࠺࡟ࡊࡗ࡜୪ࢇ࡛࣋ࢫࢺࢭ࣮ࣛ࡟࡞ࡗ࡚
࠸ࡿࠋࡇࡇࡣࠊ౛࠼ࡤࣁ࣮ࣂ࣮ࢻ኱Ꮫࡢၥ㢟㞟࡜࠿࡞࠸࡛ࡍࡼࡡࠋࡇࡇࡣ㠀ᖖ࡟᪥ᮏⓗ࡞࡜ࡇࢁ࡛ࠊ࠿ࡘࠊࡸࡣࡾࡇࡢ⭾኱࡞᫬㛫࡜࠸࠺ࡢࡀࠊ◊✲⪅ࠊᩍ⫱⪅ࡢ⭾኱࡞᫬㛫ࢆྲྀࡗ࡚ࡋࡲࡗ࡚࠸ࡿࡇ࡜ࡣ኱ࡁ࡞ၥ㢟࡜ࡋ࡚ឤࡌࡿ࡜ࡇࢁ࡛࠶ࡾࡲࡍࡢ࡛ࠊࡇࡢࡲࡲ࡛࠸࠸࡜
ᛮࡗ࡚࠸ࡿࢃࡅ࡛ࡣ࠶ࡾࡲࡏࢇࠋᨵၿࡣᚲせࡔ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡑ࠺࠸ࡗࡓ㢼ᅵࡀ᪂ࢸࢫࢺ࡟ࡼࡗ࡚ኚ࠼ࡽࢀࡿ࠿࡝࠺࠿ࠊࡇࢀࡣ᳨ウࡍ࡭ࡁ࡜ࡇࢁ࠿࡞࡜ࠋ
ࡓࡔࠊࠕ㢼ᅵࠖ࡜࠸࠺ゝⴥࢆ౑ࡗࡓ᫬Ⅼ࡛ࡶ࠺ኚࢃࡽ࡞࠸࡜࠸࠺ឤࡌࡶࡋࡲࡍࡀ㸦➗㸧ࠊ࠶ࡿ࠸ࡣࠕ኱Ꮫධヨࡢ᪥ᮏⓗ͐͐ࠖ࡜ゝࡗ࡚ࡶࠊ௒ࠊゝࡗ࡚࠸ࡿࡇ࡜ࡣ࠿࡞ࡾᮾி኱Ꮫ࡛
㇟ᚩⓗ࡟ࡸࡗ࡚࠸ࡿࡇ࡜࡞ࡢ࡛ࠊࠕᮾ኱ⓗ㢼ᅵࠖࡀኚࢃࡿ࠿࡝࠺࠿ࡣ኱ࡁ࡞࡜ࡇࢁ࠿࡞࡜
ᛮ࠸ࡲࡋࡓࠋ
᭱ᚋࡢࢫࣛ࢖ࢻ 28 ࡣᑠࡲ࡜ࡵ࡛ࠊ1 ࡘ┠ࡣࠊᐃㄽ࡜࠸࠺ほⅬ࠿ࡽࡢ㆟ㄽࡶᚲせ࡟࡞ࡿࡔࢁ࠺࡜࠸࠺ࡇ࡜ࠋ2 ࡘ┠࡟ࡣࠊࡸࡣࡾ⌮ㄽࡸᢏ⾡ࡶࡇࢀ࠿ࡽ㛤Ⓨࠊྥୖࡋ࡚࠸࠿࡞ࡃ
࡚࠸ࡅ࡞࠸ࡅࢀ࡝ࡶࠊࡑࡢࡓࡵ࡟࠿࠿ࡿ᫬㛫࡜࠸࠺ࡇ࡜ࡶ࠶ࡿࡢ࡛ࠊᐇ⾜ྍ⬟ᛶ࡜࠸࠺ࡇ
࡜ࠊ᫬㛫㍈ࢆࡼࡃ⪃࠼࡞ࡃ࡚ࡣ࠸ࡅ࡞࠸ࡔࢁ࠺࡜࠸࠺ࡲ࡜ࡵ࡛࠶ࡾࡲࡍࠋ
௨ୖ࡛ࡍࠋ㸦ᢿᡭ㸧‫ྖۑ‬఍༡㢼ཎඛ⏕ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

㠀ᖖ࡟ࢃ࠿ࡾࡸࡍࡃゎㄝࡋ࡚࠸ࡓࡔࡁࡲࡋࡓࡅࢀ࡝ࡶࠊఱ࠿஦ᐇ☜ㄆⓗ࡞ࡈ㉁ၥࡀ࠶ࡾ
ࡲࡋࡓࡽ࠾ཷࡅ࠸ࡓࡋࡲࡍࠋࡼࢁࡋ࠸࡛ࡋࡻ࠺࠿ࠋ

－68－ᗈ⠊ᅖࡢ㞴᫆ᗘ࡜࠸࠺ࡢࡣࠊ⚾ࡣࡑࡢࡇ࡜ࡀ⟅⏦᱌࡟┒ࡾ㎸ࡲࢀࡓ࡜ࡁ࡟ࠊ᝟ሗ㔞ࡢ㒊ศ࡛ࡣ㑅ᢤᛶࡢ㧗࠸࡜ࡇࢁࡣ☜࠿࡟ୖࡀࡿ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊ࠸ࢃࡺࡿඹ㏻ヨ㦂➼ࡢᯟ⤌ࡳࡢ୰࡛ࡣ㧗㞴ᗘࢆᣢࡘၥ㢟࡜࠸࠺ࡢࡣࡏ࠸ࡐ࠸ 1 㢟 2 㢟ࡄࡽ࠸ࡋ࠿ฟࡏ࡞࠸࡜࡞ࡿ
࡜ࠊෆᐜⓗጇᙜᛶ࡜࠸࠸ࡲࡍ࠿ࠊ㡯┠ࡢ㡿ᇦ௦⾲ᛶⓗ࡞࡜ࡇࢁ࡛࡝࠺ࡋ࡚ࡶ㝈⏺ࡀฟ࡚ࡃࡿࡢ࡛ࡣ࡞࠸࠿࡜ឤࡌࡲࡋࡓࠋ࡜࠸࠺ࡇ࡜࡛ࠊ1 ࡘࡢヨ㦂ࡢ୰࡛ᗈ⠊ᅖࡢ㞴᫆ᗘࢆ࢝ࣂ࣮
ࡍࡿ࡜࠸࠺ࡢࡣ࡞࠿࡞࠿གྷ௓࡞ࡇ࡜࡛ࡣ࡞࠸࠿࡜ᛮࡗࡓࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢⅬ࠸࠿ࡀ࡛ࡋࡻ࠺࠿ࠋ‫ۑ‬༡㢼ཎẶࡑ࠺࡛ࡍࡡࠋ௒ࡢෆᐜⓗ࡞ࡇ࡜ࡶ࠶ࡾࡲࡍࡅࢀ࡝ࡶࠊ౛࠼ࡤ㑅ᢤᛶࡢ㧗࠸኱Ꮫ࡟࡜ࡗ࡚ࡣࠊᗈ⠊ᅖࡢࢸࢫࢺ࡟࡞ࡿ࡜ࡇࡢ㎶ࡢ᫆ࡋ࠸㡯┠ࡣ࡯࡜ࢇ࡝౑࠼࡞࠸ࠋ඲ဨࡀ
࡛ࡁ࡚ࡋࡲ࠺ࡢ࡛せࡽ࡞࠸㡯┠ࡀࡓࡃࡉࢇධࡗ࡚࠸ࡿࢃࡅ࡛ࡍࡼࡡࠋࡔ࠿ࡽ 100 㡯┠ࡸࡗ࡚ࡶ౑࠼ࡿࡢࡣ 10 㡯┠ࠊ20 㡯┠࡜࡞ࡿࡢ࡛ࠊ࡛ࡁࢀࡤࡑࡢ኱Ꮫ࡟࡜ࡗ࡚ࡣ 100 㡯┠౑࠼ࡿࡼ࠺࡞ࢸࢫࢺ࡟ࡋࡓ࠸ࢃࡅ࡛ࡍࡼࡡࠋᗈ⠊ᅖࡢࢸࢫࢺ࡜࠸࠺ࡢࡣࠊᐇࡣㄡ࡟࡜ࡗ࡚ࡶ࣋
ࢫࢺ࡛ࡣ࡞࠸࡜࠸࠺ࡇ࡜ࡣゝ࠼ࡿࠋ‫ྖۑ‬఍࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ

ࡑࡢ㎶ࡶ௒ᚋࠊ୰ᩍᑂ⟅⏦ࡀฟࡲࡋࡓࡽᑓ㛛ᐙ఍㆟࡞࡝ࡢሙ࡛ワࡵ࡚࠸ࡃࡇ࡜࡟࡞ࡿ࡜
ᛮ࠸ࡲࡍࠋ୍᪉࡛ࠊࢭࣥࢱ࣮ヨ㦂ࡀ௒ࠊ」㞧㐣ࡂࡿ࡜࠸࠺ࡇ࡜࡛ࠊ⛉┠ᩘࢆῶࡽࡍ࡜࠸࠺
ࡇ࡜࡛ྜᩍ⛉࣭⛉┠ᆺ࡞࡝࡜࠸࠺ᥦ᱌ࡶฟ࡚ࡁ࡚࠸ࡲࡍࡢ࡛ࠊ࠶ࡲࡾࣂ࣮ࢪࣙࣥࢆቑࡸࡍ
ࡇ࡜ࡣ࡛ࡁ࡞࠸࡜ゝࡗࡓ࠶ࡓࡾࡢ❧ሙ࡜ࡢࡏࡵࡂྜ࠸࡜࠸࠺ឤࡌ࡟࡞ࡿ࠿࡞࡜ᛮࡗ࡚࠾ࡾ
ࡲࡍࠋ௚࡟ఱ࠿ࡈࡊ࠸ࡲࡏࢇ࡛ࡋࡻ࠺࠿ࠋ
ࡑࢀ࡛ࡣࠊࡲࡓᚋ࡯࡝ࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥࡢ࡜ࡁ࡟ࠋ
༡㢼ཎඛ⏕ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧ࡑࢀ࡛ࡣࠊ௒᪥ࡣ㛗୎ሙ࡛ࡍࡢ࡛ࠊࡇࡇ࡛ࡶࡕࡻࡗ࡜ఇ᠁ࢆධࢀࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
ࠑ༗ᚋ 3 ᫬ 25 ศ

ఇ᠁ࠒ

－69－

኱Ꮫධヨ䝉䞁䝍䞊䝅䞁䝫䝆䜴䝮䛂኱Ꮫධヨ䛾᪥ᮏⓗ㢼ᅵ䛿ኚ䛘䜙䜜䜛䛛䛃(2014.11.29䠈ᮾிᕤᴗ኱Ꮫ)

ධヨ㑅ᢤ䛾

ᐃ䛾┠ᶆ
䜚䛯䛔≉ᛶ䜢䠈
䛷䛝䜛䛰䛡㧗䛔ጇᙜᛶ䛷

ᐃၥ㢟

䜛䛣䛸

㔜せ䛺䛣䛸䛿䞉 䜚䛯䛔≉ᛶ䠈 䜛䜉䛝≉ᛶ䛿ఱ䛺䛾䛛䞉ጇᙜᛶ䛜㧗䛔䛸䛔䛖䛣䛸䛿䛹䛖䛔䛖䛣䛸䛺䛾䛛༡㢼ཎᮅ࿴䠄ᮾி኱Ꮫ኱Ꮫ㝔ᩍ⫱Ꮫ◊✲⛉䠅

2୰ᩍᑂ⟅⏦᱌䜘䜚ᐃ್䛾ᡂ䜚❧䛱䛸ጇᙜᛶ኱ᏛධᏛᕼᮃ⪅Ꮫຊホ౯䝔䝇䝖䠄௬⛠䠅䛷䛿䠈䛂▱㆑䞉ᢏ⬟䜢ά⏝䛧䛶䠈⮬䜙ㄢ㢟䜢Ⓨぢ䛧䠈䛭䛾ゎỴ䛻ྥ䛡䛶᥈✲䛧䠈ᡂᯝ➼䜢⾲⌧䛩䜛䛯䜑䛻ᚲせ䛺ᛮ⪃ຊ䞉ุ᩿ຊ䞉⾲⌧ຊ➼䛾⬟ຊ䛃䜢୰ᚰ䛻ホ౯ᐃ್䠄䡔䠅 䠙䜚䛯䛔≉ᛶ䜢཯ᫎ䛩䜛ᡂศ䠄䠿䠅䠇 ⣔⤫ㄗᕪ䠄䡏䠅 䠇 അ↛ㄗᕪ䠄e䠅
䡔 䛾ศᩓ
䠿 䛾ศᩓ

䛣䛾䛂 䜚䛯䛔≉ᛶ䛃䜢䠈䛔䛛䛻㧗䛔ጇᙜᛶ䛷䜛䛣䛸䛜䛷䛝䜛䛛䛜ၥ㢟䡏 䛾ศᩓ e 䛾ศᩓ䛣䛾๭ྜ䛜ጇᙜᛶ䠄validity䠅
34ጇᙜᛶ䜢㧗䜑䜛䛻䛿䕔䜚䛯䛔≉ᛶ䛾ಶேᕪ䜢཯ᫎ䛩䜛㡯┠䜢㛤Ⓨ䛩䜛౛䠖ᛮ⪃ຊ䞉ุ᩿ຊ䞉⾲⌧ຊ➼䕔䜚䛯䛔≉ᛶ௨እ䛾せᅉ䛻䜘䜛⣔⤫ㄗᕪ䜢ῶ䜙䛩౛䠖༢⣧䛺▱㆑䞉ᢏ⬟ධヨᑐ⟇ᐃ್䛾ಙ㢗ᛶ
ᐃ್䠄䡔䠅 䠙䜚䛯䛔≉ᛶ䜢཯ᫎ䛩䜛ᡂศ䠄䠿䠅┿್䠇 ⣔⤫ㄗᕪ䠄䡏䠅䠄t䠅䠇 അ↛ㄗᕪ䠄e䠅䕔 അ↛せᅉ䛻䜘䜛ㄗᕪ䜢ῶ䜙䛩౛䠖ㄡ䛜㠃᥋䜔ᑠㄽᩥ䛾ホ౯䛻䛒䛯䜛䛛㠃᥋䜔ᑠㄽᩥ䛷ఱ䜢ၥ䛖䛛䠿 䛾ศᩓ䡏 䛾ศᩓ e 䛾ศᩓ䛣䛾๭ྜ䛜ಙ㢗ᛶ䠄reliability䠅
6

5

－70－

ጇᙜᛶ䛸ಙ㢗ᛶ䛾㛵ಀ෌䜃䠈୰ᩍᑂ⟅⏦᱌䜘䜚ಙ㢗ᛶ䛜ప䛔䛸
ጇᙜᛶ䛿㧗䛟䛺䜙䛺䛔኱ᏛධᏛᕼᮃ⪅Ꮫຊホ౯䝔䝇䝖䠄௬⛠䠅䛷䛿䠈䞉㑅ᢤᛶ䛾㧗ప䛻䛛䛛䜟䜙䛪ከ䛟䛾኱Ꮫ䛷ά⏝䛷䛝䜛䜘䛖䠈ᗈ⠊ᅖ䛾㞴᫆ᗘ䛸䛩䜛䚹≉䛻䠈㑅ᢤᛶ䛾㧗䛔኱Ꮫ䛜ධᏛ⪅㑅ᢤ䛾ホ౯䛾୍㒊䛸䛧䛶༑ศά⏝䛷䛝䜛Ỉ‽䛾䠈㧗㞴ᗘ䛾ฟ㢟䜢ྵ䜐䜒䛾䛸䛩䜛䚹э 㧗䛔ጇᙜᛶ䛾䛯䜑䛻䛿䠈㧗䛔ಙ㢗ᛶ䛜ᚲせಙ㢗ᛶ䛾㧗䛥䛿䠈᝟ሗ㔞䜢䛒䜙䜟䛩э ᝟ሗ㔞䛜ᑡ䛺䛡䜜䜀ጇᙜ䛷䛒䜚䛘䛺䛔䛯䛰䛧䠈᝟ሗ㔞䛿ከ䛟䛶䜒䠈䛭䜜䛜▱䜚䛯䛔᝟ሗ䛷䛺䛔ྍ⬟ᛶ䜒䠄ጇᙜᛶ䛾ၥ㢟䠅

䞉䛂䠍Ⅼ้䜏䛃䛾ᐈほᛶ䛻䛸䜙䜟䜜䛯ホ౯䛛䜙⬺䛧䠈ྛ኱Ꮫ䛾ಶู㑅ᢤ䛻䛚䛡䜛ከᵝ䛺ホ౯᪉ἲ䛾ᑟධ䜢ಁ㐍䛩䜛ほⅬ䛛䜙䠈኱Ꮫཬ䜃኱ᏛධᏛᕼᮃ⪅䛻ᑐ䛧䛶䠈ẁ㝵ู⾲♧䛻䜘䜛ᡂ⦼ᥦ౪䜢⾜䛖䚹78

㞴᫆ᗘ䛾⠊ᅖ䛸᝟ሗ㔞㞴᫆ᗘ䛾⠊ᅖ䛸᝟ሗ㔞䛩䜉䛶୰⛬ᗘ䛾㞴᫆ᗘ䛾㡯┠䠄㯮䛾᭤⥺䠅䛾䜏䛾ሙྜ䛸䠈㞴᫆ᗘ䛜ᗈ⠊䛻䜟䛯䜛㡯┠䠄㉥䛾᭤⥺䠅䛾ሙྜ䠈᝟ሗ㔞䛿䛹䛖䛺䜛䛛㞴᫆ᗘ䛾⠊ᅖ䜢ᗈ䛢䜛䛣䛸䛻䜘䛳䛶䠈୰ኸ㒊ศ䛾᝟ሗ㔞䛿ῶᑡ䛩䜛䛜䠈≉ᛶ್䛾㧗䛔㒊ศ䛷䛾᝟ሗ㔞䛿䠈☜䛛䛻ቑ኱䛩䜛㡯┠཯ᛂ⌮ㄽ䠄Item
Response Theory;
IRT䠅䜢⏝䛔䛶ィ⟬

910㞴᫆ᗘ䛾⠊ᅖ䛸᝟ሗ㔞ẁ㝵ู⾲♧䛸᝟ሗ㔞୰⛬ᗘ䛾㞴᫆ᗘ䛾㡯┠100ಶ䛛䜙䛺䜛䝔䝇䝖䛻䛚䛔䛶䠈20Ⅼ้䜏䛷
5ẁ㝵⾲♧䜢⾜䛳䛯ሙྜ㞴᫆ᗘ䛜ᗈ⠊ᅖ䛾ሙྜ䛾᝟ሗ㔞䛿䠈୰⛬ᗘ䛾㞴᫆ᗘ䛾㡯┠䛾䜏䛾ሙྜ䛻ẚ䜉䠈≉ᛶ್䛾㧗䛔㒊ศ䛷䛿䠈2ಸ௨ୖ

5ẁ㝵⾲♧䛾ሙྜ䛾᝟ሗ㔞䛿䠈ẁ㝵䛾ቃ⏺௜㏆䛷䛿┦ᑐⓗ䛻኱䛝䛔
䛜䠈1Ⅼ้䜏䛾ሙྜ䜘䜚䛿䛛䛺䜚ᑠ䛥䛔

䛧䛯䛜䛳䛶䠈㑅ᢤᛶ䛾㧗䛔኱Ꮫ䛻䜒ᑐᛂ䛷䛝䛭䛖䛻䜏䛘䜛≉ᛶ್䛾㧗䛔㒊ศ䛷䛾᝟ሗ㔞䛿䠈䜋䜌䝊䝻
11

12

－71－

ẁ㝵ู⾲♧䛸᝟ሗ㔞ẁ㝵ู⾲♧䛸᝟ሗ㔞≉ᛶ್䛾㧗䛔㒊ศ䛾 ᐃ䛻↔Ⅼ䜢䛒䛶䜛䛯䜑䛻䠈㞴᫆ᗘ䛾㧗䛔㡯┠100ಶ䛛䜙䛺䜛䝔䝇䝖䛻䛚䛔䛶䠈
20Ⅼ้䜏䛷5ẁ㝵⾲♧䜢⾜䛳䛯ሙྜ5ẁ㝵⾲♧䛾ሙྜ䛾᝟ሗ
㔞䛾䠈1Ⅼ้䜏䛾ሙྜ䛾᝟ሗ㔞䛻ᑐ䛩䜛ẚ䠄┦ᑐຠ⋡䠅䛿䠈≉ᛶ್䛾ศᕸ䛾୰ኸ௜㏆䛷20䠂䡚
60䠂
э 5ẁ㝵⾲♧䛷ྠ⛬ᗘ䛾᝟ሗ㔞䜢ᚓ䜛䛻䛿䠈⣙
2䡚5ಸ䛾㡯┠ᩘ䛜ᚲせ≉ᛶ್䛾㧗䛔㒊ศ䛷䜒䠈᝟ሗ㔞䛿1Ⅼ้䜏䛾䛸䛝䛾
20䠂䡚60䠂⛬ᗘ㞴᫆ᗘ䛜ᗈ⠊ᅖ䛰䛸䛣䛾㒊ศ䛾᝟ሗ㔞䛿䜘䜚ᑡ䛺䛟䛺䜚䠈⤖ᒁ䠈ẁ㝵ู⾲♧䛷䛿䠈㑅ᢤᛶ䛾㧗䛔኱Ꮫ䜈䛾ᑐᛂ䛿↓⌮୧➃䛷䛿䠈┦ᑐຠ⋡䛿䜋
䜌0䠂

13

14

ᮾி኱Ꮫ➨2ḟᏛຊヨ㦂
ྜ᱁᭱పⅬ䠄2013ᖺᗘධヨ䠅㑅ᢤ䛾๓ẁ㝵䛷䛒䜛 ᐃ䠄䠙᝟ሗ䛾཰㞟䛸ᥦ౪䠅䛻䛚䛔䛶䠈ᚓⅬ䛭䛾䜒䛾䛷䛺䛟䠈ᚓⅬ䜢䛔䛟䛴䛛䛾ẁ㝵䛻ศ㢮䛧䛯ẁ㝵ู⾲♧䜢⾜䛖䛣䛸䛾
ၥ㢟

⛉ 㢮
ᩥ⛉୍㢮
ᩥ⛉஧㢮
ᩥ⛉୕㢮
⌮⛉୍㢮
⌮⛉஧㢮
⌮⛉୕㢮

䛱䛺䜏䛻ᮾி኱Ꮫ䛷䛿䠈䠍Ⅼ้䜏䛹䛣䜝䛛䞉䞉䞉

‶ Ⅼ
550
550
550
550
550
550

ྜ᱁᭱పⅬ
348.5333
342.5000
347.2111
315.7333
302.7333
370.38891516ᐃ䛾ẁ㝵䛻䛚䛔䛶
ẁ㝵ู⾲♧䜢⾜䛖䛣䛸䛾ၥ㢟Ⅼ(ii) ẁ㝵䜈䛾ศ䛡᪉䛾᜛ពᛶ䜰䝯䝸䜹䛻䛚䛡䜛䝭䝙䝬䝮䞉䝁䞁䝢䝔䞁䝅䞊䝔䝇䝖䠄ㄢ⛬ಟ஢䛾䛯䜑䛾᭱పᇶ‽䜢䜽䝸䜰䛧䛶䛔䜛䛛ྰ䛛䛾2ẁ㝵⾲♧䜢⾜䛖䝔䝇䝖䠅䛾⤒㦂(i) ᝟ሗ㔞䛾ῶᑡ䠄᪤㏙䠅(ii) ẁ㝵䜈䛾ศ䛡᪉䛾᜛ពᛶ௨ୗ䠈䛣䛾㡯䛾ᘬ⏝䛿䠈Jaeger, R. M. (1989). Certification of student competence. In R. L.
Linn (Ed.), Educational measurement (3rd ed.). New York:
Macmillan. pp. 485-514. (஭ୖಇဢ (ヂ) (1992). Ꮫ⏕䛾䝁䞁䝢䝔䞁䝇䛾ド᫂ ụ⏣ ኸ௚䠄┘ヂ) ᩍ⫱ ᐃᏛ ୗᕳ 䜏䛟䛻ฟ∧ pp.215-257.)
䜘䜚(iii) ẁ㝵ෆ䛷䛾ಶேᕪ䜔ಶேෆኚ໬䛾↓ど䛸ẁ㝵㛫䛷䛾ᕪ䜔ኚ໬䛾㄂ᙇ(iv) 㑅ᢤ䛻䛚䛡䜛㜼ᐖᐃ⤖ᯝ䛾ከᵝ䛺ά⏝䜢

17

18

－72－

ᇶ‽タᐃ䛻⏝䛔䜙䜜䛶䛔䜛᪉ἲ䛂ᇶ‽タᐃ䛻㛵䛩䜛ᩥ⊩䛜ఱ䛛Ỵᐃⓗ䛺Ⅼ䜢䜒䛳䛶䛔䜛䛸䛧䛯䜙䠈䛭䜜䛿䠈䝁䞁䝢䝔䞁䝅䞊䝔䝇䝖䛷䠈᧦ㆤ䛧䛖䜛ᇶ‽䜢タᐃ䛩䜛䛣䛸䛾ᅔ㞴䛥䛻䛴䛔䛶䛷䛒䜛䚹䛃䠄p.226䠅

Angoff䛾᪉ἲᇶ‽䜢᭱ప㝈䜽䝸䜰䛧䛶䛔䜛⏕ᚐ䜢᝿ᐃ䛧䠈䛭䛾⏕ᚐ䛜䠈䝔䝇䝖䛾ྛ㡯┠䛻ṇ⟅䛩䜛☜⋡䜢᥎ᐃ䛧䠈඲㡯┠䛻䜟䛯䜛ṇ⟅☜⋡䛾⥲࿴䜢ศ๭Ⅼ䛸䛩䜛䚹䛂Glass (1978) 䜒Shepard (1979, 1980) 䜒䠈䝁䞁䝢䝔䞁䝇䛿ᴫᛕୖ᏶඲䛺㐃⥆ኚᩘ䛷䛒䜛䛸ᣦ᦬䛩䜛䚹䛭䜜䛺䜙䜀䠈䝁䞁䝢䝔䞁䝇䜢䜒䛴䛛ྰ䛛䛷Ꮫ⏕䜢2䛴䛾ูಶ䛾䜹䝔䝂䝸䛻௬䛻ศ䛡䜛ศ๭Ⅼ䠄Cut-off
score䠅䜢タᐃ䛩䜛䛣䛸䛿䠈㠀⌧ᐇⓗ䛛䛴㠀ㄽ⌮ⓗ䛷䛒䜛䚹䛃䠄p.227䠅

Nedelsky䛾᪉ἲ䠄ከ⫥㑅ᢥᘧ㡯┠⏝䠅ᇶ‽䜢᭱ప㝈䜽䝸䜰䛧䛶䛔䜛⏕ᚐ䜢᝿ᐃ䛧䠈䛭䛾⏕ᚐ䛜䠈䝔䝇䝖䛾ྛ㡯┠䛾㑅ᢥ⫥䛾䛖䛱䠈ㄗ⟅㑅ᢥ⫥䛷䛒䜛䛸ᣦ᦬䛷䛝䛺䛡䜜䜀䛺䜙䛺䛔䜒䛾䜢㑅䜆䚹䛭䛧䛶䠈䛭䜜䜙䛾⏕ᚐ䛿䠈ṧ䜚䛾㑅ᢥ⫥䛾୰䛛䜙䝷䞁䝎䝮䛻ゎ⟅䜢㑅䜆䛣䛸䜢௬ᐃ䛧䛶䠈ྛ㡯┠䛻ṇ⟅䛩䜛☜⋡䜢᥎ᐃ䛧䠈඲㡯┠䛻䜟䛯䜛ṇ⟅☜⋡䛾⥲࿴䜢ศ๭Ⅼ䛸䛩䜛䚹
1920

ᇶ‽タᐃ䛻⏝䛔䜙䜜䛶䛔䜛᪉ἲ䠄⥆䠅౑⏝䛩䜛᪉ἲ䛻䜘䜛⤖ᯝ䛾㐪䛔ቃ⏺⥺䜾䝹䞊䝥ἲᙜヱ䛾䝔䝇䝖䛸䛿⊂❧䛾᝟ሗ䛻䜘䛳䛶䠈ศ๭䛾ቃ⏺⥺䛻䛔䜛⏕ᚐ䛯䛱䜢㑅䜃䠈ᙼ䜙䛻ᙜヱ䛾䝔䝇䝖䜢ཷ䛡䛶䜒䜙䛖䚹䛭䛾ᚓⅬ䛾ศᕸ䛾୰ኸ್䜢ศ๭Ⅼ䛸䛩䜛䚹ᴟ➃䛺ሙྜ䠈᪉ἲ䛾㐪䛔䛷䠈䛂୙ྜ᱁⋡䛃䛜
⣙30ಸ㐪䛳䛯䚹

䜾䝹䞊䝥ᑐ↷ἲᙜヱ䛾䝔䝇䝖䛸䛿⊂❧䛾᝟ሗ䛻䜘䛳䛶䠈ᇶ‽䜢䜽䝸䜰䛧䛶䛔䜛⏕ᚐ䛯䛱䛸䠈䜽䝸䜰䛧䛶䛔䛺䛔⏕ᚐ䛯䛱䜢㑅䜃䠈ᙼ䜙䛻ᙜヱ䛾䝔䝇䝖䜢ཷ䛡䛶䜒䜙䛖䚹䛭䛾2䜾䝹䞊䝥䛾
ᚓⅬศᕸ䜢⏝䛔䛶䠈䛯䛸䛘䜀┦ᑐᗘᩘศᕸ䛜ྠ䛨䛻䛺䜛ᚓⅬ䜢ศ๭Ⅼ䛸䛩䜛䚹
21

22(iii) ẁ㝵ෆ䛷䛾ಶேᕪ䜔ಶேෆኚ໬䛾↓ど䛸ẁ㝵㛫䛷䛾ᕪ䜔ኚ໬䛾㄂ᙇᑡ䛺䛔ẁ㝵ᩘ䛷䠈ูẁ㝵䛻ホᐃ䛥䜜䜛䛸䠈ᐇ㝿䛻䛿䜟䛪䛛䛺ᕪ␗䛷䜒䠈䛭䜜䛜ᣑ኱䛥䜜䠈ྜྰุᐃ䛻Ỵᐃⓗ䛺ᙳ㡪䚹ẁ㝵ู⾲♧䛷䛿䠈ྠ୍ẁ㝵ෆ䛿ᇶᮏⓗ䛻➼㉁䛺䜒䛾䛸䛥䜜䜛䚹䛧䛛䛧䠈ᐇែ䛿䠈ྠ୍ẁ㝵ෆ䛻኱䛝䛺ಶேᕪ䛜䛒䜛䚹ẁ㝵䛾ቃ⏺௜㏆䛻䛔䜛⪅䛿䠈Ꮫຊ䛭䛾䜒䛾䛻ኚ໬䛜䛺䛟䛶䜒䠈ㄗᕪኚື䛰䛡䛷ୖ䜎䛯䛿ୗ䛾ẁ㝵䛻⛣⾜䚹䛒䜛ẁ㝵䛾୰ᚰ௜㏆䛻఩⨨䛩䜛⪅䛿䠈䛛䛺䜚䛾ྥୖ䛜䛒䛳䛶䜒ྠ䛨ẁ㝵䛻䛸䛹䜎䜚䠈ດຊ䛜⤖ᯝ䛻཯ᫎ䛥䜜䛺䛔䚹

䠍Ⅼ้䜏䛾ᑠ䛥䛺ᕪ䛻䛿ᐇ㉁ⓗ䛺ព࿡䛿䛺䛔䛜䠈䛭䛾䛣䛸䛿஢ゎ䛥䜜䜔䛩䛔䚹ẁ㝵䛻䛺䜛䛸䠈ᐇ㉁ⓗ䛺ព࿡䛾䛺䛔ቃ⏺௜㏆䛾ᕪ䛜ᐇ㉁ⓗ䛺ព࿡䜢䜒䛴䛛䛾䜘䛖䛻ㄗゎ䛥䜜䜛䚹

䠿䡂. 䝎䜲䜶䝑䝖䛸య㔜 ᐃ䛾౛ẁ㝵䛻䜘䜛୙ᙜ䛺ᕪู໬䛸䛭䛾ᅛᐃ໬䜒ᠱᛕ䛥䜜
䜛䚹
23

24

－73－

(iv) 㑅ᢤ䛻䛚䛡䜛 ᐃ⤖ᯝ䛾ከᵝ䛺ά⏝䜢㜼ᐖẁ㝵ู⾲♧䛜᥎ዡ䛥䜜䜛౛䛂䜒䛧䠈ᑠㄽᩥ䛾᥇Ⅼ⤖ᯝ䜢㑅ᢤ䛻౑䛖䛸䛧䛯䜙䠈䛫䛔䛬䛔䠑ẁ㝵⛬ᗘ䛾ศ㢮䛻䛩䜉䛝䛷䛒䛳䛶䠈௚䛾ಙ㢗ᛶ䛾㧗䛔䝔䝇䝖䛾ᚓⅬ䛸ྠ䛨䜘䛖䛻䠈䛯䛸䛘䜀䠍䠌䠌Ⅼ‶Ⅼ䛷ᑠㄽᩥ䜢ホ౯䛧䛶䠈䛭䛾⤖ᯝ䜢䛭䛾䜎䜎㑅ᢤ䛻฼⏝䛩䜛䛺䛹䛿䠈䛛䛺䜚༴㝤䛺䛣䛸䛷䛒䜛䛸䛔䜟䛦䜛䜢䛘䛺䛔䛾䛷䛒䜛䚹䛃䠄ᰘᒣ┤䠈䛂᪥ᮏ䛾䝔䝇䝖ᩥ໬䛻䛴䛔䛶䛃䠈䛄ே஦ヨ㦂◊✲䛅No.206䠈2008ᖺ9᭶䠅䝔䝇䝖䛾ᚓⅬ䛻㔜䜏䜢䛴䛡䛶௚䛾ከᵝ䛺᝟ሗ䛸⤌䜏ྜ䜟䛫䛯䜚䠈኱Ꮫ⊂⮬䛾ᇶ‽䛷ẁ㝵ศ䛡䛧䛯䜚䛩䜛䛺䛹䠈 ᐃ⤖ᯝ䜢ከᵝ䛻ά⏝䛩䜛䛖䛘䛷䠈 ᐃ䛾ẁ㝵䛷䛾䠈ึ䜑䛛䜙䛾ẁ㝵ู⾲♧䛿㞀ᐖ䛸䛺䜛䚹

25

26

኱Ꮫධヨ䛾᪥ᮏⓗ㢼ᅵᑠ䜎䛸䜑୰ᩍᑂ⟅⏦䜢ྵ䜑䠈ධヨ㑅ᢤ䛻㛵䛩䜛ㅖᥦ᱌䛻䛴䛔䛶䛿䠈 ᐃㄽ䛾ほⅬ䛛䜙䛾ྫྷ࿡䛜ᚲせྛ኱Ꮫ䛜䠈⊂⮬䛻䠈⭾኱䛺᫬㛫䞉ே䞉䝁䝇䝖䜢䛛䛡䛶䠈ಶูᏛຊヨ㦂䜢సᡂ䞉᥇Ⅼ኱Ꮫ䛤䛸䛾䛂㉥ᮏ䛃䛜᭩ᗑ䛻୪䜃䠈䝧䝇䝖䝉䝷䞊䛻⌮᝿䜢ᐇ⌧䛩䜛䛯䜑䛾 ᐃ⌮ㄽ䜔ᢏ⾡䛾㛤Ⓨ䛜ồ䜑䜙䜜䜛䛸ྠ᫬䛻䠈㛤Ⓨ䛻ᚲせ䛺᫬㛫➼䜢຺᱌䛧䛶䠈ᐇ⾜ྍ⬟ᛶ䜢ุ᩿䛩䜛䛣䛸䜒ᚲせ᪂䝔䝇䝖䛻䜘䛳䛶䠈䛭䛾㢼ᅵ䛜ኚ䛘䜙䜜䜛䛛䠛

27

28

－74－

࠙ሗ࿌㸱ࠚ

ࢭࣥࢱ࣮ヨ㦂࡛ఱࡀࡽࢀࡿࡢ࠿㸽

⊂❧⾜ᨻἲே኱Ꮫධヨࢭࣥࢱ࣮◊✲㛤Ⓨ㒊㛗

኱ὠ㉳ኵ
‫ྖۑ‬఍

෌㛤࠸ࡓࡋࡲࡍࠋࡑࢀ࡛ࡣࠊ኱Ꮫධヨࢭࣥࢱ࣮࠿ࡽ◊✲㛤Ⓨ㒊㛗ࡢ኱ὠ㉳ኵࡼࡾࠕࢭࣥࢱ࣮ヨ㦂࡛ఱࡀࡽࢀࡿࡢ࠿㸽ࠖ࡜㢟ࡋࡲࡋ࡚ࠊ௒ࠊ୰ᩍᑂࡢ㆟ㄽࡢ୰࡛ࡶࢭࣥࢱ࣮ヨ㦂ࡣ▱㆑ࡋ࡚ࡗ࡚࠸࡞࠸࡜࠸࠺ࠊఱ࠿

ᑡࡋ೫ࡗࡓぢ᪉ࡶ࠶ࡿࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺༳㇟ࡶ࠶ࡾࡲ
ࡍࡢ࡛ࠊࡑࡢ㎶ࡶྵࡵ࡚ࡈሗ࿌ࢆ࠾㢪࠸ࡋࡲࡍࠋ
‫ۑ‬኱ὠẶ

኱Ꮫධヨࢭࣥࢱ࣮ࡢ኱ὠ࡛ࡍࠋࡼࢁࡋࡃ࠾㢪࠸ࡋࡲࡍࠋࢱ࢖ࢺࣝࡣࠕࢭࣥࢱ࣮ヨ㦂࡛ఱࡀࡽࢀࡿࡢ࠿㸽ࠖ࡜ࡕࡻࡗ࡜኱ࡁࡃฟࡓࢇ࡛ࡍࡀࠊ௒᪥ࡣࠊࢭࣥࢱ࣮ヨ㦂ࡢᐇ᝟ࡣ࡝ࡢࡼ࠺࡞ࡶࡢ࠿࡜࠸࠺ࡇ
࡜࡟ࡘ࠸࡚ሗ࿌ࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
⌮᝿ⓗ࡟ࡣࠊே㛫ࡢ▱ᛶ࡜࠸࠺ࡢࡣ࡝ࡢࡼ࠺࡞ᵓ㐀ࢆᣢࡗ࡚࠸࡚ࠊࡑࡇࡢ࡝࠺࠸࠺ᶵ⬟ࢆࢁ࠺࡜ࡋ࡚࡝࠺࠸࠺ࡩ࠺࡟ヨ㦂ࡀタィࡉࢀ࡚࠸ࡿ࠿࡜࠸ࡗࡓࣞ࣋ࣝࡲ࡛ࡢヰࡀ࡛ࡁࢀࡤ࠸࠸࡜ࡣᛮ࠺ࢇ࡛ࡍࡀࠊࡑࡇࡲ࡛ࡣࡼࡃࢃ࠿ࡽ࡞࠸ࡢ࡛ࠊእ࠿ࡽぢࡓ࡜࠸࠺࠿ࠊ⤫ィⓗ࡞≉ᚩࡢヰ࡟㝈ᐃࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
ࢭࣥࢱ࣮ヨ㦂ࡢཷ㦂⋡ࡀ㐣ཤ࡝ࡢࡼ࠺࡟ኚࢃࡗ࡚ࡁࡓ࠿ࠊ⚾❧ࡢ࡯࠺ࡣฟ㢪ࡋࡓࡇ࡜ࡣ
ࢃ࠿ࡿࢇ࡛ࡍࡀྜྰࡣࢃ࠿ࡾࡲࡏࢇࡢ࡛ࠊᅜබ❧኱ࡢྜྰ࡜ࢭࣥࢱ࣮ヨ㦂ࡢᚓⅬࡀ࡝ࡢࡼ࠺࡟࡞ࡗ࡚࠸ࡿ࠿࡜࠸࠺≧ἣ࡟ࡘ࠸࡚ሗ࿌ࡋࡲࡍࠋࡑࢀ࠿ࡽࠊඛ࡯࡝࠿ࡽ๓ᕝඛ⏕ࡢヰ࡟ࡶ࠶ࡗࡓࡼ࠺࡟ࠊẖᖺ⤖ᵓⅬᩘࡀኚࢃࡿࢃࡅ࡛ࡍࠋඹ㏻ᑻᗘ࡜ࡋ࡚౑࠺ࡇ࡜ࡣ࡞࠿࡞࠿㞴ࡋ࠸ࠋࡑࢀ࡛ࠊࡑࢀࡒࢀࡢ⛉┠ࡢᖹᆒⅬࡀ࡝ࡢࡼ࠺࡟᥎⛣ࡋ࡚࠸ࡿ࠿࡟ࡘ࠸࡚⤂௓ࡋࡲࡍࠋ
࠶࡜ࡣࡕࡻࡗ࡜⣽࠿࠸ヰ࡟࡞ࡿࢇ࡛ࡍࡀࠊࢭࣥࢱ࣮ヨ㦂ࡢ⛉┠ࡢⅬᩘࡣࠊ1 ேࡀ 1 ࡘࡔࡅཷࡅࡿࢃࡅ࡛ࡣ࠶ࡾࡲࡏࢇ࠿ࡽࠊ௚ࡢ」ᩘࡢ⛉┠ࡢ┦㛵ࢆィ⟬ࡍࡿࡇ࡜ࡀ࡛ࡁࡲࡍࠋࡑࢀ
ࡀ඲య࡜ࡋ࡚࡝࠺࠸࠺ᵓ㐀࡟࡞ࡗ࡚࠸ࡿ࠿ࠊ࡝࠺࠸࠺⛉┠࡜࡝࠺࠸࠺⛉┠ࡢ㛵ಀࡀ῝ࡃ࡚ࠊ࡝࠺࠸࠺࡜ࡇࢁࡀูࡢືࡁࢆࡋ࡚࠸ࡿ࠿࡜࠸࠺඲యീࢆ⤂௓ࡋࡲࡍࠋ
࠶࡜ࠊ⡆༢࡟⛉┠㑅ᢥᩘࡢᆅᇦᕪࡢヰࠊࡑࢀ࠿ࡽⱥㄒ⛉┠ᚓⅬ࡜୰ᩍᑂࡸⱥㄒᩍ⫱࡟㛵ࡍࡿ᭷㆑⪅఍㆟࡛ヰ㢟࡟࡞ࡗ࡚࠸ࡲࡍ㈨᱁᳨ᐃヨ㦂࡜ࡢ㛵ಀ࡛ࠊࢃࡎ࠿࡛ࡍࡅࢀ࡝ࡶࢹ࣮
ࢱࢆ࡜ࡗ࡚࠶ࡾࡲࡍࡢ࡛ࠊࡑࡢ⤂௓ࢆࡋࡼ࠺࡜ᛮ࠸ࡲࡍࠋ
ࢭࣥࢱ࣮ヨ㦂ࡢཷ㦂⋡ࡢኚ໬ࡲࡎࠊࢭࣥࢱ࣮ヨ㦂ࡢཷ㦂⋡ࡢኚ໬࡟ࡘ࠸࡚࡛ࡍࠋ
ඹ㏻ 1 ḟヨ㦂ࡣ 1979 ᖺؐ᫛࿴ 54 ᖺ࡟ࢫࢱ࣮ࢺࡋ࡚࠸ࡲࡍࡅࢀ࡝ࡶࠊࢭࣥࢱ࣮ヨ㦂ࡣᖹ
ᡂ 2 ᖺؐ 1990 ᖺ࠿ࡽ࡛ࡍࠋࡇࡢ➨ 1 ᅇࡢ࡜ࡁࠊ⌧ᙺ㧗ᰯ⏕ࡢཷ㦂⪅ࡣ኱య 25 ୓ே࡛ࡍࠋ
ࡑࢀ࠿ࡽᾉே➼ࡢᏛ⏕ࡀ 15 ୓ 6,000 ேࡄࡽ࠸ࠋࡇࡢ࡜ࡁࡢ᪥ᮏ඲యࡢ㧗ᰯ 3 ᖺ⏕ࡢ࠺ࡕࠊ－75－

ࢭࣥࢱ࣮ヨ㦂ࢆཷࡅࡓࡢࡣ 14 㸣࡟ࡍࡂࡲࡏࢇࠋ࡜ࡇࢁࡀ௒⌧ᅾࠊᖹᡂ 26 ᖺؐ௒ᖺ 1 ᭶࡛ࡍࠋ⌧ᙺ⏕࡛ཷࡅࡓேࡣ 41 ୓ 7,000 ேࠊ42 ୓ே㏆࠸ࢃࡅ࡛ࡍࠋ୍᪉ࠊᾉேࡣࡎࡗ࡜ῶࡗ
࡚኱య 11 ୓ேࠋ⌧ᙺࡢཷ㦂⋡ࡣ⌧ᅾ࡛ࡣ 40 㸣ࢆ㉸࠼࡚࠸ࡲࡍࠋ࡛ࡍ࠿ࡽࡇࡢ஧༑ᩘᖺࠊ
ᅄ༙ୡ⣖ࡢ㛫࡟≧ἣࡀ኱ࡁࡃኚࢃࡗࡓࠋࢭࣥࢱ࣮ヨ㦂࡜࠸࠺ࡶࡢࡀ࠿࡞ࡾ࣐࢖ࣀࣜࢸ࢕ࠊ
≉Ṧ࡞୍㒊ࡢᏛ⏕ࡉࢇࡀཷࡅࡿࡶࡢ࠿ࡽࠊ༙ᩘ࡟ࡣ࠸ࡁࡲࡏࢇࡅࢀ࡝ࡶࠊ㧗ᰯ⏕ࡢ 4 ๭ࡀཷࡅࡿ࣏ࣆ࣮ࣗࣛ࡞ヨ㦂࡟ࡔࢇࡔࢇኚ໬ࡋ࡚ࡁࡓ࡜࠸࠺ࡇ࡜࡛ࡍࠋ≉ ࡟ཷ㦂 ⪅ᩘࡀ ቑຍࡋ ࡓࡢ ࡣࠊ1990 ᖺ௦ ࡛ࡍࠋࡔ࠿ࡽᖹᡂ 1 ᱆ࡢ࡜ࡁ࡟ཷ㦂⪅ᩘࡀ࡝ࢇ࡝ࢇୖࡀࡗ࡚ࡁࡲࡋࡓࠋᖹᡂ 10 ᖺࢆ㐣ࡂࡿ࡜ᩘ࡜ࡋ࡚ࡣᏳᐃࡋ࡚࠾ࡾࡲࡍࠋࡇ ࢀ ࡣ ෆ ⏣ ࡉ ࢇ ࡀ ධ ◊ ༠ ࢭ ࣑ ࢼ ࣮ ࡢ ࡓ ࡵ ࡟ ࡘ ࡃ ࡗ ࡓ ㈨ ᩱ ࡛ ࡍ ࡅ ࢀ ࡝ ࡶ ࠊ 18 ṓ ே ཱྀ ࡣ ࡝ࢇ ࡝ ࢇ ῶ ࡗ ࡚ ࡁ ࡚࠸ ࡿ ࢃ ࡅ ࡛ ࡍ㸦 ࢫ ࣛ ࢖ ࢻ 4㸧ࠋ ࡜ ࡇ ࢁࡀ ࢭ ࣥ ࢱ ࣮ ヨ㦂 ࡢ ཷ 㦂 ⪅ᩘ ࡣ ࠊ ࡇࡢࡼ࠺࡟ᖹᡂ 1 ᱆ࡢ࡜ࡁ࡟ࡎࡗ࡜ᩘࡣୖ᪼ࡋ࡚ࠊࡑࢀ௨㝆ࡣᩘ࡜ࡋ࡚ࡣ୍ᐃࡋ࡚࠸ࡿࢇ࡛
ࡍࡡࠋ࡜ࡇࢁࡀࠊ㧗ᰯ⏕ࡢ࡯࠺ࡣ࡝ࢇ࡝ࢇῶࡗ࡚࠸ࡲࡍ࠿ࡽࠊཷ㦂⋡ࡣ┦ᑐⓗ࡟ୖࡀࡗ࡚
ࡁ࡚࠸ࡿࠊࡑ࠺࠸࠺≧ἣ࡛ࡍࠋࡔ࠿ࡽᖺ௦࡟ࡶࡼࡿ࡜ᛮ࠸ࡲࡍࡀࠊ⚾௨ୖࡄࡽ࠸ࡢேࡓࡕ
ࡀ᝿ᐃࡋ࡚࠸ࡓࢭࣥࢱ࣮ヨ㦂࠶ࡿ࠸ࡣඹ㏻ 1 ḟヨ㦂࡜࠸࠺ࡶࡢ࡜⌧ᅾࡢࢭࣥࢱ࣮ヨ㦂ࡔ࡜ࠊࡑࡢࢱ࣮ࢤࢵࢺ࡜ࡍࡿᏛ⏕ࡉࢇࡢᒙࡀ඲↛㐪ࡗ࡚࠸ࡿ࡜ゝ࠼ࡿ࠿࡜ᛮ࠸ࡲࡍࠋ
⛉┠ᖹᆒࡢᖺ᥎⛣ḟ࡟ࠊ⛉┠ᖹᆒࡢᖺ᥎⛣࡟ࡘ࠸࡚࡛ࡍࠋ
ࢭࣥࢱ࣮ヨ㦂࡜࠸࠺ࡢࡣᇶᮏⓗ࡟⛉┠ᚓⅬࡢᖹᆒࡀ኱య 6 ๭࡛࠶ࡿࡼ࠺࡟ࡋࡼ࠺࡜ࡋ࡚
࠸ࡿࢃࡅ࡛ࡍࠋ࡞ࡐ 6 ๭࡞ࡢ࠿࡜࠸࠺ヰࡣฟ࡚ࡃࡿ࡜ᛮ࠸ࡲࡍࡀࠊᇶᮏⓗ࡟ࠊ࡯࡜ࢇ࡝ࡢ
ሙྜ 5 ᢥ࠶ࡿ࠸ࡣ 4 ᢥࡢከ⫥㑅ᢥᆺࡢฟ㢟ࢆࡋ࡚࠸ࡲࡍࠋࡑࢀࡀ㠀ᖖ࡟ᢈุࢆᾎࡧࡓࡾࡍ
ࡿࡇ࡜ࡶከ࠸ࢃࡅ࡛ࡍࠋከ⫥㑅ᢥࡢၥ㢟ࡢ㝈⏺࡜࠸࠺࠿ࠊḞⅬ࡜ࡋ࡚ࡣࠊせࡍࡿ࡟ࠊᴟ➃࡟㞴ࡋ࠸タၥࡣฟࡏࡲࡏࢇࠋฟࡏ࡞࠸࡜࠸࠺ࡢࡣࠊฟࡍࡇ࡜ࡣ࡛ࡁࡿࢇ࡛ࡍࡀࠊヨ㦂࡜ࡋ࡚వࡾࡼࡃ࡞࠸ࠋせࡍࡿ࡟ࠊඛ࡯࡝๓ᕝඛ⏕ࡢ࠾ヰ࡟ࡶ࠶ࡾࡲࡋࡓࠊ

ᐃࡢࢶ࣮ࣝ࡜ࡋ࡚

ࡼࡃ࡞࠸࡜࠸࠺ࡇ࡜࡛ࡍࠋࡑࢀࡣ࡞ࡐ࠿࡜࠸࠺࡜ࠊ㠀ᖖ࡟㞴ࡋ࠸ၥ㢟ࠊࢱ࣮ࢤࢵࢺ࡜ࡍࡿ
Ꮫ⏕ࡉࢇ࡟࡜ࡗ࡚㠀ᖖ࡟㞴ࡋ࠸ၥ㢟ࢆฟࡍ࡜ࠊࡑࡢၥ㢟࡟ṇ⟅ࡋࡓேࡢ࡯࡜ࢇ࡝ࡀࡲࡄࢀ
ᙜࡓࡾ࡟ࡼࡗ࡚ṇ⟅ࡋࡓே࡟࡞ࡗ࡚ࡋࡲ࠺ࠋࡑ࠺ࡍࡿ࡜ࠊ

ᐃࡢᑻᗘ࡜ࡋ࡚ぢࡓ࡜ࡁ࡟ࠊ

࡯࡜ࢇ࡝ࣀ࢖ࢬ࡜ࡋ࡚ࡋ࠿ᶵ⬟ࡋ࡞࠸ࠋࡔ࠿ࡽࠊ࠶ࡿ⛬ᗘࡢேࡀ࡛ࡁࡿၥ㢟ࢆ㞟ࡵ࡞࠸࡜ࠊ
࡞࠿࡞࠿࠸࠸ヨ㦂࡟࡞ࡽ࡞࠸ࢃࡅ࡛ࡍࠋ
ࢭࣥࢱ࣮ヨ㦂ࡢሙྜࡣࠊࡓࡔࡘࡃࡾࡓ࠸ேࡀࡘࡃࡾࡓ࠸ࡼ࠺࡟ࡘࡃࡗ࡚࠸ࡿࢃࡅ࡛ࡣ࡞
ࡃ࡚ࠊࣞࣅ࣮ࣗࡢጤဨ఍ࡶ࠶ࡾࡲࡍࡋࠊ」ᩘࡢጤဨ఍ࡢࢳ࢙ࢵࢡࢆཷࡅ࡚࠸ࡿࢃࡅ࡛ࡍࠋ
㧗ᰯ㛵ಀ⪅ࡢࢳ࢙ࢵࢡࡶཷࡅ࡚࠸ࡲࡍࠋ࡜ࡣ࠸࠼ึฟၥ㢟ࠊせࡍࡿ࡟ࣉࣜࢸࢫࢺ࡞ࡋ࡛ᮇ
ᚅ࡝࠾ࡾࡢ⛉┠ᖹᆒⅬࢆᚓࡿࡢࡣ⌧ᐇ࡟ࡣ㠀ᖖ࡟㞴ࡋ࠸࡜࠸࠺ࡇ࡜ࡣࠊ⤒㦂ⓗ࡟ࡣࢃ࠿ࡾ
ࡲࡍࠋ≉࡟⌮⣔ࡢ⛉┠࡛ᖹᆒⅬࡢኚືࡀ㠀ᖖ࡟኱ࡁ࠸࡜࠸࠺ࡇ࡜ࡀ࠶ࡾࡲࡍࠋࡇ ࢀ ࡣ ᩘ Ꮫ ࡢ ౛࡛ ࡍ 㸦 ࢫ ࣛ ࢖ࢻ 8㸧ࠋ ࡇ ࢀࡣ ௒ ࡢ ࢝ ࣜ࢟ ࣗ ࣛ ࣒ ࡛ ࡍࡡ ࠋ ᮶ ᖺ ࠿ࡽ ࢝ ࣜ ࣒࢟ࣗࣛࡀኚࢃࡾࡲࡍࡀࠋ2006 ᖺ࠿ࡽ 2014 ᖺࡲ࡛ྠ୍ࡢ࣒࡛࢝ࣜ࢟ࣗࣛࠊࡇࢀࡀᩘᏛϨ㸿࡛ࡍࠋᩍ⛉ࡢ୰୍࡛␒࣏ࣆ࣮ࣗࣛ࡞⛉┠࡛ࡍࡅࢀ࡝ࡶࠊࡇࢇ࡞࡟ኚࢃࡿࢇ࡛ࡍࡡࠊᖹᆒⅬࡀ ࠋ 2013 ᖺ ؐ ཤ ᖺ ࡣ ࠿ ࡞ ࡾ 㞴 ࡋ ࠿ ࡗ ࡓ ࠋ ௒ ᖺ ࡣ ࡑ ࢀ ࡼ ࡾ ࡣ ᑡ ࡋ ࡸ ࡉ ࡋ ࡃ ࡞ ࡗ ࡚ ࠸ ࡲ ࡍ ࠋࡇࢀࡄࡽ࠸ኚࢃࡗ࡚ࡋࡲ࠺ࠋ
౛࠼ࡤ 2010 ᖺᗘ࡟ཷࡅࡓ㧗ᰯ⏕ࡢᏛຊࡀప࠿ࡗࡓࡢ࠿࡜ゝࢃࢀࡿ࡜ࠊࡑ࠺࠸࠺ࡇ࡜ࡣ
ከศ࡞࠸ࡢ࡛ࠊࡸࡣࡾၥ㢟ࡢ㞴ࡋࡉࡀࡇࢀࡔࡅኚືࡋ࡚࠸ࡿ࡜⪃࠼ࡊࡿࢆᚓࡲࡏࢇࠋ

－76－ᩘϩ B ࡢ࡯࠺ࡣࡲࡔᑡࡋᏳᐃࡋ࡚࠸ࡲࡍࠋప࠸࡜ࡇࢁ࡛Ᏻᐃࡋ࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊ
ࡇࢀࡄࡽ࠸ࠋࡑࢀ࡛ࡶࡸࡣࡾ⤖ᵓኚࢃࡿࠋ
ࡶ࠺୍ࡘࠊࡇࢀࡣ⌮⛉࡛ࡍ㸦ࢫࣛ࢖ࢻ 9㸧ࠋP ࡣ≀⌮ I ࡛ C ࡀ໬ᏛϨࠊB ࡀ⏕≀Ϩ࡛ E ࡀᆅᏛϨ࡛ࡍࡡࠋࡇࢀࡶࠊ౛࠼ࡤ໬Ꮫࢆぢ࡚ࡶࡽ࠸ࡲࡍ࡜ࠊࡇ࠺ୗࡀࡗ࡚ୖࡀࡗ࡚࢞ࣥ࡜ⴠ
ࡕ࡚ࡲࡓୖࡀࡗ࡚ࠊ୍ᗘⴠࡕ࡚ࡲࡓୖࡀࡿࠋᆅᏛࡶࡇࡢࡼ࠺࡟ࠊࡇࡇࡀୗࡀࡗ࡚ࡑࡢḟ࡟ୖࡀࡗ࡚ࠊࡇࢀࡄࡽ࠸࡛᥎⛣ࡋ࡚௒ᖺࡣ࠿࡞ࡾ㞴ࡋ࠿ࡗࡓ࡜࠸࠺ࡼ࠺࡞ࡇ࡜ࡀࢃ࠿ࡾࡲࡍࠋ
࡝࠺ࡋ࡚ࡶኚࢃࡗ࡚ࡋࡲ࠺ࠋ
ᩘᏛࡶ⌮⛉ࡶࠊ࠸ࡎࢀࡶࡣࡗࡁࡾ࡜ࡋࡓ኱ၥᙧᘧ࡛ࡍࠋせࡍࡿ࡟ࠊ1 ࡘࡢࢸ࣮࣐ࡀ࠶ࡗ
࡚ࡑࢀ࡟ࡘ࠸࡚ᗄࡘ࠿ࡢᑠၥࡀࡘ࠸࡚࠸ࡿ࡜࠸࠺ᙧ࡛ࡍࠋࡑࡢᑠၥࡢ㛫ࡢ㛵㐃ᛶࡀ㧗࠸ࠋࡑࢀࡀࡓࡵ࡟ࠊ1 ࡘ㛫㐪࠼ࡿ࡜௚ࡢၥ㢟ࡶ㛵㐃ࡋ࡚㛫㐪࠼࡚ࡋࡲ࠺ྍ⬟ᛶࡀ࠿࡞ࡾ㧗࠸ࡢ࡛ࠊవィࠊၥ㢟ࡢ㞴ࡋࡉࢆ࠺ࡲࡃࢥࣥࢺ࣮ࣟࣝࡍࡿࡇ࡜ࡀ⤖ᵓ㞴ࡋࡃ࡞ࡗ࡚࠸ࡿࡢࡀᐇ᝟
࡛ࡍࠋࡇࢀࡶ」ᩘࡢඛ⏕᪉࡟ 6 ๭࡜ࢀࡿࡼ࠺࡟஦๓࡟ៅ㔜࡟⪃࠼࡚࠸ࡓࡔ࠸࡚ࡣ࠸ࡿࢇ࡛
ࡍࡀࠊࡑࢀ࡛ࡶࡸࡣࡾ㞴ࡋ࠸ࠊࡇࢀࡀᐇ᝟࡛ࡍࠋ
⛉┠ࡈ࡜ࡢྜ᱁⪅࡜୙ྜ᱁⪅ࡢᖹᆒḟ࡟ࠊྜྰ࡟㛵ࡍࡿ᝟ሗࢆ⤂௓ࡋࡲࡍࠋ
Ⅼࡀࡓࡃࡉࢇ࠶ࡗ࡚ぢ࡙ࡽ࠸ࢇ࡛ࡍࡀࠊࡇࢀࡽࡢⅬࡀ 2013 ᖺؐཤᖺࡢ๓ᮇ᪥⛬ࡢᏛ㒊࡜࠿ࢥ࣮ࢫ࡛ࡍ ࢫࣛ࢖ࢻ 10 ࠋࡇࢀ 1 ࡘࡀ 1 ࡘࡢ㑅ᢤࡢ༢఩ࢆ࠶ࡽࢃࡋ࡚࠸ࡲࡍࠋᶓ㍈ࡀྜ᱁⪅ࡢᖹᆒⅬࠊࡇࡢሙྜࡣⱥㄒ➹グヨ㦂ࡢྜ᱁⪅ࡢᖹᆒⅬࠋ⦪㍈ࡀ୙ྜ᱁⪅ࡢᖹᆒⅬ࡛ࡍࠋᙜ↛࡛ࡍࡅࢀ࡝ࡶࠊྜ᱁⪅ࡢ࡯࠺ࡀ୙ྜ᱁⪅ࡼࡾᡂ⦼ࡀ࠸࠸ࢃࡅ࡛ࡍ࠿ࡽࠊྜ᱁⪅ࡢᖹᆒࡢ࡯࠺ࡀ㧗࠸࡛ࡍࠋ190 Ⅼࡄࡽ࠸࡜ࡽ࡞࠸࡜ྜ᱁ࡋ࡞࠸ࡼ࠺࡞࡜ࡇࢁࡶ࠶ࡾࡲࡍࡀࠊࡇ
ࡇࡽ㎶㸦ྜ᱁⪅ᖹᆒ 150 Ⅼࡃࡽ࠸ࡢ࡜ࡇࢁ㸧୍࡛㈏ࡋ࡚ྜ᱁⪅ࡢ࡯࠺ࡀ 10 Ⅼ࠿ 20 Ⅼࡄࡽ࠸࡛ࡋࡻ࠺࠿ࠊࡇࡕࡽࡢ࡯࠺ࡀⅬᩘࡀ㧗࠸ࡇ࡜ࡀࢃ࠿ࡾࡲࡍࠋࢭࣥࢱ࣮ヨ㦂ࡢⱥㄒ࡞ࢇ࡚
ࡸࡉࡋ࠸࠿ࡽࡳࢇ࡞‶Ⅼࡔ࡜࠿࠾ࡗࡋࡷࡿேࡶ࠸ࡿࢇ࡛ࡍࡀࠊࡑࢀ࡛ࡶ⤖ᵓୖࡢ࡯࠺࡛ࡶ
ࡸࡣࡾከᑡࡣᕪࡀࡘ࠸࡚࠸ࡲࡍࠋࡓࡔࡋࠊ୍␒ྜ᱁⪅࡜୙ྜ᱁⪅ࡢᕪࡀࡘࡃࡢࡣ┿ࢇ୰ࠊ࣮࣒࣎ࣜࣗࢰ࣮ࣥࡢ࡜ࡇࢁ࡛ࡍࡡࠋࡇࡇࡽ࠶ࡓࡾ୍࡛␒ᕪࡀࡘ࠸࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࠋ
ࡶ࠺୍ࡘࡔࡅ࠾ぢࡏࡋࡲࡍࠋ
ࡇࢀࡣ 2013 ᖺࡢᩘᏛϨ㸿ࡢⅬᩘ࡛ࡍ ࢫࣛ࢖ࢻ 11 ࠋࡇࡢᖺࡢᩘᏛϨ㸿ࡣ኱ኚ㞴ࡋ࠿ࡗࡓࢇ࡛ࡍࡡࠋࡑࡢࡓࡵ࡟ࠊࡇࢀࢆぢࡿ࡜ࡎࡗ࡜ࡇ࠺࠸࠺ᙧ࡟࡞ࡗ࡚࠸࡚ࠊᡂ⦼ୖ఩ᒙࠊᡂ
⦼ࡢ࠸࠸࡜ࡇࢁ࡛ྜ᱁⪅࡜୙ྜ᱁⪅ࡢᕪࡀࡓࡃࡉࢇฟ࡚࠸ࡿഴྥࡀ࠶ࡾࡲࡍࠋࡇࢀࡀࡸࡉ
ࡋ࠸࡜ࡲࡓࡉࡗࡁࡢࡼ࠺࡟ᘪ࡞ࡾ࡟࡞ࡗ࡚ࠊୖࡢ࡯࠺࡛ࡣవࡾᕪࡀࡘ࠿࡞࠸࡜࠸࠺ࡇ࡜ࡀ
㉳ࡁࡲࡍࡅࢀ࡝ࡶࠊࡇࡢᖺࡣୖࡢ࡯࠺࡛ᕪࡀࡘࡃ࡜࠸࠺ഴྥࡀぢࡽࢀࡲࡋࡓࠋ
ࡇࢀࡣ┬␎ࡋࡲࡍ㸦ࢫࣛ࢖ࢻ 12㸧ࠋ⛉┠ᚓⅬ┦㛵
ࡇࢀࡣḟࡢヰ㢟࡛ࠊ⛉┠㛫ࡢᚓⅬࡢ㛵ಀ࡛ࡍ㸦ࢫࣛ࢖ࢻ 13㸧ࠋ࠸ࢁ࠸ࢁ⣽ᕤࢆࡋࡲࡋࡓࠋࡇࡇ࡟♧ࡋࡓ⛉┠ࡣ඲㒊࡛ࡣ࠶ࡾࡲࡏࢇࠋཷ㦂⪅ᩘࡢᑡ࡞࠸
⛉┠ࡣ┬࠸࡚࠶ࡾࡲࡍࠋᆅṔࡢ㸿⛉┠࡜࠿᝟ሗ㛵ಀᇶ♏࡜࠿ࣇࣛࣥࢫㄒ࡜࠿ࢻ࢖ࢶㄒ࡜࠿ࠊ
ࡑ࠺࠸࠺ࡶࡢࡣ┬࠸࡚࠶ࡾࡲࡍࠋࡑࢀ࡛┦㛵ಀᩘࠊせࡍࡿ࡟ࢹ࣮ࢱࡀ࡝ࡢ⛬ᗘࣜࣥࢡࡋ࡚
ືࡃ࠿ࠋࡑࡢ┦㛵ಀᩘࡢ኱ࡁࡉࢆ‫ࠋࡍࡲࡾ࠶࡚ࡋࢃࡽ࠶࡛ۑ‬ᙜࡓࡾ๓࡛ࡍࡅࢀ࡝ࡶᩘϨ㸿࡜ᩘϩ㹀ࡢⅬᩘࡣ┦㛵ࡀ㧗࠸ࠋ∦᪉ࡀ㧗ࡅࢀࡤࡶ࠺୍᪉ࡶ㧗࠸ࠋ

－77－ࡑࢀ࠿ࡽࠊࡇࢀࡶᙜࡓࡾ๓࡜ゝ࠼ࡤᙜࡓࡾ๓࡛ࡍࡀࠊࡇࢀࡣⱥㄒ➹グヨ㦂࡛ࠊࡇࡇࡀࣜ
ࢫࢽࣥࢢ࡛ࡍࠋู⛉┠࡜ࡋ࡚ぢ࡚࠸ࡲࡍ࠿ࡽࠊⱥㄒ➹グヨ㦂࡜ⱥㄒࡢࣜࢫࢽࣥࢢࡢヨ㦂ࡣࡍࡈࡃ㛵ಀࡀ㧗࠸ࠋ඲⯡ࢆぢ࡚ࡸࡾࡲࡍ࡜ࠊࡇࡢ࠶ࡓࡾ㸦ᕥୖ㒊ศ㸧ࡣࡳࢇ࡞ࡑࢀ࡞ࡾ࡟
┦㛵㛵ಀࡀ㧗࠸ࢇ࡛ࡍࡡࠋ⌮⣔ࡢ⛉┠࡛ࡍࠋࡇࡇࡀᩘᏛ࡛≀⌮Ϩࠊ⌮⛉⥲ྜ㸿ࠊ໬ᏛϨࠊ⏕≀ϨࠊᆅᏛϨࠊࡑࢀ࠿ࡽⱥㄒࡢ➹グ࡜࠸࠺ឤࡌ࡛୪ࢇ࡛࠸ࡲࡍࠋ
ࡑࢀ࠿ࡽࠕ೔⌮ࠊᨻ⤒ࠖࡀ⤖ᵓ࠸ࢁ࠸ࢁ࡞⛉┠࡜┦㛵ࡀ㧗࠸ࠋ୍␒ከࡃࡢ⛉┠࡜┦㛵ࡀ㧗 ࠸ ࡢ ࡣⱥ ㄒ ࡢ ➹ グヨ 㦂 ࡛ ࡍ ࡅࢀ ࡝ ࡶ ࠊࠕ ೔ ⌮ ࠊᨻ⤒ ࠖࡶ⤖ ᵓ┦㛵 ࡣ㧗࠸ ࠋពእ ࡜ᆅṔ ࡢ⛉┠࡜⌮⛉ࡀ኱ࡁ࡞┦㛵ࢆᣢࡗ࡚࠸ࡲࡍࠋ
ࡓࡔࡋࠊ⌮⛉࡜࠿ᆅṔࠊබẸࡢ⛉┠ࡣ➨ 1 ゎ⟅⛉┠ࡢⅬᩘࡔࡅࢆ࡜ࡗ࡚࠸ࡲࡍࠋ2 ࢥ࣐࠶ࡗ࡚ࠊᆅṔࠊබẸࡢ୰࠿ࡽ᭱኱㸰ࡘ࡜ࢀࡿࢇ࡛ࡍࡀࠊ➨ 1 ゎ⟅⛉┠ࡢⅬᩘࡔࡅࢆࡇࡇ࡛
ࡣ౑ࡗ࡚࠸ࡲࡍࠋࡑ࠺ࡍࡿ࡜ࠊᆅṔࠊබẸ࡛ࡣ 1 ேࡢཷ㦂⪅࡟㛵ࡋ࡚ 1 ࡘࡢ⛉┠ࡢࢹ࣮ࢱࡋ࠿࠶ࡾࡲࡏࢇࠋᮏ᮶ࠊᆅṔ㛫ࡢ┦㛵ࡣ᥎ᐃ࡛ࡁ࡞࠸ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ࠸ࢁ࠸ࢁ⤫ィⓗ࡞௬ᐃࢆ⨨࠸࡚ࡸࡗ࡚ࠊࡑࢀ࡛ࡶᩘᏛ࡜࠿ⱥㄒ࡜࠿ᅜㄒࡣඹ㏻࡟ཷ㦂ࡋ࡚࠸ࡿࡢ࡛ࠊࡑࡢ᝟
ሗࢆ౑ࡗ࡚࠶ࡿ✀ࡢ⿵᏶ࢆ⾜ࡗ࡚ࠊ┦㛵ಀᩘࢆ᥎ᐃࡋ࡚࠸ࡲࡍࠋ
⾲♧ࡢ኱ࡁࡉ࡟ࡘ࠸࡚ࡣࠊࢮࣟࡢ࡜ࡁ࡟ࢮࣟ࡟ࡋ࡚ࡋࡲ࠺࡜㐪࠸ࡀవࡾࢃ࠿ࡽ࡞࠸ࡢ࡛ࠊ
ࡕࡻࡗ࡜ᙉㄪࡋ࡚ࠊ┦㛵ಀᩘࡀ 0.3 ࡢ࡜ࡁ࡟኱ࡁࡉࡀࢮࣟ࡟࡞ࡿࡼ࠺࡟ࡋ࡚࠸ࡲࡍࠋࡑࢀ
࠿ࡽ┦㛵ಀᩘࡀ 0.8 ࡢ࡜ࡁ࡟ࠊࡇࡇ࡟┠┒ࡾࡀࡘ࠸࡚࠸ࡿࢇ࡛ࡍࡀࠊ༙ᚄࡀ 1 ࡟࡞ࡿࡼ࠺࡟タᐃࡋ࡚࠸ࡲࡍࠋ࡛ࡍ࠿ࡽ୍␒኱ࡁ࠸࡜ࡇࢁ࡛኱య┦㛵ಀᩘ 0.8 ࡄࡽ࠸࡛ࡍࠋ୍␒ᑠࡉ
࠸࡜ࡇࢁ࡛ࡶ┦㛵ಀᩘࡣ 0.45ࠊࡑࢀࡄࡽ࠸ࡢ㛵ಀࡣ࠶ࡾࡲࡍࠋ
඲యࡢࡲ࡜ࡲࡾࡀ࡞ࡿ࡭ࡃࡼࡃ࡞ࡿࡼ࠺࡟⛉┠㡰␒ࢆ୪࡭࠿࠼࡚࠸ࡿࢇ࡛ࡍࡀࠊࡇࡇࡽ
㎶㸦ᕥୖ㒊ศ㸧ࡀ⌮⣔ࡢ⛉┠ࠊᆅṔࠊࡑࢀ࠿ࡽᨻ⤒ࠊ೔⌮ࠊ⌧♫ࠊ୍␒཯ᑐഃࡣࠊᩘᏛ࡜
୍␒㛵ಀᛶࡢⷧ࠸⛉┠࡜ࡋ࡚ᅜㄒࡀ୪ࢇ࡛࠸ࡲࡍࠋࡲ࠶ᙜ↛࠿࡞࡜࠸࠺ᵓ㐀࡟࡞ࡗ࡚࠸ࡲ
ࡍࠋࡇࢀࡣูࡢศᯒࢆࡋࡓࡶࡢ࡛ࡍ㸦ࢫࣛ࢖ࢻ 15㸧ࠋ⾲♧ࡋ࡚࠸ࡿⅬࡣࡉࡗࡁ࡜ྠࡌࡼ࠺࡟୍ࡘ୍ࡘࡢᏛ㒊࡛ࡍࡅࢀ࡝ࡶࠊᶓ㍈ࡀⱥㄒࡢ➹グ
ヨ㦂ࠊྜ᱁⪅ࡢᖹᆒⅬ࡛ࡍࠋ⦪㍈ࡀᅜㄒࡢᚓⅬ࡛ࡍࡡࠋ᏶඲࡟ྠࡌ࡛ࡣ࡞࠸ࡅࢀ࡝ࡶࠊࡑ
ࢀ࡞ࡾࡢ㛵ಀࡀ࠶ࡾࡲࡍࠋࡓࡔࡋ୰㛫ᒙࠊ࣮࣒࣎ࣜࣗࢰ࣮࡛ࣥࡍ࡜Ⅼᩘࡣ࠿࡞ࡾࡤࡽࡅࡿࠋ
࡜ࡇࢁࡀᩘᏛϨ㸿࡜ᩘᏛϩ㹀ࡔ࡜㸦ࢫࣛ࢖ࢻ 16㸧ࠊಶேࡣᙜ↛࠶ࡿ⛬ᗘⅬᩘࡀࡤࡽࡅࡿࢇ
࡛ࡍࡀࠊ㞟ᅋ࡛ぢࡿ࡜ࡣࡗࡁࡾ࡜ࡋࡓ᭤⥺㛵ಀࡀฟ࡚ࡁ࡚࠸ࡲࡍࠋ
⛉┠㑅ᢥࡢ≧ἣࡇࡢࢩ࣏ࣥࢪ࣒࢘ࡢᩥ⬦࡜ࡣࡕࡻࡗ࡜㐪࠺࠿ࡶࡋࢀࡲࡏࢇࡀࠊࢹ࣮ࢱࢆぢ࡚࠸࡚ᙜ஦⪅࡜ࡋ࡚ࡕࡻࡗ࡜Ẽࡀ࠿ࡾ࡞ࡇ࡜ࡣࠊ⛉┠ᩘ࡛ࡍ㸦ࢫࣛ࢖ࢻ 20㸧ࠋせࡍࡿ࡟ࠊᮾிࠊᇸ⋢ࠊ༓ⴥࠊ⚄ዉᕝࠊࡇࡢ 4 㒔┴ࡀ࠿࡞ࡾ⛉┠㑅ᢥ࡟㛵ࡋ࡚≉␗࡞≧ἣ࡛ࡍࠋࡎࡗ࡜ぢ࡚ࡳࡲࡍ࡜ 3 ⛉┠ཷ㦂⪅ࡀ 37 㸣ࠊ7 ⛉┠ཷ㦂⪅ࡀ 29 㸣࡛ࡍࠋ࡜ࡇࢁࡀࠊ౛࠼ࡤྠࡌ኱㒔ᕷ࡛ࡶி㜰ᅪࠊி㒔ࠊዉⰋࠊ኱㜰ࠊරᗜ࡛ࡣ 3 ⛉┠ཷ㦂⪅ࡀ 18 㸣ࠊ7 ⛉┠ཷ㦂⪅ࡀ 56 㸣࡛ࡋࡓࠋࡑࢀ௨እࡢᆅ᪉࡛ࡣ 3 ⛉┠ཷ㦂⪅ 11 㸣ࠊ7 ⛉┠ཷ㦂⪅ 57 㸣࡛ࡋࡓࠋせࡍࡿ࡟ࠊᮾி࡛ࡣ
≉␗ⓗ࡟ཷ㦂⛉┠ᩘࡀᑡ࡞࠸ࠊࡑ࠺࠸࠺ഴྥࡣ࠶ࡾࡲࡍࡢ࡛ࠊࡕࡻࡗ࡜೫ࡗ࡚࠸ࡿࡢ࡛ࡣ
࡞࠸࠿࡞࡜࠸࠺ᚰ㓄ࡣࡋ࡚࠸ࡲࡍࠋ
ⱥㄒ㈨᱁᳨࣭ᐃヨ㦂࡜ࡢẚ㍑
࠶࡜㈨᱁ヨ㦂ࡢヰ࡛ࡍࡀࠊࡇࢀࡣᮏ␒ࡢᐇ㝿ࡢཷ㦂⪅࡟࡞࠿࡞࠿⪺ࡃࢃࡅ࡟࠸࠿࡞࠸ࡢ

－78－࡛ࣔࢽࢱ࣮ㄪᰝࡢཧຍ⪅࡟ㄪᰝࢆࡋࡲࡋࡓࠋࣔࢽࢱ࣮ㄪᰝ࡜࠸࠺ࡢࡣࠊࢭࣥࢱ࣮ヨ㦂ࡢᙜ᪥࡟኱Ꮫ 1 ᖺ⏕ࢆ㞟ࡵ࡚ࠊ࢔ࣝࣂ࢖ࢺ࡜ࡋ࡚ࡑࡢᖺࡢࢭࣥࢱ࣮ヨ㦂ࡢၥ㢟ࢆゎ࠸࡚ࡶࡽࡗ
࡚࠸ࡲࡍࠋᮏヨ࡜㏣ヨ୧᪉ゎ࠸࡚ࡶࡽࡗ࡚࠸ࡿࢇ࡛ࡍࡀࠊཧຍ⏦ࡋ㎸ࡳ᫬࡟ⱥㄒࡢ㈨᱁ヨ㦂ࡢⅬᩘࢆ⮬ᕫ⏦࿌ࡋ࡚ࡶࡽ࠸ࡲࡋࡓࠋࡑࡢ⮬ᕫ⏦࿌ࡋࡓෆᐜ࡜ᮏ࣭㏣ࡢⱥㄒヨ㦂ࡢྜィⅬࡢ㛵ಀࢆぢ࡚ࡳࡲࡋࡓ㸦ࢫࣛ࢖ࢻ 22㸧ࠋࡇࢀࡣᶓ㍈ࡀᮏヨ㦂ࠊඛ࡯࡝࠿ࡽㄗᕪࡢヰࡀฟ࡚࠸ࡲࡍࡅࢀ࡝ࡶࠊ⦪㍈ࡀ㏣ヨ㦂࡛ࡍ㸦ࢫ
ࣛ࢖ࢻ 23㸧ࠋྠࡌస㢟ࢳ࣮࣒ࡀྠࡌࢥࣥࢭࣉࢺࡢࡶ࡜࡟ࠊྠࡌࡼ࠺࡞ၥ㢟ᵓᡂ࡛ 2 ࢭࢵࢺࡢၥ㢟ࢆࡘࡃࡗ࡚࠸ࡿࢃࡅ࡛ࡍࠋࡑࢀࢆྠࡌᏛ⏕ࡓࡕ࡟ཷ㦂ࡉࡏࡿ࡜࡝࠺࡞ࡿ࠿࡜࠸࠺ࡇ࡜࡛ࡍࠋぢ࡚ࢃ࠿ࡿࡼ࠺࡟ࠊࡇࡇࡽ㎶㸦ྑୖ㒊ศ㸧࡟㞟୰ࡋ࡚࠸ࡲࡍࠋᐇࡣ㠀ᖖ࡟ඃ⚽࡞Ꮫ⏕ࡉࢇࡀከ࠸ࡢ࡛ࠊ➹グ 200 Ⅼࠊࣜࢫࢽࣥࢢ 50 ⅬࠊྜࢃࡏࡓⅬᩘ࡛ 200 Ⅼ㉸࠼ࡀ⤖ᵓࡓࡃࡉࢇ࠸ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀ࡛ࡶࠊ┦㛵ಀᩘࢆぢࡿ࡜ 0.8 ࢆ㉸࠼࡚࠸ࡲࡍࠋࡇ࠺࠸࠺ࡩ
࠺࡟೫ࡗࡓࢹ࣮ࢱ࡛ࡶ 0.8 ㉸࠼࡚࠸ࡿࠋ
ࡇࢀࡀ㸦ࢫࣛ࢖ࢻ 24ࠊ25㸧ⱥ᳨ࡢⅬᩘ࡛ࡋ࡚ࠊ‽ 2 ⣭ࡔ࡜ࡇࢀࡄࡽ࠸ࡢⅬᩘࠊ2 ⣭࡛ࡇ࠺ࠊ‽ 1 ⣭ࡀࡇࡇ࡛ࠊ1 ⣭ࡢ᪉ࡣ 2 ே࡛ࡍࠋᙜࡓࡾ๓࡜ゝ࠼ࡤᙜࡓࡾ๓࡛ࡍࡅࢀ࡝ࡶࠊ⣭ࡀୖࡀࡿ࡯࡝Ⅼᩘࡣ㧗ࡃ࡞ࡗ࡚࠸ࡿࠋࡓࡔࡋࠊ‽ 1 ⣭࡛ぢ࡞ࡋ‶Ⅼ࡜࠿ゝࢃࢀࡿࢇ࡛ࡍࡀࠊ
ᑡ࡞ࡃ࡜ࡶᮏヨ㦂࡜㏣ヨ㦂୧᪉࠶ࢃࡏ࡚ࡸࡿ࡜ࠊࢭࣥࢱ࣮ヨ㦂ࡢ‶Ⅼࡣ⤖ᵓ㞴ࡋ࠸࡛ࡍࠋ
⌧ᐇ࡟ࡣࠋ࠶࡜ TOEIC ࡜ TOEFL ࡟ࡘ࠸࡚ࡢ᝟ሗࡀ࠶ࡾࡲࡍࡀࠊTOEFL ࡣࡕࡻࡗ࡜ேᩘᑡ࡞࠿ࡗࡓ
ࡢ࡛ TOEIC ࡔࡅࡈ⤂௓ࡋࡲࡍ㸦ࢫࣛ࢖ࢻ 26ࠊ27㸧ࠋTOEIC ࡜࠸ࡗ࡚ࡶࠊࡇࡢሙྜࠊ࡯࡜ࢇ
࡝ࡣ኱Ꮫࡢࣉࣞ࢖ࢫ࣓ࣥࢺࢸࢫࢺ࡛ࡢ TOEIC-IP ࡢཷ㦂⪅࡛ࡍࠋᶓ㍈ࡀࢭࣥࢱ࣮ヨ㦂࡛ 500
Ⅼ‶Ⅼࠊ⦪㍈ࡀ TOEIC ࡲࡓࡣ TOEIC-IP ࡢࢫࢥ࢔࡛ࡍࠋභ༑ᩘே࠸ࡿࢇ࡛ࡍࡀࠊࡇ࠺࠸࠺
ឤࡌ࡛ࡍࠋࡇࢀࡣᅇᖐศᯒ࡛ࡣ࡞ࡃ࡚ࠊศᕸࡢᕊࢆ᥎ᐃࡋࡓࡶࡢ࡛ࡍࠋ┿ࢇ୰ࢆ࠶ࡿ✀ࡢ⤫ィⓗ࡞᪉ἲ࡛᥎ᐃࡋ࡚ࡳࡲࡋࡓࠋࡑ࠺ࡍࡿ࡜ࠊTOEIC800 Ⅼ࡛ 9 ๭ࡣ㉸࠼ࡿ࡜ᛮ࠺ࢇ࡛ࡍࡀࠊࡕ
ࡻࡗ࡜‶Ⅼࡣ㞴ࡋ࠸ࡢ࡛ࡣ࡞࠸࠿ࠋ900 Ⅼࡄࡽ࠸࠸࠿࡞࠸࡜ࠊࢭࣥࢱ࣮ヨ㦂ࡢᮏ࣭㏣ྜィࡢ‶Ⅼࡣ࡞࠿࡞࠿㞴ࡋ࠸ࡢ࡛ࡣ࡞࠸࠿࡞࡜ᛮ࠸ࡲࡋࡓࠋ
࠶࡜ࠊࣔࢽࢱ࣮ཧຍ⪅ࡣ࠿࡞ࡾඃ⚽࡞Ꮫ⏕ࡉࢇࡓࡕ࡛ࡍࡅࢀ࡝ࡶࠊὀពࡍ࡭ࡁࡇ࡜ࡣࠊ
ᮏ≀ࡢ TOEFL-iBT ࡣ 368 ே୰ 2 ྡࡋ࠿ཷ㦂⤒㦂⪅ࡀ࠸࡞࠿ࡗࡓࠋࡑࡢ⛬ᗘࡢᬑཬ⋡ࡔ࡜࠸࠺ࡇ࡜࡛ࡍ㸦ࢫࣛ࢖ࢻ 28㸧ࠋࡲ࡜ࡵࡣࠊ᭱ᚋ࡟ࡇ࠺࠸࠺ࡩ࠺࡟᭩࠸࡚࠶ࡾࡲࡍ㸦ࢫࣛ࢖ࢻ 29㸧ࠋ
௨ୖ࡛ࡍࠋ
‫ྖۑ‬఍

኱ὠඛ⏕ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧ఱ࠿ࠊ஦ᐇ☜ㄆ⛬ᗘࡢࡈ㉁ၥࡣ࠶ࡾࡲࡍ࡛ࡋࡻ࠺࠿ࠋ
‫⩏ۑ‬ᮏẶ

ᩥ㒊⛉Ꮫ┬ࡢ⩏ᮏ࡛ࡍࠋ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

≉࡟㤳㒔ᅪ࡛ 3 ⛉┠ཷ㦂ࡀከࡃ࡚㛵す࡜኱ࡁ࡞㐪࠸ࡀ࠶ࡿ࡜࠸࠺ࡢࡣࠊ࡝࠺࠸࠺⫼ᬒࡀ࠶ࡿࢇ࡛ࡋࡻ࠺࠿ࠋ౛࠼ࡤ⚾Ꮫ࡜ࡢే㢪ࡀከ࠸࠿ࡽࠊࡑ࠺࠸࠺ࡩ࠺࡞ࡇ࡜ࡀ࠶ࡿࢇ࡛ࡋࡻ
࠺࠿ࠋ‫ۑ‬኱ὠẶ

ᜍࡽࡃࡣࡑ࠺ࡔ࡜ᛮ࠸ࡲࡍࠋ㛵すࡶ⚾Ꮫࡢཷ㦂⪅࠸ࡿ࡜ࡣᛮ࠺ࢇ࡛ࡍࡀࠊ㛵す－79－࡛ࡣࢭࣥࢱ࣮ヨ㦂ࡢཷ㦂⋡⮬యࡀ㛵ᮾ࡟ẚ࡭࡚ప࠸ࡢ࡛ࠊࡑࡢࡓࡵ࡟వࡾ㛵すࡔ࡜ᕪࡀぢ࠼࡚࠸࡞࠸ࠊ3 ⛉┠ཷ㦂⪅ᩘࡀࡑࢀ࡯࡝ከࡃぢ࠼࡚࠸࡞࠸ࡢ࠿ࡶࡋࢀࡲࡏࢇࠋࡑࡇࡲ࡛ࡣ
௒ࠊࡕࡻࡗ࡜ヲࡋࡃᩘࢆᣢࡗ࡚࠸ࡲࡏࢇࡢ࡛ࡍࡳࡲࡏࢇࠋ
‫ۑ‬ᒣᓮẶ

ᇸ⋢་⛉኱Ꮫࡢᒣᓮ࡜⏦ࡋࡲࡍࠋ㠀ᖖ࡟⯆࿡῝࠸࠾ヰࠊ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋ

ࡓࠋ㏵୰࡛⌮⛉ࡢ࠾ヰࢆࡋ࡚࠸ࡓࡔ࠸ࡓ࡜ࡇࢁ࡛ࠊ኱ၥᙧᘧࡢၥ㢟ࡔ࡜ᑠၥࡢ㛫࡛㛵㐃ᛶࡀ㧗࠸࡜࠸࠺ࡼ࠺࡞ࡇ࡜ࢆ࠾ࡗࡋࡷࡗࡓ࡜ᛮ࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠊඛ࡯࡝ࡢࢹ࢕ࢫ࢝ࢵࢩࣙ
ࣥࡢ୰࡛ࠊ኱ၥᙧᘧ࡛ࡶᑠၥࡢ㛫ࡢࢹ࢕࣌ࣥࢹࣥࢩ࣮ࡣ㧗࠸࡜࠸࠺ウ㆟ࡶ࠶ࡗࡓࡼ࠺࡟ᛮ
࠸ࡲࡍࠋᐇ㝿࡟ࢭࣥࢱ࣮ヨ㦂࡛኱ၥᙧᘧࡢ୰ࡢᑠၥࡢၥ㢟ࡢ┦㛵ࡀ㧗࠸࡜࠸࠺ࢹ࣮ࢱࢆ࠾
࡜ࡾ࡟࡞ࡗ࡚࠸ࡿ࡞ࡽᩍ࠼࡚࠸ࡓࡔࡅ࡞࠸࡛ࡋࡻ࠺࠿ࠋ
‫ۑ‬኱ὠẶ

ࡕࡻࡗ࡜௒ࠊᡭඖ࡟ࡣ࡞࠸ࢇ࡛ࡍࡀࠊ౛࠼ࡤᩘᏛࡢሙྜ࡛ࡋࡓࡽ୍ࡘ୍ࡘࡢၥ

㢟ࢆᬑ㏻ࡢᙧࡢ IRT ୍࡛ၥ୍ၥࣂࣛࣂࣛ࡟ᙜ࡚ࡣࡵࡼ࠺࡜ࡍࡿ࡜ࠊࡕࡻࡗ࡜ᐇ᝟࡜ࡣ㐪࠺࡞࡜࠸࠺ࡇ࡜ࡣ᫂ࡽ࠿࡟ぢ࠼ࡲࡍࠋࡑࢀࡄࡽ࠸ࡢ⟅࠼࡟࡞ࡿࢇ࡛ࡍࡀࠋ
‫ۑ‬ᒣᓮẶ

⌮⛉࡛ࡶࡑ࠺࡛ࡋࡻ࠺࠿ࠋ

‫ۑ‬኱ὠẶ

௒ࠊ඲㒊ࡣぢ࡚࠸࡞࠸ࡢ࡛ヲࡋࡃࡣゝ࠼ࡲࡏࢇࡀ͐͐

‫ۑ‬ᒣᓮẶ

ᐇ㝿ࡣࠊࡑࡢࢹ࣮ࢱࡣ࠾᥇ࡾ࡟࡞ࡗ࡚࠸ࡿࢃࡅ࡛ࡍࡡࠋ

‫ۑ‬኱ὠẶ

ࡑ࠺࡛ࡍࠋ

‫ྖۑ‬఍

௚࡟ࡼࢁࡋ࠸࡛ࡋࡻ࠺࠿ࠋ

ࡑࢀ࡛ࡣࠊ኱ὠඛ⏕ࡶࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥ࡟ཧຍࡉࢀࡲࡍࡢ࡛ࠊࡲࡓࡑࡢ࡜ࡁ࡟࠾
㢪࠸࠸ࡓࡋࡲࡍࠋ
኱ὠඛ⏕ࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧

－80－

ᴫ せ

኱Ꮫධヨ䝉䞁䝍䞊䞁䝫䝆䜴䝮 ᖹᡂ26ᖺ11᭶29᪥
ᮾிᕤᴗ኱Ꮫ ♫఍⌮ᕤᏛ◊✲⛉ 䝕䝆䝍䝹ከ┠ⓗ䝩䞊䝹䝉䞁䝍䞊ヨ㦂䛾ᐇ᝟䛿䛹䛾䜘䛖䛺䜒䛾䛛䠛䝉䞁䝍䞊ヨ㦂䛷ఱ䛜 䜙䜜䜛䛾䛛䠛• 䝉䞁䝍䞊ヨ㦂䛾ཷ㦂⋡䛿䛹䛖ኚ䜟䛳䛯䠛• 䝉䞁䝍䞊ヨ㦂䛸ᅜබ❧኱䛾ྜྰ䛾㛵ಀ• ⛉┠ᖹᆒⅬ䛾᥎⛣• 䝉䞁䝍䞊ヨ㦂⛉┠ᚓⅬ䛾┦㛵㛵ಀ• 㑅ᢥ⛉┠ᩘ䛾ᆅᇦᕪ• ⱥㄒ⛉┠ᚓⅬ䛸㈨᱁䞉᳨ᐃヨ㦂኱Ꮫධヨ䝉䞁䝍䞊 ◊✲㛤Ⓨ㒊
኱ὠ ㉳ኵ

2

1

18ṓேཱྀ䛸䝉䞁䝍䞊ヨ㦂ᚿ㢪⪅ᩘ䝉䞁䝍䞊ヨ㦂 ཷ㦂⋡䛾ኚ໬ᅋሢ䝆䝳䝙䜰ୡ௦• 䝉䞁䝍䞊ヨ㦂➨1ᅇ (ᖹᡂ2ᖺ,1990)⌧ᙺ⣙䠎䠑.1୓ே䠈᪤༞௚15.6୓ே䛜ཷ㦂⌧ᙺཷ㦂⋡䛿⣙䠍䠐䠂(⌧ᙺᚿ㢪⋡15䠂䠅• ⌧ᅾ (ᖹᡂ26ᖺ,2014)⌧ᙺ⣙41.7୓ே䠈᪤༞௚10.8୓ே⌧ᙺཷ㦂⋡䛿⣙40䠂 (⌧ᙺᚿ㢪⋡ 42%)• ཷ㦂⪅ᩘ䛿1990ᖺ௦䛻ቑຍཷ㦂⋡䛿1990ᖺ௨ᚋ᭱㏆䜎䛷ቑຍഴྥ58䠂䜎䛷ῶᑡ

䝉䞁䝍䞊ᚿ㢪⪅ᩘ䛿Ᏻᐃ໬

43

ෆ⏣↷ஂ (2013) ධ◊༠䝉䝭䝘䞊

2013䠄ᖹᡂ䠎䠑䠅๓ᮇ᪥⛬ Ꮫ㒊ูᚿ㢪ಸ⋡᪂༞ྜ᱁⋡䛾᥎⛣ᑐᩘᑻᗘᏛ㒊䞉䝁䞊䝇䛾
ᩘ䛿540(」ᩘ
䛾㑅ᢤ༢఩䜢
ྵ䜐ሙྜ䜒䛒
䜛䠅
㉥2ಸ䚸
⥳3ಸ䚸
㟷4ಸ
ከ䛟䛜ᚿ㢪ಸ
⋡2䡚4䛾⠊ᅖෆ⏣↷ஂ (2013) ධ◊༠䝉䝭䝘䞊
5

6

－81－

⛉┠ᖹᆒⅬ䛾᥎⛣ ᩘᏛ
ᖹᡂ18-26(2006-2014)ᖺ

⛉┠ᖹᆒ䛾ᖺ᥎⛣• 䝉䞁䝍䞊ヨ㦂䛿⛉┠ᚓⅬ䛾ᖹᆒ䛜䛚䛚䜘䛭6๭䛷䛒䜛䜘䛖䛻䠈ពᅗ䛥䜜䛶䛔䜛• ከ⫥㑅ᢥᆺ䛾ฟ㢟䛷䛿䠈ᴟ➃䛻㞴䛧䛔タၥ䛿䠈㆑ูຊ䜢ᦆ䛺䛖䠄䠯䠪ẚ䛜ᝏ䛔䠅• 」ᩘ䛾ጤဨ఍䛻䜘䜛䝏䜵䝑䜽䜢ཷ䛡䛶䛿䛔䜛䛜䠈ึฟၥ㢟䛷ᮇᚅ㏻䜚䛾⛉┠ᖹᆒ䜢ᚓ䜛䛣䛸䛿䠈⌧ᐇ䛻䛿㞴䛧䛔• ⌮⣔䛾⛉┠䛷ᖹᆒⅬ䛾ኚື䛜኱䛝䛔
7

8⛉┠ᚓⅬ䛾ᖹᆒⅬ ⌮⛉ᖹᡂ18-26(2006-2014)ᖺ

ྛⅬ䛿኱Ꮫ䛾Ꮫ㒊䜎䛯䛿
䝁䞊䝇䜢♧䛩
๓ᮇ᪥⛬䛻䛚䛡䜛Ꮫ㒊䞉
䝁䞊䝇ᩘ =
540

9

10

11

12

－82－

H25ᖺ⛉┠ᚓⅬ䛾┦㛵ᆅṔ䞉බẸ䞉⌮⛉䛿➨1ゎ⟅⛉┠

᥎ᐃ䛧䛯┦㛵⾜ิ䛻ᇶ䛵䛟
୺ᡂศศᯒ䠄䠬
䠟䠝䠅䛾⤖ᯝ

r=0.3䛜┤ᚄ0䠈
r=0.8䛜┤ᚄ䠍䛸䛺䜛䜘䛖䛻⾲
♧ཎⅬ䛛䜙䛾᪉
ྥ䛜㏆䛟䠈㛗䛥
䛜䠍䛻㏆䛔䛸
┦㛵䛜኱䛝䛔Ḟ 䛾ሙྜ䛿䠈
௚⛉┠䜢⪃៖䛧䛶᥎ 䠄MAR䜢௬ᐃ䠈R䛾
norm䝟䝑䜿䞊
䝆䜢฼⏝䠅➨䠍୺ᡂศ(ᶓ㍈䠅䛜㐣༙䛾
ኚື䜢ㄝ᫂┦㛵ಀᩘ䛾⠊
ᅖ䛿䠈ซ䛭
0.45䠉0.80
1314

ྛⅬ䛜ྛ኱Ꮫ䛾Ꮫ㒊䜎䛯䛿䝁䞊
䝇䜢♧䛩

15

16

17

18

－83－

⛉┠㑅ᢥ䛾≧ἣⱥㄒタၥ㡯┠䛾⤫ィⓗ≉ᚩ••ᖹᡂ25ᖺ ⱥㄒᮏヨ㦂䠄➹グ䠈䝸䝇䝙䞁䜾䠅
ゎ⟅䛾ᵓ㐀

•
•

䠎ḟඖ䛾䠥䠮䠰䠄䠎䠬䠨䠅䛻䜘䛳䛶ᚓ䜙䜜䛯㆑ูຊ䝟䝷䝯䞊䝍
䛯䛰䛧䠈䝸䝇䝙䞁䜾䛾䠍䠔䚸䠍䠕䛿ᒁᡤ౫Ꮡᛶ䛜㧗䛔䛯䜑䠈ẁ㝵཯ᛂ䝰䝕䝹(㩪ᓥ䝰䝕䝹䠅䛷䜎䛸䜑䛶୍ၥ䛸䛧䛯䚹Ẽ䛜䛛䜚䛺䛣䛸䠖 㑅ᢥ⛉┠䛾೫䜚ᮾி௜㏆䠄ᇸ⋢䠈༓ⴥ䠈⚄ዉᕝ䠅䛷䠈3⛉┠ཷ㦂⪅䛜7⛉┠ཷ㦂⪅䜢ୖᅇ䛳䛶䛔䜛
ᖹᡂ25ᖺ䛾ᆅᇦูཷ㦂⛉┠ᩘᮾிᅪ䛷䛿3⛉┠ཷ㦂⪅䛜䠏䠓䠂䠈ி㜰ᅪ䠄ி㒔䠈ዉⰋ䠈኱㜰䠈රᗜ䠅䛷䛿䠍䠔䠂䠈௚䛷䛿䠍䠍䠂
































䠖⋡䠂





  

  

ி㜰ᅪ

















⋡䠂





 





 

 

䠖䛭䛾௚













  

䠖⋡䠂





 





 

 

⛉┠ᩘ
ᮾிᅪ

䠬䠖➹グ➨䠍ၥ䠄Ⓨ㡢䠅䠣䠖➹グ➨2ၥ䠄ᩥἲ䠅䠮䠖➹グ➨3䡚6ၥ䠨䠖䝸䝇䝙䞁䜾

ィ 



20

19

ⱥㄒ㈨᱁䞉᳨ᐃヨ㦂䛸䛾ẚ㍑

䠍䠖ᇸ⋢䠈༓
ⴥ䠈ᮾி䠈⚄
ዉᕝ䠎䠖ி㒔䠈ዉ
Ⰻ䠈኱㜰䠈ර
ᗜ䠏䠖䛭䜜௨እ• 㞴᫆ᗘ䛜ᶆ‽໬䛥䜜䛶䛔䜛䠄䛿䛪䠅䛾ヨ㦂䛸ẚ
㍑䛧䛯䛔䚹䠄䝔䝇䝖䛾┠ⓗ䜔ᵓᡂ䛿䠈ᚲ䛪䛧䜒ྠ䛨䛷䛿䛺䛔䛜….)• 䝰䝙䝍䞊ㄪᰝ䛾ཧຍ⪅䜢౑䛖䠄䝉䞁䝍䞊ヨ㦂䠈ᮏ㏣ヨ㦂ྠ᪥䛻䠈ྠᖺᗘ䛾ヨ㦂ၥ㢟䜢᫬㛫㐜䜜䛷ゎ⟅䛥䛫䜛䠅㒔ෆᅜ❧኱Ꮫ䠍ᖺ⏕䠄368ྡ᭷ຠ䠅ཧຍ⏦䛧㎸䜏᫬䛻䠈ⱥㄒእ㒊ヨ㦂䛾ཷ㦂Ṕ䠈ᚓⅬ䜢グ㏙䛥䛫䛯䠄ᚓⅬ䡡ྜྰ䛿⮬ᕫ⏦࿌䠅䚹• 䝉䞁䝍䞊ヨ㦂䛾ⱥㄒᚓⅬ䛿䠈ᮏ㏣䛾ྜィ䜢౑䛖䚹㧗ᰯᡤᅾᆅ䛻ᇶ䛵䛟༊ศ
䝉䝹䛾㠃✚
䛜ேᩘ䜢♧
䛩

኱ὠ(2014) 䝔䝇䝖Ꮫ఍

21

2014䝰䝙䝍䞊 ⱥㄒᮏヨ㦂䛸㏣ヨ㦂

222014䝰䝙䝍䞊:እ㒊ⱥㄒヨ㦂䛾ཷ㦂⤒㦂䝰䝙䝍䞊ㄪᰝཧຍ⪅䛾ⱥㄒᚓⅬ䠄➹グ䠇䝸䝇䠅ཧຍ⪅䛿㒔ෆ䛾኱Ꮫ䠍ᖺ⏕ᶓ㍈䠖ᮏヨ㦂 ⦪㍈䠖㏣ヨ㦂ᚓⅬ䛾┦㛵䛿 r=0.827

኱ὠ(2014) 䝔䝇䝖Ꮫ఍

23

－84－

24

2014䝰䝙䝍䞊䠖ⱥ᳨ྲྀᚓ⣭䛸䝉䞁䝍䞊ヨ㦂

2014䝰䝙䝍䞊䠖䝉䞁䝍䞊ヨ㦂䛸TOEIC/TOEIC-IP• TOEIC: ⫈ゎ䛸ㄞ䜏䛛䜙ᵓᡂ䛥䜜䜛䠄䝉䞁䝍䞊ヨ㦂䛸㢮ఝ䠅• ୡ⏺䛷ᖺ㛫ᘏ䜉200୓ே㉸䛾ཷ㦂⪅䠄㞟ᅋཷ㦂䛾᪉䛜䜔䜔ከ䛔䠅
source 䠖ᅜ㝿䝡䝆䝛䝇䝁䝭䝳䝙䜿䞊䝅䝵䞁༠఍• 䝰䝙䝍䞊ཧຍ⪅䛾TOEICཷ㦂䛿䠈ከ䛟䛜኱ᏛධᏛᚋ䚹኱Ꮫ䛷ᐇ᪋䛥䜜䛶䛔䜛䝥䝺䞊䝇䝯䞁䝖䝔䝇䝖䛸䛧䛶䛾ཷ㦂䛜ከ䛔䛸᥎ 䛥䜜䜛䚹• ⱥ᳨䛸䛾㛵ಀ䛻䛴䛴䛔䛶䛿䠈www.toeic.or.jp 䛻㈨ᩱ䛒䜚䚹• TOEIC-SW䠈TOEIC-Bridge䛾ሗ࿌䛿䛺䛧䚹ⱥ᳨䛾ཷ㦂䛿䜋䛸䜣䛹䛜኱ᏛධᏛ௨๓䚹ⱥ᳨䛾ྲྀᚓ䛾෌㧗⣭䜢♧䛧䛯䚹䠏⣭௨ୗ䛿┬␎• TOEIC䝇䝁䜰䠄TOEIC-IPྵ䜐䠅ሗ࿌⪅63ྡ
TOEIC䝇䝁䜰ᖹᆒ 619.1Ⅼ(S.D. 153.5)
䝉䞁䝍䞊ᮏ㏣ィ 395.0/500Ⅼ(S.D. 57.6)┦㛵ಀᩘ
r = 0.803
䠄2013ᖺ䛿䠈N=55,r=0.768䠅

኱ὠ(2014) 䝔䝇䝖Ꮫ఍

26

25

2014䝰䝙䝍䞊䠖䝉䞁䝍䞊ヨ㦂䛸TOEIC/TOEIC-IP

2014䝰䝙䝍䞊䠖䝉䞁䝍䞊ヨ㦂䛸TOEFL-ITP• ␃Ꮫ㈨᱁䛻౑䜟䜜䜛TOEFL-iBT(䜸䞁䝷䜲䞁4ᢏ⬟
ヨ㦂䠅䛾ཷ㦂⤒㦂䛾ሗ࿌䛿368ྡ୰2ྡ䛾䜏
• TOEFL-ITP䠄㞟ᅋᐇ᪋∧䠗⫈ゎ䠈ᩥἲ䛸⾲⌧䠈ㄞゎ䛾3㡿ᇦ䠅ୖ⣭䝺䝧䝹(LEVEL1)310Ⅼ䡚677Ⅼ䠈ሗ࿌⪅䛿29ྡ䠄䛖䛱1ྡ䛿ᚓⅬㄗグ䛸᥎ᐃ䠅• TOEFL-ITP 28ྡ䠈ᖹᆒ505.5Ⅼ䠈S.D. 41.3
䝉䞁䝍䞊ヨ㦂䛸䛾ᚓⅬ┦㛵 r=0.699(2013ᖺ䛿 N=9, r=0.730)
2014ᖺ TOEIC䛸䛾ඹ㏻ཷ㦂⪅䛿1ྡ䛾䜏ᶓ㍈䠖䝉䞁䝍䞊ヨ㦂ᚓⅬ䠄䠎䠌䠍䠐䠅 ᮏ㏣ヨ㦂ⱥㄒᚓⅬྜィ 500Ⅼ‶Ⅼ
⦪㍈䠖TOEIC/TOEIC-IP䛾⮬ᕫ⏦࿌Ⅼศᕸ䛾ᓠ䜢ᢡ䜜⥺䛷᥎ᐃ(㠀⥺ᙧᅉᏊศᯒ䠅

኱ὠ(2014) 䝔䝇䝖Ꮫ఍

27

28

䜎䛸䜑
• ᖹᡂ䠎ᖺ䛛䜙ᖹᡂ䠎䠒ᖺ䛾㛫䛻䚸⌧ᙺᚿ㢪⋡䛿䠍䠑䠂䛛䜙䠐䠎䠂䜈ୖ᪼• ึฟၥ㢟䛷䛾⛉┠ᚓⅬ䛾ኚື• ⱥㄒ䛿䠈ᅜබ❧୰఩䛷ྜྰ䛻ᙉ䛟㛵୚ᩘᏛ䛿ୖ఩䛷㛵୚䛾⛬ᗘ䛜ᙉ䛔• ඲䛶䛾⛉┠ᚓⅬ䛾㛫䛷ṇ䛾┦㛵䛜䛒䜛• ᇸ⋢,༓ⴥ,ᮾி,⚄ዉᕝ䛷䠏⛉┠ཷ㦂⪅䛜ከ䛔• 䝉䞁䝍䞊ヨ㦂ⱥㄒᚓⅬ䛿㈨᱁䞉᳨ᐃᚓⅬ䛸ᙉ䛟
㛵㐃
29

－85－

࠙ሗ࿌㸲ࠚ㧗኱᥋⥆ࡢࡇࢀ࠿ࡽ
୰ி኱Ꮫ⌧௦♫఍Ꮫ㒊㛗

ᮧୖ
‫ྖۑ‬఍

㝯

ሗ ࿌ ࡢ ᭱ ᚋ ࡜ ࡞ ࡾ ࡲ ࡍ ࡀ ࠊࠕ 㧗 ኱ ᥋ ⥆ ࡢ ࡇ ࢀ࠿ࡽࠖ࡜㢟ࡋࡲࡋ࡚ࠊ୰ி኱Ꮫࡢᮧୖඛ⏕࡟ࡈሗ࿌ࢆ
࠾㢪࠸ࡋࡲࡍࠋ

‫ۑ‬ᮧୖẶ

ᮧୖ࡛ࡈࡊ࠸ࡲࡍࠋࡼࢁࡋࡃ࠾㢪࠸࠸ࡓࡋࡲࡍࠋ኱ኚ඘ᐇࡋࡓሗ࿌ࡀ⥆࠸࡚࠾ࡾࡲࡍࡢ࡛ࠊࡶ࠺ⓙᵝ࠾⑂ࢀ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࡀࠊ⚾ࡢሙྜࡣ⌮ㄽⓗ࡟ࡑ࠺㞴ࡋ࠸ヰࡀ࡛ࡁࡿࢃࡅ࡛ࡶࡈࡊ࠸ࡲࡏࢇ࡛ࠊᑓࡽ
⤒㦂࡟ᇶ࡙࠸࡚ࠊ௒ࠊ⪃࠼ࡽࢀ࡚࠸ࡿࠊ࠸ࢃࡺࡿධヨᨵ㠉࡟࠾࠸࡚௒ᚋ࡝ࢇ࡞㞀ᐖࡀ㉳ࡇࡾࡑ࠺࠿࡜࠸ࡗࡓ
ࡇ࡜ࢆ୰ᚰ࡟࠾ヰࡋࡋ࡚ࡳࡓ࠸࡜ᛮࡗ࡚࠸ࡲࡍࠋ
㧗኱᥋⥆ࡢᨵၿ࡟ࡣࠊᙜ↛ධヨᨵ㠉ࡀྵࡲࢀࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊ௒ࡢ᫬Ⅼ࡛࡞ࡐࡇ࠺
࠸࠺ࡇ࡜ࡀၥ㢟࡟࡞ࡿ࠿࡜࠸࠺࡜ࠊ1 ࡘࡣࠊᩍ⫱࡟ᑐࡋ࡚♫఍ⓗせㄳࡀኚ໬ࡋࡘࡘ࠶ࡿࠋ
ࡇࢀࡣ኱ࡁ࡞⏘ᴗᵓ㐀ࡢኚ໬࡜ࡘ࡞ࡀࡗ࡚࠸ࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࡀࠊࡑ࠺࠸࠺ࡇ࡜
ࡣ௒ࠊㄽࡌࡿࡘࡶࡾࡣ࠶ࡾࡲࡏࢇࠋࡑࡢ୍᪉࡛ࠊධヨ࡟ᑐࡋ࡚฼⏝ྍ⬟࡞ᢏ⾡㠉᪂ࡀ㉳ࡇࡗ࡚ࡁ࡚࠸ࡿࡇ࡜࡬ࡢᮇᚅࡶ࠶ࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀࡲࡍࠋ
ࡲࡓࠊ⚾⮬㌟ࡣ⮬ศࡀ࠿࠿ࢃࡗࡓබⓗヨ㦂ࡢ❧ࡕୖࡆ࡟ᙜࡓࡗ࡚ࠊᗄࡘ࠿⤒㦂ࡋࡓ࢚ࣆࢯ࣮ࢻࡀ࠶ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀ࡟ࡘ࠸࡚࠾ヰࡋࡋ࡚ࠊࡑࡢୖ࡛ࠊࡇࢀࡣ௒ᚋࡢධヨᨵ㠉࡟࠿࠿ࢃࡿ㆟ㄽࡢ୍ࡘࡢ↔Ⅼ࡟࡞ࡿ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊCBT ؐࢥࣥࣆ࣮ࣗࢱ࣮࣭࣋
࣮ࢫࢻ࣭ࢸࢫࢸ࢕ࣥࢢ̿ࡀ௒᪥ၥ㢟࡟࡞ࡗ࡚࠸ࡲࡍࡀࠊࡑࢀࡀ࠸ࢃࡺࡿ᪥ᮏⓗࢸࢫࢺᩥ໬
ࡢ㛫࡟࡝ࢇ࡞ᦶ᧿ࢆ⯆ࡍྍ⬟ᛶࡀ࠶ࡿ࠿࡜࠸ࡗࡓࡇ࡜ࢆ୰ᚰ࡟㆟ㄽࡋ࡚ࡳࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
ࡃࢃ࠼࡚ࠊ࠶ࡿ⬟ຊほ࡜࠸࠸ࡲࡍ࠿ࠊ⌮ᛕ࡜࠸࠺ࡶࡢࡀ࠶ࡿ⛬ᗘᡂ⇍ࡋ࡚ࡃࡿ࡜࠸࠺ࡇ
࡜ࡀ࠶ࡿ࡜ࡋ࡚ࠊࡑࢀ࡜࡜ࡶ࡟ࠊࡑࢀ࡟ᑐᛂࡍࡿᢏ⾡ࡀ࠶ࡿ⛬ᗘᩚࡗࡓ࡜ࡋ࡚ࡶࠊࡑࢀࡀ
ᮏᙜ࡟ࠊ౛࠼ࡤ௒ࡢ୰ᩍᑂ⟅⏦ࡢࡼ࠺࡞ࡶࡢ࡛ᮇᚅࡉࢀ࡚࠸ࡿࠊ࠸ࢃࡺࡿ┿ࡢᏛຊࡢホ౯࡟ᮏᙜ࡟ࡘ࡞ࡀࡗ࡚࠸ࡃࡢ࠿࡝࠺࠿ࠋࡇࢀࡣ᪥ᮏࡢࢸࢫࢺ㢼ᅵ࡜࠸࠺ࡇ࡜ࡔࡅ࡛ࡣ࡞ࡃ࡚ࠊ
ࡸࡣࡾ⬟ຊホ౯࡜࠸࠺ࡢࡣࠊ⾜㈈ᨻࡢၥ㢟࡟኱࠸࡟౫Ꮡࡍࡿ࡜⚾ࡣᛮࡗ࡚࠸ࡲࡍࠋࡑࡢ࠶
ࡓࡾ࡟ࡘ࠸࡚ࡶᑡࡋ࠾ヰࡋࡋࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
᪥ᮏ␃Ꮫヨ㦂ࡢ⤒㦂࡜ࡇࢁ࡛ࠊ⚾ࡀ❧ࡕୖࡆࢆ⤒㦂ࡋࡓヨ㦂࡜࠸࠺ࡢࡣࠊᐇࡣඛ࡯࡝๓ᕝඛ⏕࠿ࡽࡈ⤂௓࠸ࡓࡔࡁࡲࡋࡓ᪥ᮏ␃Ꮫヨ㦂࡛ࡈࡊ࠸ࡲࡋ࡚ࠊࡇࡢヨ㦂ࡣࡓࡧࡓࡧヰ㢟࡟࡞ࡗ࡚࠾ࡾࡲࡍ㡯┠཯ᛂ⌮ㄽ࡟౫ᣐࡋࡓᚓⅬ➼໬ࢆ⾜ࡗࡓࠊᜍࡽࡃᡃࡀᅜ࡛᭱ึࡢࠊ࠸ࢃࡺࡿࣁ࢖ࢫࢸ࣮ࢡࢫ ࡞ ヨ 㦂 ࠊ ࠸ ࢃ ࡺ ࡿ ධ Ꮫ ヨ 㦂 ࡛ ࠶ ࡿ ࠿ ࡜ ᛮ ࠸ ࡲ ࡍ ࠋ CBT ࡟ ࡣ ࡯ ࡝ 㐲 ࠸ ⣬ ࣋ ࣮ ࢫ ࡢ ヨ 㦂 ࡛－87－ࡈࡊ࠸ࡲࡍࡅࢀ࡝ࡶࠊධヨࢭࣥࢱ࣮ヨ㦂࠶ࡿ࠸ࡣ௒ࠊ⪃࠼ࡽࢀ࡚࠸ࡿࡑࡢᚋ⥅ヨ㦂ࡀ CBT໬ࡉࢀࡿ࡜ࡍࢀࡤࠊ㑊ࡅ࡚㏻ࢀ࡞࠸ࡼ࠺࡞ၥ㢟ࡀᗄࡘ࠿ᥦ㉳ࡉࢀ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࡢ࡛ࠊ
ࡑࡢⅬ࡟ࡘ࠸࡚ከᑡ࡜ࡶࡈཧ⪃࡟࡞ࢀࡤ࡜ᛮࡗ࡚࠸ࡿḟ➨࡛ࡍࠋ᪥ᮏ␃Ꮫヨ㦂ࠊࡇࡢࢫࣛ࢖ࢻࡣ Wikipedia ࠿ࡽࡢᘬ⏝࡛ࡈࡊ࠸ࡲࡍࡢ࡛┬␎ࡉࡏ࡚࠸ࡓ
ࡔࡁࡲࡍࡀࠊᖺ㛫 2 ᅇᐇ᪋ࡉࢀ࡚࠸ࡿࠊࡑࢀ࠿ࡽ 2 ᖺ㛫࡟ࢃࡓࡗ࡚᭷ຠ࡛࠶ࡿࠊࡑࡢࡓࡵ
࡟ࡣᚓⅬࡀ┦஫࡟ẚ㍑ྍ⬟࡛࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜࠸࠺᮲௳ࡀ࠶ࡗࡓ࡜࠸࠺ࡇ࡜ࠋ࠿ࡘࠊࡇ
ࡢヨ㦂ࡢཷ㦂⪅ࡀ᪥ᮏே࡛ࡣ࡞࠸࡜࠸࠺Ⅼ࡟࠾࠸࡚ࠊඛ࡯࡝࠿ࡽฟ࡚࠾ࡾࡲࡍᅜ㝿ᶆ‽࡜࠸࠺ࡶࡢ࡟ࠊࡸࡣࡾྜࢃࡏ࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜࠸࠺ࡇ࡜ࡀ࠶ࡗࡓ࡜ࡶᛮ࠸ࡲࡍࠋ
ࡑࡢࡓࡵ࡟ࠊ≉ู◊✲᥎㐍㈝ࠊ࠸ࢃࡺࡿᨻ⟇⛉◊࡜࠸࠺ࡶࡢࢆ࠸ࡓࡔࡃࡇ࡜ࡀ࡛ࡁ࡚ࠊ
⚾⮬㌟ࡀࡑࡢ࡜ࡁࡣൗ㉺࡟ࡶ◊✲௦⾲⪅࡛ࡋࡓࡅࢀ࡝ࡶࠊ௒ࠊࡇࡢ఍ሙࢆࡊࡗ࡜ぢᅇࡋ࡚ࡶ 6 ྡ࠿ 7 ྡࡢ᪉࡟ࠊ◊✲༠ຊ⪅࠶ࡿ࠸ࡣࡑࡢᚋࡢᐇົ࡟ᦠࢃࡗ࡚࠸ࡓࡔࡃᙧ࡛࠿࠿ࢃࡗ
࡚࠸ࡓࡔࡁࡲࡋࡓࠋࡑࡇ࡛ᢅࡗࡓၥ㢟ࡣࠊࡸࡣࡾ IRT ࡟ࡼࡿᚓⅬ➼໬ࠊࡑࢀ࠿ࡽၥ㢟సᡂయไࡢᩚഛࠊࡑࢀ࠿ࡽࠊඛ࡯࡝࠿ࡽฟ࡚࠾ࡾࡲࡍࢹ࣮ࢱ࣮࣋ࢫࡢᵓ⠏ࠋࡲࡓࠊヨ㦂඲య࡬ࡢࣂࢵࢡ࢔ࢵࣉయไࠊேⓗయไ࡜࠸࠺ࡶࡢࡀ࠶ࡾࡲࡍࠋࡑࢀ࠿ࡽࡶ࠺୍ࡘࡣἲⓗၥ㢟࡬ࡢᑐᛂ࡜࠸࠺ࡢࡀࠊ⚾⮬㌟ࡣࡑࡢ࡜ࡁࠊᾏእ࡛ࡇࡢヨ㦂࡟ᑐࡍࡿッゴࡀ㉳ࡇࡉࢀࡿࡼ࠺࡞ࡇ
࡜ࢆ༴᝹ࡋ࡚࠾ࡾࡲࡋࡓࡢ࡛ࠊࡑࢀ࡟ࡘ࠸࡚ࡶ◊✲ࡋ࡞ࡅࢀࡤ࠸ࡅ࡞࠸࡜ᛮࡗࡓḟ➨࡛ࡍࠋ
ᚓⅬ➼໬࡛ࡍࡅࢀ࡝ࡶࠊ࡝ࢇ࡞ࡇ࡜ࡀၥ㢟࡟࡞ࡗࡓࡢ࠿ࠋ≉࡟ࢸࢫࢺࡢࢯࣇࢺഃࡢ㠃࡜ゝࡗ࡚ࡶ࠸࠸࠿࡜ᛮ࠺ࡢ࡛ࡍࡀࠊබⓗヨ㦂࡜࠸࠺ࡢࡣ࡝࠺ࡋ࡚ࡶ㠀ᖖ࡟೧࠸᪉ࡢጤဨ఍ࡀ
ୖ࡟ࡘ࠸࡚ࡲ࠸ࡾࡲࡍࠋࡑ࠺࠸࠺ேࡓࡕࡢࡈពぢ࡛࠶ࡿ࡜࠿ࠊ࠶ࡿ࠸ࡣస㢟⪅ࡢࡈពぢࠊ࠶ࡿ࠸ࡣ࣮ࣘࢨ࣮ഃࡢࡈពぢࡀ࠸ࢁ࠸ࢁ࡞ᙧ࡛᢬ᢠ࡜ࡋ࡚ࠊ୍✀ࡢᦶ᧿࡟ࡘ࡞ࡀࡿ࡜࠸࠺
ࡇ࡜ࡀࡈࡊ࠸ࡲࡍࠋ࡛ࡶࠊࡇࢀࡣ௒᪥ࡢ࠾ヰࡢ୰࡛ࡣ┬␎ࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
ࡑࡢ࡜ࡁఱࢆ⌮ゎࡋ࡚ࡶࡽ࠺࡭ࡁࡔࡗࡓࡢ࠿࡜࠸࠺ࡇ࡜࡛ࡍࡀࠊࡇࡢࢫࣛ࢖ࢻ࡟᭩ࡁࡲ
ࡋࡓࡢࡣࠊඛ࡯࡝⏦ࡋୖࡆࡓᨻ⟇⛉◊ࢆ⏦ㄳࡋࡓ㝿࡟ࡘ࠸࡚ࡁࡓࢥ࣓ࣥࢺ࡛࠶ࡾࡲࡍࠋࡲ
ࡎ➨ 1 ࡟ࠊḢ⡿࡛㛤Ⓨࡉࢀࡓᡭἲࡀ࠶ࡿࡢ࡛࠶ࢀࡤࠊࡑࢀࢆࡑࡢࡲࡲ౑࠼ࡤ࠸࠸ࡢ࡛ࡣ࡞
࠸࠿ࠋࡇࢀࡣ௒᪥ࡢၥ㢟Ⅼࡢ 1 ࡘ࡛ࡈࡊ࠸ࡲࡍࠋ
ࡑࢀ࠿ࡽࠊᡃࡀᅜ࡟ࡶࡑ࠺࠸࠺ᚓⅬ➼໬ࢆྍ⬟࡟ࡋ࡚࠸ࡿヨ㦂ࡣ᪤࡟Ꮡᅾࡍࡿࡢ࡛ࡣ࡞
࠸࠿ࠋ☜࠿࡟ࡑࢀࡣࡈࡊ࠸ࡲࡋࡓࠋ౛࠼ࡤࠊ༡㢼ཎඛ⏕ࡓࡕࡢᮾி኱Ꮫࡢࢢ࣮ࣝࣉ࡛࠾ࡘ
ࡃࡾ࡟࡞ࡗࡓㄒᙡ⬟ຊࡢヨ㦂࡜࠸ࡗࡓࡶࡢࡣ᪤࡟Ꮡᅾࡋ࡚࠸ࡓࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡇࢀࡣ
ࣁ࢖ࢫࢸ࣮ࢡࢫ࡞ヨ㦂࡜࠸࠺ࢃࡅ࡟ࡣ࠸࠿࡞࠿ࡗࡓࡔࢁ࠺࡜ᛮ࠸ࡲࡍࠋ
ࡑࡢ௚࡟ࡶࠊᏛ⾡◊✲࡜ࡋ࡚⾜ࢃࢀࡓ౛ࡀ࠶ࡿࡢ࡛ࡣ࡞࠸࠿ࠋࡇࢀࡣࡑࢀࡇࡑࠊࡑ࠺࡛
ࡍࡡࠋࣞ࣋ࣝࡢ㧗࠸ࡶࡢࡀᏑᅾࡋ࡚࠾ࡾࡲࡋࡓࡀࠊ⌧ᐇሙ㠃࡛౑ࢃࢀࡿヨ㦂࡜࠸࠺ࢃࡅ࡛
ࡣ࡞࠿ࡗࡓࠋࡲࡓࠊࢹ࣮ࢱ࣮࣋ࢫࢆ⛉◊࡛ࡘࡃࡗ࡚࠾࠸࡚ࠊࡑࢀࢆ஦ᴗ࡟㌿⏝ࡋ࡚࠸࠸ࡢ
࠿ࠋࡲ࠶ࠊ࠶ࡾࡑ࠺࡞ヰ࡛ࡣ࠶ࡾࡲࡍࠋ┠ᣦࡋ࡚࠸ࡿࡢࡣࠊ࠶ࡃࡲ࡛ࡶ◊✲ẁ㝵࡛ࠊᐇ⏝໬ࡲ࡛ࡣ↓⌮ࡔ࡜⟅࠼ࡲࡋࡓࠋ
௒ࡶࡇ࠺࠸ࡗࡓ཯ㄽࡣࠊ࡜ࡾ࠶࠼ࡎฟ࡚ࡃࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠺ࢇ࡛ࡍࡀࠊࡑࡢ୰࡛ࡶ
ࡲࡎࠊIRT ࡜࠸࠺ࡶࡢ࡟ᑐࡋ୍࡚ᐃࡢㄗゎࡀ࠶ࡗࡓ࡜࠸࠺ࡇ࡜ࠊࡇࢀࡣ⏦ࡋୖࡆ࡚࠾࠿࡞
ࡅࢀࡤ࡞ࡽ࡞࠸࡜ᛮ࠸ࡲࡍࠋ
㡯┠཯ᛂ⌮ㄽ IRT ࡟㛵ࢃࡿㄗゎ࡜ㄢ㢟
IRT ࡜࠸࠺⌮ㄽࡀࠊ࠶ࡿ࠸ࡣࣉࣟࢢ࣒ࣛࡀ࠶ࢀࡤᚓⅬ➼໬ࡣ࡛ࡁࡿࠋࡑࢀ࠿ࡽヨ㦂ࡢ኱

－88－㒊ศࡢၥ㢟ࡣゎỴ࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀ࡚࠸ࡓ೧࠸᪉ࠎࠊࡇࢀࡣィ㔞ᚰ⌮Ꮫࡢᑓ㛛ᐙ࡛ࡣ࡞࠸᪉ࠎ࡛ࡍࡅࢀ࡝ࡶࠊࡑ࠺࠸࠺᪉ࠎࡀ࠸ࡽࡗࡋࡷࡗࡓࠋኻ♩࡞ࡀࡽࠊ⚾ࡣࡑ࠺ࡋࡓ᪉ࠎࢆ༙ྍ㏻࡜࿧ࡤࡏ࡚࠸ࡓࡔࡁࡲࡍࡀࠊ౛࠼ࡤࠊࡇࢀࡣࡕࡻࡗ࡜ᴟ➃࡞౛࡛ࡍࡅࢀ࡝
ࡶࠊIRT ࡜࠸࠺ࡶࡢࠊࡇࢀࡣ latent trait ࡜࠸࠺ࠊ௒ࡲ࡛௒᪥ࡢࡈሗ࿌ࡢ୰࡛ࡣ༢࡟ trait ࡜࿧ࡤࢀ࡚࠸ࡿሙྜࡀከ࠿ࡗࡓ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡇࡢ latent trait ࡜࠸࠺ゝ࠸᪉ࠊࡇࢀࡣ
せࡍࡿ࡟ࠊ┤᥋ほࡍࡿࡇ࡜ࡣ࡛ࡁ࡞࠸ࡅࢀ࡝ࡶࠊ㡯┠཯ᛂ࠿ࡽ᥎

ࡉࢀࡿ୍ࡘࡢ⬟ຊࡢḟඖ࡜࠸ࡗࡓࡶࡢࢆᣦࡋ࡚࠸ࡿࢃࡅ࡛ࡍࠋࡅࢀ࡝ࡶࠊࡇࡢ latent ࡜࠸࠺ゝⴥࡣࠊ₯ᅾࡋ࡚࠸ࡿࠊ௒ࡣ࠶ࡽࢃࢀ࡚࠸࡞࠸ࡅࢀ࡝ࡶᑗ᮶㛤ⰼࡍࡿ࠿ࡶࡋࢀ࡞࠸ࠊࡑ࠺࠸࠺ྍ⬟ᛶ࡜ࡋ࡚ࡢ⬟ຊࡢࡼ࠺࡞ࡶࡢࢆᣦࡍ࡜࠸࠺㎡᭩ⓗព࿡ࡣࠊ☜࠿࡟࠶ࡿࢇ࡛ࡍࡡࠋࡘࡲࡾࠊྠࡌࢹ࣮
ࢱ࡛ࡶ IRT ࡜࠸࠺ࣉࣟࢢ࣒ࣛࢆ㏻ࡍࡇ࡜࡟ࡼࡗ࡚₯ᅾ⬟ຊࡢᣦᶆ࡟࡞ࡾᚓࡿࡢ࡛ࡣ࡞࠸࠿
࡜࠸࠺㢼࡟ᛮࡗ࡚࠾ࡽࢀࡓ᪉ࡀ࠶ࡿࠊࡇࢀࡣ࡜ࢇ࡛ࡶ࡞࠸ㄗゎࡔ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡲ
ࡎࡣࡑ࠺࠸࠺࡜ࡇࢁࢆṇࡍࡇ࡜࠿ࡽጞࡵ࡞ࡅࢀࡤ࡞ࡽ࡞࠿ࡗࡓࡢ࡛ࡍࠋ
⚾ࡣࡓࡲࡓࡲ௒ࡢ⛉◊ࡢ௦⾲⪅࡛࠶ࡿ࡜࡜ࡶ࡟ࠊᗄࡘ࠿ࡢⅬ࡛እ㒊࡜ࡢ࢖ࣥࢱ࣮ࣇ࢙࢖
ࢫࡢᙺࢆࡋ࡚࠾ࡾࡲࡋࡓࠋᐇ㝿࡟ࢸࢡࢽ࢝ࣝ࡞㠃࡛ࡈά㌍࠸ࡓࡔ࠸ࡓࡢࡣࠊ௒᪥ࡶࡇࡇࡢ
࠾࠸࡛࡟࡞ࡗ࡚࠸ࡿఱே࠿ࡢඛ⏕᪉࡛ࡍࡅࢀ࡝ࡶࠊ⚾ࡢ᪉ࡣࠊ࢖ࣥࢱ࣮ࣇ࢙࢖ࢫ࡜ࡋ࡚࠸
ࢁ࠸ࢁ࡞⤒㦂ࢆࡋࡓ࡜࠸࠺ࡇ࡜ࢆࠊ௒᪥ࡣ࠾ヰࡋࡋ࡚࠸ࡿࢃࡅ࡛ࡍࠋ
࡜ࡶ࠿ࡃఱ࠿ィ⟬ࡍࢀࡤ」ᩘᅇヨ㦂ࡢ㛫ࡢᚓⅬࢆẚ㍑࡛ࡁࡿ࡜ᛮࡗ࡚࠾ࡽࢀࡓ᪉ࡀከࡃ
࡚ࠊࡇࢀࡣࡕࡻࡗ࡜ࡇࡢศ㔝ࡢ◊✲⪅ࡢ㄂኱ᐉఏ࡟ࡶཎᅉࡀ࠶ࡿ࡞࡜ࠊᐇࡣᛮࡗ࡚࠸ࡲࡍࠋ
ࡑࢀ࠿ࡽࠊࡇࡢᡭࡢࡶࡢࡣᩘᏛ⪅࡟ࡣ࡯࡜ࢇ࡝ㄆࡵ࡚ࡶࡽ࠼࡞࠸ࠊࡇࡇࡶ࠾ࡶࡋࢁ࠸࡜ࡇ
ࢁ࡛ࡍࠋ࡜ࡶ࠿ࡃఱࢆࢃ࠿ࡗ࡚ࡶࡽࢃ࡞ࡅࢀࡤ࠸ࡅ࡞࠸࠿࡜࠸࠺࡜ࠊᚓⅬ➼໬ࡀྍ⬟࡟࡞ࡿࡓࡵ
࡟ࡣ IRT ࡜࠸࠺ᩘ⌮ࣔࢹࣝࡣ࠶ࡗࡓ࡜ࡋ࡚ࡶࠊᚓⅬ㡯┠ࡀ࠶ࡿ⛬ᗘඹ㏻ࡋ࡚࠸ࡿ 2 ࡘࡢヨ㦂ࡢ㛫࠿ࠊ࠶ࡿ࠸ࡣཷ㦂⪅ࡀඹ㏻ࡋ࡚࠸ࡿ 2 ࡘࡢヨ㦂ࡢ㛫࡛ࡋ࠿ᚓⅬㄪᩚࡣྍ⬟࡛࡞࠸࡜࠸࠺ࡇ࡜࡞ࡢ࡛ࡍࠋࡓࡔࡋࠊཷ㦂⪅ࡣ᫬㛫ࡀࡓ࡚ࡤ⬟ຊࡀୖࡀࡗࡓࡾୗࡀࡗࡓࡾࡋࡲࡍ࠿
ࡽࠊ㠀ᖖ࡟᥋㏆ࡋࡓ᫬Ⅼ࡛ྠࡌཷ㦂⪅࡟ 2 ࡘࡢヨ㦂ࢆཷࡅ࡚ࡶࡽࡗࡓሙྜ࡟㝈ࡽࢀࡲࡍࠋ
ࡓࡔࡋࠊࡑࢀ࡛ࡣࠊ࠸ࡁ࡞ࡾⰋၥࢆࡑࢁ࠼ࡿࡢࡀ㞴ࡋ࠸࠿ࡽࠊணഛࢸࢫࢺ࡜࠸ࡗ࡚ࠊᐇ㝿ࡢཷ㦂⪅㞟ᅋ࡜࡯ࡰྠ➼࡜ᛮ࠼ࡿᑡᩘࡢཷ㦂⪅࡟࡜ࡾ࠶࠼ࡎཷࡅ࡚ࡶࡽ࠺ࡇ࡜ࡀᮃࡲࡋ࠸
࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋࡍࡿ࡜ࠊ࠶ࡿ୍ᐃᩘࡢேࡓࡕ࡟ࠊࡑࡢᚋ࡛౑ࢃࢀࡿၥ㢟ࢆぢࡏ࡞ࡅࢀࡤ࡞ࡽ࡞ࡃ࡞ࡿࠋࡋ࠿ࡋࠊ➨ 1 ࡢ᮲௳ࠊࡘࡲࡾ 2 ࡘࡢヨ㦂࡟ྠ୍ࡢၥ㢟ࢆྵࡵࡿ࡜࠸
࠺ࡢࡣࠊ➃ⓗ࡟ゝ࠼ࡤၥ㢟ࡢ෌฼⏝࡜࠸࠺ࡇ࡜࡛ࡍ࠿ࡽࠊணഛࢸࢫࢺࡶ࡛ࡁࡿࡔࡅ⛎ᐦࡀಖ࡚ࡿ᮲௳࡛⾜ࡗ࡚ࠊᇶᮏⓗ࡟ၥ㢟ࡣ㠀බ㛤࡟ࡍࡿࡋ࠿࠶ࡾࡲࡏࢇࡀࠊIRT ࢆࡍࡈࡃ᥎㐍ࡋ࡚࠸ࡽࡗࡋࡷࡿ࡜ᛮࡗ࡚࠸ࡓඛ⏕ࡀࠕヨ㦂ࢆࡸࡗࡓࡽ඲㒊ࡢၥ㢟ࢆ┤ࡕ࡟බ㛤ࡍࡿࡢࡀᖖ㆑࡛࠶ࡿࠖ࡜࠾ࡗࡋࡷࡗࡓࡾࡍࡿࡢ࡛ࠊࡇࢀࡶ኱ኚᅔࡾࡲࡋࡓࠋ
ࡶ࠺୍ࡘࡣࠊࡸࡣࡾ IRT ࡢ⌮ㄽᵓᡂୖࠊ௒᪥ࡢࡈሗ࿌ࡢ୰࡟ࡶ࠶ࡗࡓ࡜ᛮ࠸ࡲࡍࡅࢀ࡝
ࡶࠊᇶᮏⓗ࡟ࡣᑠၥ୺⩏ࠊࡘࡲࡾከᩘࡢᑠၥ࠿ࡽ࡞ࡿࢸࢫࢺ࡟ࡍࡿࡇ࡜ࡣ୙ྍ㑊࡛ࡍࠋࡇࡢࡇ࡜ࡣࠊᖖ࡟ၥ㢟౪⤥ࡢయไࢆᵓ⠏ࡋ࡚࠾࠿࡞ࡅࢀࡤ࠸ࡅ࡞࠸ࠊࡑࡋ࡚ࡑࡢࢹ࣮ࢱࢆ⵳
✚ࡋ࡚࠾࠿࡞ࡅࢀࡤ࠸ࡅ࡞࠸ࡇ࡜ࢆព࿡ࡋࡲࡍࠋࡑࢀ࠿ࡽࠊၥ㢟ࢆ୍㒊ศࡣබ㛤ࡋ࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜ࡍࢀࡤࠊࡑࡢබ㛤ྍ⬟ᛶࡢ⠊ᅖࢆ࡝࠺Ỵࡵࡿ࠿࡜࠸࠺ࡇ࡜ࡀ࠶ࡾࡲࡍࡋࠊࡉ
ࡽ࡟ணഛヨ㦂ࡀ࡛ࡁࡿࡢ࠿࡛ࡁ࡞࠸ࡢ࠿ࠊࡉࡽ࡟ࠊࡇࢀࡣ࠶ࡿព࿡࡛᭱ᚋࡢᡭẁ࡛ࡍࡀࠊ－89－ࣔࢽࢱ࣮ヨ㦂࡜࠸࠺ࠊ␃Ꮫ⏕࡛ࡣ࡞࠸ேࡓࡕࢆ౑ࡗ࡚ၥ㢟ࡢ㞴᫆ᗘࢆ᥎ᐃࡍࡿ࡜࠸ࡗࡓࡇ
࡜ࡀ㉳ࡇࡗ࡚ࡁࡓ࡜ࡁ࡟ࠊࡑࡢࢭ࢟ࣗࣜࢸ࢕ࢆ࡝࠺ࡍࡿࡢ࠿࡜࠸ࡗࡓከࡃࡢགྷ௓࡞ၥ㢟࡟ࡘ࡞ࡀࡗ࡚࠸ࡁࡲࡍࠋ
ࡇࡢ㎶࡟㠀ᖖ࡟⚄⤒ࢆ౑ࢃࡊࡿࢆᚓ࡞࠸ࡢࡣࠊࡇࢀࡣᮾ࢔ࢪ࢔ⓗヨ㦂㢼ᅵ࡜࠾ࡗࡋࡷࡿ
᪉ࡶ࠶ࡾࡲࡍࡅࢀ࡝ࡶࠊ㐣ཤၥ࡟㠀ᖖ࡟ࡇࡔࢃࡿேࡓࡕࡀከ࠸࡜࠸࠺ࡇ࡜࡟ࡶ㉳ᅉࡍࡿ࠿ࡶ▱ࢀࡲࡏࢇࠋ㐣ཤၥࢆఱ࡜ࡋ࡚ࡶᡭ࡟ධࢀࡓ࠸ࠊ࠶ࡿ࠸ࡣヨ㦂ၥ㢟ࢆぢࡓࡽࡍࡄ࡟ࢥࣆ
࣮ࡍࡿ័⩦ࡀ࠶ࡿࡼ࠺࡞ࠊㄒᏛᏛᰯ࡞࡝࡛⪺࠸࡚ࡳࡿ࡜ࡑ࠺࠸࠺ࡇ࡜ࡀ࠶ࡿ࡜࠸࠺ࡇ࡜࠿
ࡽࠊ࡝࠺ࡋ࡚ࡶࢭ࢟ࣗࣜࢸ࢕ࡢၥ㢟࡟⚄⤒ࢆ౑ࢃ࡞ࡅࢀࡤ࡞ࡽ࡞࠿ࡗࡓࠋ
ヨ㦂ࡢᐇ᪋యไࡢㄢ㢟ࡇࢀ௨እ࡟ࡶࠊࡸࡽ࡞ࡅࢀࡤ࠸ࡅ࡞࠸ࡇ࡜ࡀᗄࡘ࠿࠶ࡾࡲࡋࡓࠋࡓ࡜࠼ࡤࠊᾏእ࡛ッゴࢆ㉳ࡇࡉࢀࡿࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺ࡇ࡜ࡣࠊ⚾ࡣ࠿࡞ࡾᮏẼ࡛ᚰ㓄ࡋ࡚࠾ࡾࡲࡋࡓࡀࠊ௒ࡲ
࡛ࡢ࡜ࡇࢁࠊࡇ࠺ࡋࡓࡇ࡜ࡣ㉳ࡇࡗ࡚࠸࡞࠸ࡼ࠺࡛ࡍࠋ
࡜ࡶ࠿ࡃࠊ඲య࡜ࡋ࡚࡝ࡢࡄࡽ࠸ࣄࢺ࡜࢝ࢿࢆᢞධࡋ࡞ࡅࢀࡤ࠸ࡅ࡞࠸࠿ࠋࡑࡢ᫬Ⅼ࡛⚾ࡣཱྀࢆ㛤ࡅࡤࠕࣄࢺ࡜࢝ࢿࠖ࡜ゝࡗ࡚࠾ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊ࡛ࡣࠊලయⓗ࡟࡝ࡢࡄࡽ࠸
ࡢࡶࡢࡀᚲせ࡞ࡢ࠿ࡣࡼࡃࢃ࠿ࡗ࡚࠾ࡾࡲࡏࢇ࡛ࡋࡓࠋࡶࡕࢁࢇࠊᢏ⾡ⓗ࡞㠃࡛ࡣࠊඛ࡯
࡝ࡈⓎ⾲࠸ࡓࡔ࠸ࡓ๓ᕝඛ⏕ࢆึࡵ࡜ࡍࡿ⌧ሙࡢ᪉ࠎࠊ㠀ᖖ࡞ࡈⱞປࡀ࠶ࡗࡓ࡜ᛮࡗ࡚࠸
ࡲࡍࠋ୍ࡘࡢ౛࡜ࡋ࡚ࠊࡸࡣࡾ࢔࢖ࢸ࣒ࣛ࢖ࢱ࣮㞟ᅋ࡜࠸࠺ࡶࡢࢆ࡝࠺ࡋ࡚ࡶᣢࡗ࡚࠸࡞ࡅࢀ
ࡤ࠸ࡅ࡞࠸ࠋ኱Ꮫࡢඛ⏕࡟∦ᡭ㛫࡛ၥ㢟ࢆࡘࡃࡗ࡚࠸ࡓࡔࡃࡼ࠺࡞≧ἣ࡛ࡣ࡜࡚ࡶ㏣࠸ࡘ
ࡁࡲࡏࢇࡢ࡛ࠊእ㒊࡟࢔࢖ࢸ࣒ࣛ࢖ࢱ࣮㞟ᅋࢆᢪ࠼ࡿᚲせࡀ࠶ࡾࡲࡋࡓࠋ
ࡇ࠺࠸ࡗࡓࡇ࡜ࡀẚ㍑ⓗࢫ࣒࣮ࢬ࡟࠸ࡗࡓࡢࡣࠊ᪥ᮏㄒᩍ⫱࡜࠸࠺ศ㔝ࡣẚ㍑ⓗᾏእ஦
᝟࡟㏻ࡌࡓேࡀከ࠸ࠊ࠶ࡿ࠸ࡣᾏእ࡛ᩍ࠼࡚ࡁࡓࠊ࠶ࡿ࠸ࡣᾏእ࡛ᩍ⫱ࢆཷࡅ࡚ࡁࡓேࡓ
ࡕࡀከ࠿ࡗࡓࡢ࡛ࠊࡇ࠺࠸ࡗࡓࢫࢱ࢖ࣝࢆ࡜ࡾᚓࡓࢇ࡛ࡍࡀࠊ௚᪉࡛ࠊࡇࡢᚋ࡛⏦ࡋୖࡆࡿࡼ࠺࡞ࠊ࡝࠺ࡋ࡚ࡶၥ㢟ࢆࡼࡾ࣮࢜ࢭࣥࢸ࢕ࢵࢡ࡞ࡶࡢ࡟ࡋࡓ࠸࡜࠸࠺せồࡀᙉࡃ࡚ࠊ
ࡇࡇࡢ࡜ࡇࢁࡢጇ༠ࡀ⤖ᵓ㞴ࡋ࠸࡜࠸࠺ࡇ࡜ࡀ࠶ࡾࡲࡋࡓࠋࢺ࣮ࣞࢽࣥࢢࢆࡋࡓ࢔࢖ࢸ࣒
ࣛ࢖ࢱ࣮ࡣࠊ኱య኱Ꮫ㝔⏕ࣞ࣋ࣝࡢேࡓࡕ࡛ࡋࡓࡅࢀ࡝ࡶࠊࡇࡢேࡓࡕ࡛ࡶࢫ࢟ࣝࡢ⩦ᚓࡣẚ㍑ⓗ᪩࠿ࡗࡓࡼ࠺࡟ᛮࡗ࡚࠸ࡲࡍࠋ
ࡑࢀ࠿ࡽࠊవࡾ⪃࠼࡚࠸࡞࠿ࡗࡓࡇ࡜ࡀᗄࡘ࠿ࡈࡊ࠸ࡲࡋࡓࠋࡲࡎࠊࡔࢀࡀヨ㦂┘╩ࢆࡍࡿࡢ࠿ࠋࡶࡣࡸࠊ኱Ꮫᩍဨࡣືဨ࡛ࡁ࡞࠸ࡢ࡛኱Ꮫ⫋ဨࡢ᪉ࠎ࡟࠾㢪࠸ࡋࡓࢃࡅ࡛ࡍࡀࠊ
⚾ࡶヨ㦂ሙ࡟ධࢀ࡚ࡶࡽ࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊࡸࡣࡾ࡞࠿࡞࠿័ࢀ࡞࠸࡜࠸࠺ࡇ࡜࡟ၥ㢟ࡀ࠶ࡗࡓࠋࡑࢀ࠿ࡽࠊእᅜேࡀཷࡅࡿ࡜࠸࠺ࡇ࡜ࡣࠊ᪥ᮏࡢヨ㦂ࡢᖖ㆑ࡀవࡾ㏻⏝ࡋࡲࡏࢇࠋヨ㦂ሙ࡟࠶ࡿධᏛヨ㦂ࡢᙇࡾワࡵࡓࡼ࠺࡞㞺ᅖẼࡣࠊవࡾឤࡌࡽࢀࡲࡏࢇࠋ⤖ᯝⓗ࡟㠀ᖖ࡟୙
ṇ⾜ⅭࡀࡓࡃࡉࢇⓎ⏕ࡋ࡚ࠊࡑࢀࡢ᦬Ⓨ࡟㏣ࢃࢀࡿࡼ࠺࡞ࡇ࡜࡟ࡶ࡞ࡾࡲࡋࡓࠋࡉࡽ࡟ゝ࠺࡜ࠊࡇࢀࡣࠊูࡢヨ㦂࡛ࡢヰ࡛ࡍࡀࠊヨ㦂ၥ㢟ࢆඛ࡟㏦௜ࡋ࡚࠾ࡃ࡜ࠊ࡞ࢇ࡜ࡑࢀࢆ㛤ᑒࡋ࡚㓄ࡗ࡚ࡋࡲ࠺᪉ࡀᏑᅾࡍࡿ࡜࠸࠺ࡇ࡜ࡶ࠶ࡗ࡚ࠊࢢ࣮ࣟࣂࣜࢮ࣮ࢩࣙࣥ࡜࠸࠺ࡢࡣ
ࡦࡻࡗ࡜ࡍࡿ࡜ࡇ࠺࠸࠺ࡇ࡜࡛ࡶ࠶ࡿࡢ࠿࡞࡜ᛮࡗࡓࡇ࡜ࡶ࠶ࡾࡲࡋࡓࠋ
᪥ᮏேᑐ㇟࡟ࡋ࡚ࡶࠊࡇࢀ࠿ࡽၥ㢟ࢆ᧜ᙳࡉࢀ࡚ࡋࡲ࠺࡜࠿ࠊ࠶ࡿ࠸ࡣ㡢ኌࢆ㘓㡢ࡉࢀ࡚ࡋࡲ࠺࡜࠸ࡗࡓࡇ࡜ࡣᙜ↛㉳ࡇࡗ࡚ࡃࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࠋ

－90－ᑠၥᙧᘧၥ㢟࡜ authentic ၥ㢟స 㢟 ࡟ 㛵 ࡋ ࡚ ࡛ ࡍ ࡀ ࠊ CBT ໬ ࡉ ࢀ ࡓ ヨ 㦂 ࢆ ⪃ ࠼ ࡿ ࡜ ࡍ ࢀ ࡤ ࠊ ࡝ ࠺ ࡋ ࡚ ࡶ ከ ⫥ 㑅 ᢥ ࡢ ၥ㢟࡟࡞ࡿࡔࢁ࠺࡜ᛮ࠸ࡲࡍࠋࡑࡋ࡚ࠊඛ࡟ࡶ⏦ࡋୖࡆࡓᑠၥᙧᘧ࡜࠸࠺ࡶࡢࡀࠊヨ㦂ࡢᚓⅬࡢಙ㢗ᛶࠊጇᙜᛶࢆ☜ಖࡍࡿୖ࡛኱ኚ㔜せ࡞ഃ㠃ࡔ࡜࠸࠺ࡇ࡜ࡶ࠶ࡿࢃࡅ࡛ࡍࠋࡅࢀ࡝
ࡶࠊ≉࡟ࡤࡽࡤࡽࡢ▱㆑࣭ᢏ⬟ࠊࡇࡇࡢ࡜ࡇࢁࡀ୰ᩍᑂ⟅⏦࡞࡝࡛㠀ᖖ࡟ᢈุࡉࢀ࡚࠸ࡿ
࡜ࡇࢁࡔ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊㄒᏛࡢሙྜ࡟ࡣࠊࡇ࠺࠸࠺ࡶࡢࢆ discrete points 㞳ᩓⓗⅬ࡜࿧ࡪࡼ࠺࡛ࡍࠋヨ㦂⠊ᅖࢆබ♧ࡍࡿ࡜ࡍࢀࡤࠊࡇ࠺࠸࠺ࡶࡢࢆ⥙⨶ⓗ࡟ิᣲࡍࡿࡢࡣẚ
㍑ⓗᐜ࡛᫆ࡍࠋࡑࢀࢆ୍ぴ⾲ࡢᙧ࡟ࡋ࡚࠾ࡅࡤࠊ࡯࡜ࢇ࡝⮬ືⓗ࡟ၥ㢟ࡀ࡛ࡁࡿࡢ࡛ࡣ࡞
࠸࠿࡜࠸࠺⪃࠼ࡶ࠶ࡾࡲࡋࡓࠋึᮇࡢ᪥ᮏㄒࢸࢫࢺࡢၥ㢟ࡣࠊࡇ࠺࠸ࡗࡓᙧࡢࡶࡢࡔࡗࡓ
ࡢ࡛ࡍࡅࢀ࡝ࡶࠊࡇࢀ࡛ࡣ┿ࡢゝㄒ⬟ຊࡣࢀ࡞࠸ࡢ࡛ࡣ࡞࠸࠿ࠊせࡍࡿ࡟ࠊ࠸ࡲࠊධᏛ

ヨ㦂࡛ၥ㢟࡟࡞ࡗ࡚࠸ࡿࡼ࠺࡞⾲⌧ຊ࡛࠶ࡿ࡜࠿ᛮ⪃ຊ࡛࠶ࡿ࡜࠿ุ᩿ຊ࡛࠶ࡿ࡜࠸ࡗࡓ
ࡶࡢࡣࢀ࡞࠸ࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺ࡇ࡜࡟࡞ࡾࡲࡍࠋ࡛ࡣࠊ࠸ࢃࡤ┿ࡢᏛຊ࡟┦ᙜࡍࡿࡶࡢࢆࡿࡓࡵ࡟ࡣࠊࡶࡗ࡜ authentic㸦᭱㏆ࡣࠕ┿ṇ࡞ࠖ࡜ヂࡉࢀ࡚࠸ࡿࡼ࠺࡛ࡍ㸧ၥ㢟࡜ࡣ࡝ࡢࡼ࠺࡞ࡶࡢ࠿࡜ゝ࠺࡜ࠊࡇࡢࢫࣛ࢖ࢻࡢࡼ࠺࡞ࡶࡢ࡟࡞ࡾࡲࡍࠋ2 ࣮࣌ࢪ࡟ࢃࡓࡿ
ࡇࡢ⛬ᗘࡢ㛗ࡉࡢၥ㢟࡛ࡍࡀࠊࡇࢀ࡛ࡓࡗࡓ 1 ࡘࡢ⟅࠼ࡋ࠿ồࡵࡽࢀ࡞࠸ࠋࡘࡲࡾࠊඛ࡯
࡝๓࡟ᫎࡋࡓၥ㢟࡛࠶ࢀࡤ 30 ศࡢࢸࢫࢺ࡟ 60 ၥ࡜࠿ࡑ࠺࠸ࡗࡓࡇ࡜ࡀ࡛ࡁࡿࢃࡅ࡛ࡍࡅ
ࢀ࡝ࡶࠊࡇ࠺࠸࠺ၥ㢟ࡣ 1 ၥࡸࡿࡢ࡟ 2 ศ⛬ᗘࡀ࠿࠿ࡾࡲࡍࡢ࡛ࠊࡲࡎࠊྠࡌ᫬㛫࠿ࡅ࡚ࡶࠊಙ㢗ᛶࡀపୗࡋ࡚ࡋࡲ࠺࡜࠸࠺ၥ㢟ࡶ࠶ࡿࢃࡅ࡛ࡍࠋࡉࡽ࡟ࠊࡇ࠺ࡋࡓၥ㢟ࡢస㢟ࡢ㞴ࡋࡉࡀ࠶ࡾࡲࡍࡋࠊࡑࢀ௨እ࡟ࡶࠊ࠸ࢁ࠸ࢁ࡜ၥ㢟ࡀฟ࡚ࡁࡲࡍࠋ
ࡓ࡜࠼ࡤࠊᐇ㝿࡟ࡣ」ᩘࡢၥ㢟ࢆ⤌ࡳྜࢃࡏ࡚ 1 ࡘࡢࢸࢫࢺࢆసࡗ࡚࠸ࡃࠋࡑࢀࡶ」ᩘ
ࡢࣂ࣮ࢪࣙࣥసࡗ࡚࠸ࡃ࡜ࡁ࡟ࠊ౛࠼ࡤ௒ࡢࣜࢫࢽࣥࢢࡢၥ㢟࡞࡝ࡣࠊ୍ᐃ᫬㛫࡟ࡕࡻ࠺࡝࠾ࡉࡵࡿࡇ࡜ࡀ㠀ᖖ࡟㞴ࡋࡃ࡞ࡗ࡚ࡁࡲࡍࠋ࠶ࡿᩍᐊ࡛ࡣ 2 ศࡄࡽ࠸᫬㛫ࡀ࠶࠸࡚ࡋࡲ
࠺࡜࠿ࠊูࡢᩍᐊ࡛ࡣᮏᙜ࡟ࡂࡾࡂࡾ࡟࡞ࡿ࡜࠸ࡗࡓࡇ࡜ࡶࠊ࠶ࡿ⛬ᗘࡢ㛗ࡉࡢၥ㢟ࢆ⪃࠼ࡼ࠺࡜࠸࠺ࡇ࡜࡟࡞ࡿ࡜㉳ࡇࡗ࡚ࡲ࠸ࡾࡲࡍࠋ
࡜ࡇࢁࡀࠊ࡝࠺ࡋ࡚ࡶࡇ࠺࠸ࡗࡓ᪉ྥ࡟⾜࠿ࡊࡿࢆᚓ࡞࠸ࡢࡣࠊࢸࢫࢺࡢෆᐜࡀᐇ㝿ࡢᩍ⫱ㄢ⛬࡟ᑐࡋ࡚㏫࡟ᙳ㡪ࡋ࡚࠸ࡃ࡜࠸࠺ࡇ࡜ࠊࡇࢀࢆ㐳ཬຠᯝ࡜࿧ࡪࡼ࠺࡛ࡍࡅࢀ࡝ࡶࠊ
࡝࠺ࡶࡇࡢࢸࢫࢺ࡛

ࢀ࡚࠸࡞࠸┿ࡢᏛຊࡀᏑᅾࡍࡿࡢ࡛ࡣ࡞࠸࠿࡜࠸ࡗࡓࡇ࡜ࡢ᰿ᣐ

ࡣࠊࡇࡢࡇ࡜࡟࠶ࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࠋࢸࢫࢺࡢᙧᘧࠊෆᐜࡀᩍ⫱ෆᐜࢆ㏫࡟つᐃࡋ࡚ࡋࡲ࠺ࠋࡘࡲࡾࠊࢸࢫࢺࡢ‽ഛᩍ⫱࡜࠸࠺ࡢࡀᩍ⫱ࡢ┠ᶆ࡟࡞ࡗ࡚ࡋࡲ࠸ࡲࡍ࡜ࠊࢸ
ࢫࢺ࡛Ⅼᩘࢆ࡜ࡿࡇ࡜ࡑࡢࡇ࡜ࡀࠊࡸࡣࡾࢸࢫࢺࡢⅬᩘ࡟ᑐࡋ࡚ᙳ㡪ࢆ୚࠼ࡿࠋࡘࡲࡾࠊ
┿ࡢ⬟ຊ࡜࠸࠺ࡶࡢࡀ࠶ࡿ࡜ࡍࢀࡤࠊࡇࡢࢸࢫࢺࡣ┿ࡢ⬟ຊࢆ࠸ࢃࡤࡇࡢࢸࢫࢺࡢࡓࡵࡢ⬟ຊࢆࡗ࡚࠸ࡿࡢ࡛ࡣ࡞ࡃ࡚ࠊ

ࡗ࡚࠸ࡿࡇ࡜࡟࡞ࡗ࡚ࡋࡲ࠺ࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺ࡇ࡜

ࡀࠊ௒ᅇࡢࡳ࡞ࡽࡎᖖ࡟ᨵ㠉ࡢࣔࢳ࣮࣋ࢩࣙࣥ࡟࡞ࡗ࡚ࡁࡓࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࠋ
᪥ᮏㄒࡢࢸࢫࢺ࡟ࡘ࠸࡚ゝ࠼ࡤࠊ⚾ࡣ᪥ᮏㄒᩍ⫱ࡢඛ⏕࡟ࠊḟࡢࡼ࠺࡞㆟ㄽࢆ྿ࡁ࠿ࡅ
ࡓࡇ࡜ࡀ࠶ࡾࡲࡋࡓࠋඛ࡯࡝ࠊauthentic ࡛࡞࠸ࢸࢫࢺࡢ౛࡜ࡋ࡚࠾ぢࡏࡋࡓ㞳ᩓⓗ࡞▱㆑ࡸᢏ⬟ࡢࢸࢫࢺࡣࠊ᪥ᮏㄒࡢ native speaker ࡛࠶ࢀࡤࠊ࡯ࡰ㛫㐪࠸࡞ࡃṇゎ࡛ࡁࡿࡼ࠺࡞
ࡶࡢ࡛ࡍࠋࡘࡲࡾࠊࡇࢀࡽࡣ᪥ᮏㄒ⬟ຊࡢᚲせ᮲௳࡛ࡣ࠶ࡿࢃࡅ࡛ࡍࡡࠋࡶࡋࡶࠊࡇ࠺࠸
࠺ᙧࡢࢸࢫࢺ࡛㧗࠸ᚓⅬࡀྲྀࢀࡿࡼ࠺࡞ᩍ⫱࡜࠸࠺ࡶࡢࡣࠊ㞳ᩓⓗ࡞Ⅼࢆ⥙⨶ⓗ࡟୸ᬯグࡉࡏࡿࡼ࠺࡞ࠊ࠸ࢃࡤ౑࠼࡞࠸▱㆑ᢏ⬟ࢆ᠈࠼ࡇࡲࡏࡿࡼ࠺࡞ࡶࡢ࡛ࡣ࡞ࡃ࡚ࠊᐇᆅ࡟

－91－

authentic ࡞ゝㄒ౑⏝ࢆ㏻ࡌ࡚⋓ᚓࡍࡿࡼ࠺࡞ࡶࡢ࡛࠶ࡿࡇ࡜ࢆࠊ᪥ᮏㄒᩍ⫱ࡢඛ⏕᪉ࡀド᫂ࡋ࡚ࡃࡔࡉࢀࡤࠊࢸࢫࢺࡢ᪉ࡣస㢟ࡀ㞴ࡋ࠸๭࡟ࡣಙ㢗ᛶࡢప࠸ authentic࡞ࡶࡢࢆసࡽ࡞ࡃ࡚ࡶࠊ㞳ᩓⓗ࡞Ⅼࢆ୪࡭ࡓࡼ࠺࡞ࡶࡢ࡛࠸࠸ࡢ࡛ࡣ࡞࠸࠿ࠊ࡜ゝࡗ࡚ࡳࡓࢃࡅ࡛ࡍࠋ
ࡋ࠿ࡋࠊࡸࡣࡾᏛ⩦⪅ࡣᡭࡗྲྀࡾ᪩ࡃⅬᩘࡀ࡜ࢀࡿᬯグ୺⩏࡟㉮ࡿࢃࡅ࡛ࡍࡋࠊ⤖ᒁࠊヨ㦂ࡢᙧࡀ࠸ࢃࡤⷧࡗ࡮ࡽ࡞౑࠼࡞࠸⬟ຊࡢ⋓ᚓ࡜࠸࠺᪉ྥ࡟࡞ࡿࡇ࡜ࢆ཯ドࡍࡿࡇ࡜ࡣࠊ
ᐇ㝿ၥ㢟࡜ࡋ࡚㞴ࡋ࠸ࡼ࠺࡛ࡋࡓࠋ
グ㏙ᘧၥ㢟࣭PISA ᆺၥ㢟࡜ಙ㢗ᛶࡑ࠺ࡍࡿ࡜ࡉࡽ࡟ࠊ୍㊊㣕ࡧ࡟ࠊᐈほᙧᘧ࡛ࡣ࡞ࡃ࡚グ㏙ᙧᘧࡢၥ㢟࡟ࡋࡓ࡯࠺ࡀ࠸࠸ࡢ ࡛ ࡣ ࡞ ࠸ ࠿ ࡜ ࠸ ࠺ ୺ ᙇ ࡶ ฟ ࡚ ࡃ ࡿ ࢃ ࡅ ࡛ ࡍ ࡡ ࠋ CBT ࠿ ࡽ ࡣ ࡔ ࢇ ࡔ ࢇ 㐲 ࡊ ࠿ ࡗ ࡚ ࡃ ࡿ ࠿࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠋࡇࢀ࡟ࡘ࠸࡚ࡣ᥇Ⅼ࡟୺ほࡀධࡿࠊ᥇Ⅼ⪅࡟ࡼࡗ࡚ᚓⅬࡀኚࢃࡿࡢ
࡛ࡣ࡞࠸࠿࡜࠸࠺ヰࡣᖖ࡟㉳ࡇࡾࡲࡍࠋࡅࢀ࡝ࡶࠊ⚾ࡢ⤒㦂࠿ࡽࡍࢀࡤࠊࡇࢀࡣ༑ศ࡞ᡴ
ࡕྜࢃࡏࢆࡍࡿࠊ࠶ࡿ࠸ࡣ୍ᐃ௨ୖࡢⅬᕪࡀࡘ࠸ࡓ࡜ࡁ࡟ᨵࡵ࡚༠㆟ࢆࡍࡿ࡜࠸ࡗࡓࡇ࡜
࡟ࡼࡗ࡚┦ᙜࡢಙ㢗ᛶࡀᚓࡽࢀࡿ࡜ᛮ࠸ࡲࡍࠋ
ࡴࡋࢁၥ㢟࡟࡞ࡿࡢࡣࠊࡇࡢ✀ࡢヨ㦂࡛ࡣࠊࡲࡉ࡟኱ၥ㛫ࡢ┦㛵ࡀᴟࡵ࡚ప࠸࡜࠸࠺ࡇ
࡜ࡔ࡜ᛮ࠸ࡲࡍࠋࡇࢀࡣࡶ࠺ 30 ᖺ࡯࡝๓࡟᪥ᮏ࡛᥎⸀ධヨࡀ㛤ጞࡉࢀࡓࡇࢁ࡟ࠊࡓࡲࡓ
ࡲ 2 ᅇࡢᑠㄽᩥࢆཷ㦂ࡍࡿཷ㦂⪅ࡢᚓⅬࢆẚ࡭࡚ࡳࡓ࡜ࡁ࡟Ẽ࡙࠿ࢀ࡚࠸ࡲࡋࡓࠋ⚾ࡣࠊࡇࢀࡣࡲࡓࡶ࠺୍ࡘูࡢ⤒㦂࡟࡞ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊྡྂᒇ኱Ꮫ࡛ࡣ」ᩘࡢၥ㢟ࢆྵࡴᑠㄽᩥヨ㦂ࠊ࠸ࢃࡤᑠㄽᩥࡢၥ㢟ࢆ࣒࢜ࢽࣂࢫ࡟ࡋࡓࡼ࠺࡞ㄽ㏙ⓗᏛຊ᳨ᰝ࡜࠸࠺ྡ⛠ࡢ
ヨ㦂ࢆࡸࡗ࡚࠸ࡓࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡇ࡛ࡶࡣࡗࡁࡾ☜ㄆࡍࡿࡇ࡜ࡀ࡛ࡁࡓࢃࡅ࡛ࡍࠋ
௚᪉ࠊࡇࢀ࡟ࡘ࠸࡚ࡣࠊࡸࡣࡾ㏫ࡢ㐳ཬຠᯝ࡜࠸࠺ࡶࡢࡶ࠶ࡿࢃࡅ࡛ࠊ᭱ᚋ࡟᭩ࡁࡲࡋ
ࡓࡼ࠺࡟ࠊࡇ࠺࠸࠺ࡇ࡜࡟ࡼࡗ࡚࠶ࡿ✀ࡢཷ㦂⪅ࢆ᤼㝖ࡍࡿࡇ࡜ࡀ࡛ࡁࡿࠋࡘࡲࡾࠊ௚ࡢ
ヨ㦂࡛ࡣࡑࡇࡑࡇᏛຊࡀ㧗࠸ࡢ࡟ఱ࠿グ㏙ࡉࡏ࡚ࡳࡿ࡜ A4 ⏝⣬ࡢୖࡢ᪉࡟ 2 ⾜ࡄࡽ࠸ࡋ࠿᭩ࡅ࡞࠸ࡼ࠺࡞ே㛫ࡣ࠸ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢࡼ࠺࡞ཷ㦂⪅ࢆ᭱ึ࠿ࡽ᤼㝖ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ࡜࠸ࡗࡓព࿡࡛ࡣࠊࡑࡢࡇ࡜ࡢၿࡋᝏࡋࡣ࡜ࡶ࠿ࡃࠊຠᯝࡣ࠶ࡿ࡜⪃࠼ࡿ࡜ࠊࡇ࠺࠸࠺᪉ྥᛶࡣ࠶ࡿࡢ࠿ࡶࡋࢀ࡞࠸࡜ࡣᛮ࠸ࡲࡍࠋ
࠸ࡎࢀ࡟ࡋ࡚ࡶࡇࡢ࠶ࡓࡾࠊ୰ᩍᑂࡀ♧ࡋ࡚࠸ࡿ୍✀ࡢࠕ⌮᝿ࠖ࡜࠸࠺ࡶࡢࢆ⪃࠼ࡓ࡜ࡁ࡟ࠊ㉺࠼ࡿ࡭ࡁࣁ࣮ࢻࣝࡣ㠀ᖖ࡟ከ࠸ࡢ࡛ࡣ࡞࠸࠿࡜ᛮࡗ࡚࠸ࡿࢃࡅ࡛ࡍࠋ
௒᪥ࡐࡦఛ࠸ࡓ࠸࡞࡜ᛮࡗ࡚࠸ࡓࡢࡣࠊࡸࡣࡾ PISA ᆺࢸࢫࢺ࡟ࡘ࠸࡚࡛ࡍࠋ୰ᩍᑂ⟅⏦࡟ࡶ㠀ᖖ࡟᫂☜࡟ࠕࡇࢀࢆ┠ᣦࡍࠖ࡜᭩࠿ࢀ࡚࠾ࡾࡲࡍࡀࠊඛ࡯࡝ࡢᒣᮏඛ⏕ࡢ࠾ヰ࠿ࡽ⚾ࡣ⌮ゎࡋࡓࡘࡶࡾ࡛ࡍࡅࢀ࡝ࡶࠊ௒ࡢ PISA ᆺࢸࢫࢺࡣಶேࡢホ౯ࢆ┠ⓗ࡟ࡍࡿࡶࡢ
࡛ࡣ࡞࠸ࡼ࠺࡟ᛮࡗ࡚࠾ࡾࡲࡍࠋࡇࢀࡣࡴࡋࢁᨻ⟇ࡢホ౯ࡢࡓࡵࡢࡶࡢ࡛ࡣ࡞࠸࠿࡜ࠋ⚾ࡶࠊ࡜࠶ࡿ㐍Ꮫᰯ࡛Ꮫᰯホ㆟ဨࢆࡋ࡚࠾ࡾࡲࡋ࡚ࠊࡑࡇࡣࢫ࣮ࣃ࣮࣭ࢧ࢖࢚ࣥࢫ࣭ࣁ࢖ࢫ
ࢡ࣮ࣝࡢᣦᐃࢆཷࡅ࡚࠸ࡿᏛᰯ࡛ࡍࠋࡇࡇ࡛ᩥ⛉┬ࡉࢇ࠿ࡽ PISA ᆺࡢホ౯᪉ἲࢆ⪃࠼ࡿࡼ࠺࡟࡜࠸࠺ᐟ㢟ࢆࡶࡽࡗ࡚ࡁࡓ࡜࠸࠺ヰࡀ࠶ࡾࡲࡋࡓࠋ⚾ࡶ࡟ࢃ࠿ຮᙉ࡛ PISA ᆺࡢၥ㢟ࢆ᳨ウࡉࡏ࡚ࡶࡽࡗࡓࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊಶேࢆホ౯ࡍࡿ࡜࠸࠺ࡇ࡜ࡣࠊࡸࡣࡾࡇࡢࢱ
࢖ࣉࡢࢸࢫࢺ࡛ࡣㅉࡵࡓ࡯࠺ࡀࡼࢁࡋ࠸ࡢ࡛ࡣ࡞࠸࠿ࠋ㠀ᖖ࡟▷࠸᫬㛫ࠊ୍ᐃࡢ㧗ᰯࡢ 1ࢥ࣐ࡢᤵᴗࡄࡽ࠸ࡢ᫬㛫࡛⾜ࢃࢀࡿ࡜ࡍࢀࡤࠊࡓࡲࡓࡲඛ࡯࡝⏦ࡋୖࡆࡓ」ᩘࡢᑠㄽᩥࡢ
ሙྜࡢࡼ࠺࡟ࠊࡓࡲࡓࡲࡑࡢ᫬㛫ෆ࡟ṇ⟅ࢆᛮ࠸ࡘ࠸ࡓ⏕ᚐࡢ๭ྜࡀቑ࠼ࡿࠊࡑࢀࡣỴࡋ࡚ಶேࡢ⬟ຊࡢಙ㢗ᛶࡢ㧗࠸ᐃ࡟ࡣ࡞ࡽ࡞࠸ࡅࢀ࡝ࡶࠊࡑ࠺࠸࠺୰࡛ṇゎ࡟㐩ࡍࡿ☜⋡－92－ࡀ㧗ࡲࡗ࡚࠸ࡅࡤࠊ౛࠼ࡤࢫ࣮ࣃ࣮ࢧ࢖࢚ࣥࢫⓗ࡞࢔ࢡࢸ࢕ࣈ࣮ࣛࢽࣥࢢࢆࡸࡗࡓຠᯝࡢ
ᐃ࡟ࡣ࡞ࡿࡔࢁ࠺ࠋࡘࡲࡾࠊᖹᆒ್ࡢኚ໬ࢆ㏣࠺ࡇ࡜ࡣྍ⬟࠿ࡶࡋࢀ࡞࠸ࡅࢀ࡝ࡶࠊࡇ
࠺࠸ࡗࡓ኱ࡁ࡞ၥ㢟࡟ࡼࡗ࡚ಶேࢆホ౯ࡍࡿ࡜࠸࠺ࡇ࡜࡟ࡘ࠸࡚ゝ࠼ࡤࠊ㠀ᖖ࡟኱ࡁ࡞ၥ
㢟ࠊ㞴ၥࡀ᥍࠼࡚࠸ࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡓḟ➨࡛ࡍࠋ࠸ࡉࡉ࠿㥑ࡅ㊊࡟࡞ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊࡇࢀ࡛⤊ࢃࡽࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
ࡈΎ⫈࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧‫ྖۑ‬఍ᮧୖඛ⏕ࠊ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ

ఱ࠿ࡈ㉁ၥ࠶ࡾࡲࡍ࡛ࡋࡻ࠺࠿ࠋࡼࢁࡋ࠸࡛ࡍ࠿ࠋ
␃Ꮫ⏕ヨ㦂ࡢ࡜ࡁ࡟ࠊၥ㢟సᡂࡢ㝿࡟࠸ࢁ࠸ࢁ࡞ពぢࡀฟ࡚ࡃࡿ࡜࠸࠺ࡢࡣᐇ㝿࡟⤒㦂ࡉࢀࡓࡇ࡜࡞ࢃࡅ࡛ࡍࡼࡡࠋࡑࢀ࡛ࠊࡸࡣࡾ࣮࢜ࢭࣥࢸ࢕ࢵࢡ࡞ၥ㢟࡜ゝ࠸ࡲࡍ࠿ࠊ᭱㏆
ࡢ PISA ᆺࡢၥ㢟ࡀ᥎ዡࡉࢀࡿ࡜࠸࠺ࡢࡶྠࡌὶࢀ࡟࡞ࡿ࠿࡜ᛮ࠸ࡲࡍࡀࠊࡑࡢࡼ࠺࡞ព
ぢࡀከ࠿ࡗࡓ࡜࠸࠺ࡇ࡜࡛ࡋࡻ࠺࠿ࠋ

‫ۑ‬ᮧୖẶ

࠶ࡢ⛬ᗘࡢࢧ࢖ࢬࡢၥ㢟࡛ࡍ࡜ࠊၥ㢟ᩘࡀᑡ࡞࠸࡜࠸࠺ព࿡࡛వࡾಙ㢗ᛶࢆ㧗ࡵࡽࢀ࡞࠸࡜࠸࠺ၥ㢟ࡣ࠶ࡿ࡟ࡏࡼࠊẚ㍑ⓗ┦஫┦㛵ࡣ㧗࠸ࡢ࡛ࠊIRT ࡟㍕ࡏ࡚࠸ࡃࡇ࡜
࡟Ỵᐃⓗ࡞ᅔ㞴ࡀ࠶ࡗࡓࢃࡅ࡛ࡣ࡞࠸ࠋࡔ࠿ࡽࡇࡑࠊ௒ࠊ࡛ࡁ࡚࠸ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠋ
࡛ࡍ࠿ࡽࠊࡑࢀ࡛ࡣࡲࡔ௒ࠊၥ㢟࡟ࡉࢀ࡚࠸ࡿࡼ࠺࡞ࠊᜍࡽࡃ⾲⌧ຊ࡜࠸࠺ࡢࡣ඲ࡃ㞴ࡋ࠸࡛ࡋࡻ࠺࠿ࡽࠊࡑࢀࢆࡸࡿ࡜࡞ࡿ࡜ඛ࡯࡝࠾♧ࡋࡋࡓࡢࡼࡾࡶ࠺୍ẁ㝵኱ࡁ࡞኱ၥ࡟
ྥ࠿ࢃ࡞ࡅࢀࡤ࠸ࡅ࡞ࡃ࡞ࡗ࡚ࠊࡇࡢ࡜ࡁ࡟ᮏᙜ࡟㞴ࡋ࠸ၥ㢟ࡀฟ࡚ࡃࡿࡢ࡛ࡣ࡞࠸࠿࡜
ᛮࡗ࡚࠸ࡲࡍࠋ
‫ྖۑ‬఍

グ㏙ᘧࡢၥ㢟࡞࡝ࡶ᪂ࡋ࠸Ꮫຊࢸࢫࢺ࡟ྵࡵࡿ࡜࠸ࡗࡓࡇ࡜ࡀ⟅⏦᱌࡟ࡣ᭩࠿ࢀ

࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑ࠺࠸ࡗࡓ᥇Ⅼ⪅ࡢಙ㢗ᛶࡢၥ㢟࡞࡝࡟ࡘ࠸࡚ࡣࠊඛ⏕ࡢࡈ⤒㦂࡛ࡣࠊ࠶ࡿ⛬ᗘカ⦎ࡍࢀࡤ኱୔ኵ࡜࠸࠺ឤࡌ࡞ࢇ࡛ࡋࡻ࠺࠿ࠋ
‫ۑ‬ᮧୖẶ

ᐇ㝿࡟ࡣࠊ኱Ꮫᩍဨࡣ࣮ࣟࢸ࣮ࢩ࡛ࣙࣥ᥇Ⅼࢆᢸᙜࡋࡓࡾࡋࡲࡍࡢ࡛ࠊカ⦎࡜࠸࠺࡜ࡇࢁࡲ࡛ࡣ࡞࠿࡞࠿㞴ࡋ࠸࡛ࡍࡅࢀ࡝ࡶࠊ஦๓ࡢ༠㆟ࡣ㠀ᖖ࡟኱஦࡛ࡍࡡࠋ
ࡑࢀ࠿ࡽࠊࡸࡣࡾ᥇Ⅼࡋࡓࡽศᯒࡋ࡚ࠊ࡜ࡶ࠿ࡃᕪࡢ࠶ࡿ࡜ࡇࢁࢆぢࡘࡅ࡚ࡶ࠺୍ᗘ༠
㆟ࢆࡍࡿ࡜࠸ࡗࡓࡇ࡜ࢆࡍࢀࡤࠊ┦ᙜࡢಙ㢗ᛶࡣ☜ಖ࡛ࡁࡿ࡜ᛮ࠸ࡲࡍࡀࠊၥ㢟ࡣࠊ」ᩘ
ࡢၥ㢟㛫࡛┦㛵ࡀప࠸ࠊሙྜ࡟ࡼࡗ࡚ࡣ࡯࡜ࢇ࡝┦㛵ࡀ࡞࠸ࡇ࡜࡛࠶ࡗ࡚ࠊ࡛ࡣࠊࡇࢀࡣ
୍యఱࢆࡗ࡚࠸ࡿࡢ࠿࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋ࠸ࡘ࠿ᚓព࡞ၥ㢟ࡀฟ࡚ࡃࢀࡤࠊ௒ࡲ࡛వࡾ

㧗ᚓⅬ࡛࡞࠿ࡗࡓᏛ⏕ࡶࡑࡢၥ㢟࡛ࡣ㧗ᚓⅬࢆ࡜ࡿྍ⬟ᛶࡀ࠶ࡿ࡜࠸࠺ࡇ࡜࡛ࡍ࠿ࡽࠊ୍
యᗄࡘၥ㢟ࢆ⏝ពࡋࡓࡽ࠸࠸ࡢ࠿ࠊఱᅇࡸࡗࡓࡽ࠸࠸ࡢ࠿࡟ࡘ࠸࡚ࡣࠊࡕࡻࡗ࡜⟅࠼ࡀ࡞࠸ࡼ࠺࡞Ẽࡀࡋࡲࡍࠋ
‫ྖۑ‬఍᥇Ⅼࡍࡿഃࡢ㈇ᢸឤࡶ኱ࡁ࠸࡜ᛮ࠸ࡲࡍࡋࠊ᥇Ⅼࡢ᪥࡟ࡕࡶ㝈ࡽࢀࡲࡍ࠿ࡽࠊ኱

Ꮫධヨࡢ୰࡛グ㏙ᘧ࡞࡝ࡢヨ㦂ࢆࡸࡿࡢࡣ኱ኚ࠿࡞࡜࠸࠺༳㇟ࡶ࠶ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠋ

‫ۑ‬ᮧୖẶ

ㄒᏛヨ㦂࡛ࡋࡓࡋࠊ⚾࡝ࡶࡣẚ㍑ⓗᑠつᶍ࡞Ꮫ㒊࡛ࡋࡓࡢ࡛ࠊࡑࡢⅬࡣࡑࢀ࡯࡝ၥ㢟࡟࡞ࡽ࡞࠿ࡗࡓ࡜グ᠈ࡋ࡚࠸ࡲࡍࠋࡓࡔࠊࡶࡕࢁࢇධᏛᐃဨࡢࡍ࡭࡚ࢆࡇ࠺࠸࠺᪉
ᘧ࡛࡜࡞ࢀࡤࠊ⌧ᅾࡢ㝕ᐜ࡛ࡣ୙ྍ⬟ࡔ࡜ࡣᛮ࠸ࡲࡍࠋ
‫ྖۑ‬఍ࡑࢀ࡛ࡣࠊࡲࡓᚋ࡯࡝ࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥࡢ࡜ࡁ࡟ࠋ

ᮧୖඛ⏕ࠊ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧－93－⚾䛜⤒㦂䛛䜙ㄒ䜜䜛䛣䛸᭗‫ٻ‬੗ዓƷƜǕƔǒ• 㧗኱᥋⥆䛾ᨵၿ䛾ᥦ᱌䛻䛿䠈ᙜ↛䠈ධヨᨵ㠉䛜ྵ䜎䜜䜛䚹⌧᫬Ⅼ䛷䛿䠈≉䛻䠈૙Ꮛ↧↝ᅈ˟ႎᙲᛪ↝‫҄٭‬䜈䛾ᑐᛂ䛸䠈λᚾ↚↱МဇӧᏡ↗ᎋⅷ↸↻↺২ᘐ᪃ૼ䜈䛾ᮇᚅ䛜䠈ᨵ㠉䛾ᥦ᱌䛻䛴䛺䛜䛳䛶䛔䜛䜘䛖䛻ᛮ䜟䜜䜛䚹ᮧୖ 㝯• ⚾䛿䠈⮬ศ䛾䛛䛛䜟䛳䛯බⓗヨ㦂䛾❧䛱ୖ䛢䛻䛒䛯䛳䛶䛾䛔䛟䛴䛛䛾䜶䝢䝋䞊䝗䜢䛚ヰ䛧䛧䠈௒ᚋ䛾ධヨᨵ㠉䛻䛛䛛䜟䜛㆟ㄽ䛾䠍䛴䛾↔Ⅼ䛸䛺䜛䛸ᛮ䜟䜜䜛 CBT
䠄computer based testing䠅䛜䠈᪥ᮏⓗ䝔䝇䝖ᩥ໬䛸䛾㛫䛻㉳䛣䛩ྍ⬟ᛶ䛾䛒䜛䛂ᦶ᧿䛃䜢୰ᚰ䛻ㄽ䛨䛯䛔䚹• 䛭䛣䛛䜙䠈⬟ຊホ౯䛾᪂䛯䛺⌮ᛕ䛜䠄䛒䜛⛬ᗘ䠅ᡂ⇍䛧䠈䛭䜜䛻ᑐᛂ䛩䜛ᢏ⾡䛜䠄୍ᐃ䛾Ỉ‽䛷䠅ᩚ䛳䛯䛸䛧䛶䜒䛺䛚䠈䛭䜜䛜ᮇᚅ䛥䜜䛶䛔䜛䛂┿䛾Ꮫຊ䛃ホ౯䛻䛴䛺䛜䜛䛛䛹䛖䛛䛿䠈ଐஜ↝᫘‫↏ם‬ↀ↖↙ⅾ≏↾ⅻ‫↝׎‬ᘍᝠ૎
↝ⅱ↹૾↚̔‫܍‬䛩䜛䛣䛸䜢♧၀䛩䜛䛴䜒䜚䛷䛒䜛䚹୰ி኱Ꮫ䞉⌧௦♫఍Ꮫ㒊⚾䛜⤒㦂䛛䜙ㄒ䜜䜛䛣䛸᪥ᮏ␃Ꮫヨ㦂䛸䛿䠛• ᪥ᮏ␃Ꮫヨ㦂䠄䛻䜋䜣䜚䜕䛖䛜䛟䛧䛡䜣䚸㻱㼤㼍㼙㼕㼚㼍㼠㼕㼛㼚㻌 㼒㼛㼞㻌㻶㼍㼜㼍㼚㼑㼟㼑㻌㼁㼚㼕㼢㼑㼞㼟㼕㼠㼥㻌㻭㼐㼙㼕㼟㼟㼕㼛㼚㻌㼒㼛㼞㻌㻵㼚㼠㼑㼞㼚㼍㼠㼕㼛㼚㼍㼘㻌㻿㼠㼡㼐㼑㼚㼠㼟䚸␎⛠㻱㻶㼁䚸᪥␃ヨ䠅䛿䚸⊂❧⾜ᨻἲே᪥ᮏᏛ⏕ᨭ᥼ᶵᵓ䛜୺ദ䛩䜛䚸᪥ᮏ䛾኱Ꮫ䠄Ꮫ㒊䠅䜔ᑓಟᏛᰯ䛻ධᏛ䜢ᕼᮃ䛩䜛እᅜே␃Ꮫ⏕䜢ᑐ㇟䛸䛧䛯ඹ㏻䛾ධᏛヨ㦂䛷䛒䜛䚹䛭䜜䜙䛾ᩍ⫱ᶵ㛵䛷ᚲせ䛸䛥䜜䜛᪥ᮏㄒ⬟ຊ䛚䜘䜃ᇶ♏Ꮫຊ䛾ホ౯䜢┠ⓗ䛸䛩䜛䚹㻞㻜㻜㻞ᖺ䛛䜙ᐇ᪋䛥䜜䛶䛔䜛䚹• ⚾䛜❧䛱ୖ䛢䜢⤒㦂䛧䛯ヨ㦂䛸䛿䠈䠎䠌䠌䠎ᖺ䛛䜙ᐇ᪋䛥䜜䛶䛔䜛䛂᪥ᮏ␃Ꮫヨ㦂䛃䛷䛒䜛䚹• 䛂᪥ᮏ␃Ꮫヨ㦂䛃䛿䠈㡯┠཯ᛂ⌮ㄽ 䠄item response
theory; IRT) 䛻౫ᣐ䛧䛯ᚓⅬ➼໬䠄test scoreequating䠅䛜⾜䜟䜜䛯䠈䛚䛭䜙䛟䜟䛜ᅜ䛷᭱ึ䛾䠈䛔䜟䜖䜛 high stakes 䛺බⓗヨ㦂䛷䛒䜛䛸ᛮ䜟䜜䜛䚹• CBT 䛛䜙䛿⛬㐲䛔⣬䝧䞊䝇䛾ヨ㦂䛷䛒䜛䛜䠈䜒䛧䠈ධヨ䝉䞁䝍䞊ヨ㦂䠄䛒䜛䛔䛿䛭䛾ᚋ⥅ヨ㦂䠅䛜 CBT໬䛥䜜䜛䛸䛩䜜䜀䠈㑊䛡䛶㏻䜜䛺䛔ၥ㢟䛜䛔䛟䛴䛛ᥦ㉳䛥䜜䛯䛸ᛮ䜟䜜䜛䚹ከᑡ䛸䜒䛤ཧ⪃䛻䛺䜜䜀ᖾ䛔䛷䛒䜛䚹• ᅜෆ䛒䜛䛔䛿୺せ䜰䝆䜰ㅖᅜ䛸䜴䝷䝆䜸䝇䝖䜽䛷ཷ㦂䛩䜛䛣䛸䛜䛷䛝䜛䚹䛯䛰䛧䚸୰ᅜᮏᅵ䠄㤶 䛿㝖䛟䠅䛷䛿ᐇ᪋䛧䛶䛔䛺䛔䚹• ⌧ᅾከ䛟䛾኱Ꮫ䞉▷ᮇ኱Ꮫ䜔䚸୍㒊䛾኱Ꮫ㝔䜔ᑓಟᏛᰯ䛺䛹䛜እᅜே␃Ꮫ⏕䛾ධヨ㑅ᢤ䛻฼⏝䛧䛶䛔䜛䚹䛣䜜䜙฼⏝ᰯ䜢ᚿᮃ䛩䜛ཷ㦂⏕䛿኱Ꮫධヨ䝉䞁䝍䞊ヨ㦂䛸ྠᵝ䚸฼⏝ᰯ䛾ධᏛ㑅ᢤヨ㦂ฟ㢪᫬䛻᪥ᮏ␃Ꮫヨ㦂䛾ᡂ⦼䜢ᥦ♧䛩䜛䚹ヨ㦂䛿㻢᭶䠄➨㻝ᅇ䠅䛸㻝㻝᭶䠄➨㻞ᅇ䠅䛾ᖺ㻞ᅇᐇ᪋䛥䜜䚸ఱᅇཷ㦂䛧䛶䜒䛛䜎䜟䛺䛔䚹(᪥ᮏ␃Ꮫヨ㦂 Wikipedia)

᪥ᮏ␃Ꮫヨ㦂䛸䛿䠛

2000䡚2002ᖺᗘ≉ู◊✲᥎㐍㈝䠄䛔䜟䜖䜛ᨻ⟇⛉◊䠅• ヨ㦂⛉┠䛿ᚿᮃᰯ䛾ᣦᐃ䛻ᚑ䛳䛶㑅ᢥ䛩䜛䚹ᩥ⣔Ꮫ⛉䛿᪥ᮏㄒ䚸⥲ྜ⛉┠䠄♫఍䠅䚸ᩘᏛ䝁䞊䝇㻝䛜䚸⌮⣔Ꮫ⛉䛿᪥ᮏㄒ䚸⌮⛉㻞⛉┠䚸ᩘᏛ䝁䞊䝇㻞䛜୍⯡ⓗ䛷䛒䜛䚹䜒䛱䜝䜣୍㒊⛉┠䛰䛡䛷䜘䛔Ꮫ⛉䜒䛒䜛䛜䚸᪥ᮏㄒ䛿䜋䛸䜣䛹䛾Ꮫ⛉䛷ᚲ㡲䛷䛒䜛䚹⥲ྜ⛉┠䠄♫఍䠅䚸⌮⛉䚸ᩘᏛ䛾ၥ㢟䛾㞴᫆ᗘ䛿䝉䞁䝍䞊ヨ㦂䜘䜚䜒ⱝᖸప䛟タᐃ䛥䜜䛶䛔䜛䚹኱Ꮫ䛻䜘䛳䛶䛿䚸᪥ᮏ␃Ꮫヨ㦂䛾ᡂ⦼䛾௚䛻㼀㻻㻱㻲㻸䛺䛹䛾ᡂ⦼䜢ᚲせ䛸䛩䜛Ꮫ⛉䜒䛒䜛䚹• 䛂ᡃ䛜ᅜ䛾බⓗヨ㦂䛻䛚䛡䜛ᚓⅬ➼໬䛾ᑟධ䛻ྥ䛡䛯ᚰ⌮䞉ᩍ⫱ ᐃⓗ◊✲䛃• ◊✲௦⾲⪅䛿ᮧୖ㝯䠈◊✲༠ຊ⪅䛿䠈䛭䛾᫬Ⅼ䛷䠈ィ㔞ᚰ⌮Ꮫ䛾᭱ඛ➃䛾◊✲⪅䠈ධヨ䝉䞁䝍䞊䛻䛚䛡䜛◊✲㛤Ⓨ䛾ᢸᙜ⪅䠈䛥䜙䛻䠈ἲᏛ◊✲⪅䜢ྵ䜑䛯 9 ྡ䛷䛒䛳䛯䚹• ᢅ䛳䛯ၥ㢟䛿䠈IRT 䛻䜘䜛ᚓⅬ➼໬䠈ၥ㢟సᡂయไ䛾ᩚഛ䠈䝕䞊䝍䝧䞊䝇䛾ᵓ⠏䠈ヨ㦂඲య䜈䛾䝞䝑䜽䜰䝑䝥యไ䛾䛒䜚᪉䠈ἲⓗၥ㢟䜈䛾ᑐᛂ䠈䛸䛔䛳䛯䜒䛾䛷䛒䜛䚹• ᡂ⦼䛭䛾䜒䛾䛾᭷ຠᮇ㛫䛿㻞ᖺ䛷䛒䜛䚹᪥ᮏ␃Ꮫヨ㦂䛿」ᩘᅇศ䛾
ヨ㦂ᡂ⦼䜢ྠ᫬䛻ẚ㍑䛧䛶㑅ᢤ䛻฼⏝䛩䜛䛯䜑䚸ၥ㢟䛾㞴᫆䛻䜘䛳䛶᭷฼୙฼䛜⏕䛨䜛䚹䛣䛾䛯䜑䚸㡯┠ᛂ⟅⌮ㄽ䛸䛔䛖⤫ィᏛⓗ᪉ἲ䛻䜘䛳䛶ᚓⅬ䜢➼໬䛧䛶䛔䜛䚹䛣䜜䛻䜘䛳䛶ᚓ䜙䜜䛯䛂ᑻᗘⅬ䛃䜢ᡂ⦼䛸䛧䛶฼⏝䛧䛶䛔䜛䚹
(᪥ᮏ␃Ꮫヨ㦂 Wikipedia)－94－

CBT ᑟධ䛾ຠᯝ䠈᪂䛯䛺ၥ㢟ᚓⅬ➼໬䛾ᐇ⌧䛻䛴䛔䛶䠄䛒䛟䜎䛷䜒䠈ᮧୖ䛾᥎ 䛷䛒䜛䠅• 䝉䞁䝍䞊ヨ㦂䜢௒䛾䜎䜎ᐇ᪋䛩䜛௨ୖ䛾㈇ᢸ䛻䛿䠈኱Ꮫഃ䛿⪏䛘䜙䜜䛺䛔䠄䛷䛒䜝䛖䠅䚹• 䛣䛣䛷䛿䠈IRT 䛒䜛䛔䛿䠈䛭䛾௚䛾ᡭἲ䛻䜘䜛ᚓⅬ➼໬䛾᪉ἲ䛻䛴䛔䛶䠈ᨵ䜑䛶ゎㄝ䛩䜛ᚲせ䛿䛺䛔䛸⪃䛘䜛䚹• 䜘䛳䛶䠈᪂䛯䛺䛂฿㐩ᗘ䝔䝇䝖䛃䜢䠈⌧⾜䛾యไ䛷ᐇ᪋䛩䜛䛾䛿䠈䠄䝉䞁䝍䞊ヨ㦂䛜Ꮡ⥆䛩䜛㝈䜚䠅↓⌮䚹• 䜐䛧䜝䠈ᚓⅬ➼໬䜢ᐇ᪋䛩䜛䛻䛒䛯䛳䛶䠈ఱ䛜ၥ㢟䛰䛳䛯䛾䛛䠈䛣䜜䜢ᐇ᪋䛩䜛䛯䜑䛻䠈䝔䝇䝖㛵ಀ⪅䛻ఱ䜢⌮ゎ䛧䛶䜒䜙䛖ᚲせ䛜䛒䛳䛯䛾䛛䠈௚᪉䠈IRT ௨እ䛻䛹䜣䛺‽ഛ䛜ᚲせ䛰䛳䛯䛛䠈䜎䛯䠈స㢟䛻ᙜ䛯䜛ே䛯䛱䛛䜙䠄ព㆑ⓗ䠈↓ព㆑ⓗ䛻䠅䛹䛾䜘䛖䛺᢬ᢠ䜢ཷ䛡䛯䛛䠈䛸䛔䛳䛯䛣䛸䛻䛴䛔䛶䛚ヰ䛧䛯䛔䚹• ௚᪉䛷䠈䝉䞁䝍䞊ヨ㦂䜢䛂฿㐩ᗘ䝔䝇䝖䛃䛻⨨䛝᥮䛘䜛䛺䜙䠈䠄௒䛾ᵓ᝿䛰䛸䠅」ᩘᅇᐇ᪋䛿ᅇ㑊䛷䛝䛺䛔䚹• CBT໬䛷䠈኱Ꮫ䛾㈇ᢸ䛿ᅇ㑊䛷䛝䜛䠄᪋タ䠈䝉䜻䝳䝸䝔䜱➼䛾ၥ㢟䛿᪂䛯䛻Ⓨ⏕䛩䜛䛸ᛮ䛖䛜䠅䚹• 䛧䛛䛧䠈」ᩘ䛾ヨ㦂㛫䠄䛒䜛䛔䛿ཷ㦂ᶵ఍㛫䠅䛾ᚓⅬ➼໬䛸䠈኱㔞䛾ၥ㢟㡯┠సᡂ䛾ၥ㢟䛜䛚䛣䜛䚹• 䛷䛒䜛䛸䛩䜜䜀䠈CBT໬䛻㝿䛧䛶䠈ၥ㢟సᡂ䛾䠄䛒䜛⛬ᗘ䛾䠅⮬ື໬䜒ᮇᚅ䛥䜜䛶䛔䜛䠛• 䛯䛰䠈⌧᫬Ⅼ䛷䛿䠈グ᠈䛜ⷧ䜜䛶䛔䜛㒊ศ䜒ከ䛔䚹䜒䛧ㄗ䜚䛻Ẽ䛵䛛䜜䛯䜙䠈◊✲༠ຊ⪅䜢䛚㢪䛔䛧䛯ඛ⏕᪉䛷䛣䛣䛻䛤ิᖍ䛾᪉䜒䛔䜙䛳䛧䜓䜛䛾䛷䠈䛤ྏṇ䜢䛔䛯䛰䛝䛯䛔䚹

IRT 䛥䛘䛒䜜䜀䛔䛔䛾䛛䠛䜎䛪䠈ఱ䜢⌮ゎ䛧䛶䜒䜙䛖䜉䛝䛛• 㡯┠཯ᛂ⌮ㄽ䠄IRT䠅䛥䛘ᑟධ䛩䜜䜀䠈ᚓⅬ➼໬䛾䜏䛺䜙䛪䠈ヨ㦂䛾኱㒊ศ䛾ၥ㢟䛿ゎỴ䛷䛝䜛䛸ᛮ䛳䛶䛔䛯䠄ィ㔞ᚰ⌮Ꮫ䛾እ㒊䛾䠅ே䚻䛜ᑡ䛺䛟䛺䛛䛳䛯䠄༙ྍ㏻䠅䚹• ඛ䛾ᨻ⟇⛉◊䛾⏦ㄳ䛾㝿䛾䠈ᩥ㒊┬䠄ᙜ᫬䠅䛾ពぢ䠄䠍䠅Ḣ⡿䛷㛤Ⓨ䛥䜜䛯ᡭἲ䜢䛭䛾䜎䜎౑䛘䜀䛔䛔䛾䛷䛿䠛• IRT 䛜䛒䜜䜀䠈₯ᅾ⬟ຊ䛜 䜜䜛䛸䛔䛖኱ㄗゎ і ⌮ㄽ䛜₯ᅾ≉ᛶ䠄latent trait䠅䛸䛔䛖䛂┤᥋ほ ୙ྍ⬟䛺⬟ຊᑻᗘ䛃䛾Ꮡᅾ䜢๓ᥦ䛻⤌䜏❧䛶䜙䜜䛶䛔䜛䛸䛣䜝䛛䜙䛭䛖ᛮ䜟䜜䛯䠛ї 䛣䛾Ⅼ䛜௒᪥䛾ヰ㢟䛾䠍䛴䠄䠎䠅ᡃ䛜ᅜ䛻䜒ᚓⅬ➼໬䜢ྍ⬟䛻䛧䛶䛔䜛ヨ㦂䛿䛩䛷䛻Ꮡᅾ䛩䜛䛾䛷䛿䛺䛔䛛䠛 ї 䛒䛳䛯䛜ヨస⛬ᗘ䠄䠏䠅Ꮫ⾡◊✲䛸䛧䛶⾜䜟䜜䛯౛䛿䛒䜛䛾䛷䛿䠛• 䛸䜒䛛䛟䠈䝁䞁䝢䝳䞊䝍䛷ఱ䛛ィ⟬䜢䛩䜜䜀䠈」ᩘᅇ䛾ヨ㦂䛾㛫䛾ᚓⅬ䜢ẚ㍑ྍ⬟䛻䛷䛝䜛䛸ᛮ䛳䛶䛔䜛ே䛿䠈┦ᙜከ䛛䛳䛯䛸ᛮ䛖䚹ї 䝺䝧䝹䛾㧗䛔䜒䛾䛜Ꮡᅾ䛧䛯䛜䠈high stakes 䛺ヨ㦂䛷䛿䛺䛔䚹
䠄䠐䠅⛉◊䛷㛤Ⓨ䛧䛯䝕䞊䝍䝧䞊䝇䜢䠄᪥ᮏ␃Ꮫヨ㦂䛸䛔䛖䠅஦ᴗ䛻㌿⏝䛩䜛䛣䛸䛾ၥ㢟• 䛣䜜䛿䠈◊✲⪅䛾䛂㄂኱ᐉఏ䛃䛻䜒ཎᅉ䛜䛒䜛䛸⪃䛘䜛䚹• 䛣䛾䛒䛯䜚䛜ᖖ㆑ⓗ཯ᛂ䛷䛒䛳䛯䠄௒䜒䛒䜛䠛䠅䛸ᛮ䜟䜜䜛䚹ᩘᏛ⪅䛻䛿ㄆ䜑䜙䜜䛺䛔䠄䠛䠅 IRT

䝕䞊䝍䛜‶䛯䛥䛺䛡䜜䜀䛺䜙䛺䛔᮲௳䛜䛒䜛䛣䛸䛻䛴䛔䛶• ពእ䛻䜒䠈ᩘᏛ䛾ၥ㢟సᡂ⪅䛛䜙䛿䠈IRT 䛿䜎䛳䛯䛟⌮ゎ䛧䛶䜒䜙䛘䛺䛛䛳䛯䚹• ၥ㢟㡯┠䛛ཷ㦂⪅䛛䠈䛹䛱䜙䛛䛻ඹ㏻䛾䜒䛾䛜ྵ䜎䜜䛶䛔䛺䛡䜜䜀䠈➼໬䛿୙ྍ⬟䛷䛒䜛䠄㛫᥋ⓗ䛺䛴䛺䛜䜚䛷䜒䜘䛔䛜䠅䚹
• 䛹䛱䜙䜒䛷䛝䛺䛔䛺䜙䠈ணഛ䝔䝇䝖䠈䛭䜜䜒䛷䛝䛺䛔䛺䜙䠄୍ᛂ䠈ヨ㦂䛸↓㛵ಀ䛺ே䜢ᑐ㇟䛸䛧䛯䠅䝰䝙䝍䞊ヨ㦂䚹• IRT 䛾⌮ㄽᵓᡂୖ䜒䠈䠄䛒䜛⛬ᗘ䛾䠅ᑠၥ୺⩏䛿୙ྍ㑊䚹• ᩘᏛ䛾⌧ᐇ䜈䛾㐺⏝䛻䛴䛔䛶䛿䠈䛝䜟䜑䛶ៅ㔜䛺ே䛜ከ䛔䛾䛿஦ᐇ䚹• ௚᪉䛷䠈⬟ຊホ౯䛻䛴䛔䛶䛿䠈ᴦኳⓗ䚹䛒䜛䛔䛿䠈䛭䛣䜢䛒䜎䜚ၥ㢟䛻䛧䛯䛟䛺䛔䠛• 䛔䜟䜖䜛ᑠၥ୺⩏䜈䛾ᙉ䛔᢬ᢠឤ䚹• 䛸䜒䛛䛟䠈⤯䛘䛦䜛ၥ㢟౪⤥䛾యไ䛾ᵓ⠏䛸䝕䞊䝍䛾⵳✚䠈䛥䜙䛻䠈ၥ㢟䛾බ㛤ྍ⬟ᛶ䛾⠊ᅖ䠈ணഛヨ㦂䠈䝰䝙䝍䞊ヨ㦂➼䛻䛚䛡䜛ၥ㢟䛾䝉䜻䝳䝸䝔䜱䛸䛔䛳䛯᪂䛯䛺ၥ㢟䛜Ⓨ⏕䛩䜛䚹• ᇶ♏ᩍ⫱䛸䛧䛶䛾ᩘᏛ䛸ᛂ⏝ศ㔝䛸䛾ᑐᛂ䛿䠈኱Ꮫᩍ⫱䛷䜒኱ၥ㢟䛷䛒䜚⥆䛡䛶䛔䜛䛜䞉䞉䞉䚹• 㐣ཤၥ䜈䛾䠄␗ᖖ䛺䠛䠅ᇳ╔䛸䛔䛖䛂ᮾ䜰䝆䜰䛃ⓗヨ㦂㢼ᅵ䜒㞀ᐖ䛾䠍䛴

－95－స㢟యไ䛾☜❧䠖◊✲䛧䛺䛡䜜䜀䛺䜙䛺䛛䛳䛯䛣䛸䜰䜲䝔䝮䝷䜲䝍䞊䛾ເ㞟䛸㣴ᡂ• 䜒䛿䜔䠈኱Ꮫᩍဨ䛜∦ᡭ㛫䛷ၥ㢟䜢స䛳䛶䛔䛶䛿㏣䛔䛴䛛䛺䛔䚹኱㔞䛾ၥ㢟᱌䜢౪⤥䛩䜛䛯䜑䛻䛿䠈እ㒊䛻䜰䜲䝔䝮䝷䜲䝍䞊䛾㞟ᅋ䜢ᢪ䛘䜛䛣䛸䛿ᚲ㡲䛷䛒䛳䛯䚹• ᪥ᮏㄒᩍ⫱䛾ሙྜ䠈ẚ㍑ⓗᾏእ䛾஦᝟䛻㏻䛨䛯ᩍဨ䛜ከ䛛䛳䛯䛣䛸䛛䜙䠈ᑠၥ୺⩏䜒ᚓⅬ➼໬䜒䛩䜣䛺䜚ཷ䛡ධ䜜䜙䜜䛯䠄䜒䛧䠈䛣䜜䛜ᅜㄒᩍ⫱䛰䛳䛯䜙䛹䛖䛺䜛䛰䜝䛖䛛䠅䚹• 䛭䜜䛷䜒䠈ᵿᶓᶒᶆᶃᶌᶒᶇᶁᴾễբ᫆䠄ᚋ㏙䠅䜈䛾せồ䛿ᙉ䛟䠈ᐃ䝃䜲䝗䛿኱䛝䛺ጇ༠䜢ᙉ䛔䜙䜜䛯䛜䠈⌧≧䛿䠈䛰䛔䛯䛔䛔䛔䝞䝷䞁䝇䛻䛺䛳䛶䛔䜛䜘䛖䛻ᛮ䜟䜜䜛䚹• 䜰䜲䝔䝮䝷䜲䝍䞊䛾䝖䝺䞊䝙䞁䜾䝁䞊䝇䛻䛿䠈ᑡ䛧䛰䛡䛚௜䛝ྜ䛔䛧䛯䛜䠈኱Ꮫ㝔⏕䝺䝧䝹䛾ᛂເ⪅䛾ၥ㢟సᡂ䝇䜻䝹䛾⩦ᚓ䛿㏿䛛䛳䛯䜘䛖䛻ᛮ䛖䚹• ᪥ᮏ䛾⌧ᐇ䠄䛯䛸䛘䜀䠈᪥ᮏㄒᏛᰯ䛾ᩍဨ䛿䠈ヨ㦂ၥ㢟䜙䛧䛝䜒䛾䛜䛒䛳䛯䜙䠈䛯䛰䛱䛻䝁䝢䞊䛩䜛䛾䛜⩦័䠅䛾୰䛷䠈䛹䛖 IRT ౑⏝䛾䛯䜑䛾๓ᥦ᮲௳䜢‶䛯䛩䝕䞊䝍䜢཰㞟䛩䜛䛛䠛• 䝕䞊䝍䝧䞊䝇䛾ᩚഛ䚹స㢟䛾యไ䛾☜❧䚹• ᾏእ䛷ッゴ䛜㉳䛣䛥䜜䛯䛸䛝䛾ᑐᛂ䠄䛹䛖䜔䜙䠈ᮯ៧䛰䛳䛯䜙䛧䛔䛜䠅䚹• 䛸䜒䛛䛟䠈䛹䛾䛟䜙䛔䝠䝖䛸䜹䝛䜢ᢞධ䛩䜜䜀䛔䛔䛾䛛䠛 䠄䛣䛾䛒䛯䜚䛾▱㆑䛸⤒㦂䛿䠈ᙜ᫬䛾ᮧୖ䛻䛿Ỵᐃⓗ䛻㊊䜚䛺䛛䛳䛯䚹䠅• 䜒䛱䜝䜣䠈IRT䛾䝥䝻䜾䝷䝮䜢ᐇ㝿䛻㐠⏝䛧䛶ᚓⅬ➼໬䜢⾜䛖䛻䛒䛯䛳䛶䛿䠈๓ᕝඛ⏕䜢䛿䛨䜑䛸䛩䜛⌧ሙ䛾᪉䚻䛾኱ኚ䛺䛤ⱞປ䛜䛒䛳䛯䚹ᑐฎ䛧䛶䛔䛺䛛䛳䛯䛣䛸䠖స㢟䛻䛴䛔䛶䠖๓ᥦ䛸䛺䜛䛣䛸䛜䜙஦ົ䝃䜲䝗➼䛾䛤ⱞປ• CBT ໬䛥䜜䛯ヨ㦂䛿䠈䠄ẚ㍑ⓗከᩘ䛾䠅ከ⫥㑅ᢥᙧᘧ䛾ၥ㢟㡯┠䛛䜙䛺䜛䛸ᛮ䜟䜜䜛䚹• 䛣䜜௨ୖ䛾䠈㧗➼ᩍ⫱䛾ᇶ♏䛸䛺䜛Ꮫຊ䜢཯ᫎ䛧䛯
performance 䜢䠈䝁䞁䝢䝳䞊䝍䝧䞊䝇䛷ホ౯䛩䜛䛣䛸䛿䠈
10ᖺᚋ䛻䛚䛔䛶䜒ᅔ㞴䛷䛒䜛䛸ண᝿䛩䜛䚹• 䜒䛱䜝䜣䠈ධຊ䛿䝁䞁䝢䝳䞊䝍䠈᥇Ⅼ䛿ே㛫䛸䛔䛖ᙧ䛷ᐇ᪋䛥䜜䜛グ㏙ᙧᘧ䛾ヨ㦂䛿䛒䜚䛖䜛䚹䛥䜙䛻䠈ேᕤ▱⬟䛻䜘䜛䝥䝻䜾䝷䝮䛜᥇Ⅼ䛻䛒䛯䜛䛸䛔䛳䛯ᢏ⾡㠉᪂䜒⪃䛘䜙䜜䛺䛟䛿䛺䛔䛜䠈」ᩘ䛾᥇Ⅼ⪅䛾䛖䛱䛾ᑡ䛺䛟䛸䜒୍ே䛿ே㛫䛷䛺䛡䜜䜀䛺䜙䛺䛔䛷䛒䜝䛖䚹• ከᩘ䛾ከ⫥㑅ᢥ䛾ၥ㢟㡯┠䜢䜒䛱䛔䜛䛣䛸䛻䜘䜚䠈ヨ㦂䛾ಙ㢗ᛶ䛸ጇᙜᛶ䜒䠄グ㏙ᘧ䛾኱ၥᙧᘧ䛾ၥ㢟䜘䜚䠅㧗䜑䜙䜜䜛䚹• ௨ୗ䛾Ⅼ䛿䠈஦๓䛻䛿䛒䜎䜚⪃䛘䜙䜜䛶䛔䛺䛛䛳䛯䛣䛸䛷䛒䜛䚹• ㄡ䛜ヨ㦂┘╩䜢䛩䜛䛾䛛䚹 ї 䜒䛖ᩍဨ䛿ືဨ䛷䛝䛪኱Ꮫ⫋ဨ䠈䜔䛿䜚୙័䜜䛜┠❧䛳䛯䚹• ཷ㦂⪅䛜እᅜே䛷䛒䜛䛣䛸䚹᪥ᮏ䛾ヨ㦂䛾ᖖ㆑䛿㏻⏝
䛧䛺䛔䚹ヨ㦂ሙ䛻䠈䛒䛾ධᏛヨ㦂䛾ᙇワ䜑䛯㞺ᅖẼ䛿䛺䛔䚹⤖ᯝ䠈኱㔞䛾୙ṇ⾜Ⅽ䛾᦬Ⓨ䚹• ᾏእᐇ᪋యไ䚹ヨ㦂ၥ㢟䜢ᐇ᪋๓䛻㛤ᑒ䛧䛶䠄Ẽ䛻ධ䛳䛯䠅᪥ᮏㄒᏛᰯ䛻㓄䛳䛶䛧䜎䛖䛸䛔䛖஦౛䛜䠈ඛ⾜䛩䜛᪥ᮏㄒ⬟ຊヨ㦂䛷䛿䛒䛳䛯䚹• 䜾䝻䞊䝞䝸䝊䞊䝅䝵䞁䛸䛿䛣䛖䛔䛖䛣䛸䛷䜒䛒䜛䟿• 䛧䛛䛧䠈᪥ᮏேᑐ㇟䛷䜒䠈ཷ㦂⪅䛻䜘䜛ၥ㢟䛾᧜ᙳ䜔㘓㡢䛿䠈⾜䜟䜜䜛䛣䛸䜢ぬᝅ䛩䜉䛝䛷䛒䜝䛖䚹⺬㊊䛺䛜䜙ᑠၥ୺⩏䛸┦ᛶ䛜䛔䛔䛾䛿 discrete points• ᑠၥᙧᘧ䛾ၥ㢟㡯┠䛿䠈ಶ䚻䛻䛿኱䛝䛺䝷䞁䝎䝮ㄗᕪ䜢཯ᫎ䛩䜛䛸䛧䛶䜒䠈」ᩘಶ䛾཯ᛂ䜢ఱ䜙䛛䛾᪉ἲ䛷ྜᡂ䛩䜛䛣䛸䛻䜘䜚䠈඲ศᩓ୰䛾ㄗᕪศᩓ䛾๭ྜ䜢ῶ䛨䜛䛣䛸䛜䛷䛝䜛䚹䛩䛺䜟䛱䠈㡯┠ᩘ䜢ቑ䜔䛫䜀䠈ヨ㦂䛾Ⅼᩘ䛾ಙ㢗ᛶ䛜㧗䜑䜙䜜䜛䚹 䞊 䜎䛠䜜ᙜ䛯䜚䛿⥆䛛䛺䛔䚹• Discrete points 䛸䛿䠈䛔䜟䜀䝞䝷䝞䝷䛺▱㆑䠄ᢏ⬟䠅䚹ゝㄒ⬟ຊ䛾ヨ㦂䛷䛿䠈ᩥᏐ䞉ㄒᙡ䞉ᩥἲ➼䛻䛴䛔䛶䠈䛂㡯┠䛃䛜ิᣲ䛥䜜䜛䚹• ᑠၥᙧᘧ䛾ၥ㢟㡯┠䛿䠈 ᐃ䛧䜘䛖䛸䛩䜛⬟ຊ䛾⠊ᅖ䠄䝺䞁䝆䠅䛜ᗈ䛔ሙྜ䛻䜒䠈䛭䜜䜢䠄ẚ㍑ⓗ䠅೫䜚䛺䛟䜹䝞䞊䛩䜛䜘䛖䛻స㢟䛩䜛䛣䛸䛜䛷䛝䜛䚹䛣䜜䛿㡿ᇦ௦⾲ᛶ䛸࿧䜀䜜䜛 ᐃ䛾䠄ෆᐜⓗ䠅ጇᙜᛶ䠄䛾୍䛴䛾ഃ㠃䠅䜢㧗䜑䜛䛣䛸䛻䛴䛺䛜䜛䚹 䠉 䝲䝬䛜䛛䛡䛻䛟䛔䚹

• 䛣䛖䛔䛖䜒䛾䛜⏝ព䛷䛝䜛䛺䜙䠈䛭䛣䛛䜙ၥ㢟㡯┠䛿䜋䛸䜣䛹⮬ືⓗ䛻స䜛䛣䛸䛜䛷䛝䜛䛸䛔䛖ᕼᮃ䛜ฟ䛶䛟䜛䚹• 䛧䛛䛧䠈䛭䛖䛔䛖ၥ㢟䛰䛡䛾䝔䝇䝖䛻䛿䠈authentic 䛷䛺䛔➼䠈ከ䛟䛾ᢈุ䛜㞟୰䛩䜛䛣䛸䛻䛺䜛䚹• 䛛䛴䛶䛾᪥ᮏㄒ⬟ຊヨ㦂䛻䛿䠈⣭䠄䠍䡚䠐䠅䛤䛸䛻䠈₎Ꮠ䠈ㄒᙡ䠈ᩥἲ஦㡯䛾୍ぴ⾲䠄䝅䝷䝞䝇䛸⛠䛧䛶䛔䛯䠅䛜Ꮡᅾ䛧䛶䛔䛯䚹

• 䛭䛾㛫䛾䠈ጇ༠Ⅼ䜢ồ䜑䜛䛣䛸䛜ᚲせ䛻䛺䜛䚹䛧䛛䛧䠈ᑡ䛺䛟䛸䜒┬ຊ໬䛛䜙䛿㐲䛦䛛䜛䚹ї ᮧୖ䠄2001)－96－㐳ཬຠᯝ䠖 䝔䝇䝖䛷 䜜䛺䛔Ꮫຊ䛜Ꮡᅾ䛩䜛䛸䛔䛖ಙᛕ䛾※Ἠ

䛣䜜䛷䛿䛂┿䛾ゝㄒ⬟ຊ䛃䛿 䜜䛺䛔䠛• 䝔䝇䝖⌮ㄽ䛸䛾┦ᛶ䛜䛔䛔䛾䛿䠈▷䛔ከ⫥㑅ᢥ㡯┠䛷䛒䜛䛛䜙䠈discrete points 䜢ၥ䛖䝍䜲䝥䛾㡯┠䛜ዲ䜎䜜䜛䚹䛧䛛䛧䠈䛭䜜䛻䛿స㢟䝃䜲䝗䠈䛥䜙䛻䛿ヨ㦂䛾䝴䞊䝄䞊䝃䜲䝗䛻୙‶䛜⏕䛨䜛䛣䛸䛻䛺䜛䚹• ゝㄒᩍ⫱䛷ゝ䛘䜀䠈స㢟䝃䜲䝗䛿䠈ゝㄒ▱㆑䛷䛿䛺䛟ゝㄒ㐠⏝⬟ຊ䜢ၥ䛔䛯䛔䚹• ྠᵝ䛻䠈ධᏛヨ㦂䛷䛿䠈䠄㞳ᩓⓗ䛺䠅▱㆑䛰䛡䛷䛿䛺䛟䠈ᛮ⪃ຊ䞉ุ᩿ຊ䞉⾲⌧ຊ䠄䟿䠅➼䜒ၥ䛖䛣䛸䛜ồ䜑䜙䜜䜛䚹• ಙ㢗ᛶ䜢≛≅䛻䛧䛶䜒䠈䠄䜘䜚ᮏ㉁ⓗ䛺䠅ጇᙜᛶ䜢㧗䜑䜛䛸䛔䛖┠ᶆ䜢స㢟䝃䜲䝗䛿䜒䛱䜝䜣䠈䝴䞊䝄䞊䝃䜲䝗䜒ᮇᚅ䛩䜛䚹Authentic 䛺ၥ㢟(౛䠅ゝㄒ䛾▱㆑䛷䛿䛺䛟㐠⏝⬟ຊ䛸䛿䠛䠄䠍䠅 ゝㄒ⬟ຊ䛿䠈༢䛺䜛▱㆑䛾㞟✚䛷䛿䛺䛟䠈ᐇ㝿ሙ㠃䛻䛚䛔䛶䝁䝭䝳䝙䜿䞊䝅䝵䞁άື䜢⾜䛔䛖䜛⬟ຊ䛸䛧䛶ᤊ䛘䜙䜜䜛䚹䛣䛾⤖ᯝ䠈ゝㄒ䝔䝇䝖䛻䜘䜛 ᐃ䛾ᑐ㇟䛿,ゝㄒ䛾▱㆑䛷䛿䛺䛟䝁䝭䝳䝙䜿䞊䝅䝵䞁⬟ຊ䛸䛺䜛䚹䠄䠎䠅 䛣䜜䛻ᑐᛂ䛧䛶ၥ㢟㡯┠䛿䠈䛔䛛䛻䜒䝔䝇䝖䛾䛯䜑䛻タᐃ䛥䜜䛯ேᕤⓗ䛺ሙ㠃䛷䛿䛺䛟䠈⮬↛䛷⌧ᐇⓗ䛺䜒䛾(䛣䜜䛜 authentic 䛸䛔䛖䛣䛸䛷䛒䜛)䛜ồ䜑䜙䜜䜛䚹䠄䠏䠅 䝁䝭䝳䝙䜿䞊䝅䝵䞁άື䛭䛾䜒䛾䛻䠈ㄆ▱ᚰ⌮Ꮫ䠈♫఍ゝㄒᏛⓗ䛺ศᯒ䛜ຍ䛘䜙䜜䠈㠀ᖖ䛻ከ㠃ⓗ䛻༊ู䛥䜜䜛䜘䛖䛻䛺䜛.
• 䛭䛾⤖ᯝ䠈ಶ䚻䛾ၥ㢟㡯┠䛿ᐇ㝿䛾䝁䝭䝳䝙䜿䞊䝅䝵䞁ሙ㠃䜢タᐃ䛧䛯ୖ䛷䠈䜘䜚⌧ᐇⓗ䛺ゝㄒάື䛾㐙⾜䜢᝿ᐃ䛧䛯཯ᛂ䛜⏝ព䛥䜜䜛䚹ᐈほᙧᘧ䛾䝔䝇䝖䜈䛾⬣ጾ• 䛚Ẽ䛵䛝䛾䜘䛖䛻䠈authentic 䛺ၥ㢟㡯┠䜈䛾ഴᩳ䛿స㢟䛭䛾䜒䛾䜢ⴭ䛧䛟ᅔ㞴䛻䛩䜛䠈ከ⫥㑅ᢥᙧᘧ䛾䝔䝇䝖䛿䠈༢⣧䛺▱㆑䛾᭷↓䜢ၥ䛖ሙྜ䛷䛩䜙,㑅ᢥ⫥䛾タᐃḟ➨䛷㞴᫆ᗘ䛜ⴭ䛧䛟ኚ䜟䜚䛖䜛䚹• ㄗ⟅䛾㑅ᢥ⫥(㏞䜟䛧䠈distracter 䛸࿧䜀䜜䜛)䛾స䜚᪉䛻䜘䛳䛶䛿,ཷ㦂⪅䛾ุ᩿䜢୙Ᏻᐃ䛺䜒䛾䛻䛧䛯䜚(ಙ㢗ᛶ䛾పୗ)䠈䛷䛝䜛ཷ㦂⪅䜢䛛䛘䛳䛶ㄗ⟅䛻ᑟ䛔䛯䜚(ጇᙜᛶ䛾పୗ)䛩䜛䛣䛸䛜䛒䜛䚹• స㢟⪅䛜ᛮ䛔䜒䛛䛡䛺䛛䛳䛯䛸䛣䜝䛻䠈ṇ⟅䜢㑅ᢥ䛩䜛ᡭ䛜䛛䜚䛜䛷䛝䛶䛧䜎䛖䛣䛸䛜䛒䜛➼䠈ಙ㢗ᛶ䠈ጇᙜᛶ䛾పୗ䜢ᣍ䛟ྍ⬟ᛶ䛜୍ᒙ㧗䜎䜛䚹• 䛭䜜䛷䜒䠈䛣䛖䛧䛯ၥ㢟䛻⛣⾜䛫䛦䜛䜢ᚓ䛺䛔⌮⏤䛿☜䛛䛻Ꮡᅾ䛩䜛䚹 ї 㐳ཬຠᯝ－97－㐳ཬຠᯝ䠖 䝔䝇䝖䛷 䜜䛶䛔䛺䛔Ⴧ↝‫ܖ‬щ䛜Ꮡᅾ䛩䜛䛸䛔䛖⪃䛘䛜⏕䜎䜜䜛᰿ᣐ䛭䜜䛷䜒䛺䛚䠈ᐈほᙧᘧ䛾䝔䝇䝖䜈䛾⬣ጾ• 䛣䛾✀䛾ၥ㢟䛿䠈ᙜ↛䠈䠍ၥ䛒䛯䜚䛾ゎ⟅ᡤせ᫬㛫䛜㛗䛟䛺䜛䚹䛣䛾䛣䛸䛿䠈ᚲ↛ⓗ䛻䠍䛴䛾䝔䝇䝖䛒䛯䜚䛾㡯┠ᩘ䜢ῶᑡ䛥䛫䠈ᑠၥ୺⩏䛾ᐈほ䝔䝇䝖䛻ẚ䛧䛶䠈ಙ㢗ᛶ䛾పୗ䜢䛝䛯䛩䛣䛸䛻䛺䜛䠄๓㏙䛾䜘䛖䛻䠈ጇᙜᛶ䜈䛾ᙳ㡪䛿」㞧䛷䠈୍ᴫ䛻䛿ゝ䛘䛺䛔䠅䚹• 䝔䝇䝖䛾ᙧᘧ䜔ෆᐜ䛜ᩍ⫱ෆᐜ䜢㏫䛻䠈つᐃ䛧䛶䛧䜎䛖䛣䛸䜢㐳ཬຠᯝ䠄wash-back effect䠅䛸࿧䜆䚹• ஦ᐇ䠈䝔䝇䝖䛾ᙧᘧ䛸ෆᐜ䛜䠈䛭䛾䝔䝇䝖䛻ᑐ䛩䜛‽ഛᩍ⫱䜢ᢸ䛖ᩍ⫱ᶵ㛵䛾ᩍ⫱䛾᪉ἲ䛸ෆᐜ䛻኱䛝䛺ᙳ㡪䜢ཬ䜌䛩䛣䛸䛿␲䛖వᆅ䛜䛺䛔䚹• 䛥䜙䛻䠈ᤵᴗ䛾┠ᶆ䛜䝔䝇䝖ᑐ⟇䛻↔Ⅼ໬䛥䜜䠈䛭䛾䛯䜑䛾ᣦᑟᢏ⾡䠈Ꮫ⩦᪉ἲ䛜䛂㐍Ṍ䛃䛩䜛䛸䠈⩦ᚓ䛥䜜䜛䜒䛾䛜䛂≉ᐃ䛾䝔䝇䝖䛷㧗ᚓⅬ䜢䛸䜛⬟ຊ䛃䛻೫䛳䛶䛔䛟䛾䜒஦ᐇ䛷䛒䜝䛖䠄ᐇド䛥䜜䛶䛔䜛䛛䠛䠅䚹• 䛣䜜䛿䠈ホ౯᪉ἲ䛾ຎ໬䛸䜏䛺䛥䜜䜛䛣䛸䛻䛺䜚䠈ఱ䜙䛛䛾ᨵ㠉䜈䛾䝰䝏䝧䞊䝅䝵䞁䛻䛺䜛䚹• ゝㄒ⬟ຊ䛾ሙྜ䠈䛣䜜䛿▱㆑䛛䜙㐠⏝䜈䛸䛔䛖ὶ䜜䛷䛒䛳䛯䚹 ї ኱ᏛධᏛ⪅㑅ᢤ䛷䛿䠈䛂ᛮ⪃ຊ䛃䠈䛂ุ᩿ຊ䛃䠈䛂⾲⌧ຊ䛃䚹• 䜎䛯䠈䛣䛖䛧䛯㡯┠䛾ᡤせ᫬㛫䛿୙ᥞ䛔䛸䛺䜚䠈㡯┠䜢ධ䜜᭰䛘䛶」ᩘ䛾∧䜢⏝ព䛧䛶⾜䛖䜘䛖䛺ᚓⅬ➼໬䛾సᴗ䛻୙౽䜢䛝䛯䛩䚹• 䛩䛺䜟䛱䠈㐠⏝⬟ຊ䛻ⓗ䜢⤠䛳䛯䝔䝇䝖䛿䠈ᙧ䛾ୖ䛷䛿ᑠၥ୺⩏䛾ᐈほ䝔䝇䝖䛷䛿䛒䜛䛜䠈䝔䝇䝖⌮ㄽⓗほⅬ䜙ぢ䛯䛭䛾㛗ᡤ䛾ከ䛟䛜ኻ䜟䜜䜛䛣䛸䛻䛺䜛䚹• ↄ↸↚≏ᚡᡓࡸ↧↝ᆆᘍⅻᎋⅷ↸↻↺↗≏‫ࣱ࢘ڲ‬↝ᚇໜⅺ↸≏ↄ↸↚‫ٻ‬ⅼ↙բ᫆ⅻႆဃↈ↺⅛

グ㏙ᘧ䝔䝇䝖䛾ၥ㢟Ⅼ

グ㏙ᘧ䝔䝇䝖䛾ၥ㢟Ⅼ• ᚑ᮶⾜䜟䜜䛶䛝䛯ᑠㄽᩥ䛻㏆䛔䜒䛾䜢᝿ᐃ䛩䜛䚹• 䛣䛾ၥ㢟䛿䠈䜒䛧௬䛻㧗ᗘ䛺ேᕤ▱⬟䛻䜘䜛ᶵᲔ᥇Ⅼ䛜䠄᏶඲䛻䠅ྍ⬟䛻䛺䛳䛯䛸䛧䛶䜒䠈ᮍゎỴ䛾ၥ㢟䛸䛧䛶ṧ䜛ྍ⬟ᛶ䛜㧗䛔䚹

• 䛧䜀䛧䜀ၥ㢟䛻䛺䜛䛾䛜䠈᥇Ⅼ䛻୺ほ䛜ධ䜛䛣䛸䠈ලయⓗ䛻䛿䠈」ᩘ䛾᥇Ⅼ⪅㛫䛷ホ౯䛜୍⮴䛧䛺䛔䛣䛸䛷䛒䜝䛖䚹䛧䛛䛧䠈䛣䛾ၥ㢟䛿䠈᥇Ⅼ⪅㛫䛾༑ศ䛺ᡴ䛱ྜ䜟䛫䛸䠈୙୍⮴䛾㝿䛾ᑂ㆟䠈䛥䜙䛻䛿᥇Ⅼ⪅ᩘ䜢ቑ䜔䛩䛣䛸䛻䜘䛳䛶ゎỴ䛷䛝䠈┦ᙜ䛾ಙ㢗ᛶ䛿ᚓ䜙䜜䜛䚹• 䛴䜎䜚䠈䛯䜎䛯䜎ྲྀ䜚ୖ䛢䜙䜜䛯䝔䞊䝬䜈䛾┦ᛶ䛻䜘䛳䛶䠈ྠ୍ཷ㦂⪅䛾Ⅼᩘ䛻኱䛝䛺ኚື䛜㉳䛣䜚䛖䜛䚹䛩䛺䜟䛱䠈䝖䞊䝍䝹䛻䜏䜜䜀ಙ㢗ᛶ䛾ప䛔ホ౯᪉ἲ䛷䛒䜛䛾䛿䠄ᑡ䛺䛟䛸䜒⌧᫬Ⅼ䛷䛿䠅☜ᐇ䛷䛒䜛䚹• ၥ㢟䛿䜐䛧䜝䠈」ᩘ䛾኱ၥ䛾㛫䛾┦㛵䛜ⴭ䛧䛟ప䛔䛣䛸䛷䛒䜛䚹䛣䛾Ⅼ䛿䠈ᑠㄽᩥヨ㦂䛾᥇⏝䛾ึᮇ䛛䜙䠄」ᩘ
ᅇཷ㦂⪅䛾⤖ᯝ➼䜢㏻䛨䛶䠅ᣦ᦬䛥䜜䛶䛝䛯䛣䛸䛷䛒䜛䛜䠈ゎỴ䜢ぢ䛶䛔䛺䛔䚹䛣䛾䛣䛸䛿䠈ᖖ䛻䠏䛴䛾኱ၥ䛷ᵓᡂ䛥䜜䛶䛔䛯ྡྂᒇ኱Ꮫᩍ⫱Ꮫ㒊䛻䛚䛡䜛䛂ㄽ㏙ⓗᏛຊ᳨ᰝ䛃䛻䛚䛔䛶⧞䜚㏉䛧☜ㄆ䛥䜜䛯䚹• 䜒䛱䜝䜣䠈䛣䛖䛧䛯ၥ㢟㛫䛾┦㛵䛾ప䛥䛿䠈䛛䛺䜚㑅ᢤᛶ䛾㧗䛔኱Ꮫ䛷䛾ධヨ䛰䛳䛯䛛䜙䛰䛸䛿⪃䛘䜙䜜䜛䚹䜎䛯䠈䛣䛖䛧䛯ၥ㢟䛾䜒䛴䠄䝫䝆䝔䜱䝤䛺䠅㐳ཬຠᯝ䜒ྰᐃ䛿䛷䛝䛺䛔䚹• ྡྂᒇ኱Ꮫ䛾ྠ൉䛛䜙䠈䛂᭱㏆䠈ㄞ䜐䛻⪏䛘䛺䛔䝺䝫䞊䝖䜢᭩䛟Ꮫ⏕䛜䛔䛺䛟䛺䛳䛯䛃䛸䛔䛖ឤ᝿䜢⪺䛔䛯䛣䛸䛿ᛀ䜜䜙䜜䛺䛔䚹

䛣䜜䛛䜙䛾ධᏛ⪅㑅ᢤ䛾୰䛷䛤Ύ⫈䠈䛒䜚䛜䛸䛖䛤䛦䛔䜎䛧䛯• ௒䠈୰ᩍᑂ➼䛷ᥦ᱌䛥䜜䛶䛔䜛᪉ྥᛶ䛻䛿䠈୍ᐃ䛾䝯䝸䝑䝖䛜䛒䜛䛣䛸䜒஦ᐇ䛷䛒䜛䛜䠈䛂⌮᝿䛃䛾ᐇ⌧䛻䛿䠈㉺䛘䜛䜉䛝ከ䛟䛾䝝䞊䝗䝹䛜䛒䜛䚹• ㆟ㄽ䜢㐍䜑䜛䛯䜑䛻䛿䠈ồ䜑䜙䜜䛶䛔䜛䛂┿䛾Ꮫຊീ䛃䛜䠈䠄᝿ᐃ䛥䜜䛶䛔䜛♫఍ീ䛸䛸䜒䛻䠈䠅䜒䛳䛸ලయ໬䛥䜜䜛ᚲせ䛜䛒䜛䚹䛭䛣䛷䛿䛨䜑䛶䠈⌧ᐇ䛾ホ౯᪉ἲ䠈≉䛻䠈CBT 䛷ྍ⬟䛺䛣䛸䛸䠈䛥䜙䛻䛭䛾ᐇ᪋䛻䛒䛯䛳䛶䛾ၥ㢟Ⅼ䛜䜘䜚᫂☜䛻ぢ䛘䛶䛟䜛䛾䛷䛿䛺䛔䛛䚹• 䛭䛾ୖ䛷䠈䛣䛾ඛ䛻ᚅ䛱ཷ䛡䜛」ᩘ䛾䝝䞊䝗䝹䜢஌䜚㉺䛘䜛䛯䜑䛻䛿䠈༑ศ䛺㈈ᨻⓗ䠈ேⓗែໃ䛜䛺䛡䜜䜀䛺䜙䛺䛔䚹䛭䜜䛜䛹䛣䜎䛷స䜜䜛䛛䛜䠈✲ᴟ䛾ၥ㢟䛷䛒䜛䛸⪃䛘䜛䚹• ཧ⪃ᩥ⊩
ᑠᔱ⚽ኵ䞉ᮧୖ 㝯 㻔㻝㻥㻥㻝㻕㻌㻌ྡྂᒇ኱Ꮫᩍ⫱Ꮫ㒊䛻䛚䛡䜛ㄽ㏙ⓗᏛຊ᳨ᰝ㻝㻥㻥㻜ᖺᗘ⛉◊㈝⥲ྜ◊✲ 㻔㻭㻕㻌㻌䛂኱Ꮫධヨ䛻䛚䛡䜛ᐇᢏ䞉㠃᥋䞉ᑠㄽᩥ➼䛾ホ౯䛻㛵䛩䜛◊✲䛃ሗ࿌᭩㻘㻌㻟㻝㻙㻢㻜㻚 䠄◊✲௦⾲⪅䠖ᒾᆤ⚽୍䠅ᮧୖ 㝯 㻔㻞㻜㻜㻝㻕㻌➨䠎ゝㄒ䛸䛧䛶䛾᪥ᮏㄒ⬟ຊ䝔䝇䝖䛾㛤Ⓨ 䇷୍⯡ⓗ䛺ၥ㢟䛸ᅛ᭷䛾ᅔ㞴䇷 ィ 䛸ไᚚ䠈㻠㻠㻜㻘㻌㻡㻣㻢㻙㻡㻤㻜㻚ᮧୖ 㝯 䠄◊✲௦⾲⪅䠅 㻔㻞㻜㻜㻟㻕㻌 ᡃ䛜ᅜ䛾බⓗヨ㦂䛻䛚䛡䜛ᚓⅬ➼໬䛾ᑟධ䛻ྥ䛡䛯ᚰ⌮䞉ᩍ⫱ ᐃⓗ◊✲ᖹᡂ䠍䠎ᖺᗘ䡚ᖹᡂ䠍䠐ᖺᗘ ⛉Ꮫ◊✲㈝⿵ຓ㔠䠄≉ู◊✲ಁ㐍㈝㻔㻝㻕䠅◊✲ᡂᯝሗ࿌᭩

－98－

CBT ࡢࢧࣥࣉ࣭ࣝࢹࣔࣥࢫࢺ࣮ࣞࢩࣙࣥ
኱Ꮫධヨࢭࣥࢱ࣮◊✲㛤Ⓨ㒊ຓᩍ

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‫ྖۑ‬఍

௒᪥ࡣࢥࣥࣆ࣮ࣗࢱ࣭࣮࣋ࢫࢻ࣭ࢸࢫࢸ࢕ࣥࢢ

ؐ CBT ࡀヰ㢟࡟ୖࡗ࡚࠾ࡾࡲࡍࡅࢀ࡝ࡶࠊ኱Ꮫධヨࢭࣥࢱ࣮࡛ࡶ CBT ࡢ㛤Ⓨ◊✲ࢆࡋ࡚࠾ࡾࡲࡍࡢ࡛ࠊ኱ஂಖᬛ
ဢඛ⏕࡟ࡕࡻࡗ࡜ࢹࣔࣥࢫࢺ࣮ࣞࢩࣙࣥࡋ࡚ࡶࡽ࠸ࡓ࠸
࡜ᛮ࠸ࡲࡍࠋ
‫ۑ‬኱ஂಖẶ኱Ꮫධヨࢭࣥࢱ࣮ࡢ኱ஂಖࡀⓎ⾲ࡉࡏ࡚࠸ࡓࡔࡁࡲࡍࠋ
ᮏ◊✲ࠊ≉࡟ࢩࢫࢸ࣒㛤Ⓨ࡟ࡘࡁࡲࡋ࡚ࡣ኱Ꮫධヨࢭࣥࢱ࣮ࡢ⚾࡜ࠊᐑᇣ࡟ຍ࠼࡚ᮾிᕤᴗ኱Ꮫࡢᐊ⏣┿⏨ᩍᤵࠊ๓ᕝ┾୍ᩍᤵ࡟ඹྠ◊✲ࡋ࡚࠸ࡓࡔ࠸࡚࠾ࡾࡲࡍࠋࡇ ࢀ ࡣ ᐇ 㝿 ࡟ ࠊ ⣙ 1 ᖺ ๓ ࡟ ኱ Ꮫ ධ ヨ ࢭ ࣥ ࢱ ࣮ ࡢ ࣔ ࢽ ࢱ ࣮ ㄪ ᰝ ࡢ ୰ ࡛ ᐇ 㦂 ࡜ ࡋ ࡚ ࠊ CBTࢆ ౑ ࡗ ࡚ⱥㄒ ࡢ 4 ᢏ⬟ヨ 㦂ࡢ୍ 㒊ࢆᐇ ᪋ࡋࡓ ࡜ࡁࡢ ෗┿࡛ ࡍࠋCBT ࡟ࡼ ࡿၥ㢟 ࡜ࡣ࡝ ࡢࡼ࠺࡞ࡶࡢ࠿ࠊᐇ㝿࡟ࡇࡇ࡛࠾ぢࡏࡋࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
ⱥㄒ㸸ື⏬฼⏝ࣜࢫࢽࣥࢢၥ㢟ࡶࡕࢁࢇࢥࣥࣆ࣮ࣗࢱ࡛ࡍࡢ࡛ࠊࡇ࠺࠸ࡗࡓື⏬ࢆぢࡏࡿࡇ࡜ࡶ࡛ࡁࡲࡍࠋ㏻ᖖࡢࣜࢫ
ࢽࣥࢢ࡜㐪ࡗ࡚ࠊከᑡㄒᙡࡢࣞ࣋ࣝࡀୖࡀࡗ࡚ࡶᫎീࡀ࠶ࡾࡲࡍࡢ࡛ࠊཷ㦂⪅ࡣ᝟ሗࢆ⿵
᏶ࡋ࡞ࡀࡽࠊࡼࡾ⌧ᐇⓗ࡞ⱥㄒࡢ≧ἣ࡟㏆࠸ࡼ࠺࡞ࡇ࡜ࢆࠊࢸࢫࢺ࡜ࡋ࡚⾜࠺ࡇ࡜ࡀ࡛ࡁ
ࡲࡍࠋ௒ᅇࡢタᐃࡣࠊ2 ᅇື⏬ࢆὶࡋࡲࡍࠋࡑࡋ࡚ 2 ᅇࡢ㛫࡟ 1 ᅇ 30 ⛊ࡢ࢖ࣥࢱ࣮ࣂࣝࢆタࡅ ࡚ ࠊࠕ ḟ ࡢ ື ⏬ ࡢ ᚋ ࠊ ࡇ ࠺ ࠸ ࠺ ㉁ ၥ ࢆ ࡋ ࡲ ࡍ ࡼ ࠖ ࡜ ࠸࠺ ࡇ ࡜ ࢆ ⾲♧ ࡋ ࡚ ࠾ ࠸ ࡚ࠊ 2 ᅇ ┠ࡢື⏬ࢆࡇࡢࡼ࠺࡟ὶࡍࡼ࠺࡟࡞ࡗ࡚࠸ࡲࡍࠋᙜ↛ࡇ࠺࠸ࡗࡓタᐃࡣᗄࡽ࡛ࡶኚ࠼ࡿࡇ࡜
ࡀ࡛ࡁࡲࡍࠋࡑࡋ࡚ࠊࠕ୰㌟ࡣ࡝ࢀ࡛ࡍ࠿ࠊ㑅ࢇ࡛㐍ࡳ࡞ࡉ࠸ࠖ࡜ฟ㢟ࡋࡲࡍࠋⱥㄒ㸸Ⓨ㡢㘓㡢ၥ㢟ࡇࢀࡣື⏬ࢆ౑ࡗࡓ౛࡛ࡍࡅࢀ࡝ࡶࠊᐇ㝿࡟㘓㡢ࡉࡏࡿࡼ࠺࡞ၥ㢟ࡶ࡛ࡁࡲࡍࠋ⌧ᅾࡢධヨࢭࣥࢱ࣮ࡢၥ㢟࡛ࡍ࡜ࠊⓎ㡢グྕࢆ♧ࡋ࡚㸪࡝ࢀࡀ➼ࡋ࠸࡛ࡍ࠿㸽࡜࠿ࠊ࢔ࢡࢭࣥࢺ
ࡣ࡝ࡇ࡟࠶ࡾࡲࡍ࠿㸽࡜࠸࠺ࠊᮏᙜ࡟ࠕ࡛ࡁࡿ࠿࡛ࠖࡣ࡞ࡃ࡚ࠊ▱㆑ࢆࢩࣥࣉࣝࣜࢥ࣮ࣝ
༢⣧᝿㉳ ࡛ࡁࡿ࠿࡜࠸࠺ࡇ࡜࡟↔Ⅼࢆᙜ࡚࡚࠸ࡿࡢ࡛ࡍࡀࠊ㸦CBT ࡟ࡼࡿၥ㢟࡛ࡣ㸧࡝
࠺᥇Ⅼࡍࡿ࠿ࡣࡲࡓูࡢၥ㢟࡟࡞ࡿࡶࡢࡢࠊᢏ⾡࡜ࡋ࡚ࡣࠊࡇࡢࢱࣈࣞࢵࢺ࡟ཷ㦂⪅ࡀㄞࡳୖࡆࡓ㒊ศࢆグ㘓ࡍࡿࠋࡑࡋ࡚㸪ࡑࡢ㡢ኌࢆᚋ࡛⪺࠸࡚ࠊࡁࡕࡗ࡜ఏࢃࡗ࡚࠸ࡿ࠿ࠊఏ
ࢃࡗ࡚࠸࡞࠸࠿࡜࠸ࡗࡓࡇ࡜࡛Ⅼᩘ໬ࡍࡿࡼ࠺࡞ࡇ࡜ࡀ࡛ࡁࡿ࡜ᛮ࠸ࡲࡍࠋ
ⱥㄒ㸸㛗ᩥ㈨ᩱㄞゎၥ㢟௚࡟ࡶࠊ㈨ᩱࡢㄞゎၥ㢟ࢆ࣮࡛࢝ࣛぢࡏࡓࡾࠊ࠿࡞ࡾ㛗࠸ᩥ❶ࢆฟࡋ࡚ࠊ඲㒊ࡀ඲㒊ᅇ⟅࡟ᚲせ࡞᝟ሗ࡛ࡣ࡞ࡃ࡚ࠊ඲ࡃ㛵ಀ࡞࠸ᩥ❶ࡶΰࡐ࡚ࠊࡑࡇ࠿ࡽᩳࡵ࡟ㄞࡳฟࡏࡿ࠿㸽－99－

ᚲせ࡞᝟ሗࡔࡅࢆࣆࢵࢡ࢔ࢵࣉ࡛ࡁࡿ࠿㸽ࡲࡉ࡟ᡃࠎࡀᬑẁ᝟ሗࢆᚓࡿ㐣⛬࡛ࡸࡗ࡚࠸ࡿ
ࡼ࠺࡞సᴗࢆࠊ⣬࡟༳ๅࡍࡿ࡜ࠊ࡝࠺ࡋ࡚ࡶᯛᩘࡀቑ࠼࡚ࡋࡲ࠸ࡲࡍࡅࢀ࡝ࡶࠊࢥࣥࣆࣗ
࣮ࢱ࡛ࡣࡑ࠺࠸ࡗࡓࡇ࡜ࡶ⡆༢࡟࡛ࡁࡿࡼ࠺࡟࡞ࡾࡲࡍࠋ
ⱥㄒ㸸ື⏬ࣜࢫࢽࣥࢢ࡜ࣛ࢖ࢸ࢕ࣥࢢࡢ」ྜၥ㢟࠶࡜ࠊࣛ࢖ࢸ࢕ࣥࢢၥ㢟ࡶࢸ࢟ࢫࢺ࡜ࡋ࡚グ㘓࡛ࡁࡲࡍࠋࡇࡢၥ㢟࡛ࡣື⏬ࢆぢ࡚ࡶࡽࡗ࡚ࠊࡑࡢෆᐜ࡟ࡘ࠸࡚ࡲ࡜ࡵ࡞ࡉ࠸࡜࠸࠺ၥ㢟࡛ࡍࠋࡇ࠺࠸ࡗࡓື⏬࡜ࣛ࢖ࢸ࢕ࣥࢢࡢ
ࢥࣥࣅࢿ࣮ࢩࣙࣥࡳࡓ࠸࡞ࡇ࡜ࡶྍ⬟࡛ࡍࠋࡇࢀࢆ࡝࠺᥇Ⅼࡍࡿ࠿ࡣࡲࡓูࡢၥ㢟࡟࡞ࡾࡲ ࡍ ࡀ ࠊᢏ ⾡ ⓗ ࡟ࡣࡇ ࠺࠸࠺ ࡇ࡜ࡀ ྍ⬟ ࡟࡞ࡾ ࡲࡍࠋ ࡇࡢື ⏬⮬య ࡣ AFP ㏻ಙ ࠿ࡽチ ྍࢆ࠸ࡓࡔ࠸࡚ࢸࢫࢺ࡟஧ḟ฼⏝ࡍࡿࡇ࡜ࡀ࡛ࡁࡿࡼ࠺࡟࡞ࡗ࡚࠸ࡲࡍࡀࠊື⏬࡜࠸࠺ࡢࡣ
ᮏᙜ࡟࠸ࢁ࠸ࢁ࡞⣲ᮦࡀ࠶ࡾࡲࡍࡋࠊື⏬ࢆぢࡏࡿࡇ࡜࡟ࡼࡗ࡚ࠊ༢ㄒࣞ࣋ࣝࡀከᑡపࡃ࡚ ࡶ ᐇ 㝿ⱥ ㄒ ࢆ ࡋ ࡷ࡭ ࡿ ࡜ ࡁ ࡟ࡣ ࠊ 㞺 ᅖ Ẽ ࡛࡜ ࠸࠺ ࠿ࠊࠕࡇ ࠺࠸࠺ ࡇ࡜ࢆ ゝࡗ࡚ ࠸ࡿࡢ ࡛ࡣ࡞࠸࠿࡞ࠖ࡜࠺ࡲࡃ᥎ㄽࡋ࡚ࠊゝㄒ࡛ࡍ࠿ࡽࠊ᥎ㄽࡋ࡞ࡀࡽ⌮ゎࡋ࡚࠸ࡃ࡜࠸ࡗࡓࡇ࡜
ࡶࠊྍ⬟࡟࡞ࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼࡚࠾ࡾࡲࡍࠋ኱つᶍヨ㦂࡛ࡢ&%7㐠⏝᪉ἲ᱌ࡢᥦ♧࡛ࡣࠊᐇ㝿ࡇ࠺࠸ࡗࡓၥ㢟ࢆ࡝ࡢࡼ࠺࡟ࡸࡿࡢ࠿ࡀၥ㢟࡟࡞ࡿ࡜ᛮ࠸ࡲࡍࡀࠊ௒ࠊ♧ࡉࢀ࡚࠸ࡿ୰ᩍᑂࡢ⟅⏦᱌࡛ࡍ࡜ࠊ㧗ᰯ 2 ᖺ⏕ࠊ㧗ᰯ 3 ᖺ⏕ࡀሙྜ࡟ࡼࡗ࡚ࡣ඲ဨཷࡅࡿࠋ
ࡑ࠺ࡍࡿ࡜ 200 ୓ே㏆ࡃ࡟࡞ࡿࢃࡅ࡛ࡍࠋ኱㔞࡟ྠ᫬ⓗ࡟ཷࡅࡿྍ⬟ᛶࡶ࠶ࡾࡲࡍࠋࡑ࠺
ࡋࡓሙྜ࡟㔜せ࡟࡞ࡗ࡚ࡃࡿࡢࡣࠊࡸࡣࡾࢥࢫࢺ࡜ࢭ࢟ࣗࣜࢸ࢕ࡢၥ㢟ࡔ࡜ᛮ࠺ࡢ࡛ࡍࡀࠊ
௒ࠊ୍ࡘࡢ᱌࡜ࡋ࡚⪃࠼࡚࠸ࡿࡢࡣࠊࡇࡇ࡟♧ࡍᅗࡢࡼ࠺࡞ࡶࡢ࡛ࡍࠋ
ᐇ㝿࡟ࢸࢫࢺࡢሙᡤ࡛ࡣࠊࡇࡢࢱࣈࣞࢵࢺᮏయ࡟ࢹ࣮ࢱࢆධࢀ࡚ࠊࢭ࢟ࣗ࢔࡞ᙧ࡛ࠊ࢔
ࣉࣜࢣ࣮ࢩࣙࣥᙧᘧ࡟ࡋ࡚ࡋࡲࡗ࡚ࠊࡑࢀࢆᐇ㝿࡟ࢸࢫࢺࡢᐇ᪋ሙᡤࠊ㧗ᰯ࡛ࡶ࠸࠸ࡋ኱
Ꮫࡢㅮ⩏ᐊ࡛ࡶ࠸࠸ࡋࠊ࠸ࡎࢀ࡟ࡋ࡚ࡶࢿࢵࢺ࣮࣡ࢡࡶࢩࢫࢸ࣒ࡢ࢖ࣥࣇࣛタഛࡶ࡞࠸ᡤ
࡟ᣢࡗ࡚࠸ࡗ࡚ࠊࡑࡇ࡛ࢸࢫࢺࢆཷࡅ࡚ࡶࡽ࠸ࡲࡍࠋཷࡅ࡚ࡶࡽࡗ࡚ࡑࡢࢹ࣮ࢱࢆᅇ཰ࡋࠊ
ࡑࢀࢆ᪥ᮏ඲ᅜఱ⟠ᡤ࠿㓄⨨ࡋࡓࢹ࣮ࢱࡢ୰⥅ᆅⅬࠊ࣮࣮ࣟ࢝ࣝ࢜࣌ࣞࢩࣙࣥࢭࣥࢱ࣮࡜
⚾ࡣ࿧ࢇ࡛࠸ࡲࡍࡅࢀ࡝ࡶࠊࡑࡇ࡬ࢱࣈࣞࢵࢺࢆᣢࡗ࡚ᖐࡗ࡚ࠊࡑࡇ࡛ࢹ࣮ࢱࢆ྾཰ࡋ࡚ࠊ
ࡑࡇ࠿ࡽ୰᰾ࢭࣥࢱ࣮ࡲ࡛ࡣࢭ࢟ࣗ࢔࡞ᅇ⥺࡛ᅇ⟅ࢹ࣮ࢱࢆ㏦ཷಙࡍࡿࡇ࡜ࡀ⪃࠼ࡽࢀࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࠋࡇ࠺ࡍࡿࡇ࡜࡛ᘓ≀ࢆ೉ࡾࡓࡾ‽ഛࡋࡓࡾࠊࡲࡓࠊ⟶⌮ࡍࡿே㛫ࡢᩍ⫱ࡔ࡜࠿ࡑ࠺࠸ࡗࡓ⟶⌮࣭㐠Ⴀࢥࢫࢺࡶ኱ศῶࡽࡍࡇ࡜ࡀ࡛ࡁࡲࡍࡋࠊఱࡼࡾࡶ
CBT ࡜࠸࠺ᙧ࡛ᐇ㝿࡟㐠⏝࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜ࠊ୍ࡘࡢ᱌࡜ࡋ࡚௒ࡣ⪃࠼࡚࠾ࡾࡲࡍࠋ
ࢱࣈࣞࢵࢺࡢ୍ᣓ⟶⌮ᢏ⾡ࡢᥦ♧࡛ࡣࠊఱ༑୓ྎࠊఱ୓ྎ࡜࠸࠺ࢱࣈࣞࢵࢺࢆᐇ㝿࡝ࡢࡼ࠺࡟⟶⌮ࡍࡿ࠿࡜࠸࠺ၥ㢟࡛ࡍࡀࠊࡑࡢࡓࡵ࡟ࢱࣈࣞࢵࢺࢆ୍ᣓ⟶⌮ࡍࡿᢏ⾡࡜࠿௙⤌ࡳࡶ◊✲㛤Ⓨࡋ࡚࠾ࡾࡲࡍࠋ௒ࡇࡇ࡛ ࠾ぢࡏ ࡋ࡚࠸ ࡿࡢࡣ 100 ྎ ࡢࢱࣈ ࣞࢵ ࢺ࡟ࢧ ࣮ࣂ࣮࠿ࡽ࿨௧ࢆฟࡋ࡚ࠊ1 ␒࠿ࡽ 10␒ࡲ࡛࡟ࡣᩘᏛࡢၥ㢟ࠊ11 ␒࠿ࡽ 50 ␒ࡄࡽ࠸ࡲ࡛ࡣⱥㄒࡢ㞴ࡋ࠸ၥ㢟ࢆධࢀࡿ࡜࠸࠺ࡼ
࠺࡟ࡑࢀࡒࢀࢱࣈࣞࢵࢺ࡟ᣦᐃࡋ࡚ࠊࢧ࣮ࣂ࣮࠿ࡽ୍ᣓ࡛࿨௧ࢆฟࡋ࡚ࠊྠ᫬ⓗ࡟ࡑࢀࡒࢀࡢࢱࣈࣞࢵࢺ࡟␗࡞ࡿၥ㢟ࢆධࢀ࡚࠸ࡿ࡜ࡇࢁ࡛ࡍࠋ
IRT ࡟ࡼࡗ࡚ᑻᗘ໬ࡉࢀࡓヨ㦂࡛࠶ࢀࡤࠊၥ㢟ࡀኚࢃࡗ࡚ࡶࡑࢀࢆゎ࠸ࡓṇㄗࢹ࣮ࢱ࡜ࡑ ࡢ ၥ 㢟ࡢ 㞴 ᫆ ᗘ ࢆࡶ ࡜ ࡟ ඹ ㏻ᑻ ᗘ ୖ ࡛⾲ ⌧ࡍࡿ ࡇ࡜ࡀ ࡛ࡁࡿ ࡼ࠺࡟ ࡞ࡾࡲ ࡍࡢ࡛ ࠊIRTࢆ౑࠺࡜ࡑ࠺࠸ࡗࡓࡇ࡜ࡶྍ⬟࡟࡞ࡾࡲࡍࠋ
1 ࡘࡇࡇ࡛⏦ࡋୖࡆࡓ࠸ࡢࡣࠊࡇࡢ IRTࠊඛ࡯࡝࠿ࡽฟ࡚࠸ࡿ⤫ィ⌮ㄽࡢࡇ࡜࡛ࡍࡀؐ

－100－

IRT ࡜ࢥࣥࣆ࣮ࣗࢱࡢ┦ᛶࡀⰋ࠸࡛ࡍࠋࢥࣥࣆ࣮ࣗࢱࢆ౑࠺௨ୖࠊᚲࡎቯࢀࡲࡍࠋ⚾ࠊ100
ྎࢆ 1 ᖺ㛫⟶⌮ࡋ࡚ࡳ࡚ࠊ1 ྎࡀཎᅉ୙᫂ࡢ୙ලྜࡀฟࡲࡋࡓࠋࡇࢀࡣࢹࣂ࢖ࢫࡢၥ㢟ࡔ࡜ᛮ࠸ࡲࡍࠋࡑࢀࡀヨ㦂୰࡛࡞࠿ࡗࡓ࠿ࡽࡼ࠿ࡗࡓࡢ࡛ࡍࡀࠊヨ㦂୰࡟㉳ࡇࡿࡇ࡜ࡔࡗ࡚
࠶ࡾ࠺ࡿࢃࡅ࡛ࡍࠋࡑ࠺࡞ࢀࡤᚲࡎࣇ࢙࣮ࣝࢭ࣮ࣇࡀᚲせ࡛ࠊࡑ࠺࡞ࡗࡓ࡜ࡁ࡟ࡶูࡢࢸ
ࢫࢺࣇ࢛࣮࣒ࠊูࡢࢸࢫࢺ࡛ࡶẚ㍑ྍ⬟࡞ࢫࢥ࢔ࡀฟࡿࡼ࠺࡞᪉ἲࡀᏑᅾࡍࡿࡢ࡜Ꮡᅾࡋ
࡞࠸ࡢ࡜࡛ࡣ඲↛㐪࠸ࡲࡍࠋᖺ࡟୍ᗘࡁࡾࡢ୍ᩧヨ㦂࡛ࢥࣥࣆ࣮ࣗࢱࢆ౑࠺ࡢ࡛࠶ࢀࡤࠊ
ࢺࣛࣈࣝ 0 ࡛ࡢᐇ᪋ࡣࠊ࡯࡜ࢇ࡝↓⌮ࡔ࡜⚾ࡣᛮࡗ࡚࠸ࡲࡍࠋ
ゎ⟅ࢹ࣮ࢱ㏦ཷಙ⏬㠃ࡢᥦ♧ࡉࡽ࡟ၥ㢟ࡢᅇ཰ࡶࡇ࠺࠸ࡗࡓ⏬㠃࡛ຠ⋡ⓗ࡟࡛ࡁࡲࡍࡋࠊ⏬㠃࡟࠾♧ࡋࡋࡓࡢࡣࠊᅇ
⟅ࢹ࣮ࢱࡢ⟶⌮ࡶࡇࡢࡼ࠺࡞ᙧ୍࡛ᣓ⟶⌮࡛ࡁࡲࡍࠋࡔࢀࡀ࡝ࢇ࡞ၥ㢟࡟ᑐࡋ࡚࡝࠺࠸࠺
ࡩ࠺࡟⟅࠼ࡓ࠿ࠊࡑࡢ࡜ࡁࡢࢸࢫࢺࡢ ID ࡣ࡝ࢇ࡞ ID ࡛ࠊఱ␒┠ࡢࣈࣟࢵࢡ࡛ၥ㢟ࡣఱ␒┠ࡔࡗࡓ࠿ࠊࡑࡢࡼ࠺࡞グ㘓ࡀࡍ࡭࡚ࢹ࣮ࢱ࣮࣋ࢫ໬ࡉࢀࡲࡍࠋ
ヨ㦂ၥ㢟ࡢ⟶⌮࣭⦅㞟࡟ࡘ࠸࡚ࡶࡇࡢࡼ࠺࡟ࠊၥ㢟ࡀఱᅇ᭚㟢ࡉࢀ࡚ㄡࡀཷࡅ࡚ࠊ࡝࠺
࠸࠺ࣔࢹࣝࢆ౑ࡗࡓ࡜ࡁࡢࣃ࣓࣮ࣛࢱࡀ࡝࠺࡛࠶ࡿ࠿࡜࠸ࡗࡓࡇ࡜ࡀࢹ࣮ࢱ࣮࣋ࢫ࡜ࡋ࡚
ࡍ࡭࡚⟶⌮ࡉࢀࡲࡍࠋ
ඛ࡯࡝ࡣࠊື⏬ࢆぢࡏࡓࡾグ㘓ࢆࡍࡿ࡜࠸࠺ⱥㄒ 4 ᢏ⬟ࡢ୍ࡘࡢ౛࡜ࡋ࡚♧ࡋࡓࡢ࡛ࡍ
ࡀࠊᚋ༙࠾ぢࡏࡍࡿࡢࡣ 2 ࡘ࡛ࡍࠋࢥࣥࣆ࣮ࣗࢱࢆ౑࠺ࡢ࡛ࠊ௒ࡲ࡛⣬࡜㖄➹࡛ࡣ࡛ࡁ࡞
࠿ࡗࡓࡇ࡜ࡀ࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺ྍ⬟ᛶࢆ♧၀ࡍࡿព࿡࡛ 2 ࡘ౛ࢆᣲࡆࡉࡏ࡚࠸ࡓ
ࡔࡁࡲࡍࠋ
CBT ࡟ࡼࡿᩘᏛ㐃ၥᙧᘧࡢ౛♧ඛ࡯࡝࠿ࡽࠕ኱ၥᙧᘧࠖ࡜࠸࠺ゝⴥࡀฟ࡚࠸ࡲࡍࡅࢀ࡝ࡶࠊ኱ၥᙧᘧࠊ୍␒࢖࣓࣮ࢪࡋ
ࡸࡍ࠸ࡢࡣᩘᏛࡢၥ㢟ࡔ࡜ᛮ࠸ࡲࡍࠋ኱ၥᙧᘧ࡜ࡣ 1 ࡘࡢࢺࣆࢵࢡࢆ୰ᚰ࡟ࠊࡉࡲࡊࡲ࡞
ഃ㠃࠿ࡽ」ᩘࡢၥ࠸ࡀฟ㢟ࡉࢀࡿࡼ࠺࡞ᙧᘧ࡛ࡍࠋ1 ࡘ኱ࡁ࡞ၥ㢟ࡣࠊ๓ࡢ㡯┠ࡢṇㄗ࡟ࡼࡗ࡚ḟࡢ㡯┠࡟ᙳ㡪ࢆཬࡰࡋ࡚ࡋࡲ࠺ࡇ࡜࡛ࡍࠋࡓ࡜࠼⬟ຊࡢ࠶ࡿே࡛ࡶࠕ1 ၥ┠ࡢ⟅࠼࡟ᗄࡘࢆ㊊ࡋࡓࡽ 6 ࡟࡞ࡾࡲࡍ࠿ࠖࡇࢀࡔ࡜ 1 ၥ┠ࢆ㛫㐪࠼ࡓࡽ 2 ၥ┠ࡶ㛫㐪࠼࡚ࡋࡲ࠸ࡲࡍࠋࡑ࠺࠸ࡗࡓᐇ㦂ⓗ⊂❧ᛶࡀ኱ၥᙧᘧ࡛ࡣḞዴࡋࡲࡍࠋḟඖⓗ⊂❧ᛶ࡜࠸࠺ࡢࡶ࠶
ࡾࡲࡍࡅࢀ࡝ࡶࠊࡇࡇ࡛ࡣᐇ㦂ⓗ⊂❧ᛶࢆᢅ࠸ࡲࡍࠋ
኱ၥᙧᘧ࡟ࡣ࠸ࢁ࠸ࢁ࣓ࣜࢵࢺࡀ࠶ࡿ࡜࠸࠺◊✲⪅ࡶ࠸ࡿࡢ࡛ࡍࡀࠊIRT ࡟㥆ᰁࡲ࡞࠸࡜࠸࠺ࡇ࡜ࡀඛ࡯࡝ࡲ࡛㆟ㄽࡉࢀ࡚ࡁࡲࡋࡓࠋࡓࡔࡋࠊ୍㒊࡟㛵ࡋ࡚ࡣࢥࣥࣆ࣮ࣗࢱ࡛኱
ၥᙧᘧࢆࠊࡇࡢࡼ࠺࡟ࢫࢸࢵࣉ࡟༊ษࡿࡇ࡜࡟ࡼࡗ࡚๓ᡠࡾ࡛ࡁ࡞࠸ࡼ࠺࡞ᙧ࡟ࡍࢀࡤࠊ
ࡑࡋ࡚ḟࡢࢫࢸࢵࣉ࡟㐍ࢇࡔ࡜ࡁ࡟๓ࡢၥ㢟ࡢ⟅࠼ࢆࡋࡗ࠿ࡾᩍ࠼࡚࠶ࡆ࡚ࠊಟṇྍ⬟࡞≧ἣࡲ࡛ᡠࡋ࡚࠶ࡆࢀࡤࠊ኱ၥᙧᘧ࡟ఝࡓ≧ἣ࡛ࠊᒁᡤ⊂❧ᛶࢆᅇ᚟࡛ࡁࡲࡍࠋࡇࢀࡣ⣬ࡢࢸࢫࢺ࡛ࡣ࡛ࡁࡲࡏࢇࠋࢥࣥࣆ࣮ࣗࢱ࡛ၥ㢟ᥦ♧ࢆࢥࣥࢺ࣮ࣟࣝࡍࡿ࠿ࡽ࡛ࡁࡿࡇ࡜࡛
ࡍࠋࡇࡢࡼ࠺࡟ࠊࢥࣥࣆ࣮ࣗࢱࢆ౑࠺࡜ၥ㢟ࢆࢡࣜ࢔࡛ࡁࡿྍ⬟ᛶࡣቑ࠼࡚࠸ࡃ࡜ᛮ࠸ࡲࡍࠋ
ࡓࡔࡋࠊࡇࢀ࡟㛵ࡋ࡚ࡶࠊඛ࡯࡝㆟ㄽࡀ࠶ࡗࡓࡼ࠺࡟ࣉࣜࢸࢫࢺࢆࡍࡿ࡞࡝ᐇ㦂ࢆࡋ࡚ࡋࡗ࠿ࡾࢹ࣮ࢱࢆ࡜ࡗ࡚࠸࠿࡞ࡅࢀࡤ࠸ࡅ࡞࠸࡜ᛮ࠸ࡲࡍࠋ
CBT ࡟ࡼࡿ⮬グᘧゎ⟅࡜ࢥࣥࣆ࣮ࣗࢱ᥇Ⅼᢏ⾡ࡢᥦ♧᭱ᚋ࡟࠾♧ࡋࡍࡿࡢࡣᆅ⌮ࡢ㛵㐃ၥ㢟࡛ࡍࠋࡇࢀࡣఱ࠿࡜࠸࠺࡜ࠊ࠿࡞ࡾ㛗࠸࣏࣮ࣞࢺ
࡛ࡍࠋᐇ㝿࡟࠾ぢࡏࡍࡿ࡜ࡇࢇ࡞ឤࡌ࡛ࠊA ᅜࡀ࡝ࢇ࡞ᅜ࠿ࡽ࡝ࢇ࡞࢚ࢿࣝࢠ࣮ࢆ㍺ධࡋ

－101－

࡚࠸ࡿ࠿ᘏࠎ࡟ㄝ᫂ࡀ᭩࠸࡚࠶ࡾࡲࡍࠋᅜྡࡣఅࡏࡽࢀ࡚࠸ࡿࡢ࡛ࡍࡅࢀ࡝ࡶࠊA ࡣ࣏࣮ࣛࣥࢻࠊB ࡣࣟࢩ࢔࡛ࠊ඲యࡢᩥ❶ࢆㄞࡴ࡜࣏࣮ࣛࣥࢻࡀࡍࡈࡃࣟࢩ࢔࠿ࡽ࢚ࢿࣝࢠ࣮ࢆ
㍺ධࡋ࡚࠸ࡿࡼ࡜࠸࠺ࡇ࡜ࡀ๓༙࡟᭩࠸࡚࠶ࡗ࡚ࠊ୍᪉࡛ࠊ࣏࣮ࣛࣥࢻࡀ࢚ࢿࣝࢠ࣮ࡢ㛤Ⓨ࡜ࡋ࡚ཎᏊຊࢆᑟධࡋࡓࡾࠊࣟࢩ࢔࠿ࡽ㍺ධࡋ࡚࠸ࡓ࢚ࢿࣝࢠ࣮ࢆ࢘ࢡࣛ࢖ࢼ࠿ࡽᣢࡗ
࡚ࡁࡓࡾࠊ࢝ࢱ࣮ࣝ࠿ࡽࣃ࢖ࣉ࢞ࢫࢆᘬࡗᙇࡗ࡚ࡁࡓࡾࡋ࡚࠸ࡲࡍࡼ࡜᭩࠸࡚࠶ࡾࡲࡍࠋ
ࠕ࡞ࡐࡇࢇ࡞ࡇ࡜ࢆࡋ࡚࠸ࡿࡢ࠿᭩ࡁ࡞ࡉ࠸ࠖ࡜࠸࠺ၥ㢟࡛ࡍࡅࢀ࡝ࡶࠊࡇࢀࡶ⮬グᘧࠊ
ࡍ࡞ࢃࡕグ㏙ၥ㢟࡟࡞ࡗ࡚࠸ࡲࡍࠋ
⌧ᅾࡢ࡜ࡇࢁࠊࡇࢀࡣ⮬ືⓗ࡟᥇Ⅼ࡛ࡁࡿࡶࡢ࡛ࡣ࠶ࡾࡲࡏࢇࡀࠊᅜྡࠊ༢ㄒࡄࡽ࠸ࡣ
ᙧែ⣲ゎᯒࢆࡋ࡚࣐ࢵࢳࣥࢢࢆ࠿ࡅ࡚ṇゎࠊ୙ṇゎࡄࡽ࠸ࡢࡇ࡜ࡣࠊ⌧ẁ㝵࡛ࠊࡍ࡛࡟࡛
ࡁ࡚࠸ࡲࡍࠋ౛ ࠼ ࡤࡇ ࢀ ࠊࠕ ࣟ ࢩ ࢔ ࡬ࡢ ࢚ ࢿ ࣝ ࢠ࣮ ౫ Ꮡ ᗘ ࢆᘬࡁ ୗࡆࡿ ࡇ࡜ࡀ ࣏࣮ࣛ ࣥࢻࡢ ᨻ⟇ୖ ࠊ㔜せ࡛࠶ࡗࡓࡓࡵࠖ࡜࠸࠺ࡢࡀ⟅࠼࡛ࡍࡅࢀ࡝ࡶࠊࡇࡢၥ㢟ࢆከ⫥㑅ᢥᘧ࡟ࡋ࡚ࡋࡲ࠺࡜
㠀ᖖ࡟⡆༢࡟࡞ࡾࡲࡍࠋㄞࢇ࡛࠸ࢀࡤࠕ࠶ࠊࡑ࠺࠿ࠋ࣏࣮ࣛࣥࢻࡀࣟࢩ࢔࠿ࡽࡢ࢚ࢿࣝࢠ
࣮㟂⤥ࢆᘬࡁୗࡆࡓ࠸ࡢࡔࠖ࡜ࡍࡄࢃ࠿ࡗ࡚ࡋࡲ࠺ࡢ࡛ࡍࡅࢀ࡝ࡶࠊ᭩࠿ࡏࡿࡇ࡜࡟ࡼࡗ
࡚ࡑ࠺࠸ࡗࡓࡇ࡜ࢆῶࡽࡍྍ⬟ᛶࡣ࠶ࡾࡲࡍࠋ
ࡓࡔࠊࡇࢀࢆࡸࡗ࡚ࡶ㛗࠸ᩥ❶ࢆ⮬ືⓗ࡟᥇Ⅼࡍࡿࡇ࡜ࡣࠊඛ࡯࡝ᮧୖඛ⏕ࡢࡈሗ࿌࡟ࡶ ࠶ ࡾ ࡲ ࡋ ࡓ ࡅ ࢀ ࡝ ࡶ ࠊ ⚾ ࡣ ಶ ே ⓗ ࡟ ࠊ 10 ᖺ ࡓ ࡗ ࡚ ࡶ ↓ ⌮ ࡔ ࡜ ᛮ ࠸ ࡲ ࡍ ࠋ ࡞ ࡐ ࠿ ࡜ ࠸ ࠺࡜ࠊࡑࢀࡀ࡛ࡁࡿࡢ࡛࠶ࢀࡤⱥㄒࡢ⮬ື⩻ヂࡀᙜ↛࡛ࡁࡿࡼ࠺࡟࡞ࡿࢃࡅ࡛ࡍࠋ௒ࠊࡇࡇ
࡛᝿ᐃࡋ࡚࠸ࡿࡢࡣᴟࡵ࡚▷࠸ᩥ❶ࡶࡋࡃࡣ༢ㄒࢆᣠ࠼࡚࠸ࡿ࠿ࠊࡶࡋࡃࡣ㒊ศⓗ࡟ࣁ࢖ࣛ࢖ࢺ࡛ࡁࡿ࠿ࠊࡑ࠺࠸ࡗࡓࡶࡢࢆ᝿ᐃࡋ࡚◊✲㛤Ⓨࢆ㐍ࡵ࡚࠸ࡲࡍࠋ
ࡓࡔࡋࠊ◊✲㛤Ⓨࢆࡋࡓᚋࠊᐇ㝿ࢹ࣮ࢱࢆ࡜ࡗ࡚ࡑࢀࢆ☜ㄆࡋ࡚ಙ㢗ᛶࠊጇᙜᛶࠊࡑࡇ
ࡲ࡛ࡋࡗ࠿ࡾ☜ㄆࡋ࡚ࠊࡑࢀࡀ౑࠼ࡿࡶࡢ࡞ࡢ࠿ࠊ౑ࡗ࡚࠸࠸ࡶࡢ࡞ࡢ࠿ࠊࢥࢫࢺࡣ࡝࠺
࡞ࡢ࠿࡜࠸࠺᳨ウࡣࡋ࡞ࡅࢀࡤ࠸ࡅ࡞࠸ࡣࡎ࡛ࡍࠋ࡜ࡾ࠶࠼ࡎ௒ᅇࡢࡇࡢㄝ࡛᫂ࡣࠊࡇ࠺
࠸ࡗࡓᢏ⾡ࢆ୍㒊⤂௓ࡉࡏ࡚࠸ࡓࡔࡁࡲࡋࡓࠋ
ࡇࢀࡣࠊ࡝࠺࠸ࡗࡓ⬟ຊࢆࡗ࡚࠸ࡿ࠿࡜࠸࠺ࢫࣛ࢖ࢻ࡛ࡍࠋ

ࡈΎ⫈࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧‫ྖۑ‬఍኱ஂಖࡉࢇࠊ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

⡆༢࡞ࡈ㉁ၥࢆཷࡅࡓ࠸࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊ࠸࠿ࡀ࡛ࡋࡻ࠺࠿ࠋࡼࢁࡋ࠸࡛ࡍ࠿ࠋ
ࡇࢀࡣࡲࡔ◊✲㛤Ⓨࡢẁ㝵࡛ࡍ࠿ࡽࠊࡇࢀࡀ᪂ࡋ࠸ࢸࢫࢺࡢၥ㢟࡟ࡑࡢࡲࡲ࠸ࡃࡇ࡜ࡣ
࡞࠸࡜࠾⪃࠼࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ
ࡑࢀ࡛ࡣࠊࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥࡢ‽ഛࡀ࠶ࡾࡲࡍࡢ࡛ఇ᠁ࡋࡲࡍࠋ
ࠑ༗ᚋ 4 ᫬ 44 ศ

ఇ᠁ࠒ

－102－

Ⓨ⾲䛾ᴫせ኱Ꮫධヨ䝉䞁䝍䞊䝅䞁䝫䝆䜴䝮㻞㻜㻝㻠䛄኱Ꮫධヨ䛾᪥ᮏⓗ㢼ᅵ䛿ኚ䛘䜙䜜䜛䛛䛅㻞㻜㻝㻠ᖺ㻝㻝᭶㻞㻥᪥㻔ᅵ㻕㻌᪊ ᮾிᕤᴗ኱Ꮫ 䝕䝆䝍䝹ከ┠ⓗ䝩䞊䝹㻝㻚᝟ሗ➃ᮎ䜢฼⏝䛧䛯ⱥㄒ䠐ᢏ⬟ヨ㦂䛾ၥ㢟౛㻞㻚኱つᶍඹ㏻ヨ㦂䛷䛹䛾䜘䛖䛻᝟ሗ➃ᮎ䜢฼⏝䛧䛶ヨ㦂䜢㐠⏝䛩䜛䛾䛛㻟㻚኱つᶍ䛺㻯㻮㼀䜢ᨭ䛘䜛䛔䛟䛴䛛䛾ᢏ⾡䛾⤂௓㻠㻚ᩘᏛ䛺䛹䛷䛾᝟ሗ➃ᮎ䜢⏝䛔䛯ᕤኵ౛㻡㻚ฟ㢟ᙧᘧ䛾Ⓨᒎྍ⬟ᛶ㻯㻮㼀䛾䝃䞁䝥䝹
䝕䝰䞁䝇䝖䝺䞊䝅䝵䞁Ⓨ⾲⪅䠖኱Ꮫධヨ䝉䞁䝍䞊 ◊✲㛤Ⓨ㒊 ኱ஂಖᬛဢ
䝅䝇䝔䝮◊✲㛤Ⓨ䠖኱ஂಖᬛဢ㻝䞉ᐊ⏣┿⏨㻞䞉๓ᕝ┾୍㻞䞉ᐑᇣኖኵ㻝㻝኱Ꮫධヨ䝉䞁䝍䞊䞉㻞ᮾிᕤᴗ኱Ꮫ

኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻞᝟ሗ➃ᮎ䜢฼⏝䛧䛯ⱥㄒ䠐ᢏ⬟ヨ㦂䛾ၥ㢟౛䝍䝤䝺䝑䝖➃ᮎ䜢⏝䛔䛯㻯㻮㼀㻌䞉ᐇ㦂㢼ᬒⱥㄒ㻠ᢏ⬟ヨ㦂䛾ၥ㢟౛䠖ື⏬⫈ゎၥ㢟

©AFP㏻ಙ
኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻟኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

᝟ሗ➃ᮎ䜢฼⏝䛧䛯ⱥㄒ䠐ᢏ⬟ヨ㦂䛾ၥ㢟౛᝟ሗ➃ᮎ䜢฼⏝䛧䛯ⱥㄒ䠐ᢏ⬟ヨ㦂䛾ၥ㢟౛ⱥㄒ㻠ᢏ⬟ヨ㦂䛾ၥ㢟౛䠖Ⓨ㡢㘓㡢ၥ㢟ⱥㄒ㻠ᢏ⬟ヨ㦂䛾ၥ㢟౛䠖ⱥᩥ㈨ᩱㄞゎၥ㢟㻠©ᮾிᕤᴗ኱Ꮫ
኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻡኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

－103－

㻢

᝟ሗ➃ᮎ䜢฼⏝䛧䛯ⱥㄒ䠐ᢏ⬟ヨ㦂䛾ၥ㢟౛᝟ሗ➃ᮎ䜢฼⏝䛧䛯ⱥㄒ䠐ᢏ⬟ヨ㦂䛾ၥ㢟౛ⱥㄒ㻠ᢏ⬟ヨ㦂䛾ၥ㢟౛䠖ⱥᩥ㈨ᩱㄞゎၥ㢟ⱥㄒ㻠ᢏ⬟ヨ㦂䛾ၥ㢟౛䠖グ㏙ၥ㢟©AFP㏻ಙ

኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻣኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

኱つᶍඹ㏻ヨ㦂䛷䛹䛾䜘䛖䛻᝟ሗ➃ᮎ䜢฼⏝䛧䛶ヨ㦂䜢㐠⏝䛩䜛䛾䛛኱つᶍ䛺㻯㻮㼀䜢ᨭ䛘䜛䛔䛟䛴䛛䛾ᢏ⾡䛾⤂௓㻯㻮㼀䛾㐠⏝䝕䝄䜲䞁㻔᱌㻕

ヨ㦂ၥ㢟䛾᝟ሗ➃ᮎ䜈䛾䝉䝑䝔䜱䞁䜾ࢭ࢟ࣗ࢔࡞ᅇ⥺ᵓෆ㏻ಙ⥙(LAN)ࢱࣈࣞࢵࢺ➃ᮎ࣮࣭࣮ࣟ࢝ࣝ࢜࣌ࣞࢩ࣭ࣙࣥ
ࢭࣥࢱ࣮(LOC)㻤ࢿࢵࢺ࣮࣡ࢡᅇ⥺ࢆ⏝࠸࡞࠸ࢸࢫࢺᐇ᪋ሙᡤ
㓄㏦࣭ᅇ཰ࢥ࢔࣭ࢭࣥࢱ࣮
(CC)බ㛤ࢧ࣮ࣂ኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻥኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘኱つᶍ䛺㻯㻮㼀䜢ᨭ䛘䜛䛔䛟䛴䛛䛾ᢏ⾡䛾⤂௓኱つᶍ䛺㻯㻮㼀䜢ᨭ䛘䜛䛔䛟䛴䛛䛾ᢏ⾡䛾⤂௓ᅇ⟅䝕䞊䝍䛾ᅇ཰ゎ⟅䝕䞊䝍䛾⟶⌮኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻝㻝኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

－104－

㻝㻜

㻝㻞

኱つᶍ䛺㻯㻮㼀䜢ᨭ䛘䜛䛔䛟䛴䛛䛾ᢏ⾡䛾⤂௓ᩘᏛ䛺䛹䛷䛾᝟ሗ➃ᮎ䜢⏝䛔䛯ᕤኵ౛ヨ㦂ၥ㢟䛾⟶⌮䞉⦅㞟኱ၥᙧᘧ䛸㐃ၥᙧᘧ኱ၥᙧᘧ㻔ᩘᏛ㻵㻭䞉㻵㻵㻮䛺䛹㻕㻝㻚 䠍䛴䛾䝖䝢䝑䜽䜢୰ᚰ䛻ᵝ䚻䛺ഃ㠃䛛䜙」ᩘ䛾ၥ䛔䛜ฟ䛥䜜䜛ᙧᘧ 㻔▼ሯ㻘㻌㻞㻜㻜㻟㻕㻞㻚 ๓䛾㡯┠䛾ṇㄗ䛻䜘䛳䛶䚸⌧ᅾ䛾ゎ⟅䛾ṇㄗ䛻ᙳ㡪䜢ཬ䜌䛩ሙྜ䛜䛒䜛㻟㻚 ⌧ᅾ䛾㡯┠䜢ṇ⟅䛷䛝䜛⬟ຊ䛜䛒䜛䛻䜒㛵䜟䜙䛪䠈๓䛾㡯┠䛾ṇㄗ䛻౫Ꮡ䛧䛶ㄗ⟅኱ၥෆ䛾㡯┠䛻䛚䛔䛶ᒁᡤ⊂❧ᛶ䛜䛺䛔ሙྜ㻝㻚኱ၥෆ䛾㡯┠䜢⊂❧䛺㡯┠䛸䛧䛶ᢅ䛖䛸䠈⬟ຊ᥎ᐃ್䛾ಙ㢗ᛶ䛜㐣኱ホ౯䛥䜜䜛㻔㻿㼕㼞㼑㼏㼕㻘㻌㼀㼔㼕㼟㼟㼑㼚 㻒㻌㼃㼍㼕㼚㼑㼞㻘㻌㻝㻥㻥㻝㻕㻞㻚㡯┠䛻䛚䛡䜛ḟඖⓗ⊂❧ᛶ䛾Ḟዴ㻟㻚䛭䛾๓䛾ṇㄗ≧ἣ䛻౫Ꮡ䛩䜛ᐇ㦂ⓗ⊂❧ᛶ䛾Ḟዴ㐃ၥᙧᘧ኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻝㻟㻝㻚኱ၥ䜢䛔䛟䛴䛛䛾䝇䝔䝑䝥䛻༊ษ䜚䠈㡰ḟⓗ䛻ゎ⟅䛥䛫䜛ᙧᘧ㻞㻚ཷ㦂⪅䛜ゎ⟅䜢ධຊ䛧ḟ䛾䝇䝔䝑䝥䛻⏬㠃㑄⛣ᚋ䠈┤๓䛾䝇䝔䝑䝥䛾ṇ⟅䜢ᥦ♧䛧䛯ୖ䛷ᚋ⥆䛾✵ḍ䛻䛴䛔䛶ゎ⟅䜢ồ䜑䜛ᙧᘧ㻟㻚䛣䛾ᙧᘧ䛿᝟ሗ➃ᮎୖ䛷䛾䜏฼⏝ྍ⬟኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

ᩘᏛ䛺䛹䛷䛾᝟ሗ➃ᮎ䜢⏝䛔䛯ᕤኵ౛ᩘᏛ䛺䛹䛷䛾᝟ሗ➃ᮎ䜢⏝䛔䛯ᕤኵ౛኱ၥᙧᘧ䛸㐃ၥᙧᘧ኱ၥᙧᘧ䛸㐃ၥᙧᘧ኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻝㻡኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

ᩘᏛ䛺䛹䛷䛾᝟ሗ➃ᮎ䜢⏝䛔䛯ᕤኵ౛ᩘᏛ䛺䛹䛷䛾᝟ሗ➃ᮎ䜢⏝䛔䛯ᕤኵ౛㐃ၥᙧᘧ 䝇䝔䝑䝥㻝㐃ၥᙧᘧ 䝇䝔䝑䝥㻞㻝㻠㻝㻢

ステップ1の[ ア ][ イ ]正答が提示される኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻝㻣኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

－105－

㻝㻤

ᩘᏛ䛺䛹䛷䛾᝟ሗ➃ᮎ䜢⏝䛔䛯ᕤኵ౛ฟ㢟ᙧᘧ䛾Ⓨᒎྍ⬟ᛶ㐃ၥᙧᘧ 䝇䝔䝑䝥㻟⮬グᘧゎ⟅䛾ၥ㢟౛ 㻔ᆅ⌮ཬ䜃㛵㐃㡿ᇦ㻕

ス テップ2の[ ウ ][ エ ]正答が提示される኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻝㻥኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

ฟ㢟ᙧᘧ䛾Ⓨᒎྍ⬟ᛶฟ㢟ᙧᘧ䛾Ⓨᒎྍ⬟ᛶ⮬グᘧゎ⟅䛾ၥ㢟౛ 㻔ᆅ⌮ཬ䜃㛵㐃㡿ᇦ㻕⮬グᘧゎ⟅䛾ၥ㢟౛ 㻔ᆅ⌮ཬ䜃㛵㐃㡿ᇦ㻕኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻞㻝኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

኱Ꮫධヨ䝉䞁䝍䞊䝅䞁䝫䝆䜴䝮㻞㻜㻝㻠䛄኱Ꮫධヨ䛾᪥ᮏⓗ㢼ᅵ䛿ኚ䛘䜙䜜䜛䛛䛅㻯㻮㼀䛾䝃䞁䝥䝹
䝕䝰䞁䝇䝖䝺䞊䝅䝵䞁䛤㟼⫈䛒䜚䛜䛸䛖䛤䛦䛔䜎䛧䛯኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘2014ࠗ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘

㻞㻟

－106－

㻞㻜

㻞㻞

ࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥ
Kentaro Yamamoto Ặ ࣭ ๓ᕝ┾୍ Ặ ࣭ ༡㢼ཎᮅ࿴ Ặ ࣭኱ὠ㉳ኵ Ặ ࣭ ᮧୖ
⥲ྜྖ఍

኱㷁㞝స

㝯 Ặ

኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲๪⤫ᣓᐁධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗⿵బ‫ྖۑ‬఍෌㛤ࡋࡲࡍࠋࡑࢀ࡛ࡣࠊࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥࢆጞࡵࡲࡍࠋ

ࡑࢀࡒࢀࡢࡈሗ࿌ࡢᚋ࡛㉁ၥ
ࡶ ཷ ࡅ ࡲ ࡋ ࡓ ࡅ ࢀ ࡝ ࡶ ࠊ35 ศ 㛫࡜࠸࠺᫬㛫ࡢ㛵ಀࡶ࠶ࡾࡲࡍࡢ
࡛ࠊࡲࡎ᭱ึ࡟ࣇࣟ࢔ࡢ࡯࠺࠿
ࡽ㉁ၥ࡜࠿ࠊࡇ࠺࠸࠺Ⅼࡣ࡝࠺࡞ࢇࡔ࡜࠸࠺ࢥ࣓ࣥࢺࡵ࠸ࡓࡶ
ࡢ࡛ࡶᵓ࠸ࡲࡏࢇࡅࢀ࡝ࡶࠊᗄ
ࡘ࠿ཷࡅ௜ࡅ࡚ࠊࡑࢀ࡟㛵㐃ࡋ࡚ሗ࿌⪅ࡢඛ⏕᪉࡟⿵㊊ⓗ࡟࠾ヰࡋࡋ࡚ࡶࡽ࠾࠺࠿࡜ᛮ࠸ࡲࡍࠋ
࠸࠿ࡀ࡛ࡋࡻ࠺࠿ࠋ
‫ۑ‬㉁ၥ⪅ A኱ኚຮᙉ࡟࡞ࡾࡲࡋࡓࠋ

༡㢼ཎඛ⏕ࡢ࠾ヰࡢ୰࡛ࠊ୰ᩍᑂࡀሗ࿌ࡉࢀࡓෆᐜ࡟ࡘ࠸࡚ࡶ㠀ᖖ࡟ࢃ࠿ࡾࡸࡍ࠸࠾ヰࢆ࠸ࡓࡔ࠸ࡓࢇ࡛ࡍࡅࢀ࡝ࡶࠊඛ⏕ࡢ࠾ヰࢆఛ࠺࡜ࠊ୰ᩍᑂࡢ⟅⏦ࡢ᪂ࡋ࠸ࢸࢫࢺ࡛ࠊᗈ
⠊ᅖࡢ㞴᫆ᗘࡢၥ㢟ࢆ⏝ពࡋ࡚㑅ᢤᛶࡢ㧗࠸኱Ꮫ࡛ࡶ౑࠼ࡿࡇ࡜࡜ࠊẁ㝵ู࡛ᡂ⦼ホᐃࢆ⾜࠺ࡇ࡜ࡣࠊ⚾ࡢ⌮ゎ࡛ࡣ㠀ᖖ࡟▩┪ࡋ࡚࠸࡚ྠ᫬࡟㐩ࡏࡽࢀ࡞࠸ࡼ࠺࡟ᛮ࠼ࡓࢇ࡛ࡍࡅ
ࢀ࡝ࡶࠊࡇࢀࡣ୍య࡝࠺࠸࠺ࡇ࡜࡞ࡢ࠿ࠊ୰ᩍᑂࡢ⪃࠼ࡣ࡝࠺࠸࠺ࡇ࡜࡞ࡢ࠿ࡀࡼࡃ⌮ゎ࡛ࡁ࡞࠿ࡗࡓࡢ࡛ࠊࡑࡢ㎶ࢆ࠾ヰࡋ࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ
‫ྖۑ‬఍ࢃ࠿ࡾࡲࡋࡓࠋ

௚࡟࠸ࡽࡗࡋࡷ࠸ࡲࡍ࠿ࠋ‫ۑ‬㔛ぢẶ

ᩥ㒊⛉Ꮫ┬ࡢ㔛ぢ࡛ࡈࡊ࠸ࡲࡍࠋᮏ᪥ࡣ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋPISA ᆺ ࡢ ၥ 㢟 ࡀ ࠊ ࠶ ࡿ ᩍ ⫱ ᨻ ⟇ ࡢ ୗ ࡛ Ꮫ ࢇ ࡔ ே ࡀ ࡝ ࡢ ࡄ ࡽ ࠸ 㐩 ᡂ ࡛ ࡁ ࡓ ࠿ ࢆ 㞟 ᅋ ࡜ ࡋ࡚ᢕᥱࡍࡿࡶࡢ࡛ࠊಶேࡢ⬟ຊࢆࡿࡶࡢ࡛ࡣ࡞࠸࡜࠸࠺ࡇ࡜ࡀ 1 ࡘ኱ࡁ࡞ㄽⅬࡔ࡜ᛮ࠸ࡲࡍࠋ౛࠼ࡤࠊᒣᮏඛ⏕ࡀ♧ࡉࢀ࡚࠾ࡽࢀࡓၥ㢟࡛࠶ࡿ࡜࠿ࠊ኱ஂಖඛ⏕࡟ぢࡏ࡚࠸ࡓࡔ
࠸ࡓၥ㢟ࢆぢࡲࡍ࡜ࠊ♫఍ⓗ࡞ᩥ⬦ࡢ୰࡛▱㆑➼ࢆᐇ㊶ⓗ࡟ά⏝ࡋ࡞ࡀࡽᅇ⟅ࡍࡿࡇ࡜ࡀ
ồࡵࡽࢀࡿၥ㢟࡟ᛮ࠼ࡲࡍࡀࠊࡑࢀ࡛ಶேࡢ⬟ຊࢆࡗ࡚ࡶࡼ࠸ࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠼ࡿࢇ࡛ ࡍ ࡀ ࠊ ࡑ ࢀ ࡣ ࡞ ࡐ ᮃ ࡲ ࡋ ࠸ ࡇ ࡜ ࡛ ࡣ ࡞ ࠸ ࡢ ࠿ ࠊ PISA ᆺ ࡢ ၥ 㢟 ࡛ ࡣ ಶ ே ࡢ ⬟ ຊ ࢆᐃࡍࡿࡇ࡜ࡣ࡛ࡁ࡞࠸ࡢ࠿ࠊࡑࡢⅬ࡟ࡘ࠸࡚ᩍ࠼࡚࠸ࡓࡔࡅ࡞࠸࡛ࡋࡻ࠺࠿ࠋ
‫ྖۑ‬఍ࡶ࠺୍ࡘࡄࡽ࠸ฟࢀࡤࠋࡼࢁࡋ࠸࡛ࡍ࠿ࠋ

࡛ࡣࠊࡲࡓᚋ࡛ⓙࡉࢇࡢ࡯࠺࠿ࡽཷࡅ௜ࡅࡲࡍࡢ࡛ࠊఱ࠿࠶ࡾࡲࡋࡓࡽ⪃࠼࡚࠾࠸࡚࠸
ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ

－107－ࡲࡎࠊᗈ⠊ᅖࡢ㞴᫆ᗘࢆྵࡴࡇ࡜࡜ẁ㝵ูホᐃ࡜࠸࠺ࡢࡣ▩┪ࡍࡿࡇ࡜࡞ࡢ࠿࡜࠸࠺ࡈ㉁ၥࡔࡗࡓ࡜ᛮ࠸ࡲࡍࠋࡇࢀࡣ༡㢼ཎඛ⏕࡟ࡈᅇ⟅࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ
‫ۑ‬༡㢼ཎẶ⚾ࡶࠊᐃㄽⓗ࡟ࡣ▩┪ࡋ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࠋ1 ࡘ┠ࡢⅬ࡛ࡣ㞴㛵኱Ꮫ࡛ࡶ㏻⏝ࡍࡿࡼ࠺࡟㞴ࡋ࠸㡯┠ࢆྵࡵࡿ࡜ࡋ࡚࠾ࡁ࡞ࡀࡽࠊࡑࡢ㧗࠸ࣞ࣋ࣝࡢ࡜ࡇࢁࡣ඲㒊 5
ẁ㝵ࡢ 5 ࡜ࡋ࡚ࡋࡲ࠺ࡢ࡛࠶ࢀࡤࠊࡏࡗ࠿ࡃసࡗࡓࡶࡢࢆᖒᾘࡋ࡟ࡋ࡚ࡋࡲ࠺ࡢ࡛ࠊ

ᐃㄽⓗ࡟ࡣ▩┪ࡋ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࠋ
ࡑ࠺࠸࠺ព࿡࡛ࡣ⌧ᅾࠊࢭࣥࢱ࣮ヨ㦂ࡶᜍࡽࡃ࠿࡞ࡾᗈ⠊ᅖࡢᐃࡀ࡛ࡁ࡚࠸ࡿࡢ࡛ࡣ

࡞࠸࠿࡜ᛮ࠸ࡲࡍࡀࠊࡑࢀ࡛ࡶ㊊ࡾ࡞࠸㒊ศࢆ㞴㛵኱Ꮫ➼࡛ࡣಶูヨ㦂࡛㊊ࡋ࡚ࠊ౛࠼ࡤ
ᮾி኱Ꮫࡢሙྜࡔࡗࡓࡽࢭࣥࢱ࣮ヨ㦂ࢆ 110 Ⅼ࡟᥮⟬ࡋ࡚ࠊṧࡾ 440 Ⅼࡣಶูヨ㦂࡛ࡸࡿࠊࡑ࠺࠸࠺᱌ศࡀ⮬ศࡓࡕࡢ኱Ꮫ࡟࡜ࡗ࡚ࡣࡼ࠸ࡔࢁ࠺࡜࠸࠺ࡇ࡜࡛᮶࡚࠸ࡿࢃࡅ࡛ࡍࡡࠋ
ࡑ࠺࠸࠺ᙧࡢ⿵඘ࡢ௙᪉ࢆࡋ࡚ࡁ࡚࠸ࡿࢃࡅ࡛ࡍࠋ
࡞ࡢ࡛ࠊẁ㝵ู⾲♧࡜࠸࠺ࡢࡣᜍࡽࡃࡑ࠺࠸࠺⢭ᗘࡢ㆟ㄽࠊ᝟ሗ㔞ࡢ㆟ㄽࡔࡅ࡛ࡣฟ࡚
ࡇ࡞࠸ࠊูࡢほⅬ࠿ࡽฟ࡚ࡁ࡚࠸ࡿࡇ࡜ࡔ࡜ᛮ࠸ࡲࡍࠋࡑࢀࡣ㆟ㄽࡢࣉࣟࢭࢫࢆࡶࡗ࡜㏆
ࡃ࡛ࡈぴ࡟࡞ࡗ࡚࠸ࡿᩥ㒊⛉Ꮫ┬ࡢ᪉ࡸ኱Ꮫධヨࢭࣥࢱ࣮ࡢ᪉࡟ࡈㄝ᫂࠸ࡓࡔࡅࢀࡤ࡜ᛮ
࠸ࡲࡍࡅࢀ࡝ࡶࠊࡇࡇࡲ࡛ฟ࡚࠸ࡿ୰࠿ࡽ᥎

ࡍࡿ࡟ࠊྛ኱Ꮫ࡛ࡣከᵝ࡞᪉ἲ࡛㑅ᢤࢆࡋ࡚࡯ࡋ࠸࡜࠸࠺࡜ࡁ࡟ࠊ౛࠼ࡤࡶ࠺ 5 ẁ㝵ࡢ 5 ࡜࠸࠺ࡇ࡜࡛඲ဨ࢖ࢥ࣮࡛ࣝ࠶ࢀࡤࠊࡶ࠺
ࡑࢀࡣ౑࠸ࡼ࠺ࡀ࡞࠸ࢃࡅ࡛ࡍࡼࡡࠋከᵝ࡞᪉ἲ࡟㢗ࡽࡊࡿࢆᚓ࡞ࡃ࡞ࡿࢃࡅ࡛ࠊࡑ࠺࠸
࠺ࡩ࠺࡟௙ྥࡅ࡚࠸ࡿ㒊ศࡶ࠶ࡿࡢ࠿࡞࡜ࡣᛮ࠸ࡲࡍࠋ
ࡑ࠺ࡋࡓࠊ࠸ࢃࡤ
ࡏࢇࡅࢀ࡝ࡶࠊ
‫ྖۑ‬఍ᐃㄽࢆ㉸࠼ࡓ㓄៖࡜࠸࠺࠿ࠊࡡࡽ࠸ࡀ࠶ࢀࡤព࿡ࡣ࠶ࡿ࠿ࡶࡋࢀࡲ

ᐃㄽⓗ࡟ࡣ▩┪ࡋ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࠋ

ࡇ ࡢ Ⅼ ࡟ ࡘ ࠸ ࡚ ᒣ ᮏ ඛ ⏕ ࠊ ఱ ࠿ ࡈ ព ぢ ࠶ ࡾ ࡲ ࡍ ࠿ ࠋ ETS ࡀ ᐇ ᪋ ࡋ ࡚ ࠸ ࡿ SAT

㸦Scholastic Assessment Test㸧࡞࡝ࡣ࡝࠺࡞ࡢ࠿࡞࡜࠸࠺ࡇ࡜࡞ࢇ࡛ࡍࡀࠊẁ㝵ูホᐃ࡜࠸ࡗ࡚ࡶࠊ౛࠼ࡤࠊSAT ࡞࡝࡛ࡣᶆ‽ᚓⅬ࡟┤ࡋ࡚ࠊᑠᩘⅬ௨ୗࡣ♧ࡉࡎ࡟ࠊ20 Ⅼ࠿ࡽ 80
Ⅼࡢ㛫࡛⾲⌧ࡍࡿ࠿ࡽ 61 ẁ㝵ホᐃ࡜࠸࠺ឤࡌ࡛ᥦ♧ࡋ࡚࠸ࡲࡍࡼࡡࠋࡑࢀࡔࡗࡓࡽ⣲Ⅼ࡜኱ᕪ࡞ࡃࠊ᝟ሗ㔞ࡶࡑࢇ࡞࡟ⴠࡕ࡞࠸࡜࠸࠺࡜ࡽ࠼᪉ࡶ࠶ࡿࡢ࠿࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊ
ࡉࡍࡀ࡟ 5 ẁ㝵ホᐃ࡜࠸࠺ࡢࡣࡕࡻࡗ࡜᝟ሗ㔞࡜࠸࠺Ⅼ࡛ࡣཝࡋ࠸࠿࡞࡜ᛮ࠸ࡲࡍࠋࡑ ࢀ ࠿ ࡽ ࠊ PISA ࡢ ᪥ ᮏ ࡛ ࡢ ሗ ࿌ ࡛ ࡍ ࡜ ࠊ ࡸ ࡣ ࡾ ఱ ẁ 㝵 ࠿ ࡟ ࣞ ࣋ ࣝ ศ ࡅ ࡉ ࢀ ࡚ ࠸ ࡚ ࠊ ࡑࢀࡒࢀఱ㸣࠸ࡿ࠿࡜࠸࠺ᙧ࡛ሗ࿌ࡉࢀ࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀࡶᚋࢁࡢ࡯࠺ࡢၥ㢟࡜ࡶ㛵ಀࡋ࡚ࡃࡿ࡜ᛮ࠺ࢇ࡛ࡍࡀࠊࡑࡢᩘẁ㝵ࡢ࡛ࣞ࣋ࣝ౛࠼ࡤ࠶ࡿ㞟ᅋ࡟࠾࠸࡚᭱ୖ఩࡟
ఱ㸣࠸ࡿ࠿࡜࠸࠺ࡢࡣࠊࡑࢀ࡞ࡾ࡟᝟ሗ㔞ࡀ࠶ࡿ࡜ᛮ࠸ࡲࡍࡋࠊᏳᐃࡍࡿ࡜ᛮ࠺ࢇ࡛ࡍࡅ
ࢀ࡝ࡶࠊ୍ேࡢಶேࡀ࡝ࡢࣞ࣋ࣝ࠿࡜࠸࠺ヰ࡟࡞ࡿ࡜ࠊ༡㢼ཎඛ⏕ࡀ࠾ࡗࡋࡷࡗࡓࡼ࠺࡟
᝟ሗ㔞࡟ၥ㢟ࡀฟ࡚ࡃࡿ࡜ᛮ࠸ࡲࡍࡋࠊẁ㝵ࡢቃ⏺௜㏆ࡢ୙Ᏻᐃࡉ࡜࠸ࡗࡓၥ㢟ࡶฟ࡚ࡃࡿ࠿࡞࡜࠸࠺ࡢࡀ⚾ࡢ༳㇟࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠋ
ࡑࡢ㎶ࠊ࢔࣓࡛ࣜ࢝ࡢ⪃࠼᪉ࡣ࡝ࢇ࡞࡜ࡇࢁ࡟࠶ࡿࡢ࠿࡜࠸࠺ࡇ࡜࡞ࢇ࡛ࡍࡀࠋ
‫ۑ‬ᒣᮏẶ

≉࡟⚾ࡓࡕࡀ␃ពࡋ࡚࠸ࡿࡢࡣࠊ

ᐃㄗᕪࡢ኱ࡁࡉࢆ࡝࠺⾲⌧ࡍࡿ࠿࡜࠸࠺Ⅼ࡛ࡍࡡࠋ౛࠼ࡤࠊ ᐃㄗᕪࡀ኱ࡁ࠿ࡗࡓࡽ 5 Ⅼࡈ࡜࡟࣏࣮ࣞࢺࡍࡿ࡜࠿ࠊࡑ࠺࠸࠺࡜ࡁ࡟ 1
Ⅼࡈ࡜ࡢุ᩿ࢆ㑊ࡅࡓ᪉ࡀࡼ࠸࡜࠸࠺ࡇ࡜ࢆఏ࠼ࡿࡓࡵ࡟ࠊ5 Ⅼࡈ࡜࡟࣏࣮ࣞࢺࡍࡿ࡜࠸
ࡗࡓࡇ࡜ࢆࡋࡲࡍࠋࡑ ࢀ ࠿ ࡽ ࠊ ࡇ ࡢ ࡲ ࡲ ᚋ ⪅ ࡢ ㉁ ၥ ࡢ ᅇ ⟅ ࡟ ࡘ ࡞ ࡆ ࡚ ࡋ ࡲ ࠸ ࡲ ࡍ ࡅ ࢀ ࡝ ࡶ ࠊ PISA ࡣ ࣛ ࣮ ࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺࡢ࠺ࡕࡢ 1 ࡘ࡛ࡍࡅࢀ࡝ࡶࠊ࣮ࣛࢪࢫࢣ࣮ࣝ࢔ࢭࢫ࣓ࣥࢺ࡜࠸࠺ࡢ

－108－ࡣ ࡶ ࡜ ࡶ ࡜ ᨻ ⟇ ࡟ 㛵 ࡋ ࡚ ⤖ ᯝ ࢆ ฟ ࡑ ࠺ ࡜ ࠸ ࠺ ࡇ ࡜ ࡛ ࡍ ࡀ ࠊ PISA ᆺ ࡢ ࢔ ࢖ ࢸ ࣒ ࡀ ಶ ே ࡢ ࣏࣮ࣞࢺ࡟౑࠼࡞࠸ࢃࡅ࡛ࡣ࡞࠸ࢇ࡛ࡍࡼࠋ౛࠼ࡤࠊ୍ࡘࡢ

ᐃᑐ㇟࡜ࡍ࡭ࡁᵓᡂᴫᛕ

㸦 construct㸧ࠊ ࢻ ࣓ ࢖ ࣥ ࡢ ୰ ࡛ ࠊ ࢔ ࢖ ࢸ ࣒ ࡢ ᩘ ࡣ ࠿ ࡞ ࡾ ከ ࡃ ࠊ 80 ၥ ࡜ ࠿ ⏝ ព ࡉ ࢀ ࡚ ࠸ ࡲ ࡍࡅࢀ࡝ࡶࠊࡑࡢ 80 ၥࢆ඲㒊 1 ேࡢே࡟୚࠼ࡿࡢࡣ↓⌮࡛ࡍࠋ࡜࠸࠺ࡇ࡜࡛ࠊBIB ࢫࣃ࢖ࣛࣝ࡜࠸࠸ࡲࡍࡅࢀ࡝ࡶࠊࣂࣛࣥࢫࢺ࣭࢖ࣥࢥࣥࣉ࣮ࣜࢺ࣭ࢫࣃ࢖࣭ࣛࣝࢹࢨ࢖ࣥ
㸦Balanced incomplete spiral design㸸⼺᪕ᘧ㔮ྜᆺ୙᏶ഛࣈࣟࢵࢡࢹࢨ࢖ࣥ㸧ࢆ౑ࡗ୍࡚㒊ࡢ࢔࢖ࢸ࣒ࢆྛಶே࡟୚࠼࡚࠸ࡲࡍࠋࡑࢀࢆࡶ࡜࡟ࡋ࡚┦ᑐⓗ࡞ᑻᗘୖ࡟஌ࡏ࡚࠸ࡲࡍࠋ
ࡑ࠺࠸࠺ࡇ࡜࡛ࠊಶேಶே࡜ࡋ࡚ࡣ඲ࡃูࡢ࢔࢖ࢸ࣒ࡢࢭࢵࢺࢆࡘࡃࡾࡲࡍ࠿ࡽࠊࡑࡇ
࡛ ASA ࢆ౑࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠋ࡛ࡶࠊPISA ᆺࡢ࢔࢖ࢸ࣒ࢆ౑ࡗ࡚ಶேࡢ⬟ຊࢆฟࡍࡇ࡜
ࡣ☜ᐇ࡟࡛ࡁࡲࡍࠋPISA ᆺ ࡢ ࢔ ࢖ ࢸ ࣒ ࡜ ࠸ ࠺ ࡼ ࡾ ࡶ ࠊ ᛂ ⏝ ຊ ࢆ ୺ ࡟ ぢ ࡓ ࠊ グ ᠈ ຊ ࢆ ࢔ ࢭ ࢫ ࣓ ࣥ ࢺ ࡋ ࡚ ࠸ ࡞࠸ࢸࢫࢺࡶ࠿࡞ࡾ࠶ࡿࢇ࡛ࡍࡡࠋ౛࠼ࡤ⚾ࡓࡕࡢࡘࡃࡗࡓ࢔ࢲࣝࢺࣜࢸࣛࢩ࣮࡜࠿ࠊPIAAC࡛ ࡶ ౑ ࠸ ࡲ ࡋ ࡓ ࡅ ࢀ ࡝ ࡶ ࠊ PIAAC ࡣ ࠊ ௒ ᗘ ࡑ ࡢ ࢔ ࢖ ࢸ ࣒ ࡶ ౑ ࡗ ࡚ ᪂ ࡋ ࡃ ࠊ ᏶ ඲ ࡟ ࢥ ࣥ ࣆ࣮ࣗࢱ࣮࣋ࢫ࡛ࠊࡑࢀ࡜ྠ᫬࡟㐺ᛂᆺࢸࢫࢺ㸦adaptive test㸧࡛ࡍ࠿ࡽࠊࢥࣥࣆ࣮ࣗࢱ࣮࣋
ࢫ࡛ಶேࡢ⬟ຊࢆ᥎ᐃࡍࡿࢸࢫࢺ࡛ࡍࠋ㐪࠺ࡢࡣࠊ⮬ศಶேࡢぬ࠼ࡓࡇ࡜ࢆᣢࡗ࡚ࡁ࡚ᅇ
⟅ࢆฟࡍ࡜࠸࠺ၥ㢟ࡣ࠿࡞ࡾᑡ࡞ࡃ࡞ࡗ࡚ࠊࡍ࡭࡚ᚲせ࡞᝟ሗࡣ࣮࣌ࢪ࡟㍕ࡗ࡚࠸࡚ࠊࡑ
ࡢ࣮࣌ࢪࡢ᝟ሗࢆ࡝ࡢࡼ࠺࡟౑ࡗ࡚ᅇ⟅ࢆࡘࡃࡿࡢ࠿ࠊࡑ࠺࠸࠺⬟ຊࢆࢁ࠺࡜ࡋࡓࡶࡢ

࡛ࡍࠋࡑࡢࡼ࠺࡞࢔࢖ࢸ࣒ࢆࡶ࡜࡟ࡋ࡚ಶேࡈ࡜ࡢ⬟ຊࡢ࣏࣮ࣞࢺࡶ࡛ࡁࡲࡍࠋࡔ࠿ࡽᙜ↛ࠊPISA ᆺࡢ࢔࢖ࢸ࣒࡛ࡶ࡛ࡁࡲࡍࠋ
‫ྖۑ‬఍࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ

ᮧୖඛ⏕ࠊ࠾㢪࠸ࡋࡲࡍࠋ
‫ۑ‬ᮧୖẶ

ࡇࢀࡣࡐࡦఛ࠸ࡓ࠸࡜ᛮࡗ࡚࠸ࡓࢇ࡛ࡍࡀࠊ1 ேࡢಶே࡟ᑐࡋ࡚ࢡࣛࢫࢱ࣮ 4ࡘࠊࡉࡽ࡟ࡶ࠺୍ࡘ࡛ࡍ࠿ࠊ5 ࡘࡄࡽ࠸ࡢࢡࣛࢫࢱ࣮ࡢヨ㦂ࢆཷࡅࡉࡏ࡚࠸ࡿࢃࡅ࡛ࡍࡡࠋ
ࡑࡢ࡜ࡁ࡟ࠊࢡࣛࢫࢱ࣮㛫࡛┦㛵ࡀ࡝ࡢࡄࡽ࠸࠶ࡿࡶࡢ࡞ࡢ࠿ࠋࡘࡲࡾࠊ1 ࡘࡢࢡࣛࢫࢱ
࣮ࡢ୰࡛ࡢ࠸ࢃࡺࡿෆⓗᩚྜᛶ࡜࠸࠺ࡢࡣࣛࢵࢩࣗࣔࢹࣝ࡬ࡢᙜ࡚ࡣࡲࡾ࡛࠶ࡿ࡜⌮ゎ࡛ࡁࡿࡢ࡛ࡍࡀࠊ␗࡞ࡿࢡࣛࢫࢱ࣮ࡢ㛫࡛࡝ࡢࡄࡽ࠸┦㛵ࡀ࠶ࡿࡢ࠿ࠊࡶࡋࢃ࠿ࡾࡲࡋࡓࡽ
ᩍ࠼࡚࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ
‫ۑ‬ᒣᮏẶ

౛࠼ࡤ 6 ࡘࡢࢡࣛࢫࢱ࣮ࡢ୰࡛ࠊࢻ࣓࢖ࣥࡢ୰࡛ࡣ┦㛵ᛶࡣ࠿࡞ࡾ㧗࠸࡛ࡍࡡࠋ

౛࠼ࡤᩘᏛ࡜⛉Ꮫࡢ┦㛵ᛶࡣ኱య 0.85 ࡜࠿ 0.8 ࡜࠿ࡑࡢࡄࡽ࠸࡛ࡍࠋ࠿࡞ࡾ㧗࠸ࠋࡑࢀࢆ
ࡶ࡜࡟ࡋ࡚ࠊࡑࢀࡀ࠶ࡿ࠿ࡽࡇࡑࠊ౛࠼ࡤ᪂ࡋ࠸ࢹࢨ࢖ࣥ࡟㛵ࡋ࡚ඛ⛬࠾♧ࡋࡋࡓࡼ࠺࡟ࠊ
33 㸣ࡣࢧ࢖࢚ࣥࢫ࡜࣐ࢫࢆ࡜ࡗ࡚ࠊ33 㸣ࡣࢧ࢖࢚ࣥࢫ࡜࣮ࣜࢹ࢕ࣥࢢࢆ࡜ࡿࢇ࡛ࡍࡅࢀ
࡝ࡶࠊ⤖ᒁࢧ࢖࢚ࣥࢫ࡜࣮ࣜࢹ࢕ࣥࢢࢆ࡜ࡿேࡣ࣐ࢫࢆ࡜ࡗ࡚࠸࡞࠸ࢃࡅ࡛ࡍࡡࠋࡔࡅࢀ
࡝ࡶࠊࢧ࢖࢚ࣥࢫ࡜࣐ࢫࡢ┦㛵ᛶࡀ࠶ࡾࡲࡍ࠿ࡽࠊࡑࡢูࠎ࡞┦㛵ᛶࢆࡶ࡜࡟ࡋ࡚⿵᏶ࡍ
ࡿࡇ࡜ࡣ࡛ࡁࡲࡍ࠿ࡽࡡࠋࡑࡢ┦㛵ᛶࢆࡶ࡜࡟ࡋ࡚ࠊ᥎⟬್㸦Plausible Value㸧࡜࠸࠸ࡲࡍࡅ ࢀ ࡝ ࡶ ࠊ ᐇ 㝿 ࡟ ᚓ ࡽ ࢀ ࡓ ᚓ Ⅼ ࠿ ࡽ ࡑ ࡢ ᚓ Ⅼ ࡟ 㛵 ࡍ ࡿ ከ ḟ ඖ ⓗ ஦ ᚋ ศ ᕸ 㸦 posterior
multivariate distribution㸧ࢆ᥎ᐃࡋ࡚ࠊࡑࢀ࡟ᇶ࡙࠸࡚⿵᏶㸦impute㸧ࡍࡿ್ࡀ⟬ฟ࡛ࡁࡲࡍࠋ
‫ۑ‬ᮧୖẶ

ࡑࢀࡔࡅ࠶ࢀࡤࠊ࡯ࡰ୍ࡘࡢḟඖ࡛ 1 ேࡢಶேࢆ

ᐃࡍࡿࡇ࡜ࡣྍ⬟࡛࠶ࢁ࠺࡜࠸࠺ࡇ࡜࡛ࡍࡡࠋཎ⌮ⓗ࡟ࡣࠋ‫ۑ‬ᒣᮏẶ

࠼࠼ࠋ1 ࡘࡢࢡࣛࢫࢱ࣮࡟኱య 10 ၥ࠿ࡽ 15 ၥࡄࡽ࠸ධࡗ࡚࠸ࡿࢇ࡛ࡍࡼࠋࡔ

࠿ࡽࠊ౛࠼ࡤグ᠈ࡔࡅࡢ࢔࢖ࢸ࣒ࡔࡗࡓࡽࡶࡗ࡜ከࡃධࡿ࡜ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡓࡔࠊᛂ

－109－⏝ຊࢆぢࡿ࡜ࡑࡢ࡜ࡁ࡟ࡣࡸࡣࡾ 10 ၥ࡜࠿ 15 ၥࡄࡽ࠸࡟࡞ࡗ࡚ࡋࡲ࠺ࠋ30 ศ࡛ࠋ‫ۑ‬ᮧୖẶ

௒ࡢ࠾⟅࠼࡛ࠊ⚾ࡣ኱ኚ㦫ࡁࡲࡋࡓࠋࡑ࠺࠸࠺ࡇ࡜࡛࠶ࢀࡤ PISA ᆺࡢၥ㢟࡛ಶேࡢ⬟ຊࢆ

ࡿࡇ࡜ࡣྍ⬟ࡔࢁ࠺࡜ᛮ࠸ࡲࡍࠋ

⚾ࡣ࠶ࡃࡲ࡛ࡶ⮬ศࡢ⤒㦂࡛ࡋ࠿ヰࡏ࡞࠸ࡢ࡛ࡍࡀࠊ᪥ᮏ࡛ࡼࡃ࠶ࡿࡼ࠺࡞ᑠㄽᩥᆺࡢ
ၥ㢟ࡣࠊホ౯⪅ࡢ㛫࡛ࡢ┦㛵ࡣ㠀ᖖ࡟㧗࠸ࡅࢀ࡝ࡶࠊ␗࡞ࡿၥ㢟㛫࡛ࡢ┦㛵ࡀᴟࡵ࡚ప࠸
࡜࠸࠺ࡇ࡜ࠋࡇࡢ஦ᐇࡣࠊࡸࡣࡾࡑࢀࡽࡀ୍ࡘࡢḟඖࡢࡶࡢࢆ
ࢃࡅ࡛ࡍ࠿ࡽࠊせࡍࡿ࡟ࡑ࠺࠸࠺ࢱ࢖ࣉࡢ኱ၥ࡛⬟ຊࡗ࡚࠸ࡿ࡜ࡣ⪃࠼࡟ࡃ࠸ᐃࡣ㞴ࡋ࠸ࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼࡚࠸ࡓ࡜࠸࠺ࡇ࡜ࠋࡑ ࢀ ࠿ ࡽ ࠊ PISA ᆺ ࡢ ၥ 㢟 ࢆ ࡼ ࡾ 㑅 ᢤ ⓗ ࡟ ࡍ ࡿ ࡜ ࠸ ࠺ ࠿ ࠊ 㑅 ᢤ ᛶ ࡢ 㧗 ࠸ ࣞ ࣋ ࣝ ࡢ 㧗 ᰯ ࡛ᩘᏛࡢၥ㢟ࢆࡘࡃࢁ࠺࡜ࡋࡓ࡜ࡁ࡟ࠊࡇࢀࡣ୍ᐃ᫬㛫ࡢ୰࡛ࡇࡢၥ㢟ࡢᅇ⟅࡟฿㐩࡛ࡁࡿྍ⬟ᛶࡣ࠿࡞ࡾഅ↛ⓗ࡞ࡶࡢࡀ࠶ࡗ࡚ࠊࡑࡢഅ↛ࢆ㧗ࡵࡿࡇ࡜ࡣ࡛ࡁࡿࡔࢁ࠺ࠊࡘࡲࡾ㞟ᅋ࡜ࡋ࡚ࡢ⬟ຊࢆࡿࡇ࡜ࡣ࡛ࡁࡿ࠿ࡶࡋࢀ࡞࠸ࡅࢀ࡝ࡶࠊಶேࡢ⬟ຊࢆ

ࡿ࡜࠸࠺Ⅼ࡟

㛵ࡋ࡚ࡣ࠿࡞ࡾࡸࡤ࠸ࡢ࡛ࡣ࡞࠸࠿࡜࠸࠺༳㇟ࢆཷࡅ࡚࠸ࡓ࡜࠸࠺ࡇ࡜࡛ࠊඛ࡯࡝ࡢࡼ࠺
࡟Ⓨゝࡉࡏ࡚࠸ࡓࡔࡁࡲࡋࡓࠋ
㸦Ⓨゝ⪅ὀ㸸

ࡇࡢ᫬Ⅼ࡛ࡣࠊࡇࡢࡼ࠺࡟཯ᛂࡋࡓࡀࠊᨵࡵ࡚⪃࠼࡚ࡳࡿ࡜ࠊᒣᮏẶࡢᐇ᪋ࡉࢀ࡚࠸ࡿ PISA ᆺヨ㦂ࡢཷ㦂⪅ࡢ⬟ຊࡢᖜࡢᗈࡉ࡜࠸ࡗࡓࡇ࡜ࡀࠊࡇࡢ㧗࠸┦㛵ࡢࡶ࡜࡟࡞ࡗ࡚࠸ࡿ࡜ࡶ⪃࠼ࡽࢀࡿࠋ௚᪉ࠊ⚾ࡣẚ㍑ⓗ㑅ᢤᛶࡢ㧗࠸኱Ꮫࠊࡍ࡞ࢃࡕࠊཷ㦂⪅ࡀཷ㦂௨๓ࡢẁ㝵࡛┦ᙜ⛬ᗘ㑅ᢤࡉࢀ࡚࠸ࡿ኱Ꮫධヨ࡟࠾ࡅࡿᑠㄽᩥヨ㦂ࡢ⤒㦂ࢆヰࡋ࡚࠸ࡿࡢ࡛ࠊࡸࡸ㆟ㄽࡢᅵተࡀ㣗࠸㐪ࡗ࡚࠸ࡓࡼ࠺࡟ᛮࢃࢀࡿࠋࡓࡔ㸪ࡇࢀࢆゝ࠸ฟࡍ࡜ࠊ
⌧⾜ࡢከࡃࡢ㑅ᢤᛶࡢ㧗࠸኱Ꮫ࡟࠾ࡅࡿ㸰ḟヨ㦂ࡢࠕᩘᏛࠖ࡟ࡶྠᵝࡢၥ㢟ࡀ࠶ࡿࡼ࠺࡟
ᛮࢃࢀࡿࠋ࠸ࡎࢀ࡟ࡋ࡚ࡶࠊ⌧⾜ࡢ኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂ࡢၥ㢟ࢆ PISA ᆺ࡟ษࡾ᭰࠼ࢀ
ࡤၥ㢟ゎỴ࡜࠸࠺ࢃࡅ࡟ࡣ࠸࠿࡞࠸࡜⪃࠼ࡿࠋ㸧‫ྖۑ‬఍ࡕ࡞ࡳ࡟ࠊࡇࡢࢧࣥࣉࣝၥ㢟ࡣ࡝ࡢࡄࡽ࠸᫬㛫ࡀ࠿࠿ࡿࢇ࡛ࡍ࠿ࠋࡍ࡭࡚ࡸࡿࡢ

ࡣ⤖ᵓ኱ኚࡑ࠺࡞Ẽࡀࡍࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠋ
‫ۑ‬ᒣᮏẶࢩ ࣑ ࣗ ࣞ ࣮ ࢩ ࣙ ࣥ ࡢ ࢔ ࢖ ࢸ ࣒ ࡣ ࠿ ࡞ ࡾ ᫬ 㛫 ࡀ ࠿ ࠿ ࡾ ࡲ ࡍ ࡼ ࡡ ࠋ 10 ศ ࡣ ࠿ ࠿ ࡗ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࠋ᭱ึࡢ࡯࠺ࡢ࣮࣌ࢪ㸦ࢫࣛ࢖ࢻ 54㸧࡟ㄝ᫂ࡀ᭩࠿ࢀ࡚࠸ࡿࡢ࡛ࡍࡀࠊタၥ࡟ྵࡲࢀࡿ᮲௳ࡢタᐃ᪉ἲࠊ౛࠼ࡤࠊCO2 ⃰ᗘ࡜࠿ගࡢᙉࡉ࡞࡝ࡢኚᩘ࡟ࡘ࠸࡚ࢫࣛ
࢖ࢲ࣮ࢆື࠿ࡋ࡚࡝࠺࠸࠺ࡩ࠺࡟ኚ࠼ࡿࡢ࠿࡜࠿ࠊࡲࡎࡑ࠺࠸࠺ࡇ࡜ࢆ▱ࡿᚲせࡶ࠶ࡾࡲࡍࠋࡑ ࢀ ࡜ ྠ ᫬ ࡟ ࠊ ⌮ ⏤ ࡞ ࡝ ࡢ グ ㏙ ᘧ 㸦 ࢫ ࣛ ࢖ ࢻ 59㸧 ࡢ ࢫ ࢥ ࢔ ࣜ ࣥ ࢢ ࡜ ࠸ ࠺ ࡢ ࡣ ಙ 㢗 ᛶ ࡀࡍࡈࡃప࠸ࢇ࡛ࡍࡼࠋ౛࠼ࡤ๓࡟ 1 ᗘࡸࡗࡓࡢࡣࠊࢽ࣮࣮ࣗࣚࢡࡢ㧗ᰯ⏕ࡢ༞ᴗヨ㦂㸦exit
test㸧 ࡛ ࡍ ࡈ ࡃ 㔜 せ ࡞ ࢸ ࢫ ࢺ ࡀ ࠶ ࡿ ࢇ ࡛ ࡍ ࡅ ࢀ ࡝ ࡶ ࠊ ࡑ ࡢ ࣛ ࢖ ࢸ ࢕ ࣥ ࢢ ࢚ ࢵ ࢭ ࢖ ࡢ ࣜ ࣛ ࢖ࣅࣜࢸ࢕ࡣࡶ࠺ࡍࡈࡃప࠸ࡇ࡜ࡀሗ࿌ࡉࢀ࡚࠸ࡲࡋࡓࠋ࢚ࢵࢭ࢖ࡢၥ㢟ࡢ┦㛵ᛶ࡜࠸࠺ࡼ
ࡾࡶࠊࢫࢥ࢔࣮ࣛࡢ┦㛵ᛶࡀప࠸ࡢ࠿ࡶࡋࢀࡲࡏࢇࠋࡑࡇ࡛ぢ࠼ࡓࡢࡣࠊࢫࢥ࢔࣮ࣛࡢ┦㛵ᛶ࡜࠸࠺ࡢࡀࡲࡎᴟ➃࡟ప࠿ࡗࡓ࡜࠸࠺ࡇ࡜࡛ࡍࠋ
‫ۑ‬ᮧୖẶ

⚾ࡢ⤒㦂࡛ࡣࠊࡑࡇࡢ㒊ศࡣఱ࡜࠿࡞ࡿࠋࡇࢀࡣࡸࡣࡾఱே࠿࡛ 2 ࡘࢆẚ࡭ࡓ࡜ࡁ࡟ࠊࡶ࠺⤯ᑐࡇࡗࡕࡢ࡯࠺ࡀࡼࡃ࡛ࡁ࡚࠸ࡿࡼࡡ࡜࠸࠺༳㇟ࡣ࠶ࡿࢃࡅ࡛ࡍࡀࠊ࡛ࡣࠊ
ḟࡢၥ㢟ࢆぢࡓ࡜ࡁ࡟ࡑࢀ࡜ྠࡌഴྥ࡟࡞ࡗ࡚࠸ࡿ࠿࡜࠸࠺࡜ࠊࡑ࠺࡛ࡣ࡞࠸࡜࠸࠺ࡢࡀ
⚾ࡢ⤒㦂࡛ࡍࠋ‫ྖۑ‬఍ᇶ ᮏ ⓗ ࡟ ࠊ ௒ ࠊ ᒣ ᮏ ව ኴ ᮁ ඛ ⏕ ࡀ ฟ ࡋ ࡚ࡃ ࡔ ࡉ ࡗ ࡓࢧ ࣥ ࣉ ࣝ ၥ 㢟ࡣ ᭱ ప ࡛ ࡶ 10－110－ศࡄࡽ࠸ࡣ࠿࠿ࡿࡔࢁ࠺ࠊࡶ࠺ࡕࡻࡗ࡜࠿࠿ࡾࡲࡍ࠿ࡡࠋ࡜࠸࠺ࡇ࡜ࡣࠊỴࡵࡽࢀࡓ᫬㛫ࡢၥ㢟ᩘࢆῶࡽࡊࡿࢆᚓ࡞࠸࡜࠸࠺ࡇ࡜࡛ࡍࡼࡡࠋ㞟ᅋࡢᖹᆒṇ⟅⋡࡜࠿ࡑ࠺࠸ࡗࡓࡶࡢ
ࡣ୍ே୍ேࡢ⿕㦂⪅ࡀᐃᶵ఍࡟࡞ࡿࡢ࡛Ᏻᐃࡋࡓ⤖ᯝ࡟ࡣ࡞ࡿࢇࡔࡅࢀ࡝ࡶࠊ1 ேࡢ⿕㦂⪅ࡢ⬟ຊࢆ᥎ᐃࡍࡿ࡜࠸࠺ព࿡࡛ࠊၥ㢟ᩘࡀᑡ࡞࠸࡜Ᏻᐃࡋࡓ⤖ᯝࡀฟ࡟ࡃ࠸ࠊࡑ࠺࠸
࠺▩┪ࡀ࡝࠺ࡋ࡚ࡶฟ࡚ࡃࡿ࡜࠸࠺ࡇ࡜ࡣ࠶ࡿࡢ࡛ࡣ࡞࠸࡛ࡍ࠿ࡡࠋ
‫ۑ‬ᒣᮏẶ

࠶ࡾࡲࡍࡡࠋ

౛࠼ࡤࠊ༠ㄪⓗၥ㢟ゎỴ㸦Collaborative Problem Solving㸧࡜࠸࠺᪂ࡋ࠸ࢻ࣓࢖ࣥࢆࡸࡾࡲ
ࡋࡓࡅࢀ࡝ࡶࠊࡑࡢ᫬Ⅼ࡛ࡣ⤖ᒁࠊ࡝ࡢࡼ࠺࡟࢖ࣥࢱࣛࢡࢺࡍࡿ࠿࡜࠸࠺ၥ㢟࡞ࢇ࡛ࡍࡅ
ࢀ࡝ࡶࠊ30 ศ࡛ 1 ࡘࡢタၥࡋ࠿࡞࠸ࢇ࡛ࡍࡼࠋࡑࡢ୰࡛࡝ࡢࡼ࠺࡟⤒㊰ࢆࡓ࡝ࡿ࠿ࠊࡑ
ࢀࡀၥ㢟࡞ࢇ࡛ࡍࠋࡑࡢ⤒㊰ࡈ࡜࡟ぢ࡚࠸ࡃ࡜኱య 30 ၥ࡜࠿ 40 ၥ࡟࡞ࡾࡲࡍࡅࢀ࡝ࡶࠊ
ࡔࡅࢀ࡝ࡶࡑࡢ᫬Ⅼ࡛ከศࡘ࡞ࡀࡿࡢࡣࠊࡑࡢタၥࡈ࡜ࡢ┦㛵ᛶࡀప࠸ࢇ࡛ࡍࡡࠊࡑࡢ᫬
Ⅼ࡛ࡣࠋࡔ࠿ࡽ῝ࡃ࠸ࡗࡓ࡜ࡁ࡟࡝ࢀࡔࡅ┦㛵ᛶࡀ࠶ࡿ࠿ࠊࡑࡇࡣゝ࠼ࡲࡍࠋ‫ྖۑ‬఍ࡇࡢၥ㢟࡟㛵ࡋ࡚ఱ࠿ࢥ࣓ࣥࢺ࠶ࡾࡲࡍ࠿ࠋ

‫ۑ‬༡㢼ཎẶ௒ࡢࡇ࡜࡟㛵㐃ࡋ࡚ࠊඛ࡯࡝ጇᙜᛶࡢ 3 ࡘࡢ⟽ࡢヰࡀ࠶ࡾࡲࡋࡓࡡࠋࡑࢀ࡟

ᙜ࡚ࡣࡵࡿ࡜࡝࠺࠸࠺ࡇ࡜࡟࡞ࡿ࠿࡜࠸࠺ࡇ࡜࡛ࠊ௒ࠊᑠㄽᩥ࡛⾲⌧ຊࢆぢࡓ࠸࡜࠿ᵓ᝿ຊࢆぢࡓ࠸࡜࠸ࡗࡓࡇ࡜ࡀồࡵࡽࢀ࡚࠸ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊ௒ࡇࡇ୍࡛⮴ࡋ࡚࠸ࡿࡢࡣࠊ
ၥ㢟ࡀኚࢃࡿ࡜ಶேᕪࡢ㡰఩ࡀኚࢃࡗ࡚ࡃࡿࠋ࡝ࡢၥ㢟ࢆฟࡍ࠿࡟ࡼࡗ࡚ㄡࡀྜ᱁ࡍࡿ࠿
ࡀ㐪ࡗ࡚ࡃࡿ࡜࠸࠺ࡇ࡜࡛ࡍࡼࡡࠋࡑࢀࢆጇᙜᛶࡢ࠶ࡢᅗᘧ࡟ᙜ࡚ࡣࡵࡿ࡜ࠊ

ࡾࡓ࠸ࡢࡣᩥ❶ຊ࡛࠶ࡗࡓࡾ⾲⌧ຊ࡛࠶ࡗࡓࡾࡍࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊᐇ㝿࡟ࡣࡑࢀ࡜ࡣ㐪࠺ࠊ౛࠼ࡤ࢚ࢿࣝࢠ࣮ၥ㢟࡛࠶ࢀࡤ࢚ࢿࣝࢠ࣮࡟㛵ࡍࡿ⯆࿡ࡸ▱㆑ࡀࡽࢀ࡚࠸ࡿࠋࡑࢀࢆ

ࡾࡓ࠸ࢃࡅ࡛ࡣ࡞࠸ࢃࡅ࡛ࡍࡡࠋ➨ 2 ၥ࡟࡞ࡿ࡜௒ᗘࡣேᶒၥ㢟ࡀၥࢃࢀࡿࠋேᶒ࡬ࡢ㛵ᚰࠊࡎࡗ࡜⪃࠼࡚ࡁࡓࡇ࡜ࠊࡑ࠺࠸ࡗࡓࡇ࡜ࡀၥࢃࢀ࡚࠸ࡿࢃࡅ࡛ࠊ࡝ࡕࡽࡶᩥ❶ຊ࡜࠿⾲⌧ຊ࡜࠿ྵࡲࢀ࡚࠸ࡲࡍࡅࢀ࡝ࡶࠊ࠿࡞ࡾࡢ㒊ศ࢚ࢿࣝࢠ࣮࡬ࡢ⯆࿡ࠊ▱㆑ࠊேᶒ࡬ࡢ⯆࿡ࠊ▱㆑ࡀࡽࢀ࡚࠸ࡿ࡜࠸࠺ࡇ࡜࡛ࠊࡑࡇࡀ࢚࣮ࣛ࡟࡞ࡿ࡜࠸࠺ࡇ࡜࡞ࢇ࡛ࡍࡡࠋࡑ࠺࠸࠺

ព࿡࡛ጇᙜᛶࡀప࠸ࠋ
ඛ࡯࡝ఇ᠁᫬㛫࡟ࠊ࡝ࡢၥ㢟ࡀฟࡉࢀࡿ࠿ࠊㄡࡀホᐃ࡟ᙜࡓࡿ࠿࡜࠸࠺ࡇ࡜ࡣࢩࢫࢸ࣐
ࢸ࢕ࢵࢡ࢚࣮࡛ࣛࡣ࡞࠸࠿࡜࠸࠺㉁ၥࢆ࠸ࡓࡔࡁࡲࡋࡓࠋࡇࢀࡣぢ᪉࡟ࡶࡼࡾࡲࡍࡅࢀ࡝
ࡶࠊ౛࠼ࡤ࢚ࢿࣝࢠ࣮࡟㛵ࡍࡿ▱㆑ࠊேᶒ࡟㛵ࡍࡿ▱㆑࡜࠸࠺ព࿡࡛ࡣᏳᐃࡋࡓᡂศ࡛ࡍ
ࡢ࡛ࠊࡑ࠺࠸࠺ព࿡࡛ࡣࢩࢫࢸ࣐ࢸ࢕ࢵࢡ࡛ stable ࡔࡅ࡝ irrelevant ࡞ᡂศ࡜ゝࡗ࡚ࡼ࠸࡜
ᛮ࠸ࡲࡍࡅࢀ࡝ࡶࠊࡓࡃࡉࢇฟࡋᚓࡿၥ㢟ࡢ୰࡛ࡓࡲࡓࡲ࢚ࢿࣝࢠ࣮ࡢၥ㢟ࡀฟࡿࠊࡓࡲ
ࡓࡲேᶒࡢၥ㢟ࡀฟࡿ࡜࠸࠺ព࿡࡛ࡣࣛࣥࢲ࣒࡞㒊ศࡀ࠶ࡿࠋ࠸ࡎࢀ࡟ࡋ࡚ࡶጇᙜ࡛ࡣ࡞
࠸㒊ศ࡜࠸࠺ࡇ࡜࡛ࡍࠋ
࠸ࡲ㆟ㄽࡋ࡚࠸ࡿホᐃ㛫┦㛵ࡢၥ㢟ࡶጇᙜᛶࡢᯟ࡛࡜ࡽ࠼ࡿ࡜ࠊ

ࡿ࡭ࡁࡶࡢࢆ

ࡗ࡚࠸ࡿ࠿࡝࠺࠿࡜࠸࠺ほⅬ࡛⤫୍ⓗ࡟࡜ࡽ࠼ࡽࢀࡿ࠿࡞࡜ᛮ࠸ࡲࡍࡢ࡛ࠊ⿵㊊ࡋࡲࡋࡓࠋ
‫ྖۑ‬఍࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ

௚࡟ఱ࠿ࡈࡊ࠸ࡲࡍ࡛ࡋࡻ࠺࠿ࠋ
‫⩏ۑ‬ᮏẶ

ᩥ⛉┬ࡢ⩏ᮏ࡜⏦ࡋࡲࡍࠋࡇࡢࢩ࣏ࣥࢪ࣒࢘ࡢࢸ࣮࣐ࠕ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࢆኚ࠼ࡽࢀࡿ࠿ࠖ࡜࠸࠺ၥ㢟࡟ከศ㛵㐃ࡍࡿヰ࡞ࢇ࡛ࡍࡅࢀ࡝ࡶࠊᜍࡽࡃ኱Ꮫධヨࡢ㑅ᢤࡢၥ㢟࡜ࠊ౛࠼ࡤᑵ⫋࠶ࡿ࠸ࡣබົ
ဨࢆཷࡅࡿ࡜࠸࠺ࡢࡣ࠿࡞ࡾ㐪࠺㢼ᅵࡀ࠶ࡿࡼ࠺࡞ឤࡌࡀࡍࡿࢇ࡛ࡍࠋ

－111－

౛࠼ࡤᑵ⫋࡛࠶ࢀࡤࠊࡴࡋࢁ㠃᥋࡜࠿ࡸࡾ࡜ࡾࢆ⏝ពࡋ࡚ࠊ௒ࡢࡇࡢࢸࢫࢺ⌮ㄽ࡛࠸ࡅࡤጇᙜᛶ࡜࠸࠺ࡼࡾࡶࡴࡋࢁ┦ᛶ࡜࠿࠸࠺ࡇ࡜࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀ࡛ᅇࡋ࡚࠸ࡿࠋබົဨ
ࡶ୍ᛂヨ㦂ࡀ࠶ࡾࡲࡍࡅࢀ࡝ࡶࠊ㠃᥋࡛ࡢᑐᛂ࡜࠿ࡀ᥇⏝࡟࠾࠸࡚࠿࡞ࡾࡢẚ㔜ࢆ༨ࡵࡿࢃࡅ࡛ࡍࠋ࡛ࡍ࠿ࡽࠊබᖹᛶࠊጇᙜᛶࡢၥ㢟࡟࡞ࡿࡢ࠿ࠊࢸࢫࢺ⌮ㄽᑓ㛛ࡢඛ⏕᪉࡟ࡇ࠺࠸࠺ヰࢆࡪࡘࡅࡿࡢࡣࡕࡻࡗ࡜➽ࡀ㐪࠺ࡢ࠿ࡶࡋࢀࡲࡏࢇࡅࢀ࡝ࡶࠊ࡝ࡢࡼ࠺࡟ࣂࣛࣥࢫࢆ࡜ࡿࡢ
࠿࡜࠸࠺ࡇ࡜࠿࡞࡜࠸࠺ឤࡌࡶࡍࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠋࡘࡲࡾࠊ୍ᐃࡢጇᙜᛶࠊ࠶ࡿ࠸ࡣࢸ
ࢫࢺ⌮ㄽⓗ࡟ࡣ

ࢀ࡞࠸⬟ຊࠊ౛࠼ࡤࡑࡢேࡢ⬟ຊ࡜࠿ࠊ࠶ࡿ࠸ࡣ㧗ᰯ᫬௦࡝ࢇ࡞ពḧࢆᣢࡗ࡚ࡸࡗࡓ࠿࡜࠿ࠊ࠶ࡿ࠸ࡣࡑࡢẼᣢࡕ࡜࠿࠸࠺ࡇ࡜࡟ࡘ࠸࡚ࡣࠊࡸࡣࡾࢸࢫࢺࡔࡅ࡛
ࡣ࡞ࡃ࡚ࠊ࠸ࢁ࠸ࢁ࡞ࡶࡢࢆ⤌ࡳྜࢃࡏ࡚ࡸࡿ࡜࠸࠺᪉㔪ࡀᜍࡽࡃ⟅⏦ࡢ୰࡟ࡣ࠶ࡿ࡜ᛮ
࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢ㎶ࡢࣂࣛࣥࢫࢆ࡝࠺⤌ࢇ࡛࠸ࡃࡢ࠿࡜࠸࠺Ⅼ࡟ࡘ࠸࡚ඛ⏕᪉࡟࠾⪃࠼ࢆఛ࠼ࢀࡤ࡜ᛮࡗ࡚࠾ࡾࡲࡍࠋ‫ྖۑ‬఍‫ۑ‬ụ⏣Ặ

࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ
ᩍ⫱ᐃ◊✲ᡤࡢụ⏣࡛ࡍࠋ⚾ࡣࠊඛ࡯࡝༡㢼ཎඛ⏕ࡀ࠾ࡗࡋࡷࡗࡓࡇ࡜ࢆࡕࡻࡗ࡜ูࡢゅᗘ࠿ࡽ᝟ሗᥦ౪ࡋࡓ࠸࡜
ᛮ࠸ࡲࡍࠋ⤖ᒁࠊࢸࢫࢺࡢሙྜࠊ⿕㦂⪅ࡢᩘࡣ㠀ᖖ࡟ከࡃ࡜ࢀࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊၥ㢟ࡢᩘࡣ㝈ࡽࢀࡓ᫬㛫࡛ࡍࡢ࡛ཷ㦂⪅ᩘ࡟ẚ࡭ࢀࡤ඲↛ᑡ࡞࠸ࢃࡅ࡛ࡍࡡࠋ
ࡑࢀ࡛ࠊ⚾ࡢඛ⏕࡛࠶ࡿࢡࣟࣥࣂࢵࢡඛ⏕ࡀࢡࣟࣥࣂࢵࢡࡢȘ࡜࠸࠺ࡢࢆ⪃࠼࡚࠸ࡓࢃ
ࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢࢡࣟࣥࣂࢵࢡࡢȘࡣேࢆᑐ㇟࡟ࡋ࡚ࠊࡑࢀ࡜ၥ㢟࡜ࡢ㛵ಀࢆᣲࡆ࡚
࠸ࡓࢃࡅ࡛ࠊၥ㢟ᩘࡀቑ࠼ࢀࡤࡔࢇࡔࢇȘࡀ㧗ࡃ࡞ࡿࢃࡅ࡛ࡍࡡࠋࡑࡢṧࡾࡢ㒊ศࡀ࢚࣮ࣛ࡜ࡋ࡚ฎ⌮ࡉࢀࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢ࢚࣮ࣛ࡜࠸࠺ࡢࡣ⾲⌧ࡀᝏ࠸ࡢ࡛ࠊᐇࡣࡑࡢ
୰࡟ࡣ࠸ࢁ࠸ࢁ࡞せ⣲ࡀྵࡲࢀ࡚࠸ࡿࠊࡑࢀࢆศᯒࡋ࡚࠸ࡇ࠺࡜ᛮࡗ࡚ș࡜࠿Ț࡜࠿⪃࠼࡚࠸ࡓࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠋࡑࢀࡣᐇ㝿࡟ࡣཷ㦂⪅ᩘࡢࡼ࠺࡟ቑࡸࡍࡇ࡜
ࡣ↓⌮࡞ࡢ࡛ࠊ࡝࠺ࡋ࡚ࡶ᭷㝈ࡢࠊᩘࡢᑡ࡞࠸ၥ㢟࡛ฎ⌮ࡋ࡞ࡅࢀࡤ࠸ࡅ࡞࠸ࠊࡋࡓࡀࡗ
࡚ࡑࢀࡣࠊಶᩘࢆቑࡸࡍࡇ࡜ࢆㅉࡵ࡚ࠊࡴࡋࢁศᩓศᯒࡢ⪃࠼᪉ࢆྲྀࡾධࢀ࡚ࠊᑡ࡞࠸ಶ
ᩘࠊ౛࠼ࡤホᐃ⪅࡛࠶ࢀࡤ 5ࠊ6 ேࡄࡽ࠸ࡲ࡛ࡀ㝈ᗘ࡛ࡋࡻ࠺࠿ࡽࠊࡑ࠺࠸ࡗࡓࡶࡢࢆศ
ᩓ࣭ඹศᩓࡢࡼ࠺࡞⪃࠼᪉࡛ศᯒࡍࡿ࡜࠸࠺❧ሙࢆ࡜ࡾጞࡵࡓࢃࡅ࡛ࡍࡡࠋ࡛ࡍ࠿ࡽࠊᚑ
᮶࢚࣮ࣛ࡜ゝ࠺᪉ἲ࡛ฎ⌮ࡉࢀࡓࡶࡢࢆࠊࡑࡢ࢚࣮ࣛࡢせ⣲ࢆホᐃ⪅㛫ࡢศᩓ࡜࠿ࠊࡑࢀ
࠿ࡽఱᅇཷ㦂ࡋࡓ࠿ࡢᅇᩘࡢศᩓ࡜࠿ࠊࡑ࠺࠸࠺ᙧ࡛せ⣲࡟ศࡅ࡚ࠊศᩓศᯒࡢ⪃࠼᪉࡜ᣑᙇࡋ࡚࠸ࡇ࠺࡜ࡋ࡚ࠊ࠸ࢃࡺࡿ୍⯡໬ྍ⬟ᛶ⌮ㄽࡀ⏕ࡲࢀ࡚ࡁࡓࠋࡑ࠺࠸࠺せ⣲ࡀ࠶ࡿ࡜࠸࠺ࡇ࡜ࢆ⿵㊊ࡋࡓ࠿ࡗࡓࢃࡅ࡛ࡍࠋ
‫ྖۑ‬఍࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ

ụ⏣ඛ⏕ࡢᖌ໶ࡀࢡࣟࣥࣂࢵࢡ࡛࠶ࢀࡤࠊ⚾ࡢᖌ໶ࠊ᭱ึ࡟ᩍ⫱ᐃࡢᤵᴗࢆཷࡅࡓࡢ

ࡀụ⏣ኸඛ⏕ࡢᤵᴗ࡛ࡋࡓࠋࡲࡓ㈗㔜࡞᝟ሗࢆ࠸ࡓࡔࡁࡲࡋ࡚ࠊ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ
ࡑࢀ࡛ࡣࠊࡶ࠺࠶࡜ 10 ศษࡾࡲࡋࡓࡢ࡛ࠊ⩏ᮏᑂ㆟ᐁ࠿ࡽࡶฟ࡚ࡁࡲࡋࡓࡇࢀ࠿ࡽࡢ
᪥ᮏࡢ኱Ꮫධヨࠊ௒ࠊ᪂ࡋ࠸Ꮫຊࢸࢫࢺࡣධヨࢭࣥࢱ࣮ࡀᐇ᪋୺య࡜࡞ࡿ࡜࠸࠺᪉ྥᛶࡀ
ฟ࡚ࡁ࡚࠾ࡾࡲࡍࡅࢀ࡝ࡶࠊࡑࢀ࡟ຍ࠼࡚ࠊಶู኱Ꮫ࡛ࡢヨ㦂ࡣࡑࡢࣂࣛࣥࢫࢆ࡜ࡿࡼ࠺
࡞ᙧ࡛ᐇ᪋ࡋ࡚࠸ࡃ࡭ࡁࡔ࡜࠸࠺ࡼ࠺࡞ࡇ࡜ࡀ⟅⏦࡟┒ࡾ㎸ࡲࢀ࡚࠾ࡾࡲࡍࠋࡑࢀࢆᐇ⌧ࡍࡿࡓࡵ࡟ࠊᏛຊヨ㦂ࡀබᖹ࡛࠶ࡿ࡜࠸ࡗࡓᚑ᮶ࡢධヨほ࠿ࡽࠊ⟅⏦࡟ࡣࠕබṇᛶࠖ࡜࠸࠺ゝⴥࡶฟ࡚ࡁ࡚࠾ࡾࡲࡋ࡚ࠊࡑࡢほⅬ࠿ࡽከ㠃ⓗ࡞᪉ἲ࡟ࡼࡿධヨࡀᥦゝࡉࢀ࡚࠾ࡾࠊ

－112－ධヨ࡜࠸࠺ࡢࡣࡑ࠺࠸࠺ࡶࡢࡔ࡜࠸࠺ࡇ࡜ࢆඹ᭷ࡋ࡚࠸ࡃ୰࡛᪂ࡋ࠸ධヨࢆ☜❧ࡋ࡚࠸ࡃ࡜࠸ࡗࡓࡇ࡜ࡀ࠺ࡓࢃࢀ࡚࠾ࡾࡲࡍࠋ
ࡑࡢ㎶࡟ࡘ࠸࡚ࠊ௒ᗘࡣ㏫࡟ᮧୖඛ⏕ࡢ࡯࠺࠿ࡽࠊࡇࢀ࠿ࡽࡢᏛຊホ౯ࠊ᪂ࡋ࠸ࢸࢫࢺ
࡟ࡘ࠸࡚࡛ࡶᵓ࠸ࡲࡏࢇࡅࢀ࡝ࡶࠊ௒ᚋࡢධヨࡢ᪉ྥᛶ࡟ࡘ࠸୍࡚ゝࡎࡘࢥ࣓ࣥࢺࢆ࠸ࡓ
ࡔࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ
‫ۑ‬ᮧୖẶ

௒ࠊẸ㛫௻ᴗ࡬ࡢᑵ⫋࠶ࡿ࠸ࡣබົဨヨ㦂࡛ࡣࡲࡓูࡢほⅬࡀ࠶ࡿࡢ࡛ࡣ࡞࠸

࠿࡜࠸࠺࠾ヰࡀ࠶ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊࡑࢀࡣ඲ࡃࡑࡢ࡜࠾ࡾࡔ࡜ᛮ࠸ࡲࡍࠋ
ࡓࡔࠊ୍᪉࡛ࠊබົဨヨ㦂ࡢ୍㒊ศ࠶ࡿ࠸ࡣᩍဨ᥇⏝ヨ㦂ࠊ࠶ࡿ࠸ࡣ㠀ᖖ࡟ࡓࡃࡉࢇ࠶
ࡿ㈨᱁ヨ㦂ࠊࡇࢀࡽࡣ඲ࡃ௒ࡢ࡜ࡇࢁಶูࡢࠊ⚾ࡢゝࡗࡓゝⴥ࡛ゝ࠼ࡤࢹ࢕ࢫࢡ࣮ࣜࢺ࣏
࢖ࣥࢺࡢ▱㆑ࠊᢏ⬟࡛⾜ࢃࢀ࡚࠸ࡿࡢࡣ஦ᐇ࡞ࢃࡅ࡛ࡍࡡࠋ࠶ࡿヨ㦂࡟ࡘ࠸࡚⏦ࡋୖࡆࡿ࡜ ࠊ ஧ ༑ఱ 㡿 ᇦ ࠿ ࠶ࡾ ࡲ ࡍ ࠋ ࡇࢀ ࡣ ࡶ ࠺ ࡯ ࡜ࢇ ࡝ࡍ ࡭࡚ᬯ グ࡛ Ỵࡲࡿ ࠋࡉࡽ ࡟ゝ࠺ ࡜ࠊ1㡿ᇦ࡛ࡶ 0 Ⅼࡀ࠶ࡗࡓࡽ௚ࡀᗄࡽⅬࡀࡼࡃ࡚ࡶ୙ྜ᱁࡟ࡍࡿ࡜࠸࠺㦫ࡃ࡭ࡁ㈨᱁ヨ㦂ࡀ⚾ࡢ㌟㏆࡞࡜ࡇࢁ࡟ࡣ࠶ࡿࢃࡅ࡛ࠊࡑ࠺࠸࠺ࡶࡢ࡟ࡘ࠸࡚⪃࠼ࡿ࡜ࠊᐇࡣಶู▱㆑ᆺࡢධᏛ
ヨ㦂࡜㠀ᖖ࡟┦ᛶࡀ࠸࠸࡜࠸࠺࠿ࠊ⤖ᒁࡑ࠺࠸࠺ࡩ࠺࡟ࡋ࡚ࡁࡓே㛫ࡀ⤖ᵓᡂຌࡍࡿࡇ࡜
࡟࡞ࡿࡳࡓ࠸࡞࡜ࡇࢁࡀ୍㠃࡛ࡣ࠶ࡾࡲࡍࡡࠋ
ࡑࢀ࡜ࡣูࡢ㠃࡛ࠊ࠸ࢃࡤ㠃᥋࡟ࡼࡗ࡚ࢃ࠿ࡿ࡜ࡉࢀ࡚࠸ࡿே≀ホ౯࡟㛵ࡋ࡚ࡣࠊヰࡋጞࡵࡿ࡜ᜍࡽࡃ 30 ศࡄࡽ࠸࠿࠿ࡗ࡚ࡋࡲ࠸ࡑ࠺࡞ࡢ࡛ࠊࡲࡓูࡢᶵ఍࡟࠾ヰࡋࡉࡏ࡚࠸
ࡓࡔࡁࡓ࠸࡜ᛮ࠸ࡲࡍࠋ
‫ۑ‬኱ὠẶ
ࢭࣥࢱ࣮ヨ㦂࡛ఱࢆࡗ࡚࠸ࡿ࠿ࠊవࡾㄝ᫂ࡋࡓࡇ࡜࡟࡞ࡗ࡚࠸࡞࠿ࡗࡓࡢ࠿ࡶࡋࢀࡲࡏ

ࢇࡅࢀ࡝ࡶࠊ㏫࡟ఱࢆ

ࡗ࡚࠸࡞࠸࠿ࢆ⪃࠼ࡿ࡜ࠊࡣࡗࡁࡾ᰿ᣐࡀ࠶ࡿࢃࡅ࡛ࡣ࠶ࡾࡲࡏ

ࢇࡅࢀ࡝ࡶࠊᜍࡽࡃᑐேⓗ࡞⬟ຊࠊ≉࡟❧ሙࡸ౯್ほࡢ㐪࠺ேࢆゝⴥ࡛ㄝᚓࡋ࡚ື࠿ࡍ⬟ຊࠊࡑ࠺࠸࠺ഃ㠃ࢆࡗ࡚࠸ࡿ࠿࡜࠸࠺࡜ࠊከศ

ࡗ࡚࠸࡞࠸ࠋ

௻ᴗࡢ᪉࡜࠿ࠊ≉࡟እᅜ࡜ࡢ஺΅࡟ᦠࢃࡗ࡚࠸ࡿࡼ࠺࡞ၟ♫ࡢ᪉➼ࡀᙉࡃせồࡋ࡚௒ࡢຮᙉ࡛ࡣࡔࡵࡔ࡜࠾ࡗࡋࡷࡿࡢࡣࠊᜍࡽࡃᏳすඛ⏕ࡶᛕ㢌࡟⨨࠸࡚࠾ࡽࢀࡿࡢࡣࠊࡑ࠺࠸
࠺ࡇ࡜࠿࡞࡜ᛮ࠺ࢇ࡛ࡍࡀࠊࡓࡔࠊࡑࢀࢆヨ㦂࡛ࢀࡿ࠿࡜࡞ࡿ࡜ࠊ࡝࠺⪃࠼࡚ࡶࡑࢀࡣ

㞴ࡋ࠸ࡢ࡛ࡣ࡞࠸࠿ࠋࡴࡋࢁูࡢ᪉ἲࢆ⪃࠼ࡓ࡯࠺ࡀ࠸࠸ࡢ࡛ࡣ࡞࠸࠿࡞࡜࠸࠺Ẽࡀࡋ࡚࠸ࡲࡍࠋఱࡀヨ㦂࡛࢝ࣂ࣮࡛ࡁࡿ࠿ࠊศࢆࢃࡁࡲ࠼ࡿ࡜࠸࠺࠿ࠊࡑࡢ㝈⏺ࡣᚲせ࠿࡞࡜ࡣᛮ࠸ࡲ
ࡍࠋ⟅࠼࡟࡞ࡗ࡚࠸ࡿ࠿ࢃ࠿ࡾࡲࡏࢇࡀࠋ
‫ۑ‬༡㢼ཎẶᮾி኱Ꮫ࡛㛗ࡃᏛ⏕ࡓࡕࢆぢ࡚࠸ࡿ࡜ࠊ㞴ࡋ࠸ヨ㦂ࢆ㏻ࡗ࡚ࡁ࡚࠸ࡿࢇࡔࡅ

ࢀ࡝ࡶࠊ౛࠼ࡤ༞ᴗ◊✲࡜࠿࡟࡞ࡿ࡜ࣃࢱࢵ࡜࢔࢖ࢹ࢕࢔ࡀฟ࡞࠸ࠋ⮬ศ⮬㌟࡛ၥ㢟ࢆ᥈ࡍ࡜࠸࠺ࡇ࡜ࡣ⪃࠼ࡓࡇ࡜ࡶ࡞࠸ࡋࠊ࡛ࡁ࡞࠸࡜࠸࠺ࡇ࡜࡛ࠊࡑࡇ࠿ࡽ㠀ᖖ࡟ⱞປࡋ࡚ࠊ
⤖ᒁ༞ᴗ࡛ࡁ࡞࠸࡜࠸ࡗࡓࡇ࡜ࡶ࠶ࡗࡓࡾࡋࡲࡍࠋ࡛ࡍࡢ࡛ࠊ⌧ᅾࡢᮾி኱Ꮫࡢヨ㦂ࡣࡑ࠺࠸ࡗࡓᏛ⏕ࡀࣃࢫࡋ࡚ࡋࡲ࠺ഃ㠃ࢆᣢࡗ࡚࠸ࡿࢃࡅ࡛ࡍࡡࠋ
୍᪉ࠊ࢔࣓ࣜ࢝ࡢ࢔ࢻ࣑ࢵࢩࣙࣥࢬ࢜ࣇ࢕ࢫ࡟ࡼࡿホ౯ࢆぢ࡚࠸ࡿ࡜ࠊࡑ࠺࠸࠺ᶆ‽ࢸࢫࢺࠊ⢭ᗘࡢ㧗࠸ࢸࢫࢺ࡟ຍ࠼࡚㧗➼Ꮫᰯ࡛ఱࢆࡋ࡚ࡁࡓ࠿࡜࠸ࡗࡓࡇ࡜ࢆከ㠃ⓗ࡟ホ౯ࡋ࡚࠸ࡿഃ㠃ࡀ࠶ࡿ࡜ᛮ࠸ࡲࡍࠋࡔ࠿ࡽ௒ࡢᏛຊヨ㦂୍㎶ಽ࡛ࡽࢀ࡚࠸࡞࠸኱஦࡞⬟ຊࡀ࠶ࡿ࡜࠸࠺ࡇ࡜ࡣࠊࡑࡢ㏻ࡾࡔ࡜ᛮ࠸ࡲࡍࠋࡑࢀࡀ 1 ࡘࠋ௚᪉ࠊࡑ࠺࠸࠺ၥ㢟Ⓨ᝿ຊࡀ༑ศ࡛࡞࠸Ꮫ⏕ࡸ༠ㄪᛶࡢ࡞࠸Ꮫ⏕࡛ࡶࠊ⌧ᅾ࡞ࡽࡤᮾி

－113－

኱Ꮫ࡛Ꮫࡪࡇ࡜ࡀ࡛ࡁࡿࢃࡅ࡛ࡍࡡࠋࡑ࠺࠸ࡗࡓᏛ⏕ࢆษࡿࡢ࠿ࠊᙼࡽࡀᏛࡪᶵ఍ࢆྲྀࡾ
ୖࡆ࡚ࡋࡲ࠺ࡢ࠿ࠊ࡛ࡣㄡࡀᮾி኱Ꮫࡢᩍ⫱ࢆཷࡅࡿᶒ฼ࡀ࠶ࡿ࠿࡜⪃࠼ࡿ࡜ࡲࡓ㞴ࡋ࠸ၥ㢟ࡶฟ࡚ࡃࡿࠋ
⟅⏦᱌ࡢ୰࡟ࡶࠕ㞀ᐖࡢ᭷↓࡟࠿࠿ࢃࡽࡎࠖ࡜࠶ࡾࡲࡋࡓࡅࢀ࡝ࡶࠊ࡛ࡣࠊ࡝࠺࠸࠺㞀ᐖࢆ᝿ᐃࡋ࡚࠸ࡿࡢ࠿ࠊ௒ࠊධᏛヨ㦂ࡢ࡜ࡁ࡟ྜ⌮ⓗ࡞㓄៖ࢆࡋ࡚ࠊ౛࠼ࡤⓎ㐩㞀ᐖ➼ࡀ࠶ࡗ࡚ࡶఱ࡜࠿┿ࡢ⬟ຊ࡜ゝࢃࢀ࡚࠸ࡿࡶࡢࡀⓎ᥹࡛ࡁࡿࡼ࠺࡞᪉ྥ࡛ດຊࡋ࡚࠸ࡿࡇ࡜
࡜ࠊ௒ࠊヰ㢟࡟࡞ࡗ࡚࠸ࡿࡼ࠺࡞ពḧ࡜࠿஺΅ຊ࡜࠿ㄝᚓຊ࡜࠸ࡗࡓࡇ࡜ࢆホ౯ࡋ࡚࠸ࡃ
ࡇ࡜ࠊࡇࢀ඲యࢆ࡝ࡢࡼ࠺࡟⪃࠼࡚࠸ࡃࡢ࠿ࠊ┿๢࡟㆟ㄽࡍ࡭ࡁၥ㢟ࡀࡓࡃࡉࢇ࠶ࡿ࡞࡜
⪃࠼࡚࠸ࡲࡍࠋ
‫ۑ‬๓ᕝẶ
බᖹᛶ࡜࠸࠺ࡇ࡜࡛ࡍࡅࢀ࡝ࡶࠊඛ࡯࡝ࡢ⚾ࡢࢫࣛ࢖ࢻ࡟ࡶ᭩ࡁࡲࡋࡓࡼ࠺࡟ࠊ௒ࡣᖺ࡟ 1ᅇࠊྠࡌၥ㢟࡛ྠࡌ᫬ᮇ࡟ྠࡌ⎔ቃࡢࡶ࡜࡛ཷࡅࡓヨ㦂ࠊࢭࣥࢱ࣮ヨ㦂࡛ࡍࡡࠊࡑࢀ࡜ 2ḟヨ㦂ࡢၥ㢟ࢆ㔜ࡳࢆ௜ࡅ࡚㊊ࡋࡓⅬᩘࢆ౑ࡗ࡚ࠊࡑࡢୖࡢ࡯࠺࠿ࡽఱே࠿᥇ࡿ࡜࠸࠺ᙧ
࡛ࡸࡗ࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࢀࡀ௒ࠊ᪥ᮏ࡛ࡣධヨ࡟㛵ࡋ࡚බᖹࡔ࡜ᛮࢃࢀ࡚࠸ࡿࢇ
࡛ࡍࡀࠊ᭱㏆ࠊ኱ᏛࡣධᏛ⪅ࡢ࣏ࣜࢩ࣮ࢆ᫂☜࡟ࡋࢁ࡜࠿ゝࢃࢀ࡚࠸࡚ࠊ౛࠼ࡤࠕ࠺ࡕࡢ
኱Ꮫࡣዪᛶ࡜⏨ᛶࡢ๭ྜࢆ஬ศ஬ศ࡟ࡋࡲࡍࠖࡳࡓ࠸࡞࣏ࣜࢩ࣮ࢆసࡗࡓ࡜ࡋࡓࡽࠊዪᛶ
ࢆ 5 ๭ࠊ⏨ᛶࢆ 5 ๭᥇ࡿࢃࡅ࡛ࡍ࠿ࡽࠊᙜ↛ᡂ⦼ࡢ㡰ࡔࡅ࡛ࡣ᥇ࢀ࡞ࡃ࡞ࡿ࡜ᛮ࠸ࡲࡍࡋࠊ
࠶࡜ࠕ࠺ࡕࡣ኱Ꮫ㝔ࡲ࡛⾜ࡃேࢆඃ㐝ࡋࡲࡍࠖࡳࡓ࠸࡞࣏ࣜࢩ࣮ࢆసࡗࡓ࡜ࡋࡓࡽࠊࡸࡣ
ࡾ༢࡟㡰ᗎࡔࡅ࡛࡜ࡿࢃࡅ࡛ࡣ࡞ࡃ࡞ࡿ࡜ᛮ࠸ࡲࡍࠋࡑࢀ࠿ࡽࠕྠ❆⏕ࡢᏊᘵࢆඃ㐝ࡋࡲࡍࠖࡳࡓ࠸࡞࣏ࣜࢩ࣮ࡔࡗ࡚ࠊࢫࢡ࣮࣮ࣝ࢝ࣛࢆᙉㄪࡍࡿࡓࡵ࡟ࡣ࠸࠸࣏ࣜࢩ࣮࠿ࡶࡋࢀ
ࡲࡏࢇࡅࢀ࡝ࡶࠊࡑ࠺࠸ࡗࡓ࣏ࣜࢩ࣮࡟ᇶ࡙ࡃヨ㦂ࢆࡸࡿ࡜ࠊ௒ࠊࡸࡗ࡚࠸ࡿࡼ࠺࡞㔜ࡳ
௜ࡁᚓⅬ࡛ୖ࠿ࡽ୪࡭࡚࡜ࡿ࡜࠸࠺ 1 ࡘࡢసᴗࡔࡅ࡛ࡣ᥇ࢀ࡞ࡃ࡞ࡿࡢ࠿ࡶࡋࢀ࡞࠸࡜࠸
࠺Ẽࡀࡋࡲࡍࠋ
ࡑࢀ࠿ࡽࠊẁ㝵ホᐃ࡟㛵ࡋ࡚࡛ࡍࡅࢀ࡝ࡶࠊ౛࠼ࡤ 5 ẁ㝵࡟ࡋࡓࡽࠊ5 ẁ㝵࡛࡞࠸࡜ࡁࠊ
ᮾ኱ࡢᩍ⫱Ꮫ㒊࡟ධࡿࡓࡵ࡟ࡣ 450 Ⅼ௨ୖᚲせࠋᮾᕤ኱ࡢᩍ⫱Ꮫ㒊ࡣࠊࡑࢇ࡞Ꮫ㒊ࡣ↓࠸ࢇ࡛ࡍࡅࢀ࡝ࡶࠊ350 Ⅼ௨ୖ࡜࠿࠸࠺ࡢࡀࠊ5 ẁ㝵࡟ࡋ࡚ࡋࡲ࠺࡜୧᪉࡜ࡶ 5 ࡔࡗࡓࡽ࠸࠸ ࡳ ࡓ ࠸࡞ ࡇ ࡜ ࡟ ࡞ࡿ ࡜ ࠊ ᮾ ᕤ኱ ࡜ ࡋ ࡚ ࡣ Ꮀࡋ ࠸ࠋ㸦 ➗㸧ࡑ ࠺࠸࠺ ࡜ࡇࢁ ࡶ࠶ࡿ ࡢ࡛ࡣ ࡞࠸࠿࡞࡜࠸࠺Ẽࡀࡋ࡞࠸࡛ࡶ࡞࠸࡛ࡍࠋ‫ྖۑ‬఍ᒣᮏඛ⏕ࠊ᪥ᮏࡢ኱Ꮫධヨ஦᝟ࡣ࠶ࡿព

࿡࡛ᾆᓥኴ㑻≧ែ࠿ࡶࡋࢀࡲࡏࢇࡅࢀ࡝ࡶࠊ௒᪥ࡢ㆟ㄽࢆ⪺࠸࡚ఱ࠿ឤ᝿࡜࠿ࠊࡲ࡜ࡵࡋ
୍࡚ゝ࠾㢪࠸ࡋࡲࡍࠋ
‫ۑ‬ᒣᮏẶ

ࡲ࡜ࡵ࡟࡞ࡿ࠿࡝࠺࠿ࡣࢃ࠿ࡾࡲࡏࢇࡅࢀ࡝ࡶࠊ኱Ꮫධヨࢆ⪃࠼ࡓ࡜ࡁ࡟ࠊࡓ

ࡔ⬟ຊࡔࡅ࡛ࡣ࡞ࡃࠊ኱Ꮫධヨࡢ┠ⓗ࡜ࡋ࡚ࠊ኱Ꮫ࡛ఙࡧࡿᏛ⏕ࢆ㑅ࡪࡢ࠿ࠊࡑࢀ࡜ࡶ኱
Ꮫ࡛࡝࠺ఙࡧࡿࡼ࠺࡟ࡍࡿࡢ࠿ࠊࡑࢇ࡞ࡇ࡜ࢆ⪃࠼࡚ࠊ⤖ᒁࡣጇᙜᛶ㸦validity㸧ࡢၥ㢟ࡔ࡜ᛮ࠺ࢇ࡛ࡍࡡࠋࡔ࠿ࡽ኱Ꮫධヨࡢ┠ⓗࢆࡶࡗ࡜ࡣࡗࡁࡾࡉࡏ࡚ࠊ౛࠼ࡤ♫఍࡟ฟ࡚ఙࡧ
ࡿேࢆࡘࡃࡾࡓ࠸ࡢ࠿ࠊࡑࢀ࡜ࡶ኱Ꮫࡢ୰࡛ఙࡧࡿேࠊఙࡧࡿ࡜࠸࠺ࡢࡣ኱Ꮫࡢ୰࡛ᡂຌ
ࡍࡿ࡜࠸࠺ࡢࢆ࡝࠺ᐃ⩏ࡍࡿ࠿ࠊࡑࡢᐃ⩏ࢆ᫂☜࡟ࡋ࡚ࠊ௒ࡲ࡛ࡼࡃ⪺࠿ࢀ࡚࠸ࡓࡢࡣࠊ
኱Ꮫ࡟ධࡗࡓࡽ⮬⏤࡟ఱ࡛ࡶࡸࡿ࡜࠿ࠊࡑ࠺࠸࠺ࡩ࠺࡞⪃࠼᪉࡛ࡣ࡞ࡃ࡚ࠊ኱Ꮫࡢ࠶ࡾ᪉
ࢆྠ᫬࡟⪃࠼࡚ࠊࡑࢀ࡟༶ࡋ࡚኱Ꮫධヨࢆᨵၿࡋ࡚࠸ࡃࠊ኱Ꮫࡢ┠ⓗ࡟࠿࡞ࡗࡓࡶࡢࢆࡘ
ࡃࡗ࡚࠸࠿࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜ᛮ࠸ࡲࡍࠋࡑ࠺࠸࠺ព࿡࡛ࠊᛂ⏝ຊ࡜࠸࠺ࡢࡣ୍␒⥆ࡃ࠿ࡶࡋࢀ࡞࠸࡜ࡶᛮ࠸ࡲࡍࠋࡓࡔࠊࡑࢀ࡜ྠ᫬࡟ࠊ㧗ᰯࡲ࡛࡟⩦ࡗࡓ⬟ຊࡣ⤯ᑐᚲせ࡛ࡍ࠿－114－

ࡽࠊࡑࢀࢆ࡞࠸ࡀࡋࢁ࡟ࡣ࡛ࡁࡲࡏࢇࠋࡔ࠿ࡽࠊ⩦࠺⬟ຊࡀ࡞࠿ࡗࡓࡽᙜ↛ࡑࡢ᫬Ⅼ࡛⬟ຊࡀ࡞࠸ࢃࡅ࡛ࡍ࠿ࡽࠊ⬟ຊࡀ࠶ࡿ࡜࠸࠺ࡇ࡜⮬యࠊ⩦࠺ຊࡀ࠶ࡿࡇ࡜ࡀࢃ࠿ࡿࢃࡅ࡛ࡍ
ࡡࠋࡑ࠺࠸࠺ࡇ࡜࡛ࠊ⤖ᒁࠊ᭱⤊┠ⓗ࠿ࡽࣂࢵࢡ࢔ࢵࣉࡋ࡚ࠊࡑࢀ࡛኱Ꮫධヨࢆ⪃࠼ࡓ࡯࠺ࡀ࠸࠸ࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡲࡍࠋ‫ྖۑ‬఍࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡍࠋ

⚾ࡣࠊ኱Ꮫホ౯࣭Ꮫ఩ᤵ୚ᶵᵓ࡟ࡶ࠸ࡓࡇ࡜ࡀ࠶ࡗ࡚ࠊࡑࡢࡇࢁ࠿ࡽࢹ࢕ࣉ࣐࣏ࣟࣜࢩ
࣮ࠊ࣒࣏࢝ࣜ࢟ࣗࣛࣜࢩ࣮ࠊ࢔ࢻ࣑ࢵࢩ࣏ࣙࣥࣜࢩ࣮ࢆྛ኱Ꮫ࡛❧࡚ࢁ࡜࠸࠺ࡇ࡜࡛ࠊࡑ
࠺࠸࠺ព࿡࡛኱Ꮫࡢᩍ⫱ࡢ୰࡛࡝࠺࠸࠺Ꮫ⏕ീࢆᥖࡆ࡚ࠊࡑࢀࢆ⫱࡚ࡿ࠿࡜࠸࠺┠ᶆࢆࡋࡗ࠿ࡾ❧࡚ࢁ࡜࠸࠺ືࡁࡀᗈࡀࡗ࡚ࡁ࡚࠸ࡿࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡑࡢ㎶ࡀࡲࡔ࡞࠿࡞࠿ࠊ᫂
☜࡞ᙧ࡛ࡣඹ᭷࡛ࡁ࡚࠸࡞࠸㒊ศࡶ࠶ࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࠋ
ࡑ࠺࠸࠺ព࿡࡛ධヨᨵ㠉ࡀࠊࡇࢀࡣ

ᐃㄽࡔࡅ࡛࡞࠸࡜࠸࠺ࡢࡣࠊᮧୖඛ⏕ࡢゝⴥࢆ࠿ࡾࢀࡤࡸࡣࡾධヨ࡜࠸࠺ࡢࡣ㐳ཬຠᯝࡀ࠶ࡗ࡚ࠊධヨࡀኚࢃࡿࡇ࡜࡟ࡼࡗ࡚኱Ꮫᩍ⫱ࡶ㧗ᰯᩍ⫱ࡶኚࢃࡗ࡚࠸ࡃ࡜࠸࠺ഃ㠃ࡶ኱ࡁ࡞ࡇ࡜࡛ࡣ࡞࠸࠿࡜ᛮ࠸ࡲࡍࠋࡑࡢⅬ࡛ࠊ௒ࡢᏛ
ຊヨ㦂୰ᚰࡢධヨࡀࠕබᖹᛶࠖ࡜࠸࠺ࡇ࡜࡛ࡸࡽࢀ࡚࠸ࡿ࡜⟅⏦࡛ࡣ᭩࠿ࢀ࡚࠸ࡲࡍࡀࠊ
ᮏᙜ࡟ࡑࢀࡔࡅ࡞ࡢ࠿ࠊ⚾ࡣࠊ๓௵ࡣி㒔኱Ꮫ࡛ࡋࡓࡅࢀ࡝ࡶࠊࡸࡣࡾᏛຊࡀୗࡀࡗ࡚ࡁ
࡚࠸ࡿ࡜࠸ࡗࡓࡇ࡜ࡀࠊி㒔኱Ꮫࡢඛ⏕᪉ࡀᤵᴗࢆࡸࡗ࡚ᐇឤࡋ࡚࠸࡚ࠊ኱Ꮫࡢඛ⏕᪉ࡢၥ㢟ព㆑࡜ࡋ࡚ฟ࡚ࡃࡿ㒊ศࡶ࠶ࡿࢃࡅ࡛ࠊࡑ࠺࡞ࡿ࡜ࠊࡸࡣࡾᏛຊࢆୖࡆࡿࡓࡵࡢヨ㦂ࢆࡸࡽࡊࡿࢆᚓ࡞࠸࡜࠸࠺ࡢࡀࠊ௒ࡲ࡛ࡢᏛຊヨ㦂୰ᚰࡢධヨࡢせᅉ࡛࠶ࡿࡢ࠿࡞࡜ᛮࡗࡓ ࡾ ࡋ ࡲࡍ ࠋ ࡘ ࡲ ࡾࠊࠕ බᖹ ᛶ ࠖ ࡛ Ꮫຊ ヨ 㦂 ࢆ ࡸࡗ࡚ ࠸ࡿ࡜ ࠸࠺ࡼ ࡾࡣࠊ ࡑ࠺࠸ ࠺ࢽ࣮ ࢬ࡟ᇶ࡙࠸࡚Ꮫຊヨ㦂୰ᚰ࡟࡞ࡽࡊࡿࢆᚓ࡞࠸ࠊ௒ࡲ࡛࠸ࢁ࠸ࢁ࡞ධヨᨵ㠉ࢆ௒ࡲ࡛ࡸࡽࢀ
࡚ࡁࡓࢃࡅ࡛ࠊ୍ⱁධヨࡶ࠶ࡾࠊAO ධヨࡶධࡗ࡚ࡁࡓࠊࡑ࠺࠸ࡗࡓ୰࡛⤖ᒁࠊᏛຊࡀၥ
㢟ࡔ࡜࠸࠺ࡇ࡜࡟࠸ࡘࡶ㏉ࡗ࡚ࡃࡿࡢࡣࠊࡑ࠺࠸࠺኱Ꮫࡢඛ⏕᪉ࡢࢽ࣮ࢬࡸᛮ࠸࡜࠸࠺ࡢ
ࡀ኱ࡁ࠸ࡼ࠺࡞Ẽࡶࡋ࡚࠸ࡿࢃࡅ࡛ࡍࠋ
ࡓࡔࠊࡸࡣࡾࡑࢀࢆ஌ࡾ㉺࠼࡚࠸࠿࡞ࡅࢀࡤ࠸ࡅ࡞࠸᫬௦࡟ධࡗ࡚ࡁࡓ㒊ศࡶ☜࠿࡟࠶ࡾࡲࡍࡢ࡛ࠊࡇࢀࡣᡃࠎධヨࢭࣥࢱ࣮ࡢㄢ㢟࡛ࡶ࠶ࡿࢃࡅ࡛ࡍࡅࢀ࡝ࡶࠊᙜ㠃᪂ࡋ࠸Ꮫຊ
ࢸࢫࢺࢆ࡝࠺࠸࠺ᙧ࡛㐍ࡵ࡚࠸ࡗࡓࡽ࠸࠸ࡢ࠿ࠊࡇࢀࡣ㠀ᖖ࡟ከࡃࡢၥ㢟ࡀ㞃ࢀࡓ㒊ศ࡟ࡶ኱ࡁࡃ₯ࢇ࡛࠸ࡲࡍࠋ౛࠼ࡤࠊ༡㢼ཎඛ⏕ࡀゝࡗ࡚ࡃࡔࡉ࠸ࡲࡋࡓࡅࢀ࡝ࡶࠊ≉ูᥐ⨨ࡢၥ㢟ࠊࡘࡲࡾ㞀ࡀ࠸ࢆᣢࡗࡓཷ㦂⏕ᑐ㇟ࡢඹ㏻ࢸࢫࢺ࡜࠸࠺ࡢࡣࠊ኱Ꮫධヨࢭࣥࢱ࣮࡟
᮶࡚ࡳࡿ࡜ᮏᙜ࡟ᾦࡄࡲࡋ࠸ດຊࢆࡋ࡚࠸ࡿࡇ࡜ࡀࡦࡋࡦࡋ࡜ࢃ࠿ࡿࢃࡅ࡛ࡍࡡࠋࡑ࠺࠸
࠺ഃ㠃࠿ࡽࡍࡿ࡜ࠊᅗ⾲ࡀฟ࡚ࡁࡓࡾ㛗࠸ᩥ❶ࡀฟ࡚ࡁࡓࡾ࡜࠸࠺ࡢࡣࠊⅬᏐ࡟ヂࡋࡓࡾࡍࡿࡢࡣࡍࡈࡃ኱ኚ࡛ࠊⅬᏐࡣ࣮࣒࣎ࣜࣗࡀᗈࡀࡾࡲࡍ࠿ࡽࠊࡇࡢ๓ࠊึࡵ࡚ⅬᏐࡢ఍♫࡬⾜ࡗ࡚ࡳ࡚ࢃ࠿ࡗࡓࢇ࡛ࡍࡅࢀ࡝ࡶࠊᑠࡉ࠸ࢥࣥࢧ࢖ࢫⱥ࿴㎡඾ࡀ 100 ᕳࡢⅬᏐࡢᮏ࡟࡞ࡗ࡚࠸ࡿࢇ࡛ࡍࡡࠊࡑࡢࡄࡽ࠸኱ࡁ࡞ᙧ࡟࡞ࡗࡓࡾࡍࡿ࡜࠸࠺ၥ㢟ࡶྵࡵ࡚ࠊ᫬㛫ࡢไ⣙࡜ಙ㢗ᛶ࣭ጇᙜᛶ☜ಖࡢࡓࡵࡢ㔞ࡢ㛵ಀ࡞࡝ࠊ࠸ࢁ࠸ࢁ࡜▩┪ࡍࡿࡼ࠺࡞ၥ㢟ࢆྠ᫬࡟ᢪ࠼࡚࠸࠿࡞ࡅࢀࡤ࠸ࡅ࡞࠸࡜࠸࠺㎞ࡉࢆᑡࡋ࡛ࡶⓙࡉࢇ࡟ࡶඹ᭷ࡋ࡚࠸ࡓࡔࡅࢀࡤ࡜ᛮ
ࡗࡓࡾࡶࡋࡲࡍࠋࡑࡢ୰࡛ࠊᇶᮏⓗ࡟኱஦࡞ࡇ࡜ࡣᏛ⏕ࡀ⫱ࡗ࡚࠸ࡃࡇ࡜ࡔ࡜ᛮ࠸ࡲࡍࡢ
࡛ࠊࡑࡢཎⅬ࡟❧ࡕ㏉ࡗ࡚ධヨࢭࣥࢱ࣮ࡢ❧ሙ࠿ࡽධヨᨵ㠉ࡶ㐍ࡵ࡚࠸ࡁࡓ࠸࡜ᛮ࠸ࡲࡍ
ࡋࠊࡲࡓྛ኱Ꮫࠊࡑࡋ࡚௒᪥ࡣணഛᰯࡸሿࡢ㛵ಀ⪅ࡢ᪉ࡶ᮶࡚࠸ࡽࡗࡋࡷࡿ࡜ᛮ࠸ࡲࡍࡅ
ࢀ࡝ࡶࠊ࠸ࢁ࠸ࢁ࡞❧ሙ࠿ࡽヨ㦂ࢆࡑࡢࡓࡵ࡟࡝࠺ࡸࡗ࡚࠸ࡗࡓࡽ࠸࠸ࡢ࠿ࠊ௒ᚋࡶඹ࡟
⪃࠼࡚࠸ࡅࢀࡤ࡜ᛮ࠸ࡲࡍࠋ

－115－

௒᪥ࡣ㛗᫬㛫࡟ࢃࡓࡾࡈཧຍ࠸ࡓࡔࡁࡲࡋ࡚ࠊᮏᙜ࡟࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ
ㅮ₇⪅ࠊሗ࿌⪅ࡢඛ⏕᪉࡟࠸ࡲ୍ᗘᢿᡭࢆ࠾㉗ࡾࡃࡔࡉ࠸ࠋ㸦ᢿᡭ㸧࡜ࡾࢃࡅࡇࡢᮾᕤ኱ࡢ఍ሙࢆࠊ᭱㏆ࡣࠊࡇࢀࡶ኱Ꮫホ౯ࡢᝏᙳ㡪ࡢ 1 ࡘ࡛ࡣ࡞࠸࠿࡜ᛮ
࠺ࢇ࡛ࡍࡅࢀ࡝ࡶࠊࡇ࠺࠸࠺ࢩ࣏ࣥࢪ࣒࢘ࡀ⮳ࡿ࡜ࡇࢁ࡛㛤ദࡉࢀ࡚࠸࡚ࠊࡇ࠺࠸࠺఍ሙ
ࢆ☜ಖࡍࡿࡢࡀࡶࡢࡍࡈࡃ኱ኚ࡞ࢇ࡛ࡍࡡࠋࡑ࠺࠸࠺ព࿡࡛ࠊ๓ᕝඛ⏕࡟ࡣࡇࡢ఍ሙࢆ☜ಖࡋ࡚࠸ࡓࡔ࠸࡚ࠊ᫖᪥ࡶࡇࡇ࡛Ἡࡲࡾ㎸ࡳ࡛Ⲵ≀ࡢ⟶⌮ࢆࡋ࡚ࡃࢀࡓࡾ࡜࠸࠺ࡇ࡜࡛ࠊ
ᮏᙜ࡟࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧ࡲࡓࠊ௒᪥ࡲ࡛ࠊ⿬᪉࡛ධヨࢭࣥࢱ࣮ࡢ◊✲ᨭ᥼ಀࡢ஦ົ⫋ဨࡀࣂࢵࢡ࢔ࢵࣉࡋ࡚ࡃࢀ
࡚࠾ࡾࡲࡋ࡚ࠊ↓஦࡟ࡇࡇࡲ࡛᮶ࡽࢀࡓࡢࡣ◊✲ᨭ᥼ಀࡢᛂ᥼ࡀ࠶ࡗࡓ࠿ࡽࡔ࡜ᛮࡗ࡚࠾
ࡾࡲࡍࠋࡑࢀ࠿ࡽࠊ๓ᕝඛ⏕ࡢᣦᑟ኱Ꮫ㝔⏕࡟ࡶ࠾ᡭఏ࠸࠸ࡓࡔࡁࡲࡋࡓࠋᮏᙜ࡟࠶ࡾࡀ࡜࠺ࡈ
ࡊ࠸ࡲࡍࠋ࠸ࢁ࠸ࢁ࡞ࢧ࣏࣮ࢺࡀ࠶ࡗ࡚ࡇࡇࡲ࡛᮶ࡿࡇ࡜ࡀ࡛ࡁࡲࡋࡓࠋ
㞵ࡣ࡝࠺࡛ࡋࡻ࠺࠿ࠊ㊊ඖࡀ⁥ࡾࡸࡍࡃࠊ௒᪥ࡣ⋞㛵࡛⁥ࡗ࡚ᩆᛴ㌴࡛࠾ 1 ே⑓㝔࡟ᢸࡂ㎸ࡲࢀࡓ࡜࠸࠺஦௳ࡶᐇࡣ࠶ࡗࡓࡢ࡛ࠊࡃࢀࡄࢀࡶ࠾Ẽࢆࡘࡅ࡚࠾ᖐࡾࡃࡔࡉ࠸ࠋࡲࡓ
ఱ࠿࠶ࡾࡲࡋࡓࡽࠊ࢔ࣥࢣ࣮ࢺ⏝⣬ࡀධࡗ࡚࠾ࡾࡲࡍࡢ࡛ࡐࡦࡑࢀ࡟⡆༢࡟᭩࠸࡚࠸ࡓࡔ
ࡁࡲࡋ࡚ࠊฟཱྀ࡛ಀࡀཷࡅྲྀࡾࢆᚅࡗ࡚࠸ࡿ࡜ᛮ࠸ࡲࡍࡢ࡛ࠊ࠾Ώࡋ࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠸
ࡲࡍࠋࡑࢀ࡛ࡣࠊᮏᙜ࡟௒᪥ࡣ࡝࠺ࡶ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦ᢿᡭ㸧

－116－

኱Ꮫධヨࢭࣥࢱ࣮ࢩ࣏ࣥࢪ࣒࢘ 2014 ᚋグ኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂࣭◊✲๪⤫ᣓᐁධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊᐊ㛗⿵బ኱㷁㞝సࢩ࣏ࣥࢪ࣒࢘ࡢ㊃᪨࡜ᴫせᮏሗ࿌᭩ࡣࠊ2014 ᖺ 11 ᭶ 29 ᪥㸦ᅵ㸧13:00 㹼 17:30ࠊᮾிᕤᴗ኱Ꮫ኱Ꮫ㝔♫఍⌮ᕤᏛ◊✲ ⛉ ࢹ ࢪ ࢱ ࣝ ከ ┠ ⓗ ࣍ ࣮ ࣝ ࡛ ⾜ ࢃ ࢀ ࡓ ࠊ ኱ Ꮫ ධ ヨ ࢭ ࣥ ࢱ ࣮ ࢩ ࣥ ࣏ ࢪ ࢘ ࣒ 2014ࠗ ኱ Ꮫ ධ ヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿࠘
㸦୺ദ㸸኱Ꮫධヨࢭࣥࢱ࣮ධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊࠊ
ᚋ᥼㸸ᩥ㒊⛉Ꮫ┬࣭᪥ᮏࢸࢫࢺᏛ఍㸧ࡢ඲グ㘓࡛࠶ࡿࠋᢡ࠿ࡽࠊᩍ⫱෌⏕ᐇ⾜఍㆟ࠊ୰ኸᩍ⫱ᑂ㆟఍࡞࡝ࢆ୰ᚰ࡟ࠊ኱Ꮫධヨᨵ㠉ࡀ㆟ㄽࡉࢀ࡚࠸ࡓࡇ࡜ࡶ࠶ࡗ࡚ࠊ㞵ᶍᵝࡢ୰࡟ࡶ࠿࠿ࢃࡽࡎࠊᩥ㒊⛉Ꮫ┬㛵ಀ⪅ 9 ྡࠊ኱Ꮫ㛵ಀ⪅ 97 ྡࠊ㧗ᰯ㛵ಀ⪅ 10 ྡ࡞࡝ࢆྵࡴ 255ྡ ࡢ ཧ ຍ⪅ ࢆ ᚓ ࡚ ࠊ඘ ᐇ ࡋ ࡓ ሗ࿌ ࡜ ⇕ ᚰ ࡞ ㆟ㄽ ࡀ⾜ ࢃࢀࡓ ࠋ࠙ཧ⪃ ࠚ࡜ࡋ ࡚ࠊࢩ ࣏ࣥࢪ ࣒࢘࡟㛵ࡍࡿ࢔ࣥࢣ࣮ࢺ⤖ᯝࢆᮏᚋグࡢᚋࢁ࡟௜ࡅ࡚࠸ࡿࡀࠊ࠸ࢁ࠸ࢁ࡜⮳ࡽࡠⅬ࡟ࡶẼ࡙࠿ࡉࢀࡿ୍᪉ࠊ඲యⓗ࡟ࡣᴫࡡ‶㊊ࡀᚓࡽࢀࡓࢩ࣏ࣥࢪ࣒࢘ࢆ㛤ദࡍࡿࡇ࡜ࡀ࡛ࡁࡓࡇ࡜
࡟ࠊࡲࡎࡶࡗ࡚ࠊཧຍ⪅ࡢ᪉ࠎࠊࡲࡓࠊୗᨭ࠼ࡋ࡚ୗࡉࡗࡓࢫࢱࢵࣇࡢⓙࡉࢇ࡟ᚰࡼࡾࡢ
ᚚ♩ࢆグࡋ࡚⾲ࡋ࡚࠾ࡁࡓ࠸ࠋࡇ ࡢ ࢩ ࣥ ࣏ ࢪ ࢘ ࣒ ࡣ ࠊ Ⲩ ஭ ඞ ᘯ ヨ 㦂 ࣭ ◊ ✲ ⤫ ᣓ ᐁ ࡢ ㊃ ᪨ ㄝ ᫂ ࡟ ࠶ ࡿ ࡼ ࠺ ࡟ ࠊࠕ ኱Ꮫධ Ꮫ⪅㑅ᢤࡀࠊᏛຊ࢚࣮ࣜࢺࡢ㑅ᢤ࠿ࡽࠊࡇࢀ࠿ࡽࡢ▱㆑♫఍ࢆᨭ࠼ࡿᑐ㇟ᒙࢆ㑅ᢤࡋࠊᩍ⫱ࡋ ࠊ ♫ ఍ⓗ ࡟ 㓄 ศ ࡋ࡚ ࠸ ࡃ ࡜ ࠸࠺ ᙺ ๭ ࡟ ኚ ࢃࡗ ࡚ࡁ ࡚࠾ࡾ ࠊࡑ ࡢព࿡ ࡛ࠊࠕ㑅ᢤ ࠖ࡜࠸ ࠺ࡼࡾࡶᩍ⫱ࣉࣟࢭࢫࠊᩍ⫱ㄢ⛬ࡢ✚ࡳୖࡆ࡜࠸࠺㒊ศࡀᙉㄪࡉࢀࡿᚲせࡀ࠶ࡾࠊࡑࡢࡓࡵ
࡟ࠊ኱Ꮫධヨࡣྍ⬟࡞㝈ࡾጇᙜ࡞ࡑࡋ࡚ಙ㢗ᛶࡢ㧗࠸ᩍ⫱ࡽ࡞࠸ࠖ࡜࠸࠺ほⅬ࠿ࡽࠊᩍ⫱ᐃ࡟㔜Ⅼࡀ⨨࠿ࢀ࡞ࡅࢀࡤ࡞

ᐃࡢᑓ㛛ᐙࢆࣃࢿࣜࢫࢺ࡜ࡋ࡚Ⓩቭ࠸ࡓࡔ࠸ࡓࠋࡲ ࡎ ࠊ ETS ࡢ ᒣ ᮏ ව ኴ ᮁඛ ⏕ ࡟ ࠊ PISA ㄪᰝ ࡟ 㛵 ࢃ ࡿศ ᯒ ➼ ࡢ ᇶ ᮏⓗ ⪃ ࠼ ᪉ ࡟ࡘ ࠸ ࡚ ࠊᩍ⫱ᐃ ࡢ ᑓ 㛛 ⓗ ࡞ ❧ ሙ ࠿ ࡽ ㅮ ₇ ࢆ ࠸ ࡓ ࡔ ࠸ ࡓ ࠋ ධ ヨ ࢆ ࢸ ࣮ ࣐ ࡟ ࡍ ࡿ ࡞ ࠿ ࡛ ࠊ PISA ㄪ ᰝࢆྲྀࡾୖࡆࡓࡇ࡜࡟㐪࿴ឤࢆឤࡌࡓཧຍ⪅ࡶ࠾ࡽࢀࡓࡼ࠺࡛࠶ࡿࡀࠊ᪂ࡓ࡞ PISA ㄪᰝ࡟ྥࡅ࡚ࡢ‽ഛࡢࡓࡵࡢ⦓ᐦ࡞ࣇ࢕࣮ࣝࢻㄪᰝࡢィ⏬ࡸࢺࣞࣥࢻࢆᢕᥱࡍࡿࡓࡵࡢᐃ⌮ㄽⓗ⫼ᬒ࡟ᇶ࡙࠸ࡓศᯒᡭἲࡀ♧ࡉࢀࡓࡇ࡜࡟ࡼࡗ࡚ࠊㄪᰝ࡜㑅ᢤ࡜࠸࠺┠ⓗࡢ㐪࠸ࡇࡑ࠶ࢀࠊᩍ⫱ࣉࣟࢭࢫࡢὶࢀࡢ࡞࠿࡛᪂ࡓ࡞ධヨࢆ㛤Ⓨࡋ࡚࠸ࡃ㝿ࡢὶࢀࡢ୍➃࡜␃ពࡍ࡭ࡁ
࣏࢖ࣥࢺࡢ࠸ࡃࡘ࠿ࢆ❚࠸▱ࡿࡇ࡜ࡀ࡛ࡁࡓࠋ
⥆࠸࡚ࠊ๓ᕝ┾୍ඛ⏕࠿ࡽࡣࠊ᪂ࡋ࠸ࢸࢫࢺࢆᑟධࡍࡿ࡟࠶ࡓࡾࠊୡ⏺ࡢヨ㦂ࡢືྥࢆ㋃ࡲ ࠼ࡘࡘࠊ」ᩘᅇࡢཷ㦂ᶵ఍ࡢ‽ഛࡸ CBT ࡢᑟධ࡜࠸ࡗࡓㄢ㢟࡟ᑐࡋ࡚ࠊ᪥ᮏࡢධヨ㢼ᅵࢆ࡝࠺ኚ࠼࡚࠸ࡃᚲせࡀ࠶ࡿ࠿࡟ࡘ࠸࡚⤂௓ࡋ࡚࠸ࡓࡔ࠸ࡓࠋ༡㢼ཎᮅ࿴ඛ⏕࠿ࡽࡣࠊࠕබṇᛶࠖࡢᴫᛕࡢᇶᮏ࡜࡞ࡿಙ㢗ᛶ࡜ጇᙜᛶ࡟ࡘ࠸࡚ࢃ࠿ࡾࡸࡍࡃゎㄝࡋ࡚࠸ࡓࡔࡃ࡜
ඹ࡟ࠊᡂ⦼ᥦ౪ࡢ㝿ࡢẁ㝵⾲♧࡟ࡘ࠸࡚ࠊᚓࡽࢀࡿ᝟ሗ㔞ࡀ࠸࠿࡟పࡃ࡞ࡗ࡚ࡋࡲ࠺ࡇ࡜
࡟࡞ࡿ࠿࡜࠸࠺ࡇ࡜ࢆ୰ᚰ࡟ࠊබṇ࡞ࢸࢫࢺࢆ┠ᣦࡍࡇ࡜ࡢ㔜せᛶ࡟ࡘ࠸࡚ᙉㄪࡋ࡚࠸ࡓ
ࡔ࠸ࡓࠋ኱ὠ㉳ኵ◊✲㛤Ⓨ㒊㛗࠿ࡽࡣࠊ኱Ꮫධヨࢭࣥࢱ࣮ヨ㦂ࡢᐇ㝿ࡢࢹ࣮ࢱ࡟ᇶ࡙࠸࡚ࠊ
⛉┠ᚓⅬ㛫ࡢ┦㛵ศᯒࡢ⤖ᯝࠊࡲࡓࠊཷ㦂⪅ᒙࡢ≉ᚩ࡞࡝࡟ࡘ࠸࡚⤂௓ࡋ࡚࠸ࡓࡔ࠸ࡓࠋ

－117－ᮧୖ㝯ඛ⏕࠿ࡽࡣࠊ␃Ꮫ⏕ࡢࡓࡵࡢ᪥ᮏㄒヨ㦂࡟ᦠࢃࡽࢀࡓ⤒㦂࡟ᇶ࡙࠸࡚ࠊ࣮࢜ࢭࣥࢸ
࢕ࢵࢡ࡞ᩥ⬦࡟ἢࡗࡓᐃࡀồࡵࡽࢀࡿ㢼₻ࡢ࠶ࡿ࡞࠿࡛ࠊ」ᩘᅇཷ㦂ࢆಖ㞀ࡍࡿヨ㦂ࢩࢫࢸ࣒ࡢᑟධࡢ㞴ࡋࡉ࡞࡝࡟ࡘ࠸࡚ᾋࡁ᙮ࡾ࡟ࡋ࡚࠸ࡓࡔ࠸ࡓࠋ᭱ᚋ࡟኱ஂಖᬛဢຓᩍ࠿
ࡽࠊ◊✲㛤Ⓨࡢ୍⎔࡜ࡋ࡚㛤Ⓨ୰ࡢ CBT ࡢࢧࣥࣉࣝࢹࣔࡀ࠶ࡾࠊCBT ࡢලయⓗ࢖࣓࣮ࢪ࡜ྍ⬟ᛶ࡟ࡘ࠸࡚ඹ᭷ࡍࡿᶵ఍ࢆࡶࡗࡓࠋࡑࢀࡒࢀࡢሗ࿌ࡣࠊᩍ⫱ᐃㄽࡢࡋࡗ࠿ࡾࡋࡓ

ᇶ┙࡟ᇶ࡙ࡁࠊࡑࢀࡔࡅ࡟ᑓ㛛ⓗ࡟㞴ゎ࡞㒊ศࡶྵࡲࢀࡘࡘࡶࠊ඘ᐇࡋࡓෆᐜࡢࡶࡢ࡛࠶
ࡗࡓࠋ
ࡑࡢᚋࠊ㉁␲ᛂ⟅ࠊࢹ࢕ࢫ࢝ࢵࢩࣙࣥࢆ 30 ศ⛬ᗘ⾜ࡗ࡚ࠊࢩ࣏ࣥࢪ࣒࢘ࡣ⤊஢ࡋࡓࡀࠊ
ࡇࡢࢩ࣏ࣥࢪ࣒࡛࢘ࡢ㆟ㄽࡣࠊṧᛕ࡞ࡀࡽࠊ࡯ࡰ 1 ࣧ᭶ᚋࡢ 2014 ᖺ 12 ᭶ 22 ᪥࡟୰ኸᩍ
⫱ᑂ㆟఍࠿ࡽฟࡉࢀࡓ⟅⏦ࠗ᪂ࡋ࠸᫬௦࡟ࡩࡉࢃࡋ࠸㧗኱᥋⥆ࡢᐇ⌧࡟ྥࡅࡓ㧗➼Ꮫᰯᩍ⫱ࠊ኱Ꮫᩍ⫱ࠊ኱ᏛධᏛ⪅㑅ᢤࡢ୍యⓗᨵ㠉࡟ࡘ࠸࡚㹼ࡍ࡭࡚ࡢⱝ⪅ࡀክࡸ┠ᶆࢆⱆ྿࠿ ࡏ ࠊ ᮍ ᮶ ࡟ ⰼ 㛤 ࠿ ࡏ ࡿ ࡓ ࡵ ࡟ 㹼 㸦 ᕳ ᮎ ཧ ⪃ ㈨ ᩱ ཧ ↷ ࣭ ௨ 㝆 ࠕ 㧗 ኱ ᥋ ⥆ ⟅ ⏦ ࠖ㸧࠘ ࡟ ࢖ ࣥࣃࢡࢺࢆཬࡰࡍ࡟ࡣࠊ᫬ᮇⓗ࡟㐜ࡁ࡟ኻࡋࡓࠋࡋ࠿ࡋࠊᐇ㝿࡟ධヨࢆኚ࠼࡚࠸ࡃẁ㝵࡟࠾࠸࡚ࠊࡲࡎࡣ༑ศ࡟᳨ウࡋ࡚࠾ࡃ࡭ࡁᇶᮏⓗㄢ㢟ࡢከࡃࡀྲྀࡾୖࡆࡽࢀࡓ࡜ᛮࢃࢀࡿࠋ
ẚ ㍑ⓗ㏆ᖺ࡟࠾࠸࡚ࡶࠊධヨᨵ㠉࡟㛵ࡋ࡚ࡣࠊᖹᡂ 11 ᖺ㸦1999㸧୰ኸᩍ⫱ᑂ㆟఍⟅⏦ࠗึ➼୰➼ᩍ⫱࡜㧗➼ᩍ⫱࡜ࡢ᥋⥆ࡢᨵၿ࡟ࡘ࠸࡚࠘ࠊᖹᡂ 12 ᖺ㸦2000㸧኱Ꮫᑂ㆟఍⟅⏦ࠗ኱Ꮫධヨࡢᨵၿ࡟ࡘ࠸࡚࠘࡞࡝࡟࠾࠸࡚ࠊୖグ⟅⏦࡜ྠᵝࡢ㊃᪨࡛ࠊከ㠃ⓗ࡞ホ౯ࡸ」ᩘᅇཷ㦂࡞࡝ࡀᥦ᱌ࡉࢀ࡚࠸ࡓ࡟ࡶ࠿࠿ࢃࡽࡎࠊࡑࡢ୍㒊ࡣ⤖ᒁᐇ⌧ࡉࢀࡠࡲࡲ௒᪥࡟⮳ࡾࠊࡑࡋ࡚ࡲࡓྠᵝࡢᥦゝࡀ᫖ᖺᮎࡢ⟅⏦࡟࡞ࡉࢀ࡚࠸ࡿ࡜࠸࠺Ⅼࡣ␃ពࡋ࡚࠾ࡃ࡭ࡁࡇ
࡜࡛࠶ࢁ࠺ࠋ⌮ᛕⓗ࡟ࡼ࠸࡜࠸࠺ࡇ࡜࡛ࠊఱᗘ࡜࡞ࡃྲྀࡾୖࡆ࡞ࡀࡽࡶࠊᐇ㝿ࡢලయⓗᡭẁ࡟⤖ࡧ௜࠿࡞࠸࡜࠸࠺ࡇ࡜ࡀ࠶ࡿࡢࡣࠊ୍ࡘࡣධヨ࡟㛵ࢃࡿᩍ⫱ᐃ⌮ㄽࡢᇶᮏࡀඹ᭷

ࡉࢀ࡚࠸࡞࠸࡜࠸࠺ࡇ࡜࡛࠶ࡾࠊࡲࡓࠊධヨࡀ࡝࠺࠶ࡿ࡭ࡁ࡜࠸࠺ධヨほࡀࡑࡢ⌮ᛕ࡟㏣࠸ࡘ࠸࡚࠸࡞࠸࡜࠸࠺ࡇ࡜࡞࡝ࡀ⫼ᬒ࡟࠶ࡿࡢࡔࢁ࠺࡜ᛮࢃࢀࡿࠋ⌧ᐇ࡟ࠊ௒ᅇࡢ⟅⏦᱌࡟࠾࠸࡚ࡶࠊ⚾⮬㌟ࡢ┠࠿ࡽぢࡿ࡜ࠊ┦▩┪ࡍࡿᥦゝࡀ୪ิࡉࢀ࡚࠸࡚ࠊࡑࢀࢆ࠺ࡲࡃࣂ
ࣛࣥࢫࡉࡏࡿධヨᨵ㠉ࡣ⮳㞴ࡢᴗ࡛ࡣ࡞࠸࠿࡜ṇ┤ឤࡌࡉࡏࡽࢀࡿ㒊ศࡀከࡃྵࡲࢀ࡚ࡋ
ࡲࡗ࡚࠸ࡿࠋᮏࢩ࣏ࣥࢪ࣒࡛࢘ࠊᩍ⫱ᐃ࡟㛵ࢃࡿᑓ㛛ᐙࡢ᪉ࠎ࠿ࡽᥦ㉳࠸ࡋ࡚࠸ࡓࡔ࠸

ࡓࠊ࠶ࡿ✀ࢸࢡࢽ࢝ࣝ࡞ഃ㠃࠿ࡽࡢධヨᨵ㠉࡟㛵ࢃࡿㄢ㢟࡟ࡘ࠸࡚ࠊᨵࡵ࡚ჶࡳࡋࡵ࡚ࡳ
࡚࠸ࡓࡔࡅࢀࡤ࡜ᛮ࠺ࠋ
ࠕබṇᛶࠖ࡜ከ㠃ⓗ࡞ホ౯࡟࠾ࡅࡿࠕጇᙜᛶࠖࡢㄢ㢟ᙜ᪥ࡢࢩ࣏ࣥࢪ࣒࡛࢘ࡣࠊ༑ศ࡟ᩚ⌮ࡋࡁࢀ࡞࠿ࡗࡓࡀࠊ௨ୗ࡟ࠊ⚾⮬㌟ࡢಶேⓗ࡞ほⅬ࠿ࡽ࡛ࡣ࠶ࡿࡀࠊ⟅⏦࡟ྵࡲࢀ࡚࠸ࡿ࡜ᛮࢃࢀࡿ▩┪Ⅼࡸၥ㢟Ⅼ࡟ࡘ࠸࡚ࠊ௒ᚋࡢཧ⪃ࡢࡓࡵ࡟⡆༢࡟ྲྀࡾୖࡆ࡚ࡲ࡜ࡵ࡚࠾ࡃࡇ࡜࡟ࡋࡓ࠸ࠋ
ࡲࡎࠊ㧗኱᥋⥆⟅⏦࡛ࡣࠊ᪂ࡋ࠸኱Ꮫධヨࡣࠕබṇᛶࠖ࡟ᇶ♏ࢆ⨨ࡃ࡭ࡋ࡜ࡉࢀ࡚࠸ࡿࠋ
ᮏ⟅⏦࡟ࡘ࠸࡚ࡣࠊ඲ᩥࢆᕳᮎ࡟ཧ⪃㈨ᩱ࡜ࡋ࡚ᥖ㍕ࡋ࡚࠸ࡿࡀࠊࡑࡢࠕከඖⓗ࡞ホ౯࡟ྥ ࡅ ࡓ ព ㆑ ᨵ 㠉 ࡜ ࠊ ᪂ ࡓ ࡞ ホ ౯ ᡭ ἲ ࡢ ⵳ ✚ 㺃ඹ ᭷ ࠖ ࡜ ࠸ ࠺ 㡯 ࡟ ࠾ ࠸ ࡚ ࠊࠕ ಶ ู 㑅 ᢤ ࡟ ࠾ ࡅࡿホ౯࡟ᙜࡓࡗ࡚ࡣࠊ⏬୍ⓗ࡞୍ᩧヨ㦂࡛ṇ⟅࡟㛵ࡍࡿ▱㆑ࡢ෌⏕ࢆၥ࠸ࠊࡑࡢ⤖ᯝࡢⅬᩘ ࡢ ࡳ ࡟౫ ᣐ ࡋ ࡓ 㑅ᢤ ࢆ ⾜ ࠺ ᚑ᮶ ᆺ ࡢ ࠕ බ ᖹᛶ ࠖࠕᐈ ほᛶࠖ ࡜ࠊከ ᩘࡢཷ 㦂⏕࡟ ᑐࡋ࡚ ▷᫬㛫࡛ྜྰุᐃࢆ⾜࠺ࡓࡵࡢຠ⋡ᛶࢆ㔜どࡍࡿ࠶ࡲࡾࠊ㠃᥋ࠊ㞟ᅋウㄽࠊᑠㄽᩥࠊㄪᰝ᭩ࠊ
ࡑࡢ௚࡟ࡼࡿከඖⓗ࡞ホ౯ࢆ㔜どࡋ࡞࠸ഴྥࡀ࠶ࡿࠋࡇࡢⅬ࡟㛵ࡋ࡚ࡣࠊᐈほᛶ࡜ࡣఱ࠿
࡟ࡘ࠸࡚ࡢព㆑ᨵ㠉࡜ేࡏ࡚ࠊಶู㑅ᢤࢆ⾜࠺ഃࡀࠊ⮬ࡽࡢ㒔ྜࡢࡳ࡟ࡼࡾ㑅ᢤࡍࡿ᪉ἲ

－118－

࡛ࡣ࡞ࡃࠊ୍ேࡦ࡜ࡾࡢධᏛᕼᮃ⪅ࡀ⾜ࡗ࡚ࡁࡓከᵝ࡞ດຊࢆཷࡅṆࡵࡘࡘࠊධᏛ⪅࡟ồࡵࡽࢀࡿ⬟ຊࢆࠕබṇࠖ࡟ホ౯ࡋ㑅ᢤࡍࡿ᪉ἲ࡬࡜ព㆑ࢆ㌿᥮ࡋࠊ࢔ࢻ࣑ࢵࢩ࣭࣏ࣙࣥࣜ
ࢩ࣮࡟♧ࡋࡓᇶ‽㺃᪉ἲ࡟ᇶ࡙ࡃከඖⓗ࡞ホ౯ࡢጇᙜᛶ㺃ಙ㢗ᛶࢆ㧗ࡵࠊㄝ᫂㈐௵ࢆᯝࡓࡋ
࡚࠸ࡃᚲせࡀ࠶ࡿࠋࠖ࡜㏙࡭ࡽࢀ࡚࠸ࡿࠋࠕ බ ṇᛶ ࠖ ࡜ ࠸ ࠺ゝ ⴥ ࡟ ࡣ ࠊࡑ ࢀ ࡇ ࡑ ࠊ ᚑ᮶ ᆺࡢ ࠕබᖹ ᛶࠖࠕᐈ ほᛶࠖ ࡜࠸࠺ せ⣲ࡶ ྵࡲࢀ࡚࠸ࡿ࡜ぢࡿࡇ࡜ࡶ࡛ࡁࡿࡋࠊ♫఍ⓗ᱁ᕪࡢ᫝ṇ࡛࠶ࡿ࡜࠿ࠊ♫఍ⓗṇ⩏࡜ゝࡗࡓ♫఍ⓗ࡞ほⅬ࠿ࡽࡢබṇ࡜࠸࠺ᤊ࠼᪉ࡶ࠶ࡿࡋࠊከ⩏ⓗ࡛࠸ࢁ࠸ࢁ࡞ᤊ࠼ࡽࢀ᪉ࡀ࠶ࡿࡀࠊࡑࡢ ࡞࠿࡛ ⟅⏦࡛ ࡣࠊࠕಙ 㢗ᛶࠖࠊࠕ ጇᙜᛶ ࠖ࡜࠸ ࠺ࠊᩍ ⫱ᐃࡀලഛࡍ࡭ࡁ஧ࡘࡢ≉ᚩࢆවࡡഛ࠼࡚࠸ࡿ࡜࠸࠺ࡇ࡜ࡀᙉㄪࡉࢀ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡇࢀࡽࡢⅬࡣࠊᩍ⫱ᐃࡢᑓ㛛ᐙ࠿ࡽࡍࢀࡤࠊࡴࡋࢁᙜ↛ࡢࡇ࡜࡛࠶ࡾࠊ␗ㄽࢆᕪࡋᣳࡴᚲせࡢ࡞࠸ධヨ➼ࡢᐃ࡟࠾ࡅࡿᇶᮏⓗ๓ᥦ࡛ࡶ࠶ࡿࠋࡲࡓࠊ▱㆑ࡢࡳ࡞ࡽࡎࠊ▱㆑ά⏝ຊ㸦ᛮ⪃ຊุ࣭᩿ຊ࣭⾲⌧ຊ➼ 㸧 ࢆ ㌟࡟ ௜ ࡅ ࡚ ࠸ࡃ ࡇ ࡜ ࡸ ࠊࠕ ୺ యⓗ ࡞ Ꮫ ࡧ ࠖ࡟ࡘ ࡞ࡀࡿ Ꮫ⩦ព ḧࢆ㔊 ᡂࡋ࡚ ࠸ࡃࡇ ࡜ࡣࠊ㧗ᰯ࠿ࡽ኱Ꮫᩍ⫱࡟࠿ࡅ࡚㔜せ࡞ᩍ⫱┠ᶆ࡟఩⨨࡙ࡅࡽࢀࡿ࡜࠸࠺ࡇ࡜ࡶ␗ㄽࡢ࡞࠸
࡜ࡇࢁ࡛࠶ࢁ࠺ࠋ
ࡋ࠿ࡋࡔ࠿ࡽ࡜ゝࡗ࡚ࠊ▱㆑ά⏝ຊࡸᏛ⩦ពḧ࡞࡝࡟㛵ࢃࡿಶேࡢ≉ᛶࢆከ㠃ⓗ࡟ᐃࡋ࡚ࠊࡑࡢ⤖ᯝࢆ㑅ᢤ࡟฼⏝ࡍࡿ࡜࠸࠺ࡇ࡜ࡣࠊ࠶ࡿព࿡࡛▷⤡ⓗ࡟㐣ࡂࡿࡋࠊఱࡼࡾࡶࠊ
⟅⏦࡛ᙉㄪࡉࢀ࡚࠸ࡿࠕබṇᛶࠖ࡜࠸࠺ほⅬ࠿ࡽࡍࢀࡤ✀ࠎࡢ▩┪ࡀ⏕ࡌࡿࡇ࡜࡟࡞ࡿࡢ࡛ ࠶ ࡿ ࠋ౛ ࠼ ࡤ ࠊࠕ Ꮫ ⩦ ពḧ ࠖ ࡞ ࡝ ࡟㛵 ࢃ ࡿᐃࡀྛ ኱Ꮫࡢ ಶูධ ヨ࡛ᐇ ᪋ࡉࢀ ࡿ࡜࠸ ࠺ࡇ࡜࡟࡞ࡿ࡜ࠊࡑࡢほⅬ࡛ࡢⅬᩘࢆୖࡆࡿࡓࡵ࡟࡝ࢇ࡞ࡇ࡜ࡀཷ㦂ࢆ┠ᣦࡋ࡚⾜ࢃࢀࡿࡇ࡜࡟࡞ࡿ࠿ࠊ᝿ീࡍࡿࡔ࡟ᜍࢁࡋ࠸㒊ศࡶ࠶ࡿࡀࠊ኱Ꮫධヨࡣࡑ࠺࠸࠺࢖ࣥࣃࢡࢺࢆཬࡰ
ࡍ࡜࠸࠺ࡇ࡜ࡀᖖ࡟ෆໟࡉࢀ࡚࠸ࡿࡇ࡜ࢆᛕ㢌࡟⨨࠸࡚࠾ࡃᚲせࡀ࠶ࡿࠋࡓ࡜࠼ᚰ⌮Ꮫⓗ࡟ࡣጇᙜᛶࡢ㧗࠸ࠕᏛ⩦ពḧࠖࡢᐃᡭἲࡀ㛤Ⓨࡉࢀ࡚࠸ࡓ࡜ࡋ࡚ࡶࠊࡑࡢᡭἲࡀධヨ࡟᥇⏝ࡉࢀࡓ㏵➃࡟ࠊཷ㦂‽ഛ࡜࠸࠺Ἴཬຠᯝࢆ⤒࡚ࠊࡍࡄ࡟ࡑࡢᡭἲࡢࠕጇᙜᛶࠖࡣᔂࢀཤ ࡗ ࡚ ࠸ࡗ ࡚ ࡋ ࡲ ࠺ࡢ ࡛ ࠶ ࡿ ࠋࠕ ጇ ᙜᛶ ࠖ ࡣ ࡲ ࡉ࡟ࠊᐃᡭ ἲࡢᒓ ᛶ࡜ࡋ ࡚୙ኚ ࡢࡶࡢ ࡛ࡣ࡞ࡃࠊ฼⏝┠ⓗࡀኚࢃࢀࡤኚ໬ࡍࡿࡶࡢ࡛࠶ࡿࡋࠊࡲࡓࠊཷ㦂⪅ࡢ‽ഛࡢ࠶ࡾ᪉ࠊ

ᐃࡢᑐ㇟࡜࡞ࡿ㞟ᅋࡢ㐪࠸࡟ࡼࡗ࡚ࠊኚࢃࡗ࡚ࡋࡲ࠺࡜࠸࠺≉ᚩࢆᣢࡗ࡚࠸ࡿࡢ࡛࠶ࡿࠋࡉ ࡽ ࡟ࠊࠕ ጇᙜ ᛶ ࠖ ࡣ ࠊ୍ ᐃ ࡢ ࠕ ಙ㢗 ᛶ ࠖ ࡀ ☜ಖࡉ ࢀ࡞࠸ ࡜ࡑࢀ ࡀ㧗࠸ᐃ࡟ ࡣ࡞ࡾ ᚓ࡞࠸࡜࠸࠺ᛶ㉁ࢆᣢࡗ࡚࠸ࡿࠋࡑࡢⅬ࡛ࠊከ㠃ⓗ࡞ホ౯࡜ࡋ࡚᝿ᐃࡉࢀ࡚࠸ࡿࠕ㠃᥋࡛ࠖ࠶ ࡿ ࡜ ࠿ࠊ ࢢ ࣝ ࣮ ࣉ࣡ ࣮ ࢡ ➼ ࡢࠕ ほ ᐹ ࠖ ࡛ ࠶ࡿ ࡜࠿ ࠊࠕᑠㄽ ᩥ࡛ࠖ ࠶ࡿ࡜ ࠿ࠊࡑ ࠺࠸ࡗ ࡓ㢮ࡢ࠸ࢃࡺࡿࣃࣇ࢛࣮࣐ࣥࢫ࡟㛵ࡍࡿホ౯࡟ࡘ࠸࡚ࡣࠊ᥇Ⅼ⪅ࡢಙ㢗ᛶࡀᇶᮏⓗ࡟ప࠸࡜࠸࠺ၥ㢟Ⅼࢆࢡࣜ࢔ࡋ࡚࠸࠿ࡡࡤ࡞ࡽ࡞࠸ࡢ࡛࠶ࡿࠋධヨࡢሙྜࠊ㝈ࡽࢀࡓ᫬㛫ࠊ㝈ࡽࢀࡓ࣐ࣥࣃ࣮࣡ࡢୗ࡛⾜ࢃࢀࡿࡇ࡜࡛ࠊࡇࡢⅬࡣࣇ࢕࣮ࢪࣅࣜࢸ࢕ࡢㄢ㢟࡟ࡶࡘ࡞ࡀࡿࡇ࡜
࡛ࡶ࠶ࡿࠋࡑࡢ୍᪉࡛ࠊ࡛ࡣࠊಙ㢗ᛶࢆ㧗࠸ᡭἲࢆ᥇⏝ࡍࡿ࡜࠸࠺࡜ࡇࢁ࡟ຊⅬࢆ⨨ࡃ࡜ࠊ≉࡟ࠊᐃࡢᑐ㇟࡜࡞ࡿ≉ᛶࡀ」㞧࡟࡞ࢀࡤ࡞ࡿ࡯࡝ከ㠃⥺ࢆ᭷ࡍࡿࡇ࡜࡟࡞ࡾࠊጇᙜᛶ

ࢆ☜ಖࡋ࡟ࡃࡃ࡞ࡿ࡜࠸࠺ࠊಙ㢗ᛶ࡜ጇᙜᛶࡢ▩┪࡜࠸࠺ᐃㄽⓗ࡞ᇶᮏࡣᏑእඹ᭷ࡉࢀ

࡚࠸࡞࠸ࡇ࡜ࡢ୍ࡘ࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀࡿࠋ
▱㆑ά⏝ຊ࡟ࡘ࠸࡚ࡶྠᵝࡢࡇ࡜ࡀゝ࠼ࡿࠋᏛ⩦ពḧ࡯࡝࡛ࡣ࡞࠸࡟ࡋ࡚ࡶࠊࡇࡢⅬ࡟㛵ࡋ࡚⟅⏦࡛ྲྀࡾୖࡆࡽࢀ࡚࠸ࡿྜᩍ⛉࣭⛉┠ᆺࡢヨ㦂࡛࠶ࡿ࡜࠿ࠊ⥲ྜᆺࡢヨ㦂࡜࠸࠺ࡢࡣࠊࡉࡲࡊࡲ࡞せ⣲ࢆྵࡳ㎸ࡴࡇ࡜࡟࡞ࡾࠊ⤖ᒁࠊࡑࢀ࡟ࡼࡗ࡚ఱࢆ

ᐃࡋࡼ࠺࡜ࡋ࡚

࠸ࡿࡢ࠿ࡀ୙᫂░࡟࡞ࡿࠊゝ࠸᥮࠼ࢀࡤࠕጇᙜᛶࠖࡀపࡃ࡞ࡿྍ⬟ᛶࡀ㧗࠸࡜࠸࠺ၥ㢟Ⅼ－119－ࡀ ᖖ ࡟ ࡘ ࡁ ࡲ ࡜ ࠺ ࡇ ࡜ ࡟ ࡞ ࡿ ࠋ ከ ഃ 㠃 ࡢ ၥ 㢟 㡯 ┠ ࢆ ྵ ࡴ ࡜ ࠸ ࠺ ࡇ ࡜ ࡣ ࠊࠕ ጇᙜᛶ ࠖࡤ࠿ ࡾ࡛࡞ࡃࠊෆⓗ࡞ᩚྜᛶࡀ☜ಖࡋ࡟ࡃࡃ࡞ࡿ࡜࠸࠺ࡇ࡜࡟ࡶࡘ࡞ࡀࡾࠊࡑࡢ⤖ᯝࠊࠕಙ㢗ᛶࠖࢆ☜ಖࡍࡿࡇ࡜࡟ࡶᅔ㞴ࢆ⏕ࡌࡿࡇ࡜࡟࡞ࡿࠋ
ࡲࡓࠊྜᩍ⛉࣭⛉┠ᆺࠊ⥲ྜᆺࡢၥ㢟ࡣࠊᇶᮏⓗ࡟ࠊᗈ⠊ᅖࡢෆᐜ㡿ᇦࢆ࢝ࣂ࣮ࡍࡿᚲ
せࡶ⏕ࡎࡿࡇ࡜ࠊ࣮ࣜࢻᩥࡸᅗ⾲࡞࡝ࡶేࡏ࡚ᥦ♧ࡉࢀࡿ࡞࡝ࠊၥ㢟ゎ⟅࡟ࡼࡾከࡃࡢ᫬㛫ࡀᚲせ࡜ࡉࢀࡿྍ⬟ᛶࡀ኱ࡁࡃ࡞ࡿࡇ࡜࡞࡝ࠊᮃࡴࡽࡃࡣ༑ศ࡞ヨ㦂᫬㛫ࡀᚲせ࡜ࡉࢀࡿࡇ࡜࡟࡞ࡿࡀࠊ୍᪉࡛」ᩘᅇࡢཷ㦂ᶵ఍ࢆ‽ഛࡍࡿせㄳ࡞࡝ࡶྵࡵ࡚ࠊヨ㦂࡟࠿ࡅࡿ᫬
㛫ࡣ࡛ࡁࡿ㝈ࡾຠ⋡໬ࢆᅗࡿࡇ࡜ࡀồࡵࡽࢀ࡚࠸ࡿ࡜࠸࠺▩┪ࡶᢪ࠼ࡿࡇ࡜࡟࡞ࡿࠋ୍ᐃ
ࡢၥ㢟ᩘ࡟⤠ࡽࡊࡿࢆᚓ࡞࠸࡜࡞ࡿ࡜ࠊໃ࠸ࠕಙ㢗ᛶࠖࡶపࡃ࡞ࡿ࡜࠸࠺ၥ㢟Ⅼࡶࢡࣜ࢔ࡍ࡭ࡁㄢ㢟࡜࡞ࡿࠋ
ࡉࡽ࡟ࠊྜᩍ⛉࣭⛉┠ᆺࠊ⥲ྜᆺࡢၥ㢟࡜࠸࠺ࡢࡣࠊࡑࢀࢆసᡂࡍࡿࡢࡀᐜ᫆࡜ࡣゝ࠼ࡎࠊẖᖺᘬࡁ⥆ࡁ⾜࠺ᚲせࡢ࠶ࡿධヨ࡟࠾࠸࡚ၥ㢟సᡂయไࢆ㛗࠸㛫⥔ᣢࡋ࡚࠸ࡃࡢࡣࡑ
࠺⡆༢࡞ࡇ࡜࡛ࡣ࡞࠸࡜࠸࠺ࣇ࢕࣮ࢪࣅࣜࢸ࢕ࡢၥ㢟࡟ࡶ㐼㐝ࡍࡿࡇ࡜࡟࡞ࡿ࡛࠶ࢁ࠺ࠋ
࡞࠾ࠊࠕ▱㆑ά⏝ᆺࠖࡢၥ㢟࡟㛵ࡋ࡚ࡣࠊᒣᮏවኴᮁඛ⏕ࡢㅮ₇࡟࠾࠸࡚ࠊPISA ࡢ CBTࡢၥ㢟౛ࡀ♧ࡉࢀࡓࠋධヨ࡟࠾࠸࡚ࡶࠊࡑࡇ࡛

ᐃࡉࢀࡿ࡭ࡁ⬟ຊࡣࠊ௒ᚋࡉࡽ࡟ࡼࡾ㧗ḟ ࡢ ࡶ ࡢ ࡀ ྲྀ ࡾ ୖ ࡆ ࡽ ࢀ ࡚ ࠸ ࡃ ࡇ ࡜ ࡟ ࡞ ࡿ ࡛ ࠶ ࢁ ࠺ ࠋ ࡑ ࡢ 㝿 ࡟ ࠊ ࡲ ࡎ ࠊ PISA ࡢ ࡼ ࠺ ࡟ ࠊᅜูࠊᆅᇦู࡞࡝ࠊከࡃࡢ⿕ㄪᰝ⪅ࡢᖹᆒⓗ࡞ࣞ࣋ࣝࢆ▱ࡿࡓࡵࡢࠕㄪᰝࠖ࡜ࠊಶࠎࡢཷ
㦂⏕ࡢຊ㔞ࢆ㆑ูࡋ࡞ࡅࢀࡤ࡞ࡽ࡞࠸㑅ᢤࡢࡓࡵࡢࠕධヨࠖ࡜ࡢ㐪࠸ࢆㄆ㆑ࡋ࡚࠾ࡃᚲせࡀ ࠶ ࡿ ࡔ ࢁ ࠺ ࠋ PISA ㄪ ᰝ ࡣ ࠊ ▱ ㆑ ά ⏝ ᆺ ࡢ ࠕ ၥ 㢟 ࠖ ࡜ ࠕ ཷ 㦂 ⏕ ࠖ ࡢ ୧ ⪅ ࡢ ḟ ඖ ࡟ ࠾ ࡅ ࡿࢧࣥࣉࣜࣥࢢࡢᕤኵ࡟ࡼࡗ࡚ࠊᅜࡈ࡜ࡢᖹᆒ್࡟㛵ࡋ࡚ࡣࠊෆᐜⓗ࡞೫ࡾ࡞࡝ࡶᑠࡉࡃ࡞
ࡿࡼ࠺࡟ᕤኵࡉࢀࠊࡲࡓࠊᶆ‽ㄗᕪ࡞࡝ࡶᑠࡉࡃ࡞ࡿࡼ࠺࡟㓄៖ࡉࢀ࡚࠸ࡿࠋ☜࠿࡟ࠊಶேಶேࡀྲྀࡾ⤌ࢇ࡛࠸ࡿၥ㢟ࡣ␗࡞ࡗ࡚࠸ࡿ࡜࠸࠺≧ἣ࡟࠶ࡗࡓ࡜ࡋ࡚ࡶࠊ㡯┠཯ᛂ⌮ㄽ
㸦 IRT㸧ࢆ ฼ ⏝ ࡋ ࡓ⤫ ィ ⓗ ࡞ ᥎ ᐃ್ ࡜ ࡋ ࡚ ࠊ୍ ࡘ ࡢ ᑻ ᗘୖ ࡟ ಶ ே ࡢ ⬟ຊ ࢆ ఩ ⨨ ࡙ࡅ ࡿ ࡇ ࡜ࡶྍ⬟࡛࠶ࡿࠋࡓࡔࠊࡑࡢ᥎ᐃ್ࢆ኱Ꮫධヨ࡞࡝ࡢ㑅ᢤ࡟฼⏝ࡍࡿ࡜࠸࠺ࡇ࡜࡟♫఍ⓗ࡞ࢥࣥࢭࣥࢧࢫࡀᚓࡽࢀࡿࡲ࡛࡟ࡣࠊIRT ࡢᐃᑐ㇟⬟ຊࡢ୍ḟඖᛶࡢ๓ᥦࡸᒁᡤ⊂❧ࡢ௬ᐃ࡞࡝࠿ࡽࡢ⌧ᐇࡢ஋㞳ࡢၥ㢟ࠊၥ㢟㡯┠ࡢ㠀බ㛤ࡢၥ㢟ࠊ㡯┠ࣃ࣓࣮ࣛࢱ࡞࡝ࡢ⟬ฟ࡟㛵ࢃࡿಙ㢗ᛶࡸࣇ࢕࣮ࢪࣅࣜࢸ࢕ࡢၥ㢟࡞࡝ࡢከࡃࡢㄢ㢟ࡀᒣ✚ࡋ࡚࠾ࡾࠊ࠶ࡿ⛬ᗘࡣ㡹೺࡞⤖ᯝࡀᚓࡽࢀࡿ࡛࠶ࢁ࠺࡜࠸࠺ࡇ࡜ࡀ࠶ࡗࡓ࡜ࡋ࡚ࡶࠊ᪥ᮏࡢヨ㦂㢼ᅵ࡟࠾࠸࡚ࡣ࠿
࡞ࡾࡢ᫬㛫࡜ࢫࢸࢵࣉࡀᚲせ࡜ࡉࢀࡿ࡜ᛮࢃࢀࡿࠋ
ࠕ᝟ሗ㔞ࠖ࡜ẁ㝵ู⾲♧࡟ࡼࡿᡂ⦼ᥦ౪㧗 ኱᥋⥆ ⟅⏦࡛ ࡣࠊඹ ㏻ࢸ ࢫࢺ࡜ ࡋ࡚ࠊࠕ㧗 ➼Ꮫᰯ ᇶ♏ Ꮫຊࢸࢫࢺ㸦௬⛠㸧ࠖ࡜ࠊࠕ኱Ꮫධ Ꮫ ᕼ ᮃ⪅ Ꮫ ຊ ホ ౯ࢸ ࢫ ࢺ 㸦 ௬⛠ 㸧ࠖࡢ ஧ ࡘ ࡢ ኱つᶍ ࡞ࢸࢫ ࢺࡢᐇ ᪋ࡀᥦ ゝࡉࢀ ࡚࠸ࡿ ࠋࡑࡋ࡚ࠊࡑࢀࡒࢀࡢࢸࢫࢺࡣࠊከᵝ࡞ཷ㦂⪅ࢆ᝿ᐃࡋ࡚ࠊࡑࡢ㑅ᢤࡢࡓࡵࡢ㆑ูຊࢆ☜ಖࡍ ࡿ ࡓ ࡵ࡟ ᗈ ⠊ ᅖ ࡢ㞴 ᫆ ᗘ ࡢ ၥ㢟 ࢆ ྵ ࡴ ࡇ ࡜ࠊ ࡲࡓ ࠊࠕ୍Ⅼ ้ࡳࠖ ࡢධヨ ࡢᝏᙳ 㡪࠿ࡽ ⬺ࡍࡿࡓࡵ࡟ࠊࠕẁ㝵⾲♧ࠖ࡟ࡼࡿᡂ⦼ᥦ౪࡞࡝ࡀᥦゝࡉࢀ࡚࠸ࡿࠋ
ࡇࡢⅬ࡟ࡘ࠸࡚ࡣࠊ༡㢼ཎᮅ࿴ඛ⏕࠿ࡽࡢሗ࿌࡟ヲࡋ࠸ࡀࠊࡸࡣࡾ⟅⏦࡟ࡣ࠸ࡃࡘ࠿ࡢ
▩┪Ⅼࡀྵࡲࢀ࡚࠸ࡿࠋIRT ࡢࣔࢹࣝ࡟ᚑ࠼ࡤࠊྛၥ㢟㡯┠ࡣࠊ⿕㦂⪅ࡢ⬟ຊ࡟㛵ࢃࡿ≉ᛶ್ࡢ㛵ᩘ࡜ࡋ࡚ࠊ᝟ሗ㔞࡜࠸࠺ᴫᛕࡀᥦ㉳ࡉࢀ࡚࠸ࡿࠋ࠶ࡿ⬟ຊࣞ࣋ࣝ࡟࠾ࡅࡿ

ᐃࡢ᝟ሗ㔞ࡀ኱ࡁ࠸࡜࠸࠺ࡇ࡜ࡣࠊࡑࡢ⬟ຊ࡛ࣞ࣋ࣝࡢㄗᕪࡀᑠࡉࡃࠊ㆑ูຊࡀ኱ࡁ࠸࡜࠸࠺
ࡇ࡜࡟ᑐᛂࡋ࡚࠸ࡿ࡜⪃࠼࡚࠾ࡅࡤࡼ࠸ࠋࡑࡋ࡚ࠊ⬟ຊࣞ࣋ࣝࡈ࡜࡟ࠊࢸࢫࢺ࡟ྵࡲࢀࡿ

－120－ྛၥ㢟ࡢ᝟ሗ㔞ࢆࢸࢫࢺ඲య࡛ྜィࡋࡓࡶࡢࢆࢸࢫࢺ᝟ሗ㔞ࠊࢁ࠺࡜ࡋ࡚࠸ࡿ≉ᛶ್࡟᝟ሗ㔞ࢆᑐᛂ࡙ࡅࡓ㛵ᩘࢆ᝟ሗ㔞㛵ᩘ࡞࡝࡜࿧ࢇ࡛࠸ࡿࠋࢸࢫࢺࡣࠊ᝟ሗ㔞㛵ᩘࡢ኱ࡁ࠸
⬟ຊࣞ࣋ࣝ௜㏆ࡢᐃ⢭ᗘࡀ㧗࠸࡜࠸࠺ࡇ࡜࡟࡞ࡿࠋྛ㡯┠࡛ࣞ࣋ࣝࡣࠊࡑࡢ㡯┠ࡢ㞴᫆ᗘࢆ⾲ࡍ⬟ຊࣞ࣋ࣝ௜㏆࡛㧗࠸್ࢆ♧ࡍࡇ࡜ࡀ▱ࡽࢀ࡚࠸ࡿࠋ౛࠼ࡤࠊ᫆ࡋ࠸ၥ㢟ࡤ࠿ࡾ࡛࠶ࢀࡤࠊ⬟ຊࡢ㧗࠸ࣞ࣋ࣝࡢཷ㦂⏕࡟࡜ࡗ࡚ࡣࡳࢇ࡞ࡀ࡛ࡁ࡚ࡋࡲࡗ࡚ࠊ㆑ูຊࡀపࡃ
࡞ࡾ᝟ሗ㔞ࡶప࠸࡜࠸࠺ࡇ࡜࡟࡞ࡾࠊゝ࠸᥮࠼ࢀࡤࠊ᫆ࡋ࠸ၥ㢟ࢆ࠸࠿࡟㞟ࡵ࡚ࡶࠊ㧗ࣞ࣋ࣝࡢ⬟ຊࡢ㆑ู࡟ࡣ᭷ຠ࡛ࡣ࡞࠸࡜࠸࠺ࡇ࡜࡟࡞ࡿࠋ⬟ຊࡢ㧗࠸ࣞ࣋ࣝ࡟ࡣࠊࡑࢀ┦ᛂࡢ㞴ࡋ࠸ࣞ࣋ࣝࡢၥ㢟ࡀᚲせ࡜࡞ࡾࠊࡑࡢព࿡࡛ࠊ☜࠿࡟ࠊᗈ⠊ᅖࡢከᵝ࡞ཷ㦂⏕ࡀ᝿ᐃࡉࢀࡿሙྜ࡟ࡣࠊ㡯┠ࡢ㞴᫆ᗘࡶᗈ⠊࡞ࢸࢫࢺࢆ⏝ពࡍࡿᚲせࡀ࠶ࡿ࡜࠸࠺ࡇ࡜࡟ࡶ࡞ࡿࠋ
ࡋ࠿ࡋࠊヨ㦂࡜࠸࠺ࡢࡣࠊ᫬㛫ⓗ࡞ไ⣙ࡀ࠶ࡾࠊᗈ⠊࡞㞴᫆ᗘࡢ㡯┠ࢆ⥙⨶ⓗ࡟ྵࡴࡼ࠺࡞ࢸࢫࢺࡣ⌧ᐇⓗ࡟ࡣᐇ᪋ࡍࡿࡇ࡜ࡀ㞴ࡋ࠸࡜࠸࠺▩┪Ⅼࡀฟ࡚ࡃࡿࡢ࡛࠶ࡿࠋࡑࡇ࡛ࠊ
ࡑࢀࡒࢀࡢ㞴᫆ᗘࡢ㡯┠ᩘࢆῶࡽࡏࡤࡼ࠸࡜࠸࠺ࡇ࡜ࡶ⪃࠼ࡽࢀࡿࡀࠊ㡯┠ᩘࢆῶࡽࡏࡤࠊ
୍ࡘࡢ⬟ຊࣞ࣋ࣝ࡟࠾ࡅࡿ᝟ሗ㔞ࢆ኱ࡁࡃࡋ࡚࠸ࡃࡇ࡜ࡀ࡛ࡁ࡟ࡃࡃ࡞ࡿࢃࡅ࡛ࠊࢸࢫࢺ
᝟ሗ㛵ᩘࡣ඲యⓗ࡟᝟ሗ㔞ࡢప࠸ࡶࡢ࡟࡞ࡽࡊࡿࢆᚓ࡞࠸࡜࠸࠺ࡇ࡜ࡀ࠶ࡿࠋ࠸ࢃࡺࡿ㐺ᛂᆺࢸࢫࢺ࡜࿧ࡤࢀࡿࢥࣥࢭࣉࢺࡣࠊࡲࡉ࡟ࠊ⬟ຊࣞ࣋ࣝࡢ㆑ู࡟ࡩࡉࢃࡋ࠸᝟ሗ㔞ࡢ኱ࡁ࡞㡯┠ࢆḟࠎ࡟ᥦ♧ࡍࡿࡇ࡜࡟ࡼࡗ࡚ࠊᑡᩘࡢ㡯┠࡛ຠ⋡ⓗ࡟⬟ຊ᥎ᐃࢆࡋࡼ࠺࡜࠸࠺
ࡶࡢ࡛࠶ࡿࠋࡓࡔࠊ㐺ᛂᆺࢸࢫࢺࢆ࠸ࡁ࡞ࡾ኱Ꮫධヨ࡟ᑟධࡍࡿ࡟ࡣࠊࡸࡣࡾከࡃࡢቨࡀ
⫇࠼࡚࠸࡚ࠊ⌧᫬Ⅼ࡛ࡣࡲࡔ⌧ᐇ࿡ࡀ࠶ࡿ࡜ࡣゝ࠼࡞࠸ẁ㝵࡛࠶ࡿࠋࡑࡇ࡛ࠊࡑࢀࢆゎỴࡍࡿࡓࡵ࡟ࡣࠊ㞴᫆ᗘࡢ␗࡞ࡿఱࢭࢵࢺ࠿ࡢࢸࢫࢺࢆ⏝ពࡋࠊࡑࢀ
ࡒࢀࡢࢭࢵࢺ࡟୍ᐃࡢ㡯┠ᩘࡀྵࡲࢀࡿࡼ࠺࡟ࡋࠊࡑࢀࡽࢆࡑࡢ㞴᫆ᗘ࡟ࡩࡉࢃࡋ࠸⬟ຊࣞ࣋ࣝ࡟ᑐࡋ࡚ᥦ౪ࡍࡿࢩࢫࢸ࣒ࢆసࡿࡇ࡜ࡀ⪃࠼ࡽࢀࡼ࠺ࠋࡓࡔࠊࡇࡢࡼ࠺࡞ᙧࡢࢩࢫ
ࢸ࣒ࡣࠊ࡝ࡢࣞ࣋ࣝࡢヨ㦂ࢆ㑅ᢥࡍࡿ࠿࡛ࠊ኱Ꮫࡸ㧗ᰯࡢᗎิ໬࡟┤⤖ࡋ࡚ࡋࡲ࠸ࠊࡑࢀ
ࡇࡑ᪥ᮏࡢヨ㦂ᩥ໬ࡢ୰࡛ࡣᐇ᪋ࡋ࡟ࡃ࠸࡜࠸࠺▩┪Ⅼࡶࡘࡁࡲ࡜࠺ࡢ࡛࠶ࡿࠋࡓࡔࠊࣘ
ࢽࣂ࣮ࢧࣝ໬ࡢ᫬௦࡟࠶ࡗ࡚ࠊ⌧⾜ࡢࢭࣥࢱ࣮ヨ㦂ࡣ⛉┠ᩘࡀከࡍࡂ࡚」㞧࡜࠸࠺ゝㄝࡶࡋࡤࡋࡤ⪺࠿ࢀࡿࡇ࡜࡛ࡣ࠶ࡿࡀࠊ࠸ࡃࡘ࠿ࡢ㞴᫆ᗘࡢࢸࢫࢺࢆ⏝ពࡋ࡚ࠊࡑࢀࢆ㑅ᢥࡍࡿࡇ࡜ࡢ࡛ࡁࡿࡼ࠺࡞ࢩࢫࢸ࣒ࡀཷࡅධࢀࡽࢀࡿࡢ࡛࠶ࢀࡤࠊࡇࡢ▩┪ࡣ࠶ࡿ⛬ᗘゎᾘࡋ࡚࠸ࡃࡇ࡜ࡀ࡛ࡁࡿ࡛࠶ࢁ࠺ࠋࡑࢀࡀ」㞧࡛࠶ࡗࡓ࡜ࡋ࡚ࡶࠊከᵝᛶ࡟ᑐᛂ࡛ࡁࡿᰂ㌾࡞ࢩࢫࢸ࣒ࢆᑟධ࡛ࡁࡿࡢ࡛࠶ࢀࡤࠊᡃࡀᅜࡢඛ㐍ᛶࢆ♧ࡍᶵ఍࡟ࡶ࡞ࡿ࡜࠸࠺ព࿡࡛ࠊ୍
⪃ࡢ౯್ࡢ࠶ࡿ࣏࢖ࣥࢺ࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀࡿࠋ
ẁ㝵⾲♧࡟㛵ࡋ࡚ࡣࠊ༡㢼ཎඛ⏕ࡢሗ࿌࡛ヲࡋࡃゝཬࡉࢀࡓࡼ࠺࡟ࠊヨ㦂ࡢ⣲Ⅼࢆᑡᩘࡢẁ㝵࡛ホᐃࡍࡿࡇ࡜࡟ࡼࡗ࡚ࠊࢸࢫࢺࡢࡶࡘ᝟ሗ㔞ࡀ࠿࡞ࡾῶ⦰ࡋ࡚ࡋࡲ࠺࡜࠸࠺ࡇ࡜
࡛ࠊ㑅ᢤ࡟฼⏝ࡍࡿ࡜࠸࠺┠ⓗࡢࡓࡵ࡟ࡣᚲࡎࡋࡶᮃࡲࡋ࠸ࡇ࡜࡛ࡣ࡞࠸ࠋ↓ㄽࠊྛ኱Ꮫ
ࡢಶูධヨ࡟࠾࠸࡚ࠊከᵝ࡞㑅ᢤ࡟฼⏝ࡍࡿ᝟ሗࢆ཰㞟ࡋ࡚ࠊࡑࢀࡒࢀࢆẁ㝵ホᐃࡋ࡚ࠊࡑࢀࡽࢆྜᡂࡍࡿ࡞࡝ࡋ࡚㑅ᢤ࡟ά⏝ࡍࡿ࡜࠸࠺ᡭ㡰ࡀ᝿ᐃࡉࢀ࡚࠸ࡿࡢ࠿࡜ᛮࢃࢀࡿ
ࡀࠊ࠸ࡎࢀ࡟ࡋ࡚ࡶࠊࡑࡢྜᡂᚓⅬࡢࠕ୍Ⅼ้ࡳࠖࡣఱࡽ࠿ࡢᙧ࡛ṧࡿࡇ࡜࡟࡞ࡿ࡜࠸࠺
Ⅼ࡟ࡣ␃ពࡋ࡚࠾ࡃᚲせࡀ࠶ࡿࡔࢁ࠺ࠋࡲࡓࠊࡑࡢࡼ࠺࡞ྜᡂᚓⅬ࡟ඹ㏻ࢸࢫࢺ⤖ᯝࡢẁ㝵ホᐃࢆ฼⏝ࡍࡿ௙᪉ࡣࠊ኱Ꮫࡢ≧ἣ࡟ࡼࡗ࡚㐪ࡗ࡚ࡃࡿࡢ࡛࠶ࡗ࡚ࠊྛ኱Ꮫ࡬ࡣⅬᩘࡢᙧ࡛ᥦ౪ࡋࠊࡑࢀࢆẁ㝵໬ࡍࡿ࡞࡝ࡢḟࡢᕤኵࡣಶูヨ㦂ࡢẁ㝵࡛ྛ኱Ꮫࡢ஦᝟࡟༶ࡋ࡚
Ỵࡵ࡚࠸ࡃ࡜࠸࠺ࡇ࡜ࡀ⮬↛࡛࠶ࢁ࠺ࠋẁ㝵ホᐃ࡟㛵ࡋ࡚ࡣࠊ࡝ࡢ⛬ᗘࡢᩘࡢẁ㝵࡟ࡋࡓࡽࡼ࠸ࡢ࠿࡜࠸࠺ࡇ࡜ࡶࠊࡑࢀࡒࡢ஦

－121－

᝟ࡸ౯್ほ࡟ࡼࡗ࡚ࡶ㐪ࡗ࡚ࡃࡿࡇ࡜࡛࠶ࡾࠊ࡝ࡢ኱Ꮫ࡟ࡶ୍ᚊࡢࡇࢀࡀࡼ࠸࡜࠸࠺ศ㢮ࡢ௙᪉ࡀ࠶ࡿࢃࡅ࡛ࡣ࡞ࡃࠊࡏ࠸ࡐ࠸࠶ࡾᚓࡿࡢࡣࠊ᝟ሗ㔞ࢆῶ㏥ࡉࡏ࡞࠸ᙧ࡛ࡢ࠸ࡃࡘ
࠿ࡢᶆ‽໬ᡭἲ࡟ࡼࡿኚ᥮ᚓⅬࡢᥦ౪࡜࠸ࡗࡓࡇ࡜ࡀ↓⌮ࡢ࡞࠸࡜ࡇࢁ࡛࠶ࢁ࠺ࠋIRT ࡞࡝ࡀ฼⏝ࡉࢀࡿࡼ࠺࡟࡞ࢀࡤࠊᚓⅬࡣ㡯┠࡬ࡢゎ⟅ࣃࢱ࣮ࣥࡢᩘ࡟┦ᙜࡍࡿẁ㝵ࢆ᝿ᐃࡍࡿࡇ࡜ࡀ࡛ࡁࠊࡑࢀࡣ 100 Ⅼ‶Ⅼࡢ⣲Ⅼࡢẁ㝵࡟ẚ࡭ࡼ࠺ࡶ࡞ࡃ⣽࠿ࡃ⟬ฟࡉࢀࡿࡇ࡜࡟
ࡶ࡞ࡿࡋࠊࡲࡓࠊࡑࡇ࡛᥎ᐃࡉࢀࡿ≉ᛶ್ࡣᇶᮏⓗ࡟ᶆ‽ᚓⅬ࡛࠶ࡾࠊ࠸ࢃࡺࡿ೫ᕪ್࡟
┦ᙜࡍࡿᣦᶆ࡛࠶ࡿ࡜࠸࠺ࡇ࡜ࡶࠊ࡜ࡾ࠶࠼ࡎඹ᭷ࡋ࡚࠾ࡁࡓ࠸

ᐃࡢᇶᮏ࡛࠶ࡿࠋ

ࠕ」ᩘᅇཷ㦂ࠖ࡟㛵ࢃࡿㅖㄢ㢟⟅⏦࡛ࡣࠊᖺࠕ」ᩘᅇࠖࡢᐇ᪋࡜࠸࠺ࡇ࡜ࡶࠊࡑࡢᥦ᱌ࡢ㔜せ࣏࢖ࣥࢺࡢ୍ࡘ࡜ࡉࢀ࡚
࠸ࡿࠋ」ᩘᅇᐇ᪋࡜࠸࠺ࡇ࡜ࡣࠊࡲࡎࠊࡑࡢᐇ⾜ྍ⬟ᛶ࡜࠸࠺Ⅼ࡛ࠊ⌧⾜ࡢᐇ᪋ࡢᙧࢆ᰿
ᗏ࠿ࡽኚ࠼ࡿࡸࡾ᪉ࢆ⪃࠼࡚࠸࠿ࡡࡤ࡞ࡽ࡞࠸࡛࠶ࢁ࠺ࠋࡇࡢⅬ࡟㛵ࡋ࡚ࡣࠊ๓ᕝ┾୍ඛ⏕ࠊᮧୖ㝯ඛ⏕ࡢሗ࿌࡛ゝཬࡉࢀ࡚࠸ࡓࡼ࠺࡟ࠊ᪥ᮏࡢධヨ㢼ᅵ࡛ࡣ㥆ᰁࡳ࡛ࡣ࡞࠸࠸ࡃ
ࡘ࠿ࡢⅬࢆࢡࣜ࢔ࡋ࡚࠸ࡃᚲせࡀ࠶ࡿࠋ୍ࡘࡣࠊ」ᩘᅇࡢヨ㦂ࡢᚓⅬࡀྠ➼ࡢࡶࡢ࡛࠶ࡿ࡜ ࠸ ࠺ ࡇ ࡜ ࡛ ࠊ ࡑ ࢀ ࡒ ࢀ ࡢ ヨ 㦂 ࢆ ➼ ໬ ࡍ ࡿ 㸦 equating㸧 ᚲ せ ࡀ ᚲ ࡎ ⏕ ࡌ ࡿ ࡜ ࠸ ࠺ ࡇ ࡜ ࡛ ࠶ࡾࠊࡑࡢࡓࡵ࡟ࠊၥ㢟㡯┠࡞࡝ࡣ㠀බ㛤ࡢࢹ࣮ࢱ࣮࣋ࢫ࡟኱つᶍ࡟⵳✚ࡉࢀ࡚࠸ࡃᚲせࡀ࠶ࡿ࡜࠸࠺Ⅼ࡛࠶ࡿࠋ➼໬ࡢ㝿࡟ࡣࠊIRT ࡞࡝ࡢᐃ⌮ㄽ࡟౫ᣐࡍࡿࡇ࡜ࡀ⪃࠼ࡽࢀࡿࡀࠊ

IRT ࡣ㡯┠ࡢࣃ࣓࣮ࣛࢱࢆ᥎ᐃࡍࡿࡓࡵ࡟ࠊணഛㄪᰝࡀᚲせ࡜ࡉࢀࡓࡾࠊ࠶ࡿ࠸ࡣࠊᮏ␒
ࡢヨ㦂࡟ࠊ⥲ྜᚓⅬࡢ⟬ฟ࡟ࡣ฼⏝ࡉࢀ࡞࠸ࢲ࣑࣮㡯┠ࢆ୍㒊࡟ྵࢇ࡛࠾ࡃ➼ࡢᕤኵࡀᚲ
せ࡜ࡉࢀࡿࠋࡑࢀࡽࡢࡇ࡜⮬యࠊ᪥ᮏ࡟ࡣ㥆ᰁࡳࡢ࡞࠸ࡇ࡜࡛࠶ࡿࡀࠊࡑࢀ௨እ࡟ࡶࠊ㠀බ㛤࡛࠶ࡿࡣࡎࡢၥ㢟࡟ࡘ࠸࡚ணഛㄪᰝࢆࡋ࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜࠸࠺ⅬࠊࡲࡓࠊIRT ࡣᇶᮏⓗ࡟୍ḟඖᛶࡢ⬟ຊࢆ๓ᥦ࡜ࡋ࡚࠾ࡾࠊከ㠃ⓗ࠿ࡘࡼࡾ㧗ḟࡢ⬟ຊࢆᑐ㇟࡜ࡍࡿሙྜ࡬
ࡢ⌮ㄽⓗᒎ㛤ࡣࡲࡔⓎᒎ㏵ୖ࡟࠶ࡿẁ㝵࡟࠶ࡿ࡜࠸ࡗࡓ▩┪Ⅼࡶෆໟࡉࢀ࠸࡚࠸ࡿ࡜࠸࠺
ࡇ࡜ࢆࡲࡎࡣ☜ㄆࡋ࡚࠾ࡃ࡭ࡁ࡛࠶ࢁ࠺ࠋ
ࡲࡓࠊ⌧⾜ࡢࢭࣥࢱ࣮ヨ㦂࡛ࡣࠊ㐜ࡃ࡜ࡶ⩣᪥ࡢ᪂⪺࡞࡝࡛ࡣࠊヨ㦂ၥ㢟ࡀබ⾲ࡉࢀࠊ⮬ᕫ᥇Ⅼ࡟ࡼࡗ࡚ࠊ኱Ꮫฟ㢪ࢆྛཷ㦂⏕࡛ࣞ࣋ࣝỴᐃࡋ࡚࠸ࡃ࡜࠸࠺ࣉࣟࢭࢫࢆ࡜ࡿࡇ࡜
࡟࡞ࡗ࡚࠸ࡿࡀࠊ」ᩘᅇཷ㦂࡞࡝ࡢሙྜ࡟ࡣࠊヨ㦂ၥ㢟ࢆබ⾲ࡍࡿࡇ࡜ࡀ࡛ࡁ࡞ࡃ࡞ࡿ࡜
⪃࠼࡚࠾ࡃ࡭ࡁ࡛࠶ࡿࠋヨ㦂ၥ㢟ࡢබ⾲ࡀ࡛ࡁ࡞ࡃ࡞ࡿ࡜࠸࠺ࡇ࡜ࡣࠊ⌧ᅾࡣࠊබ⾲ࡉࢀࡓヨ㦂ၥ㢟ࢆ⣲ᮦ࡟ࡋ࡚ࠊࡉࡲࡊࡲ࡞࡜ࡇࢁ࡛㆟ㄽࡀ㉳ࡇࡗࡓࡾࠊࡲࡓࠊ㧗ᰯࡸ኱Ꮫ࡟࠾
࠸࡚ࡶᩍᮦ࡜ࡋ࡚฼⏝ࡉࢀࡓࡾ࡜࠸࠺ࡇ࡜ࡶᩓぢࡉࢀࡿࡀࠊࡑ࠺ࡋࡓᩍ⫱࡬ࡢά⏝ࡶ࡛ࡁ࡞ࡃ࡞ࡿࠋ⟅⏦࡟ࡣࠊ㧗ᰯᩍ⫱ࠊධヨࠊ኱Ꮫᩍ⫱ࡢ୍యⓗᨵ㠉࡜࠸࠺⌮ᛕࡀᶆ㢟࡟ࡶᥖࡆࡽࢀ࡚࠸ࡿࡀࠊၥ㢟ࢆ㠀බ㛤࡟ࡍࡿࡇ࡜࡟ࡼࡗ࡚ࠊධヨࡢ㔜せ࡞ᩍ⫱ⓗᶵ⬟ࡢ୍㒊ࡀ༑඲
࡟Ⓨ᥹ࡉࢀ࡟ࡃࡃ࡞ࡿ▩┪ࢆᢪ࠼ࡿࡇ࡜࡟ࡶ࡞ࡿ࡜࠸࠺Ⅼࡣ࠶ࡲࡾẼ࡙࠿ࢀ࡚࠸࡞࠸ࡇ࡜
࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀࡿࠋ
ࡉࡽ࡟ࠊ」ᩘᅇࡢヨ㦂ᚓⅬࡢ➼໬ࡢࡓࡵ࡟ࠊIRT ࡞࡝ࢆ฼⏝ࡍࡿ࡟ࡋ࡚ࡶࠊᚓⅬࡣᶆ‽
ᚓⅬࡢ㢮࡛⾲ࡉࢀࡿࡇ࡜ࡶᚲᐃ࡛࠶ࡾࠊ⮬ᕫ᥇Ⅼ࡞࡝ࡢᶵ఍ࡶኻࢃࢀࡿࡇ࡜࡟࡞ࡾࠊฟ㢪ࡢࡓࡵࡢࢫࢸࢵࣉ࡜ࡋู࡚ࡢ᪉ἲࢆ⟇ᐃࡋ࡚࠸ࡃᚲせࡶ⏕ࡎࡿ࡛࠶ࢁ࠺ࠋ࠸ࡎࢀ࡟ࡋ࡚ࡶࠊ
ࡑ࠺࠸ࡗࡓ⌧⾜ࡢධヨࡢᅾࡾ᪉࡟㛵ࡍࡿほᛕࢆᗫᲠࡋ࡚ࠊ᪂ࡓ࡞ධヨࡢࢥࣥࢭࣉࢺࢆᥦ᱌
ࡋᗈࡃ♫఍࡟ඹ᭷ࡋ࡚࠸ࡃࡇ࡜ࡀᚲ㡲࡜࡞ࡿࠋ
ࡶ࠺୍Ⅼࠊ㧗ᰯ࡜኱Ꮫࡢ᥋⥆࡜࠸࠺ᩍ⫱ⓗほⅬࡀ㛵ࢃࡿ኱Ꮫධヨࡢ㝿࡟⪃៖ࡋ࡚࠾ࡃ࡭
ࡁࡇ࡜ࡣࠊ㡯┠ࢹ࣮ࢱ࣮࣋ࢫ࡟ಖᣢࡍ࡭ࡁ㡯┠ࡸ IRT ࡢ㐺⏝ࡢࡓࡵ࡟ᚲせ࡜࡞ࡿ㡯┠ࣃࣛ

－122－࣓࣮ࢱ࡞࡝ࡣࠊᩍ⫱ࡢᨵゞ࡞࡝࡟ᛂࡌ࡚ࡑࡢ㒔ᗘ཰㞟ࡋ┤ࡋ࡚࠸࠿࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜࠸
࠺ࡇ࡜࡛࠶ࡿࠋ᪥ᮏ࡛ࡣ 10 ᖺࡈ࡜ࡃࡽ࠸࡟Ꮫ⩦ᣦᑟせ㡿ࡀᨵゞࡉࢀࡿ࡞࡝ࠊヨ㦂ࡢᑐ㇟࡜࡞ࡿෆᐜ㡿ᇦࡢ⠊ᅖࡀኚ࠼ࡽࢀ࡚࠸ࡃࡇ࡜࡟࡞ࡿࡀࠊࡑࡢᗘ࡟ࠊヨ㦂ၥ㢟ࡢ㡯┠ࡢධࢀ᭰࠼ࡸ㡯┠ࣃ࣓࣮ࣛࢱ࡟㛵ࢃࡿ኱つᶍ࡞஦๓ㄪᰝࡢᚲせࡀ⏕ࡌࠊᚲࡎࡋࡶຠ⋡ࡢࡼ࠸ᙧ࡛
⥔ᣢࡋ࡚ࡃࡇ࡜ࡣᐜ࡛᫆࡞࠸࡜࠸࠺ࡇ࡜ࡶ୍ࡘࡢ▩┪Ⅼ࡜ࡋ࡚␃ពࡋ࡚࠾ࡃ࡭ࡁ࡛࠶ࢁ
࠺ࠋࡉࡽ࡟ࠊ⟅⏦࡛ࡣࠊⱥㄒ࡞࡝࡟㛵ࡋ࡚ࡣࠊ4 ᢏ⬟ࡢヨ㦂ࢆࡍ࡭ࡋ࡜࠸࠺ࡇ࡜࡛ࠊእ㒊ࡢ
ⱥㄒ㈨᱁᳨ᐃヨ㦂ࡢά⏝ࡀᥦゝࡉࢀ࡚࠾ࡾࠊࡑࡢࡓࡵ࡟ࠊྛ✀ࡢ㈨᱁᳨ᐃヨ㦂ࡢᚓⅬࡢᑐᛂ⾲࡞࡝ࢆసᡂࡋබ⾲ࡍࡿࡇ࡜ࡀồࡵࡽࢀ࡚࠸ࡿࠋ␗࡞ࡿ┠ⓗࡢヨ㦂ࡢᚓⅬࡢᑐᛂ⾲ࡣࠊࡑࡶࡑࡶᐃㄽⓗ࡟ࡣព࿡ࡢ࠶ࡿࡶࡢ࡛ࡣ࡞࠸ࠋ┠ⓗࡢ㐪࠺ࠊ࠶ࡿ࠸ࡣࠊ

ᐃࡋࡓ࠸ᑐ㇟

ࡀ␗࡞ࡿࠊ࠶ࡿ࠸ࡣࠊཷ㦂⪅ࡢ᝿ᐃࡉࢀࡿẕ㞟ᅋࡀ㐪࠺࡜࠸ࡗࡓࡇ࡜ࡀ࠶ࡿ≧ἣ࡛ࡣࠊᚓ
Ⅼࡢ ➼໬࡜ ࠸࠺ゝ ⴥ࡟ᑐ ࡋ࡚ ࠊᑐᛂ ࡙ࡅ㸦 concordance㸧 ࡜࠸࠺ゝⴥࡀ౑ࢃࢀࡿࡇ࡜ࡀከ࠸
ࡼ࠺࡛࠶ࡾࠊࡑࡢᡭἲࡢ◊✲㛤Ⓨࡶ㐍ࡵࡽࢀ࡚࠸ࡿ࡜ࡇࢁ࡛ࡣ࠶ࡿࡀࠊᇶᮏⓗ࡟ࠊᑐᛂ࡙
ࡅ࡟ࡶㄗᕪࡀྵࡲࢀࡿࡇ࡜࡟࡞ࡾࠊ㧗ᚓⅬ⪅࡞࡝࡟ࡘ࠸୍࡚ⱁධヨⓗ࡞ά⏝᪉ἲࡣ᭷ຊ࡛࠶ ࡿ ࡜ ࡣᛮ ࢃ ࢀ ࡿ ࡀࠊࠕ බṇ ᛶ ࠖ ࡜ ࠸࠺ Ⅼ ࠿ ࡽ ࡍࢀࡤ ࠊゝ࠸ ᥮࠼ࢀ ࡤࠊጇ ᙜᛶࡸ ಙ㢗ᛶ ࡜࠸࠺Ⅼ࠿ࡽࡍࢀࡤࠊ✀ࠎࡢၥ㢟ࡀṧࡉࢀࡿ࡜࠸࠺ࡇ࡜ࡣ༑ศ࡟⮬ぬࡋ࡚࠾ࡃ࡭ࡁ࡛࠶ࢁ࠺ࠋ
ࡇࡢⅬ࡟㛵ࡋ࡚ࡶ࠺୍ࡘ᳨ウࡋ࡚࠾ࡃ࡭ࡁࡇ࡜ࡣࠊ」ᩘᅇཷ㦂ࡣࠊ୍Ⓨ຾㈇ࡢࣉࣞࢵࢩ
࣮ࣕ࠿ࡽཷ㦂⏕ࢆゎᨺࡍࡿ࡜࠸࠺ࡡࡽ࠸ࡶྵࡲࢀ࡚࠸ࡿࡢ࡛࠶ࡿࡀࠊ㏫࡟ࠊ㧗ᰯᩍ⫱ࡢᢸᙜ⪅ࡸཷ㦂⏕࠿ࡽࡢኌࡢ୰࡟ࡋࡤࡋࡤぢࡽࢀࡿࡢࡀࠊ」ᩘᅇ࡟࡞ࡿࡇ࡜࡟ࡼࡗ࡚ࠊࡋ࠿ࡶࠊ
ᇶ♏Ꮫຊࢸࢫࢺ࡜㑅ᢤࡢࡓࡵࡢᏛຊホ౯ࢸࢫࢺࡢ஧ᮏ❧࡚࡜࡞ࡿ࡜ࠊ㧗ᰯ⏕ࡀヨ㦂ₕࡅ࡟࡞ࡗ࡚ࡋࡲࢃ࡞࠸࠿࡜࠸࠺ࡇ࡜࡛࠶ࡿࠋ」ᩘᅇཷ㦂ࡣࠊࡑࡢᐇ᪋᫬ᮇࡢࢥࣥࢭࣥࢧࢫࢆᚓ
ࡿࡇ࡜࡛࠶ࡿ࡜࠿ࠊࢸࢫࢺࢭࣥࢱ࣮ࡢタ⨨࡞࡝ࡢヨ㦂ࡑࡢࡶࡢࡢ᪥ᖖ໬ࡢࡓࡵࡢ⎔ቃసࡾ࡛࠶ࡿ࡜࠿ࠊࡲࡓࠊࡑࢀ࡟㛵ࡍࡿ㧗ᰯࢆึࡵ࡜ࡍࡿ♫఍ࡢ⌮ゎ࡛࠶ࡿ࡜࠿ࠊࡑ࠺࠸ࡗࡓヨ㦂ࡢ࿘㎶ⓗ࡞㒊ศ࡛ࠊ♫఍඲యࡢ༠ຊయไࢆ⠏࠸࡚࠸ࡃࡓࡵࡢ㞺ᅖẼసࡾࡀࡲࡎࡣồࡵࡽࢀࡿ࡜࠸࠺ࡇ࡜࡛࠶ࢁ࠺ࠋࡇࢀࡶࡲࡓࠊ᪥ᮏࡢධヨ㢼ᅵࡢ㌿᥮ࡀồࡵࡽࢀ࡚࠸ࡿⅬ࡛ࡶ࠶ࢁ࠺ࠋ
CBT ࡢᑟධ
」 ᩘᅇཷ 㦂࡟ࡶ 㛵㐃ࡋ࡚ࠊ⟅⏦࡛ࡣࠊCBT㸦Computer-Based

Testing㸧ࡢᑟධ࡟ࡘ࠸࡚ࡶᥦ ゝ ࡉ ࢀ ࡚ ࠸ ࡿ ࡜ ࡇ ࢁ ࡛ ࠶ ࡿ ࠋ ᮏ ࢩ ࣥ ࣏ ࢪ ࢘ ࣒ ࡛ ࡶ ࠊ ᒣ ᮏ ව ኴ ᮁ ඛ ⏕ ࠿ ࡽ ࠊ PISA ࡟ ࠾ ࡅࡿ CBT ၥ㢟ࡢ౛ࡶ⤂௓ࡉࢀࠊࡲࡓࠊ኱Ꮫධヨࢭࣥࢱ࣮࡛㐍ࡵ࡚࠸ࡿ◊✲㛤Ⓨࡢ୍➃࡟ࡘ࠸࡚ࡶ኱ஂಖᬛဢຓᩍ࠿ࡽࡢሗ࿌ࡀ࠶ࡾࠊ௒ᚋࠊࢃࢀࢃࢀࡀ㑊ࡅ࡚ࡣ㏻ࢀ࡞࠸࣓ࢹ࢕࢔ࡢኚ㠉 ࢆぢᤣ࠼࡚ࠊධヨ࡟ࡘ࠸࡚ࡶ CBT ໬ࢆྵࡵࡓ฼⏝࣓ࢹ࢕࢔ࡢኚ㠉࡬ࡢᑐᛂࡣᖖ࡟ᛕ
㢌࡟⨨ࡃ࡭ࡁ㔜せ࡞࣏࢖ࣥࢺ࡛࠶ࡿࠋ࠾ ࡑ ࡽ ࡃ ࠊ ⌧ ⾜ ࡢ ࣐ ࣮ ࢡ ࢩ ࣮ ࢺ ᪉ ᘧ ࢆ ࠊ CBT ࡟ ⨨ ࡁ ᥮ ࠼ ࡿ ࡜ ࠸ ࠺ ⛬ ᗘ ࡛ ࠶ ࢀ ࡤ ࠊ ࢹ ࣂ࢖ࢫࡢ⥔ᣢ⟶⌮ཬࡧ⿦⨨ࡸࢸࢫࢺࢭࣥࢱ࣮࡞࡝ࢆᩚ࠼ࡿึᮇᢞ㈨࡞࡝ࡢၥ㢟ࡀࢡࣜ࢔ࡉ࠼
ࡍࢀࡤࠊࡑࢀ⛬኱ࡁ࡞ၥ㢟ࡶ࡞ࡃ⛣⾜ࡋ࡚࠸ࡃࡇ࡜ࡶ࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀࡿࠋࡓ
ࡔࠊࣜࢫࢽࣥࢢ࡛฼⏝ࡉࢀ࡚࠸ࡿ IC ࣉ࣮࣮࡛ࣞࣖࡉ࠼ 1 ୓ྎ࡟ 1 ྎ⛬ᗘࡢ୙ලྜࡢ⏦࿌ࡀ ࠶ ࡾ ࠊ ࡇ ࢀ ࡣ ࡇ ࡢ ✀ ࡢ ༴ ᶵ ࡛ ࡣ 㠀 ᖖ ࡟ ᑠ ࡉ ࡞ ☜ ⋡ ࡛ ࡣ ࠶ ࡿ ࡶ ࡢ ࡢ ࠊ 50 ୓ ே ࡢ ཷ 㦂 ⏕ ࡀ࠸ ࢀ ࡤ ☜ ᐇ ࡟ ࡑ ࠺ ࠸ ࠺ ୙ ල ྜ ࡀ Ⓨ ⏕ ࡍ ࡿ ࡇ ࡜ ࡟ ࡶ ࡞ ࡾ ࠊ CBT ࡞ ࡝ ࡛ ࡢ ฼ ⏝ ࡀ ᝿ ᐃ ࡉ ࢀ ࡿࢱࣈࣞࢵࢺ PC ࡞࡝ࡢࡼࡾ㧗ḟࡢࢹࣂ࢖ࢫࡣࡑࡢ☜⋡ࡣࡉࡽ࡟㧗ࡲࡿྍ⬟ᛶࡀ኱ࡁ࠸ࡔࡅ

－123－

࡟ࠊࡑ࠺࠸࠺ࣜࢫࢡ࣐ࢿࢪ࣓ࣥࢺࡣࡋࡗ࠿ࡾㅮࡌ࡚࠾ࡃᚲせࡀ࠶ࢁ࠺ࠋࡑ ࢀ ࡟ ຍ ࠼ ࡚ ࠊ CBT ᑟ ධ ࡢ ࣓ ࣜ ࢵ ࢺ ࡜ ࡋ ࡚ ⟅ ⏦ ࡛ ⪃ ࠼ ࡽ ࢀ ࡚ ࠸ ࡿ ࡇ ࡜ ࡣ ࠊ ඹ ㏻ ࢸ ࢫ ࢺ࡟࠾࠸࡚ࡶグ㏙ᘧၥ㢟ࢆྵࡵࡿ࡜࠸ࡗࡓᥦ᱌ࡀ࡞ࡉࢀ࡚࠸ࡿⅬ࡟㛵ࢃࡿࡇ࡜࡛࠶ࢁ࠺ࠋグ㏙ ᘧ ࡞ ࡝ ࡢ ၥ 㢟 ࡣ ࠊ 50 ୓ ே つ ᶍ ࡢ ヨ 㦂 ࡟ ࠾ ࠸ ࡚ ࡣ ே ࡟ ࡼ ࡿ ᥇ Ⅼ ࡜ ࠸ ࠺ ࡇ ࡜ ࡛ ࡣ ࡯ ࡜ ࢇ ࡝ᐇ⌧ ྍ⬟ᛶࡢ࡞࠸ࡇ࡜࡛࠶ࡾࠊᑡ࡞ࡃ࡜ࡶ CBT ࡀᑟධࡉࢀࠊཷ㦂⏕࡟ࡼࡗ࡚ධຊࡉࢀࡓ
ࢹࢪࢱࣝⓗ࡞ᅇ⟅ࢆ⮬ື᥇Ⅼࡍࡿࢩࢫࢸ࣒ࡀᑟධࡉࢀࡿࡇ࡜ࡀ๓ᥦ࡜࡞ࡿࡔࢁ࠺ࠋ⮬ື᥇
Ⅼࢩࢫࢸ࣒ࡣ᪤࡟ⱥㄒࡢࣛ࢖ࢸ࢕ࣥࢢࡸࢫࣆ࣮࢟ࣥࢢ࡟㛵ࡋ୍࡚ᐃࡢࡶࡢࡀ㛤Ⓨࡉࢀ࡚ࡶ࠸ࡿࡀࠊࡋ࠿ࡋࠊ▷⟅ᘧࡢグ㏙ᅇ⟅࡛࠶ࡗࡓ࡜ࡋ࡚ࡶࠊ⌧⾜ࡢ 50 ୓ேつᶍࠊ௒ᚋࡢཷ㦂⏕ᩘࡢῶᑡഴྥࢆ⪃࠼࡚ࡶࠊᩘ༑୓࣮࢜ࢲ࣮ࡢཷ㦂⏕࠿ࡽࡢከᵝ࡞ᅇ⟅㸦ㄗ⟅࡜ࡣุᐃᅔ
㞴࡞᝿ᐃࡋᚓ࡞࠸ᅇ⟅࡞࡝㸧࡟ᑐᛂࡍࡿ⮬ື᥇Ⅼࢩࢫࢸ࣒ࢆᵓᡂࡍࡿࡇ࡜ࡣࠊࡑ࠺ᐜ᫆࡞
ࡇ࡜࡛ࡣ࡞࠸࡜ᛮࢃࢀࡿࠋࡲࡓࠊ⟅⏦࡛ࡡࡽࡗ࡚࠸ࡿከ㠃ⓗ࡞⬟ຊ࡜࠸ࡗࡓࣞ࣋ࣝࡢຊࡣࠊ
⮬ື᥇Ⅼࢩࢫࢸ࣒ࡢ࢔ࣝࢦࣜࢬ࣒࡟ࡣ஌ࡾ࡟ࡃ࠸㒊ศࡶ࠶ࡾࠊࡑ࠺࠸ࡗࡓㄢ㢟ඞ᭹ࡶṧࡉࢀ࡚࠸ࡃࡇ࡜࡟࡞ࡿࡔࢁ࠺ࠋ
ࡲࡓࠊ⮬ື᥇Ⅼࢩࢫࢸ࣒ࡀ㛤Ⓨࡉࢀࡓ࡜ࡋ࡚ࠊࡑࡢ࢔ࣝࢦࣜࢬ࣒ࡣබ㛤ࡉࢀࡿࡇ࡜ࡣ࡞
࠸࡛࠶ࢁ࠺ࡀࠊࡋ࠿ࡋࠊධヨ࡛฼⏝ࡉࢀࡿ᥇Ⅼࢩࢫࢸ࣒ࡢ≉ᚩࡣࠊཷ㦂‽ഛࡢẁ㝵࡛ࡉࡲ
ࡊࡲ࡞ゅᗘ࠿ࡽศᯒࡉࢀࠊཷ㦂ࢸࢡࢽࢵࢡ࡜ࡋ࡚ཷ㦂⏕࡟ఏ࠼ࡽࢀࡿࡇ࡜࡟࡞ࡿ࡜࠸࠺ࠊ
ධヨࡢἼཬຠᯝࠊ࢖ࣥࣃࢡࢺ࡜࠸ࡗࡓⅬࡶ⪃៖࡟ධࢀ࡚࠾ࡃᚲせࡀ࠶ࡿࡔࢁ࠺ࠋグ㏙ᘧࡢၥ㢟࡟㛵ࡋ࡚ࡣࠊࣇࣛࣥࢫࡢࣂ࢝ࣟࣞ࢔ࡢဴᏛࡢၥ㢟ࡀࡋࡤࡋࡤヰ㢟࡜ࡋ࡚ྲྀࡾୖࡆࡽࢀ
ࡿࡀࠊṇゎࡢ࡞࠸ࡼ࠺࡞ᢳ㇟ⓗ࡞ၥ㢟࡟ᑐࡋ࡚ࡶࠊ኱つᶍ࡞ධᏛヨ㦂࡛ྲྀࡾୖࡆࡽࢀࡿࡇ
࡜࡟ࡼࡗ࡚ࠊཷ㦂ࢸࢡࢽࢵࢡࡀࡴࡋࢁඛ⾜ࡋ࡚ࡋࡲ࠸ࠊࡡࡽ࠸࡜ࡍࡿဴᏛⓗᛮ⪃ຊࢆᐃࡍࡿ࡜࠸࠺ព࿡࡛ࡣࠊࡈࡃୖ఩ࡢ୍㒊࡟㝈ࡽࢀ࡚ࡃࡿ࡜࠸࠺ሗ࿌ࡶ࠶ࡿ㸦ᆏᮏᑦᚿ, 2012㸧ࠋࡲࡓࠊࢩ࣏ࣥࢪ࣒࡛࢘ࡶ㆟ㄽࡉࢀ࡚࠸ࡓⅬ࡜ࡋ࡚ࠊᒣᮏවኴᮁඛ⏕ࡢゝⴥࢆ೉ࡾࡿࡢ࡛
࠶ࢀࡤࠊࠕ࢔ࢲࣉࢸ࣮ࢩࣙࣥ㸦adaptation㸧ࠖࡢၥ㢟ࡶ␃ពࡋ࡚࠾ࡃᚲせࡀ࠶ࡿࡔࢁ࠺ࠋ⮬ศࡀ ᬑ ẁ ฼ ⏝ ࡋ ࡚ ࠸ ࡿ ࢥ ࣥ ࣆ ࣗ ࣮ ࢱ ࢆ ヨ 㦂 ሙ ࡟ ᣢ ࡕ ㎸ ࢇ ࡛ ࠊ CBT ࡀ ᐇ ᪋ ࡛ ࡁ ࡿ ࡜ ࠸ ࠺ ࡼ ࠺࡞ࡇ࡜࡛࠶ࢀࡤࠊࡇࡢ࢔ࢲࣉࢸ࣮ࢩࣙࣥࡢၥ㢟ࡣ࠶ࡿ⛬ᗘᅇ㑊࡛ࡁࡿ࡜ᛮࢃࢀࡿࡀࠊࡑࡢ
ሙ࡛ึࡵ࡚᥋ࡍࡿࢹࣂ࢖ࢫ࡟㛵ࡋ࡚ࡣࠊࡑࡢ᧯స࡬ࡢ័ࢀࡢ⛬ᗘ࡟ಶேᕪࡀⓎ⏕ࡍࡿ࡜࠸࠺ࡇ࡜࡛࠶ࡿࠋࡘࡲࡾࠊ࢔ࢲࣉࢸ࣮ࢩࣙࣥ࡜࠸࠺ಶேᕪࡢࣇ࢓ࢡࢱ࣮ࡀࠊ᪂ࡓ࡞య⣔ⓗㄗᕪせᅉ࡜ࡋ࡚ࠊࡑࡇ࡛

ᐃࡉࢀࡿᚓⅬࡢጇᙜᛶࢆపࡵࡿྍ⬟ᛶࡢ࠶ࡿࡇ࡜࡟␃ពࡋ࡚࠾ࡃᚲせࡀ࠶ࡿࠋ
ࡶࡗ࡜ࡶࠊࡇࡢၥ㢟ࡣࠊ⣬➹᳨ᰝ࡟࠾ࡅࡿグ㏙ᘧ࡟࠾࠸࡚ࡶࠊᩥᏐࡢࡁࢀ࠸ࡉ࡜࠸ࡗࡓࣇ ࢓ ࢡ ࢱ ࣮ ࡀ ↓ ど ࡋ ᚓ ࡞ ࠸ ࡜ ࠸ ࠺ ࡇ ࡜ ࡶ ࠶ ࡗ ࡚ ࠊ CBT ࡔ ࡅ ࡟ ၥ 㢟 ࡜ ࡉ ࢀ ࡿ ࡇ ࡜ ࡛ ࡣ ࡞ ࠸ࡋࠊࡲࡓࠊ୍ேࡦ࡜ࡾ࡟ࢱࣈࣞࢵࢺᆺ PC ࢆ୚࠼࡚ࡢᤵᴗࡀᑠᏛᰯ࠿ࡽᑟධࡉࢀࡓࡾࠊ㟁Ꮚᩍ⛉᭩࡞࡝ࡶᬑཬࡋࡘࡘ࠶ࡿ࡞࠿࡛ࠊ㔜኱࡞ㄗᕪせᅉ࡜ࡋ࡚ྲྀࡾୖࡆࡿᚲせࡶ࡞ࡃ࡞ࡿ
᫬௦ࡣࡑ࠺ඛࡢࡇ࡜࡛ࡣ࡞࠸࡜࠸࠺ࡇ࡜࠿ࡶࡋࢀ࡞࠸ࠋࡑࡢⅬ࡛ࠊ㡰ᙜ࡞ᡭ㡰ࢆ⪃࠼ࡿࡢ࡛࠶ࢀࡤࠊࡲࡎࡣ⦎⩦ၥ㢟࡞࡝ࡢᙧ࡛ᬑẁࡢᤵᴗࡢᩍᮦ࡟⤌ࡳ㎸ࡲࢀ࡚฼⏝ࢆᗈࡆࡿ࡜࠸
ࡗࡓ࡜ࡇࢁ࠿ࡽࠊẁ㝵ⓗ࡞ᡓ␎ࢆㅮࡌࡿᚲせࡶ࠶ࡿࡔࢁ࠺ࠋࡶ ࠺ ୍ Ⅼ ࠊ CBT ࢆ ฼ ⏝ ࡍ ࡿ ࡜ ࠸ ࠺ ࡇ ࡜ ࡣ ࠊ 」 ᩘ ᅇ ࡢ ཷ 㦂 ࡜ ࠸ ࠺ య ไ ࡶ ⤌ ࡳ ㎸ ࡲ ࢀ ࡿ ࡇ࡜࡟࡞ࡿࡔࢁ࠺ࡋࠊࡲࡓࠊ௒ࡢࡼ࠺࡞୍ᩧࡢヨ㦂ᐇ᪋࡛ࡣ࡞ࡃࠊྛᆅࡢࢸࢫࢺࢭࣥࢱ࣮࡞
࡝࡟㐺ᐅཷ㦂⏕ࡀ㞟ࡲࡗ࡚ᐇ᪋ࡍࡿᙧࢆྲྀࡿ࡞࡝ࠊ⌧⾜࡜ࡣ㐪ࡗࡓయไ࡛ࡢヨ㦂ࡀᐇ᪋ࡉࢀ࡚࠸ࡃࡇ࡜࡟࡞ࡿ࡛࠶ࢁ࠺ࠋࡑࡢࡼ࠺࡞ศᩓᆺࡢヨ㦂ᐇ᪋యไ࡛ࡣࠊᑠࡉ࡞୙ලྜ࡞࡝

－124－

࡬ࡢᑐᛂ࡞࡝ẚ㍑ⓗᰂ㌾࡟ᢅ࠸ࡸࡍࡃ࡞ࡿ࡛࠶ࢁ࠺ㄢ㢟࡜ࠊࡲࡓࠊࡑࡢࡇ࡜࡛ၥ㢟₃ὤࡢ☜⋡ࡀ኱ࡁࡃ࡞ࡿ࡞࡝ࡢ᪂ࡓ࡟⏕ࡌࡿㄢ㢟࡞࡝ࢆᩚ⌮ࡋ࡚࠾ࡃࡇ࡜ࡶ⫢せ࡛࠶ࢁ࠺ࠋࡇࡢ
Ⅼࡶ᪥ᮏࡢ኱Ꮫධヨ࡛ࡣ࠸ࡲࡲ࡛⤒㦂ࡋ࡚ࡁ࡚࠸࡞࠸࡜࡛࠶ࡾࠊືࡁ࡞ࡀࡽಟṇࡋ࡚⾜࠿ࡊࡿࢆᚓ࡞࠸࡜࠸࠺㒊ศࡶ࠶ࡿ࡜ࡣᛮࢃࢀࡿࡀࠊ኱Ꮫධヨ࡜࠸࠺஦ᴗࡢᛶ᱁ୖࠊ༑ศ࡞ࢩ
࣑࣮ࣗࣞࢩࣙࣥࢆᛰࡿࢃࡅ࡟ࡣ࠸࠿࡞࠸࡛࠶ࢁ࠺ࠋ
࠸ࡎࢀ࡟ࡋ࡚ࡶࠊ⌧᫬Ⅼ࡛ࡲࡔ༑ศ࡟ඛࡀぢ㏻ࡋᚓ࡞࠸ࡇ࡜ࡣࠊࡑࢀࢆᑟධࡍࡿ㝿ࡢึ
ᮇᢞ㈨ࠊ⥔ᣢ⟶⌮࡞࡝࡟せࡍࡿ㈝⏝ࡸࠊඛ࡟ࡶゐࢀࡓࡇ࡜࡛࠶ࡿࡀࠊ⭾኱࡞㡯┠ࢹ࣮ࢱ࣮࣋ࢫࢆ‽ഛࡍࡿࡢ࡟ᚲせ࡞ປຊ࡞࡝ࠊཷ㦂ᩱ࡞࡝࡛࢝ࣂ࣮ࡋࡁࢀ࡞࠸࡛࠶ࢁ࠺㒊ศࡢࣜࢯ࣮ࢫࡀࡁࡕࢇ࡜ᡭᙜ࡚ࡉࢀࡿࡢ࠿࡝࠺࠿࡜࠸ࡗࡓⅬ࡛ࡢ୙Ᏻ࡛࠶ࡿࠋධヨ࡜࠸࠺
࡛ࣝఱࡀ

ᐃࢶ࣮

ᐃࡉࢀ࡚࠸ࡿࡢ࠿࡜࠸࠺Ⅼ࡟㛵ࡋ࡚ࡣࠊ኱ὠ㉳ኵ◊✲㛤Ⓨ㒊㛗࠿ࡽࡢሗ࿌࡟ࡶ

࠶ࡗࡓࡼ࠺࡟ࠊࡑࢀࡒࢀࡢ⬟ຊ࡟ࡣࡑࢀ࡞ࡾࡢ┦㛵ࡀほࡤ ࠊ ⌧ ⾜ ࡢ ヨ 㦂 ࡛ ࡶ ࠊ CBT ࡞ ࡝ ࡟ ࡼ ࡿ ヨ 㦂 ࡛ ࡶ ࠊࡉࢀࡿࡇ࡜࡟࡞ࡾࠊゝ࠸᥮࠼ࢀࢁ࠺࡜ࡋࡓ࠸≉ᛶ࡟㛵ࡍࡿ᝟ሗࡣࡑ

ࢀ࡞ࡾ࡟ᚓࡽࢀࡿ࡜࠸࠺ࡇ࡜ࡶ࠶ࡿࠋࡑࢀ࡞ࡽࡤࠊࢥࢫࢺࣃࣇ࢛࣮࣐ࣥࢫⓗ࡟ࠊཝࡋ࠸ᅜ
ᐙ㈈ᨻࡢ୰࠿ࡽࠊⳘ኱࡞ண⟬ࢆࡑࢀ࡟ᢞධࡋ࡞ࡅࢀࡤ࡞ࡽ࡞࠸࡜࠸࠺ㄝᚓⓗ࡞⌮⏤ࢆ♫఍࡟ఏ࠼࡚࠸ࡃࡇ࡜ࡀ࡛ࡁࡿࡢ࠿࡝࠺࠿ࡣࠊ኱࠸࡟␲ၥࡢṧࡿ࡜ࡇࢁ࡛ࡶ࠶ࡾࠊ࣓ࢹ࢕࢔ࡢ
ᾐ㏱ࡢ⤒⦋࡞࡝࡟ࡶ␃ពࡋࡘࡘࠊከᑡ࡞ࡾ࡜ࡶ᫬㛫ⓗవ⿱ࢆᣢࡗࡓྲྀ⤌ࡀᚲせ࡜ࡉࢀࡿࡇ
࡜࡟࡞ࡿ࡛࠶ࢁ࠺ࠋ
ධヨᨵ㠉ࢆ࡝࠺ື࠿ࡋ࡚࠸ࡃࡢ࠿
௨ୖࠊ⟅⏦࡟ᑐࡋ࡚ࠊ࠾ࡑࡽࡃࡣࠊ࠿࡞ࡾྰᐃⓗ࡞ࢺ࣮ࣥࡀఏࢃࡗࡓࡢ࡛ࡣ࡞࠸࠿࡜ᛮ
࠺ࡀࠊࡶࡕࢁࢇࠊ⌧⾜ࡢࢭࣥࢱ࣮ヨ㦂ࡀ᭱Ⰻࡢධヨࢩࢫࢸ࣒࡛࠶ࡿ࡜ࡣỴࡋ࡚⪃࠼࡚࠸ࡿࢃࡅ࡛ࡣ࡞࠸ࡋࠊ᫬௦ࡢὶࢀ࡟༶ࡋ࡚ࠊධヨࢆኚ㠉ࡋ࡚࠸࠿ࡡࡤ࡞ࡽ࡞࠸࡜࠸࠺Ⅼ࡟ࡘ࠸࡚ࡣࡲࡗࡓࡃ␗Ꮡࡢ࡞࠸ࡇ࡜࡛ࡶ࠶ࡿࠋࡋ࠿ࡋࠊ௒ࡢ᪥ᮏⓗධヨ㢼ᅵࡢ࡞࠿࡟࠶ࡗ࡚ࠊ▩┪Ⅼࢆከࡃྵࢇࡔࡲࡲࡢධヨࡢኚ㠉ࡔࡅࡀඛ㉮ࡾࡋ࡚ࡶࠊ࠾ࡑࡽࡃࡣ㏵୰࡛᣸ᢡࡋ࡚ࡋࡲ
࠺ࡇ࡜ࡣ┠࡟ぢ࠼࡚࠸ࡿࡇ࡜࡛ࡶ࠶ࡿࠋኚ㠉ࡋࡓ࡜ࡁࡢධヨീ࡟㛵ࡋ࡚ࠊ㧗ᰯ࣭኱Ꮫࡢ㛵ಀ⪅ࠊධヨᐇ᪋୺యࠊࡑࡋ࡚ࠊཷ㦂⏕ࢆ୰ᚰ࡜ࡍࡿࢫࢸ࣮ࢡ࣍ࣝࢲ࣮࡞࡝ࠊࡑࡢ඲యࡀࡑ
ࢀ࡛ࡸࡗ࡚࠸ࡅࡿ࡜࠸࠺ຠຊឤࢆᣢ࡚ࡿࡇ࡜࡜ࠊࡑࢀࡀ௒ࡢධヨไᗘ࡟ẚࡋ࡚ࡼࡾࡼ࠸ᩍ
⫱࡟ࡘ࡞ࡀࡗ࡚࠸ࡃ࡜࠸࠺㞺ᅖẼసࡾࡀࡲࡎࡣ኱ษ࡞ࡇ࡜࡛࠶ࡾࠊࡑࡢⅬࡶ⪃៖ࡋࡘࡘࠊ
㛗ᮇⓗ࡞ᡓ␎ࢆ❧࡚ࡿࡇ࡜ࡀ⫢せ࡛࠶ࢁ࠺࡜ᛮࢃࢀࡿࠋ
ᮾ㸦2001㸧ࡣࠊ࣓ࣜࢺࢡࣛࢩ࣮ࡢ㐍ᒎ࡟ࡼࡿ㝵ᒙ໬ࢆ⦆࿴ࡍࡿᡭẁ࡜ࡋ࡚ࠊ኱Ꮫධヨ࡟ࡃ ࡌ ࢆ ᑟධ ࡍ ࡿ ࡜ ࠸࠺ ᥦ ᱌ ࢆ ࡋ࡚ ࠸ ࡿ ࡀ ࠊࠕ ࡃ ࡌࡢᑟ ධࠖ࡜ ࠸࠺ࡇ ࡜࡜ࠊ ⌧⟅⏦ ࡟ᇶᮏ ⓗ⌮ᛕ࡜ࡋ࡚࡜ࡾ࠶ࡆࡽࢀ࡚࠸ࡿጇᙜᛶ࣭ಙ㢗ᛶ࡟౫ᣐࡋࡓࠕබṇᛶࠖ࡜࠸࠺⪃࠼᪉࡜ࡣ┦ᐜࢀ࡞࠸㒊ศࡀ࠶ࡾࠊࡑࢀࡣ࠾ࡑࡽࡃ௒࡛ࡶ♫఍࡟ࡣཷࡅධࢀࡽࢀ࡞࠸኱Ꮫධヨᡭἲ࡛࠶ࡿࡔࢁ࠺ࠋࡋ࠿ࡋࠊከ㠃ⓗ࡞ホ౯࡛࠶ࡿ࡜࠿ࠊྜᩍ⛉ⓗ࡞ヨ㦂࡛࠶ࡿ࡜࠿ࠊಙ㢗ᛶࡸጇᙜᛶ ࡢ Ⅼ ࡛࠶ ࡿ ✀ ࡢ ၥ㢟 ࢆ ᢪ ࠼ ࡚࠸ ࡿ ධ ヨ ࡣ ࠊ࠶ ࡿព ࿡࡛ࠊࠕ ࡃࡌࡢ ᑟධࠖ ࡜࠸࠺ ࡇ࡜࡟ 㝈
ࡾ࡞ࡃ㏆࠸ᡭἲ࡜ゝ࠼࡞ࡃࡶ࡞࠸࡜࠸࠺Ⅼࡣ⮬ぬࡋ࡚࠾ࡃ࡭ࡁ࡛࠶ࢁ࠺ࠋ㏫࡟ࠊጇᙜᛶ࣭
ಙ㢗ᛶ࡟౫ᣐࡋࡓࠕබṇᛶࠖ࡜࠸࠺⌮ᛕࢆᲠ࡚࡚ࠊධᏛヨ㦂ࡣࠕ㐠ࠖࡢせᅉࡀ኱ࡁࡃྵࡲ
ࢀ࡚࠸ࡿࡶࡢ࡛࠶ࡾࠊࡏ࠸ࡐ࠸ࡑࡢ⛬ᗘࡢࡶࡢࡔ࡜࠸࠺๓ᥦࡀඹ᭷ࡉࢀࡓ࡜ࡍࢀࡤࠊ⌧⾜ࡢ⟅⏦ࡢᥦ᱌ࡶ࠿࡞ࡾᐇ⌧ྍ⬟ᛶࡢ㧗࠸ࡶࡢ࡜ࡋ࡚఩⨨࡙ࡅࡽࢀࡿࡇ࡜࡟࡞ࡿ࡛࠶ࢁ࠺ࠋ࠶ ࡿ ࠸ ࡣ ࠊࠕ බ ṇ ᛶ ࠖ ࡜ ࠸ ࠺ ゝ ⴥ ࢆ ู ࡢ ぢ ᪉ ࠿ ࡽ ᤊ ࠼ ࡚ ࡳ ࡿ ࡞ ࡽ ࡤ ࠊࠕ ࡃ ࡌ ࠖ ࡜ ࠸ ࠺ ᡭ ἲࡣࡑࡢ⌮᝿ⓗᡭẁ࡟఩⨨࡙ࡅࡽࢀࡿ࠿ࡶࡋࢀࡎࠊࡑࢀࡣࡑࢀ୍࡛ࡘࡢ㑅ᢥ⫥࡜࡞ࡾᚓࡿ࡜

－125－࠸ ࠺ ࡇ ࡜࠿ ࡶ ࡋ ࢀ ࡞࠸ ࠋ ࡓ ࡔ ࡋࠊࠕ ࡃࡌ ࠖ ࡞ ࡝ ࡀᑟධ ࡉࢀࢀ ࡤࠊ⤫ ୍ⓗ࡞ Ꮫ⩦ᣦ ᑟせ㡿 ࡞࡝࡟ࡼࡿᩍ⫱ㄢ⛬ࡢయ⣔ᛶ࡞࡝ࡶⷧࢀࡿࡇ࡜࡟࡞ࡿࡋࠊ࠿࠼ࡗ࡚Ꮫࡪ┠ᶆࡢ࡜ࡽ࠼࡝ࡇࢁ
ࡶ᭕᫕࡟࡞ࡿࡇ࡜࠿ࡽࠊᏛ⩦ពḧ࡞࡝ࡶᚲࡎࡋࡶ☜ಖࡋ࡟ࡃ࠸࡜࠸࠺஦ែࡶᣍࡁ࠿ࡡ࡞࠸
࡜࠸ࡗࡓࡇ࡜ࡶ⪃࠼ࡿ࡜ࠊ⟅⏦࡛๓ᥦ࡜ࡉࢀ࡚࠸ࡿጇᙜᛶ࣭ಙ㢗ᛶ࡟౫ᣐࡋࡓࠕබṇᛶࠖ࡜࠸࠺⌮ᛕࢆそࡍࡇ࡜ࡣࡑ࠺ᐜ᫆ࡃ࡛ࡁࡿࡇ࡜࡛ࡶ࡞࠸ࡔࢁ࠺ࠋ
ࡑ࠺࡛࠶ࢀࡤࠊ౛࠼ࡤᏛຊホ౯ࢸࢫࢺ࡟㛵ࡋ࡚ࠊྜᩍ⛉ᆺࡢヨ㦂࡛࠶ࡿ࡜࠿ࠊࡲࡓࠊ▱㆑ ά ⏝ ᆺ ࡢ ၥ 㢟 ࡛ ࠶ ࡿ ࡜ ࠿ ࠊ CBT ࡞ ࡝ ࡜ ࠸ ࡗ ࡓ ᪂ ࡓ ࡞ ᐇ ᪋ ᙧ ែ ࡟ ࠸ ࡁ ࡞ ࡾ ᮏ ␒ ࡢ ヨ 㦂 ࢆኚ࠼ࡿ௨๓࡟ࠊ㧗ᰯ࡛ࡢ᪥ᖖⓗ࡞ᤵᴗ࡛࠶ࡿ࡜࠿⮬⩦άື࡛฼⏝࡛ࡁࡿࡼ࠺࡞Ꮫ⩦ᮦࡢ࡞࠿࡟ࠊࡲࡎࡣᮏ⟅⏦࡛ᥦ᱌ࡉࢀ࡚࠸ࡿࡼ࠺࡞ࡇ࡜ࢆ୍ࡘࡦ࡜ࡘ⤌ࡳ㎸ࡴ࡜ࡇࢁ࠿ࡽጞࡵ࡚࠸ ࡃ ࡜ ࠸ ࡗ ࡓ ᡭ 㡰 ࡀ ồ ࡵ ࡽ ࢀ ࡿ ࡢ ࡛ ࡣ ࡞ ࠸ ࠿ ࡜ ᛮ ࢃ ࢀ ࡿ ࠋ ᑡ ࡞ ࡃ ࡜ ࡶ ࠊ PISA ㄪ ᰝ ࡞ ࡝ ࢆཧ ⪃ ࡟ ࠊ 㧗 ᰯ ࣞ ࣋ ࣝ ࡢ Ꮫ ຊ ㄪ ᰝ ࡞ ࡝ ࡢ ᑟ ධ ࠶ ࡓ ࡾ ࠿ ࡽ ࠊ CBT ➼ ࡶ ྵ ࡵ ࡚ ࠊ ᐇ 㦂 ⓗ ࡟ ヨ ⾜ࡋ࡚࠸ࡃᡭ㡰࡞ࡋ࡟ࠊ࠸ࡁ࡞ࡾධヨࡑࡢࡶࡢ࡟᪂ࡓ࡞ヨࡳࢆ௙㎸ࢇ࡛࠸ࡃ࡜࠸࠺ࡢࡣࠊࡴ
ࡋࢁ᪥ᮏⓗධヨ㢼ᅵࡢୗ࡛ࡣ㢼ᙜࡓࡾࡀᙉ࠸࡛࠶ࢁ࠺ࡇ࡜ࡣ᝿ീ࡟㞴ࡃ࡞࠸ࠋࡑࢀࡔࡅ࡟
ࡼࡾලయⓗ࠿ࡘẁ㝵ⓗ࡞ධヨᨵ㠉ࡢࣉࣟࢭࢫࢆඹ᭷ࡋ࡚࠸ࡃࡇ࡜ࡀࠊࡲࡎࡣồࡵࡽࢀࡿࡇ
࡜࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀࡿࠋ
ࡑࢀ࡜ᖹ⾜ࡋ࡚ࠊධヨᨵ㠉ࡢᇶᮏࡣࠊᩍ⫱ᐃࢆྵࡴࠊ኱Ꮫධヨ࡟㛵ࢃࡿࣜࢸࣛࢩ࣮ࡢ

ᗏୖࡆࡶᚲせ࡜࡞ࡿ࡛࠶ࢁ࠺ࠋ᪂ࡓ࡞᪉ἲࢆᑟධࡋࡓ㝿࡟ࠊ㏣㊧◊✲ࢆࡣࡌࡵࠊ࠸ࢁ࠸ࢁ࡞ゅᗘ࠿ࡽࡑࡢᡭἲ࡟㛵ࢃࡿ᳨ドࢆࡋ࡚࠸ࡃᚲせࡶ࠶ࡾࠊࡑࢀࡣཎ๎ⓗ࡟ྛ኱Ꮫࡢ≧ἣ࡟༶ࡋ࡚⾜ࡗ࡚࠸ࡃࡇ࡜࡟࡞ࡿࡇ࡜࠿ࡽࠊྛ኱Ꮫࡢ࢔ࢻ࣑ࢵࢩࣙࣥࢬ࣭࢜ࣇ࢕ࢫ࡟ᦠࢃࡿே
ᮦࡢ඘ᐇ࡞࡝ࠊࡇࢀࡣ⟅⏦࡟ࡶゝཬࡉࢀ࡚࠸ࡿࡇ࡜࡛ࡣ࠶ࡿࡀࠊ㔜せ࡞ࢫࢸࢵࣉ࡜࡞ࡗ࡚
࠸ࡃ࡛࠶ࢁ࠺ࠋ
࠸ࡎࢀ࡟ࡋ࡚ࡶࠊධヨᨵ㠉ࡣࠊ୍ᮅ୍ኤ࡟ࡣᐇ⌧ࡍࡿࡇ࡜࡛ࡣ࡞࠸ࠋᑡ࡞ࡃ࡜ࡶࠊ኱Ꮫ
ධヨࢭࣥࢱ࣮ࡢࡼ࠺࡞ヨ㦂ᐇ᪋ᶵ㛵࡜࡛ࡶ࠸࠺࡭ࡁᶵ㛵ࡀヨ㦂ࢆసࡗ࡚ᐇ᪋ࡍࡿ࠿ࡽ࡜ゝࡗ࡚ࠊ㧗ᰯࡸ኱Ꮫࠊ♫఍ࡀࠊࡑࡢ฼⏝౯್ࢆඹ᭷ࡋ࡚ࡃࢀ࡞ࡅࢀࡤ㛗⥆ࡁࡍࡿࡶࡢ࡛ࡣ࡞
࠸ࠋࡑࡢⅬ࡛ࠊ⌧⾜ࡢࢭࣥࢱ࣮ヨ㦂ࡣࠊ࠸ࢁ࠸ࢁ࡞ᢈุࢆཷࡅ࡞ࡀࡽࡶ 30 ᖺ௨ୖ࡟ࢃࡓ
ࡗ࡚ࡢ⵳✚ࡀ࡞ࡉࢀ࡚ࡁ࡚࠾ࡾࠊ࠶ࡿព࿡࡛ࡢ᪥ᮏࡢධヨᩥ໬ࡢ୍➃ࢆᢸ࠺఩⨨࡙ࡅࡶྍ⬟࡛࠶ࡿࠋࡑࡢ㊊㊧ࢆࡁࡕࢇ࡜㋃ࡲ࠼ࠊࡑࡢ㐣⛬࡛✚ࡳ㔜ࡡࡽࢀࡓ▱ぢࢆ᪂ࢸࢫࢺ㛤Ⓨ࡟༑ศ࡟ά⏝ࡍࡿ࡜࠸࠺ᇶᮏⓗጼໃ࡟ᖖ࡟❧ࡕ㏉ࡾࡘࡘࠊ௒ᚋࡢධヨᨵ㠉ࡢⲨἼ࡟஌ࡾࡔࡋ
࡚࠸ࡁࡓ࠸ࡶࡢࡔ࡜ᛮ࠺ࠋ
ᩥ
ᮾὒ

⊩
2001 .

ᆏᮏᑦᚿᐹ̿Ꮚ࡝ࡶࡢ⬟ຊ࡜ᩍ⫱ホ౯➨ 2 ∧㸦UP 㑅᭩ 198㸧ᮾி኱Ꮫฟ∧఍2012 . ࣂ࢝ࣟࣞ࢔ဴᏛヨ㦂ࡣఱࢆホ౯ࡋ࡚࠸ࡿ࠿㸽̿ཷ㦂ᑐ⟇ཧ⪃᭩࠿ࡽࡢ⪃ி㒔኱Ꮫ㧗➼ᩍ⫱◊✲, No.18, 53-63. ி㒔኱Ꮫ㧗➼ᩍ⫱◊✲㛤Ⓨ᥎㐍ࢭࣥࢱ࣮－126－

࠙ཧ⪃ࠚ࢔ࣥࢣ࣮ࢺ㞟ィ⤖ᯝࢩ࣏ࣥࢪ࣒࢘⤊஢᫬ࡢ࢔ࣥࢣ࣮ࢺ⤖ᯝࡢᴫせࢆ௨ୗ࡟♧ࡍࠋ‫ۍ‬ᅇ཰ᩘ㸸146 ⚊ձᛶู㸸⏨㸦ᅇ཰⋡㸸60 㸣㸧

106㸦73.6 㸣㸧ղᖺ㱋㸸㹼 29 ṓ࣭ዪ38㸦26.4 㸣㸧

11㸦7.6 㸣㸧࣭ 30 㹼 39 ṓ

30㸦20.8 㸣㸧࣭ 40 㹼 49 ṓ

50 㹼 59 ṓ

54㸦37.5 㸣㸧࣭ 60 ṓ㹼

14㸦9.7 㸣㸧ճᡤᒓ㸸ᅜබ❧㧗ᰯ

5㸦3.4%㸧࣭ ⚾❧㧗ᰯ8㸦5.5%㸧࣭ ሿ࣭ணഛᰯ㛵ಀᅜබ❧኱Ꮫ35㸦24.1 %㸧࣭ ⚾❧኱Ꮫ࣐ࢫࢥ࣑㛵ಀ
մ⫋✀㸸ᩍဨ
බົဨ

30㸦20.7%㸧࣭ Ẹ㛫௻ᴗ3㸦2.1%㸧࣭ ┬ᗇ࣭ᙺᡤ➼49㸦35.0%㸧࣭ ஦ົ⣔⫋ဨ
4㸦2.9%㸧 ࣭ ࡑࡢ௚

3㸦2.1%㸧࣭ ࡑࡢ௚

43㸦30.7%㸧࣭ ᑓ㛛⫋35㸦24.3 㸣㸧࣭
27㸦18.6%㸧࣭26㸦17.9%㸧࣭8㸦5.5%㸧21㸦15.0%㸧࣭23㸦16.4%㸧

ᖹᆒ

ᶆ‽
೫ᕪ

᭷ຠ
ᅇ⟅ᩘ

䠍㻔㻑㻕

䠎㻔㻑㻕

䠏㻔㻑㻕

䠐㻔㻑㻕

࠶࡚ࡣࡲࡽ࡞࠸ЋЍ࠶࡚ࡣࡲࡿ

㸦㸯㸧ࢃ࠿ࡾࡸࡍ࠿ࡗࡓ

㻟㻚㻜㻥

㻜㻚㻢㻟

㻝㻠㻢

㻝㻚㻠

㻝㻝㻚㻢

㻢㻟㻚㻣

㻞㻟㻚㻟

㸦㸰㸧᪂ࡓ࡞Ⓨぢࡀ࠶ࡗࡓ

㻟㻚㻡㻢

㻜㻚㻡㻣

㻝㻠㻢

㻜㻚㻜

㻠㻚㻝

㻟㻡㻚㻢

㻢㻜㻚㻟

㸦㸱㸧⯆࿡῝࠿ࡗࡓ

㻟㻚㻢㻢

㻜㻚㻢㻜

㻝㻠㻢

㻝㻚㻠

㻞㻚㻣

㻞㻠㻚㻜

㻣㻝㻚㻥

㸦㸲㸧ᵓᡂࡣ㐺ษ࡛࠶ࡗࡓ

㻟㻚㻞㻥

㻜㻚㻢㻥

㻝㻠㻢

㻝㻚㻠

㻥㻚㻢

㻠㻣㻚㻟

㻠㻝㻚㻤

㸦㸳㸧᭷┈࡛࠶ࡿ࡜ᛮࡗࡓ

㻟㻚㻠㻥

㻜㻚㻢㻜

㻝㻠㻡

㻜㻚㻣

㻟㻚㻠

㻠㻞㻚㻝

㻡㻟㻚㻤

㸦㸴㸧㞟୰ࡋ࡚ཧຍ࡛ࡁࡓ

㻟㻚㻞㻤

㻜㻚㻢㻥

㻝㻠㻢

㻜㻚㻣

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㸦㸵㸧᫬㛫ࡢ㛗ࡉࡣ㐺ᙜ࡛࠶ࡗࡓ

㻟㻚㻜㻡

㻜㻚㻣㻡

㻝㻠㻢

㻝㻚㻠

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㸦㸶㸧㛤ദ᪥᫬ࡣ㐺ษ࡛࠶ࡗࡓ

㻟㻚㻟㻞

㻜㻚㻢㻤

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㸦㸷㸧⥲ྜⓗ࡟‶㊊࡛ࡁࡓ

㻟㻚㻠㻡

㻜㻚㻢㻞

㻝㻠㻠

㻝㻚㻠

㻞㻚㻤

㻠㻡㻚㻝

㻡㻜㻚㻣

‫⏤⮬࡞୺ۍ‬グ㏙㸦ᢤ⢋㸧¾ ホ౯ࡢ㞴ࡋࡉࠊከᵝࡉࡀࡼࡃㄆ㆑࡛ࡁࡓࠋධヨ¾ ࠸ࢁ࠸ࢁ࡞⪃࠼᪉ࠊ❧ሙࡀ࠶ࡿ࡜ᨵࡵ࡚ឤࡌࡲ

㑅ᢤࡢ㞴ࡋࡉࡶศࡿࡀࠊᮏ㢟ࡢ㒊ศࡢ㆟ㄽࡀ࠶ࡋࡓࠋࡑࢀࡒࢀࡢ͆❧ࡕ఩⨨͇ࢆࡶࡗ࡜⪃࠼ࡿࡲࡾ࡞ࡉࢀ࡚࠸࡞࠿ࡗࡓࠋ㸦⏨࣭60 ṓ㹼࣭ࡑࡢᚲせࡀ࠶ࡿ࡜ᛮ࠸ࡲࡍࠋ᪂ࡋ࠸Ꮫຊࢸࢫࢺࡀ࡝

௚࣭‶㊊ᗘ 4㸧࠺࡞ࡿ࠿኱ኚ࡞ၥ㢟࡛ࡍࠋ㸦⏨࣭50 㹼 59 ṓ࣭ᅜ

¾ࠕᏛຊホ౯ࡢࡓࡵࡢ᪂ࡓ࡞ࢸࢫࢺࠖࡢ఩⨨࡙ࡅබ❧኱࣭ᩍဨ࣭‶㊊ᗘ 3㸧ᐃㄽࡔࡅ࡛ࡣ࡞

¾ࠕධヨࠖ㸻ࠕࢸࢫࢺࠖ࡜࠸࠺Ⓨ᝿ࡇࡑࡀ᪥ᮏࡢ

࠸ࡇ࡜㸦⏨࣭40 㹼 49 ṓ࣭⚾❧኱Ꮫ࣭ᩍဨ࣭‶ࠕධヨࡢ㢼ᅵ࡛ࠖ࠶ࡿࠋ௒ồࡵࡽࢀ࡚࠸ࡿࡢࡣࠊ

㊊ᗘ 4㸧ࠕࢸࢫࢺࠖ㸩ㄪᰝ᭩ࠊ㠃᥋ࠊㄽᩥࢆྵࡴ⥲ྜⓗࡀ᫂☜࡟࡞ࡾࡲࡋࡓࠋ༢࡞ࡿ

¾ ᨵࡵ࡚ࠊIRT ᑟධࡢ㔜せᛶࡣ෌ㄆ㆑࡛ࡁࡓࡀࠊホ౯࡬ࡢ᪉ྥᛶࠋࡑࡢ୰࡛ࠕࢸࢫࢺ㸦CBT㸧ࠖࡣ࠸ࡊࠊࢭࣥࢱ࣮ヨ㦂࡛ CBT 㸩 IRT ⌮ㄽࢆ࠸ࡁ࡝ࡇࡲ࡛࡞ࡾᑟධࡍࡿ࡜࡞ࡿ࡜ࠊ㠀ᖖ࡟࣒ࣜࡀ࠶ࡿ࡜ᛮ

࠿ࠋ㸦ࠕࢭࣥࢱ࣮ヨ㦂ࠖᐇ᪋⤌⧊࡜ࡋ࡚ࡣ࠸ࡓࡋ

࠺ࠋ⣬࣮࣋ࢫࡢ㛗ᡤࠊࢥࣥࣆ࣮ࣗࢱ࣮ࡢ㛗ᡤࢆ࠿ࡓ࡞࠸㸧 㸦⏨࣭60 ṓ㹼࣭ᅜබ❧኱࣭ᩍဨ࣭

ά࠿ࡏࡿࡼ࠺࡞ᰂ㌾ᛶࡢ࠶ࡿࡶࡢ࡬ࡢᑐᛂࡀᚲ

‶㊊ᗘ 3㸧

せ࡛ࡣ࡞࠸࠿㸦⏨࣭30 㹼 39 ṓ࣭ሿ࣭ணഛ࣭ᑓ

ᐃㄽⓗ࡟ཝ᱁࡛࡞ࡅࢀࡤ࡞ࡽ࡞࠸ࡢ

¾ ⌮ㄽⓗ࡞▱ぢ࡜ᐇົⓗ࡞⤒㦂ࡢ୧᪉ࢆ⪺ࡃࡇ࡜㛛⫋࣭‶㊊ᗘ 3㸧ࡀ࡛ࡁࡓࡢࡀࡼ࠿ࡗࡓࠋ≉࡟༡㢼ཎඛ⏕ࡢሗ࿌

－127－

ࡣ୰ᩍᑂࡢ⟅⏦࡜ࢸࢫࢺ⌮ㄽࡢヰࢆ⤌ࡳྜࢃࡏ࡚࠸ࡿࡢ࠿ࢆ▱ࡾࡓ࠸ࠋ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋ

࡚࠾ࡾࠊᑓ㛛እࡢ᪉࡛࠶ࡗ࡚ࡶศ࠿ࡾࡸࡍ࠸ෆ

ࡓࠋ㸦⏨࣭60 ṓ㹼࣭⚾❧㧗ᰯ࣭ᩍဨ࣭‶㊊ᗘ 4㸧ᐜ࡛࠶ࡗࡓࡢ࡛ࡣ࡞࠸࠿࡜ᛮࢃࢀࡿࠋ
㸦⏨࣭㹼 29¾ ࡜࡚ࡶࣂࣛࣥࢫࡢࡼ࠸ᵓᡂࡔ࡜ᛮ࠸ࡲࡋࡓࠋ࠶

ṓ࣭Ẹ㛫௻ᴗ࣭ᑓ㛛⫋࣭‶㊊ᗘ 4㸧ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ
㸦ዪ࣭40 㹼 49 ṓ࣭⚾❧

¾CBT ࢧࣥࣉࣝࡢࢹࣔࡢヰࡀᑗ᮶ࡢᑟධྥࡅ

ࢧ኱Ꮫ࣭ᩍဨ࣭‶㊊ᗘ 4㸧༳㇟࡟ṧ¾ ࢭࣥࢱ࣮୺ദࡢࢩ࣏ࣥࢪ࣒࢘࡞ࡢ࡟ࠊ᪂ࢸࢫࢺ

ࡾࡲࡋࡓࠋ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ㸦⏨࣭50࡟ᑐࡍࡿྰᐃⓗ࡞ពぢࡀከࡃ⪺ࡅࡓࡢࡣពእ࡛

㹼 59 ṓ࣭ᅜබ❧኱࣭஦ົ⣔⫋࣭‶㊊ᗘ 3㸧ࡋࡓࠋ኱ኚຮᙉ࡟࡞ࡾࡲࡋࡓ㸟㸦⏨࣭40 㹼 49ࣥࣉ࣭ࣝࢹࣔࣥࢫࢺ࣮ࣞࢩࣙࣥᐇ᪋

¾࣭ྜᩍ⛉࣭⛉┠ᆺ࡟ࡘ࠸࡚㸸ࢸࢫࢺࡣࠊ㧗ᰯࡲṓ࣭ᅜබ❧኱࣭஦ົ⣔⫋࣭‶㊊ᗘ 4㸧࡛ࡢ࣒࢝ࣜ࢟ࣗࣛ࡜ࡢ㛵ಀࢆ⪃࠼࡚ࠊ⛉┠ู㸦ከ

¾"୰ᩍᑂࡢ⟅⏦࡟ࠊ┿ྥ࠿ࡽྰᐃࡍࡿෆᐜ࡛ࠊ࡜

ᑡ෌⦅ࡀ࠶ࡗ࡚ࡶࡼ࠸ࡀ㸧࡟⾜࠺࡭ࡁ࡛ࡣ࡞࠸

࡚ࡶ⯆࿡῝࠿ࡗࡓࠋ୰ᩍᑂ࡟ࡣ᪂ࡋ࠸ࢸࢫࢺࡢ

࡛ࡋࡻ࠺࠿ࠋࡑࡢୖ࡛ࠊ౛࠼ࡤ⏕≀ࡢၥ㢟࡛ⱥᵓ⠏࡟ྥࡅ࡚ࠊᐇ⌧ᛶࢆ⪃࠼࡚࡯ࡋ࠸ࠋ
㸦ዪ࣭40

ㄒࡢ㈨ᩱࡀ౑ࢃࢀࡓࡾࠊ⤫ィࡢࢫ࢟ࣝࡀᚲせ࡜

㹼 49 ṓ࣭ሿ࣭ணഛ࣭஦ົ⣔⫋࣭‶㊊ᗘ 4㸧
¾㸦ᇶㄪㅮ₇ࠊሗ࿌ 2ࠊ4 ࡣ≉࡟㸧ࡓ࠸࡬ࢇຮᙉ࡟

࠸ࡗࡓࡇ࡜ࡣ࠶ࡗ࡚࠸࠸࡜ᛮ࠸ࡲࡍࠋ
࣭ᑠㄽᩥࡸㄽ㏙ヨ㦂࡟ࡘ࠸࡚㸸IB ࡢࡼ࠺࡟ࠊ」࡞ࡾࡲࡋࡓࠋCBT ࢧࣥࣉࣝࢹࣔࡶ⯆࿡῝࠸ࡶࡢᩘࡢ㡿ᇦࢆ⏝ពࡋࠊྛ㡿ᇦ࡛ከࡃࡢၥ㢟ࢆ⏝ព࡛ࡋࡓࠋ఍♫࡛ࡢᴗົ㸦ᐇົ㸧࡛ฟ࡚ࡃࡿ⏝ㄒࡋ࡚ཷ㦂⪅ࡀࢸ࣮࣐ࢆ㑅ᢥ࡛ࡁࡿࡼ࠺࡟ࡋࡓ᪉

ࡢ෌☜ㄆ࡜ࠊ௒ࠊ⾜ࡗ࡚࠸ࡿ௙஦࡬☜ಙࢆᣢࡘࡀ࠸࠸࡜ᛮ࠸ࡲࡍࠋ
㸦୺࡟ಶูヨ㦂ࡔ࡜ᛮ࠸ࡲࡍ

ࡇ࡜ࡀ࡛ࡁࡲࡋࡓࠋ㸦⏨࣭50 㹼 59 ṓ࣭ሿ࣭ணࡀࠊࢭࣥࢱ࣮ࡀၥ㢟ࢆ‽ഛ࡛ࡁ࡞࠸ࡢ࡛ࡋࡻ࠺

ഛ࣭ࡑࡢ௚࣭‶㊊ᗘ 4㸧¾ ධヨࢆ୍ᚊ࡟ㄽࡌࡿࡇ࡜ࡢ㝈⏺ࢆឤࡌࡲࡋࡓࠋ

࠿㸧࣭」ᩘᅇ࡟ࡘ࠸࡚㸸኱ᏛධᏛᕼᮃ⪅Ꮫຊホ౯ࢸ᪥ᮏࡢⱝ⪅ࡀᏛࡧࠊඖẼ࡟࡞ࡿไᗘࠊࢩࢫࢸ࣒

ࢫࢺࡢ」ᩘᅇ໬ࡣࠊ࢖ࢠࣜࢫ࡛㸦ࡢ㸧㆟ㄽ࡞࡝࡜ࡋ࡚㧗኱᥋⥆ࢆ㆟ㄽ᳨࣭ウࡍࡿᚲせࡀ࠶ࡾࡲࡶཧ⪃࡟ៅ㔜࡟ุ᩿ࡋ࡚ḧࡋ࠸࡛ࡍࠋ௒ᅇࡢᨵ

ࡍࠋࡑࢀࡣࠊ
ࠕධヨ࡛ࠖࡣ࡞࠸࡜ᛮ࠸ࡲࡍࠋ
㸦⏨࣭50㠉࡛Ꮫຊపୗ࡟ࡘ࡞ࡀࡽ࡞࠸ࡼ࠺࡟ᕤኵࡀᚲせ㹼 59 ṓ࣭ሿ࣭ணഛ࣭஦ົ⣔⫋࣭‶㊊ᗘ 4㸧
¾ ࢸࢫࢺࡢศࢆࢃࡁࡲ࠼ࡿ࡭ࡁࡔ࡜࠸࠺኱ὠඛ⏕ࡔ࡜ᛮ࠸ࡲࡋࡓࠋ
ᮏ᪥ࡣከࡃࡢࡇ࡜ࢆᏛࡤࡏ࡚࠸ࡓࡔࡁࠊ࠶ࡾ

ࡢⓎゝࡀ༳㇟࡟ṧࡾࡲࡋࡓࠋ⟅⏦᱌ࡣ࡞࠸ࡶࡢ

ࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ
㸦⏨࣭50 㹼 59 ṓ࣭Ẹ㛫௻ࡡࡔࡾࢆࡋ࡚࠸ࡿ࡜ᛮ࠺ࠋ㸦⏨࣭30 㹼 39 ṓ࣭ᴗ࣭ᑓ㛛⫋࣭‶㊊ᗘ 4㸧ሿ࣭ணഛ࣭ࡑࡢ௚࣭‶㊊ᗘ 3㸧

¾ ኱Ꮫධヨࡢఱࢆኚ࠼ࡓ࠸ࡢ࠿ࠊఱᨾኚ࠼ࡿࡢ࠿ࠊ

¾ࠕ኱Ꮫ㹼Ꮫຊホ౯ࢸࢫࢺࠖࡀఱࢆ

ᐃࡋࡼ࠺࡜ࡣ௬ᐃࡏࡎ࡟ࠊఱࡀ࡛ࡁࡑ࠺࡞ࡢ࠿ࢆ㆟ㄽࡋ࡚ࡋ࡚࠸ࡿ࠿㸽ࠕ⬟ຊࠖ࡞ࡢ࠿ࠕᏛຊࠖ࡞ࡢ࠿㸽࠸ࡓࡼ࠺࡟ᛮ࠸ࡲࡍࠋ኱Ꮫ࡜࠸ࡗ࡚ࡶ࡝ࡢࡼ࠺ࡑࡢ ᐃࡍࡿࠕၥ㢟ࠖ஦౛ࡣ࢖࣓࣮ࢪ࡛ࡁࡿࡀࠊ

࡞ධヨၥ㢟ࢆ࡝ࡢࡼ࠺࡞௙⤌ࡳ࡛࡜࠸࠺ேᮦീ

ࡑࡢࠕၥ㢟ࠖࢆ࡝ࡢࡼ࠺࡟

࡜ไᗘ࡜ࠊᯟ⤌ࡳࢆ௬ᐃࡋ࡚㆟ㄽࡋ࡞࠸࡜୍ࡘ

ࠕ⬟ຊࠖ࠿ࠕᏛຊࠖ࠿ࠊ࠶ࡿ࠸ࡣ࢚࣮ࣛ㸦അⓎࡢゎࡶぢ࠼࡞࠸࠿ࡶࡋࢀ࡞࠸࡜ᛮ࠸ࡲࡍࠋၨⓎᛶ㸧࠿ࡀỴࡲࡿࡼ࠺࡞Ẽࡀࡋࡲࡍࠋ
㸦⏨࣭50 㹼 59ⓗ࡞ព࿡࡛ࡣ኱ࡁ࡞ព⩏ࡀ࠶ࡾࡲࡋࡓࡢ࡛ࠊ࠶

ṓ࣭ሿ࣭ணഛ࣭ࡑࡢ௚࣭‶㊊ᗘ 1㸧ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ
㸦⏨࣭50 㹼 59 ṓ࣭ᅜබᐃࡍࡿ࠿࡟ࡼࡗ࡚

¾࣭Ⓨ⾲㈨ᩱࢆ㓄ᕸࡋ࡚ࡶࡽ࠼ࡓࡢࡣⰋ࠿ࡗࡓ࡛
ࡍࠋ❧኱࣭ᩍဨ࣭‶㊊ᗘ 4㸧¾ ලయⓗ࡞ၥ㢟ࡢᑂᰝࢆᇶ࡟ࡋࡓලయⓗ࡞ࢹ࣮ࢱ࣭௒ᅇࠕ኱Ꮫධヨࠖ࡟ࡘ࠸࡚࡞ࡢ࡛ࠊ┬ᗇ㛵ಀ࣭බⓗᶵ㛵ࡢ᪉ࡢⓎ⾲ࡶ࠶ࢀࡤ⯆࿡῝࠸ࢆ♧ࡋ࡚㡬ࡁࡓ࠿ࡗࡓࠋࡘࡲࡾ⌧⾜ࡢࢸࢫࢺࡢ

࡜ᛮ࠸ࡲࡍࠋ

࠶ࡾᵝࡀࠊලయⓗ࡟⾲࡟㛵ಀࡍࡿࡶࡢ㸧ࡢ⤂௓㸦ࢫࣛ࢖ࢻ㸧ࡀ࠶ࡿ࡝ࡢࡼ࠺࡟ኚࢃࢁ࠺࡜ࡋ

－128－

࣭Ⓨ⾲⪅ࡢ᪉ࡢ◊✲㸦௒ᅇࡢⓎ࡜ᑦࡼ࠿ࡗࡓ࡛ࡍࠋ

࣭ࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙ

᪂㩭࡞࣓ࢵࢭ࣮ࢪࢆ㏦ࡿ࡜࠸࠺ពᚿ࡟ࡶ㈶ྠ࠸

ࣥࡣ᭷ព⩏࡛ࡋࡓࠋ
㸦ዪ࣭40 㹼 49 ṓ࣭ࡑࡢ௚࣭

ࡓࡋࡲࡍࠋ኱ὠඛ⏕ࡢࠕே㛫ࡢ▱ᛶࡣ࡝࠺࡞ࡗ

ᑓ㛛⫋࣭‶㊊ᗘ 4㸧࡚࠸ࡿࡢ࠿ࠖ࡜࠸࠺ヰ㢟࡟ࡣ࡜࡚ࡶ⯆࿡ࡀ࠶ࡾ¾ ኱ኚ᭷┈࡞ෆᐜ࡛ࡋࡓࡀࠊࡶ࠺ᑡࡋ᫬㛫ࡀ࠶ࡿࡲࡍࠋ௒ᚋධヨᨵ㠉ࡀ࡝࠺㐍ࡴ࠿ࠊ⌧ሙࡣ࡜࡚࡜ࡼ࠿ࡗࡓ࠿࡜ᛮ࠸ࡲࡍ㸦≉࡟ࣃࢿࣝࢹ࢕ࢫ࢝

ࡶẼ࡟ࡋ࡚࠸ࡲࡍࠋᨵ㠉ࡢពᅗࡀṇࡋࡃఏࢃࡿ

ࢵࢩࣙࣥ㸧ࠋࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥࡣ࠶ࡿ⛬ᗘࡼ࠺࡞᝟ሗᥦ౪ࠊබ㛤ࢆ࠾㢪࠸⮴ࡋࡲࡍࠋ㸦⏨࣭ࢸ࣮࣐ࢆ㝈ᐃࡋ࡚ࡶࡼ࠿ࡗࡓࡢ࡛ࡣ࡞࠸࡛ࡋࡻ

㹼 29 ṓ࣭⚾❧㧗ᰯ࣭ᩍဨ࣭‶㊊ᗘ 4㸧࠺࠿ࠋ㸦⏨࣭30 㹼 39 ṓ࣭Ẹ㛫௻ᴗ࣭ᑓ㛛⫋࣭‶¾࣭せࡍࡿ࡟ࠊPISA ࡢẚ㍑ࡣᑻᗘ໬ࡢ᪉ἲḟ➨ࠋࡇ࠺ゝࡗ࡚ࡣఱ࡛ࡍࡀࠊᢏ⾡ⓗ࡟୓඲࡛ࡣ࡞࠸

㊊ᗘ 4㸧
¾ ᪂ࡋ࠸ࢸࢫࢺ࡟ࡘ࠸࡚ࠊ᭱᪂ࡢ᝟ሗࢆ▱ࡿࡇ࡜᪉ἲㄽ࡛ฟࡓ⤖ᯝ࡛ୡㄽࡀື࠿ࡉࢀࠊࡑࢀࢆᇶ

ࡀ࡛ࡁࠊⰋ࠸ᶵ఍࡛࠶ࡗࡓࠋ
㸦⏨࣭40 㹼 49 ṓ࣭࡟ᨻ⟇ࡀỴࡵࡽࢀ࡚ࡁࡓ࡜࠸࠺ࡇ࡜ࡀ᫂ࡽ࠿࡟

⚾❧㧗ᰯ࣭ᩍဨ࣭‶㊊ᗘ 4㸧ࡉࢀࡓ࡜࠸࠺ព࿡࡛ࠊDr.Yamamoto ࡢㅮ₇࡟ࡣ¾ 㠀ᖖ࡟ࣞ࣋ࣝࡢ㧗࠸ㅮ₇⪅ࢆࡑࢁ࠼ࠊࢱ࢖࣒ࣜ

ព⩏ࡀ࠶ࡗࡓ࡜ᛮ࠸ࡲࡍࠋ⤖ᒁࡢ࡜ࡇࢁࠊPISA

࣮࡞ࢸ࣮࣐࡛ࡋࡓࠋ኱ኚຮᙉ࡟࡞ࡾࡲࡋࡓࠋ௒

ࡀࠕᐇ⏕ά࡟࠾ࡅࡿά⏝⬟ຊࠖࢆᚋ࡜ࡶᐅࡋࡃ࠾㢪࠸ࡋࡲࡍࠋ
㸦⏨࣭50 㹼 59 ṓ࣭࡜࠸࠺ドᣐࡣఱࡶ࡞࠸࡜ᛮ࠸ࡲࡋࡓࠋࡇ࠺࠸ࡗ

⚾❧኱Ꮫ࣭‶㊊ᗘ 4㸧ࡓၥ㢟ࢆཷ㦂ࢸࢡࢽࢵࢡⓗ࡟ᨷ␎ࡋࡼ࠺࡜ᛮࡗ

¾ ᭱ᚋࡢࣃࢿࣝࢹ࢕ࢫ࢝ࢵࢩࣙࣥࡢ᫬㛫ࡀ▷࠸ࡢᐃࡋ࡚࠸ࡿࡓࡽࠊ௒ࡢධヨၥ㢟ࡼࡾࡶ┦ᙜ࡟ࡓࡸࡍࡑ࠺࡛

ࡀṧᛕ࡛࠶ࡗࡓࠋCBT ࡢᑓ㛛ᛶࡢ㧗࠸ෆᐜࡢࢩ

ࡍࡡࠋ࣏ࣥࡸࢭ࣑ࢼ࣮ࢆ㛤ദࡋ࡚㡬ࡅࡿ࡜࠶ࡾࡀࡓ࠸

ᚚᣦ᦬ࡣྠឤ࡛ࡍࠋ

࡛ࡍࠋ㸦⏨࣭50 㹼 59 ṓ࣭ᅜබ❧኱࣭ᩍဨ࣭‶㊊ࢀࡓࡼ࠺࡞⪃࠼᪉ࢆᇶᮏⓗᩍ㣴࡜ࡋ࡚≀஦ࢆ⪃ᗘ 4㸧࠼ࡽࢀࡿேᮦࡀከᩘ㍮ฟࡉࢀࡿࡇ࡜ࡀࠊධヨࡢ¾ Ꮫ⏕ࡢ⬟ຊࢆ ࡿほⅬ࡜ධᏛ⪅㑅ᢤࡢほⅬ࡛ࡣࠊ࣭⣲Ⅼࡢ฼⏝ࡀၥ㢟࡜࠸࠺๓ᕝඛ⏕ࡢ࣭༡㢼ཎඛ⏕ࡢㅮ₇ࡉ

㆟ㄽ࡟ࡣᚲせࡔ࡜ᛮ࠸ࡲࡍࠋ

୍ᗘ࡟୧᪉⾜࠾࠺࡜ࡍࡿ࡜㊊ࡾ࡞࠸㒊ศࡸᚲせ࣭኱ὠඛ⏕ࡢㅮ₇࠿ࡽࠊᮾி࣭㤳㒔ᅪࢆぢ࡚᪥ᮏ࡞࠸㒊ศࡀࡑࢀࡒࢀᏑᅾࡋࠊ୍య࡝ࡇࡲ࡛ࡢ⠊ࡢᩍ⫱ࢆㄽࡌ࡚ࡣ࠸ࡅ࡞࠸ࡇ࡜ࡀࡼࡃศ࠿ࡾࡲ

ᅖࢆ

ࡋࡓࠋ

ࡾࠊ࡝ࡇࡲ࡛ࢆ฼⏝ࡍࡿࡢ࠿㆟ㄽࡀᚲせ࣭ᮧୖඛ⏕ࡢㅮ₇࡛㢌ࡢ୰ࡢࣔࣖࣔࣖ

࡜ᛮ࠺ࡋࠊࡲࡓヨ⾜㘒ㄗࡍࡿࡇ࡜࡟࡞ࡿࡢ࡛ࡣ

ࡀᑡࡋᬕࢀࡓẼࡀࡋࡲࡋࡓࠋࡶ࠺୍ᗘࠊෆᐜ࡜

࡞࠸࠿ࠋ࠸ࡎࢀ࡟ࡏࡼࠊ⚾ࡶࡋࡗ࠿ࡾ࡜ຮᙉࡋ

ㄽⅬࢆᩚ⌮ࡋ࡚⪃࠼࡚ࡳࡓ࠸࡜ᛮ࠸ࡲࡋࡓࠋ

ࡓ࠸࡜ᛮ࠸ࡲࡍࠋ௒ᅇࡣ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋ

㸦⏨࣭50 㹼 59 ṓ࣭ᅜබ❧኱࣭ᩍဨ࣭‶㊊ᗘ 4㸧ࡓࠋ㸦⏨࣭50 㹼 59 ṓ࣭ሿ࣭ணഛ࣭஦ົ⣔⫋࣭‶¾ ࡇࡢࡼ࠺࡞ෆᐜࡢࢩ࣏ࣥࢪ࣒࢘ࡣ௒ᚋࡶ࡜࡚ࡶ
኱ษ࡛ࡍࡋࠊ㆟ㄽࡉࢀࡿ࡭ࡁෆᐜࡔ࡜ᛮ࠸ࡲࡍࠋ

㊊ᗘ 4㸧
¾ 㧗ᰯࡀ⏕ᚐࢆ᭷ྡ኱Ꮫ࡬ఱேධࢀࡓ࠿ᅜබ❧኱ࡋ࠿ࡋࠊࡶࡗ࡜ࡣࡸ࠸᫬ᮇ࡟㛤ദࡉࢀࡿ࡜࡞࠾

Ꮫ࡬ఱேධࢀࡓ࠿࡛ホ౯ࡉࢀࡿࡼ࠺࡞㢼ᅵࡀኚࡼ࠿ࡗࡓ࠿࡞࡜ឤࡌࡲࡋࡓࠋ≧ἣⓗ࡟ࠋ㸦ዪ࣭30ࢃࡽ࡞࠸㝈ࡾࠊ࡞࠿࡞࠿㧗኱᥋⥆ࡢ⌧≧ࡣኚࢃ㹼 39 ṓ࣭ᅜබ❧኱࣭ᩍဨ࣭‶㊊ᗘ 4㸧ࡗ࡚࠸࠿࡞࠸ࡢ࡛ࡣ㸽㸦⏨࣭50 㹼 59 ṓ࣭ᅜබ

¾ ఱᨾࢭࣥࢱ࣮ヨ㦂㸦ཬࡧࡑࢀ࡟௦ࢃࡿࡶࡢ㸧ࡀᚲせ࡞ࡢ࡛ࡋࡻ࠺࠿ࠋ኱Ꮫࢆ࠸ࡃࡘ࠿ࡢࣞ࣋ࣝ

❧㧗࣭ᩍဨ࣭‶㊊ᗘ 3㸧
¾ 㧗ᰯ࡛ᅜㄒࡢᩍဨࢆࡋ࡚࠾ࡾࡲࡍࠋࢭࣥࢱ࣮ヨ࡟ศࡅࠊྛࣞ࣋ࣝࡢヨ㦂ࢆࡋࡼ࠺ࠊ࡜࠸ࡗࡓ㆟㦂ࡢᅜㄒࠊ≉࡟⌧௦ᩥࡢヨ㦂ෆᐜࡣ௚࡟࡞࠸㉁ㄽ࡟࡞ࡽ࡞࠸ࡢ࡛ࡋࡻ࠺࠿ࠋPISA ࡢࡼ࠺࡟ࠊྛ

ࡢⰋ࠸ࡶࡢࡔ࡜ᛮࡗ࡚࠾ࡾࡲࡍࠋᩥ❶ࡢᮏ㉁ࢆ

ࣞ࣋ࣝࡢᩍ⫱Ỉ‽࡜࠸ࡗࡓࡶࡢࢆ

ᩍ࠼ࡿࡓࡵࡢ㐺ษ࡞ᩍᮦ࡜ࡋ࡚఩⨨࡙ࡅࡿࡇ࡜

ཷ㦂⏕ࡢධᏛ㑅ᢤࢆ⾜࠺ࡶࡢࢆྠࡌᅵಥ࡛ㄽࡌ

ࡀ࡛ࡁࠊ࡜࡚ࡶຓ࠿ࡗ࡚࠾ࡾࡲࡍࠋᩍ⫱⌧ሙ࡟࡚ࡣ࠸ࡅ࡞࠸࡜ᛮ࠸ࡲࡍࠋ
ࠕ࡝࠺ᛂ⏝࡛ࡁࡿ࠿ࠖ

－129－

ࡿࡶࡢ࡜ࠊ

ࢆㄽࡎࡿ࡭ࡁ࡜ᛮ࠸ࡲࡍࠋ࡜࡚ࡶຮᙉ࡟࡞ࡾࡲ

ṇ┤࡟ఏ࠼࡚࠸ࡓࡔࡅࡓ༳㇟ࢆᣢࡗࡓࠋᩍ⫱ࡢࡋࡓࠋ࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ
㸦⏨࣭50 㹼 59⌧ሙ㸦⚾⮬㌟ࡣᅜㄒ࡞ࢇ࡛ࡍࡀ㸧࡟࠸ࡿ⪅࡜ࡋṓ࣭ᅜබ❧኱࣭஦ົ⣔⫋࣭‶㊊ᗘ 3㸧࡚ࡣࠊ⏕ᚐ㐩ࡢグ㏙⾲⌧ຊࢆࠊࡦ࠸࡚ࡣࠊࡑࡶ

¾࣭୰ᩍᑂ⟅⏦ࡢၥ㢟Ⅼࡣ⌮ゎ࡛ࡁࡿࡀࠊ᪂ࢸࢫ

ࡑࡶ⾲⌧ຊࢆᾰ㣴࡛ࡁࡿࢸࢫࢺࢆᐇ⌧ࡋ࡚࠸ࡓࢺࡣᐇ᪋ࡍࡿࡇ࡜ࡀ᪤࡟Ỵࡗ࡚࠸ࡿࠋධヨࢭࣥࡔࡁࡓ࠸ࠊ࡜ษ࡟㢪࠸ࡲࡍࠋ㸦⏨࣭40 㹼 49 ṓ࣭ࢱ࣮ࡀᐇ᪋ẕయࡔ࡜ᛮ࠺ࡀࠊ࡝ࡢࡼ࠺࡟ᐇ᪋ࡍ

ᅜබ❧㧗࣭ᩍဨ࣭‶㊊ᗘ 3㸧ࡿࡢ࠿㐨➽ࡀ୙࡛᫂࠶ࡿࠋ⤖ᒁྛ኱Ꮫ㸦ᅜ❧㸧

¾ࠕᏛຊࡢ⤒ᖺኚ໬ࠖ࡟ࡘ࠸࡚ࠊ௒ᚋࡢ࡜ࡽ࠼᪉

ࡣ኱Ꮫ⊂⮬ࡢᏛຊධヨࢆᤞ࡚࡞࠸ࡢ࡛ࡣ࡞࠸

ࢆ⪃࠼࡚ࡳࡓ࠸࡜ឤࡌࡲࡋࡓࠋ」ᩘᅇࠊ」ᩘᖺ

࠿㸽㸦⏨࣭60 ṓ㹼࣭ᅜබ❧኱࣭ᩍဨ࣭‶㊊ᗘ 3㸧ᗘࠊၥ㢟㠀බ㛤ࠊᑻᗘᚓⅬࡢᐇ⌧࡟ࡼࡾࠊࡇࢀ

¾ ᴦࡋ࠸◊✲఍࡛࠶ࡗࡓࡀᑓ㛛ᛶࡀ㧗ࡃࢃ࠿ࡽ࡞

ࡀྍ⬟࡟࡞ࡿࡢ࠿࡝࠺࠿ࠋࡓࡔࠊ
ࠕᏛຊࠖᴫᛕࡑ

࠸㒊ศࡶ࠶ࡗࡓࠋ
㸦⏨࣭50 㹼 59 ṓ࣭ᅜබ❧኱࣭ࡢࡶࡢࡶࠊ╔┠ࡢ௙᪉ࡀኚࢃࡿࡶࡢࡔ࡜ᛮ࠸ࡲ

ᩍဨ࣭‶㊊ᗘ 4㸧

ࡍࠋIRTࠊᏛຊࡢ⤒ᖺኚ໬ࢆ㏣࠺ࠊᏛຊほࡢኚ¾ ኱Ꮫධヨࢭࣥࢱ࣮࡛㛤Ⓨࡋࡓ CBT ࢸࢫࢺࡢࢹࣔ

㑄࡟ࡘ࠸࡚ࠊ⤌ࡳྜࢃࡏࡓ㆟ㄽࢆ῝ࡵ࡚㡬ࡁࡓၥ㢟ࢆᣏぢ࡛ࡁ࡚⯆࿡῝࠿ࡗࡓࠋ㸦ዪ࣭㹼 29 ṓ࣭࠸࡛ࡍࠋ㸦⏨࣭30 㹼 39 ṓ࣭Ẹ㛫௻ᴗ࣭ᑓ㛛⫋࣭ᅜබ❧኱࣭஦ົ⣔⫋࣭‶㊊ᗘ 3㸧

‶㊊ᗘ 4㸧

¾ ̿ᑓ㛛ⓗ࡞ෆᐜࡶࠊ㠀ᖖ࡟ࢃ࠿ࡾࡸࡍࡃලయⓗ¾ ࡇࢀࡔࡅࡢ࣓ࣥࣂ࣮ࡀ㞟ࡲࡗࡓ࡜ࡇࢁࢆᣏぢ࡛࡟࠾ヰࡋࡋ࡚࠸ࡓࡔࡁࠊᏛࡪࡇ࡜ࡀከ࠸࡛ࡋࡓࠋ

ࡁࡿࡢࡣࡍࡈ࠸࡜ᛮ࠸ࡲࡋࡓࠋࢸࢫࢺࡢጇᙜᛶࠊ

࠶ࡾࡀ࡜࠺ࡈࡊ࠸ࡲࡋࡓࠋ̿኱Ꮫධヨᨵ㠉࡟ࡘࡇࢀࡀ୍␒㔜せࡔ࡜ᛮ࠸ࡲࡍࠋ㸦⏨࣭40 㹼 49

࠸࡚ࡣࠊ⚾❧኱Ꮫࡶྵࡵ࡚ࠊ᪉ྥᛶࡀぢ࠼࡚ࡃṓ࣭Ẹ㛫௻ᴗ࣭ࡑࡢ௚࣭‶㊊ᗘ 4㸧ࢀࡤ࠸࠸ࡢ࡟࣭࣭࣭࡜ឤࡌ࡚࠾ࡾࡲࡍࠋ
㸦ዪ࣭50¾ ⌧≧ࡢධヨࡀᢪ࠼࡚࠸ࡿၥ㢟ࢆᐃㄽ࡜࠸࠺ほⅬ࡜ᐇ㝿ࡢ✀ࠎࡢヨ㦂࡜ẚ㍑ࡋ࡚⪃࠼ࡿࡇ࡜ࡀ

㹼 59 ṓ࣭⚾❧኱Ꮫ࣭ᩍဨ࣭‶㊊ᗘ 4㸧
¾࣭CBT ࡟ࡼࡿ㐠⏝㠃ࠋ≉࡟ࢥࢫࢺ࡜ᆅᇦ࡟ࡼࡿ

࡛ࡁࡲࡋࡓࠋබᖹᛶࢆ☜ಖࡍࡿࡇ࡜ࡢ㞴ࡋࡉ࡜ࠊ࣭኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵ࡜ࡋ࡚ணഛ

ධヨࢭࣥࢱ࣮࡜ྛ኱Ꮫࡢᙺ๭ศᢸࢆ᫂☜࡟ࡍࡿᰯ㸦ሿ㸧ࡸᑐ⟇ၥ㢟㞟࡛ࡲ࠿࡞࠼࡞࠸ࢸࢫࢺࡀ

ࡇ࡜ࡀ᪂ࡋ࠸Ꮫຊࢸࢫࢺ࡟ồࡵࡽࢀࡿせ௳࡛ࡣྍ⬟࡛ࡋࡻ࠺࠿㸦⏨࣭50 㹼 59 ṓ࣭ࡑࡢ௚࣭ᑓ

࡞࠸࠿࡜ឤࡌࡲࡋࡓࠋ
㸦⏨࣭30 㹼 39 ṓ࣭ሿ࣭ண㛛⫋࣭‶㊊ᗘ 3㸧ഛ࣭ᑓ㛛⫋࣭‶㊊ᗘ 4㸧

᱁ᕪၥ㢟

࣭኱

¾ ࢱ࢖ࢺࣝࡣࠕ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀ

Ꮫධヨࡔࡅ࡛࡞ࡃࠊ♫఍ࡢᩥ໬࣭ࡋࡃࡳࢆ⪃࠼ࡿ࠿ࠖ࡜࠸࠺ࡶࡢ࡛࠶ࡗࡓࡀࠊ⤖ᒁࡑࡢࢱ࢖ࢺ

࡞࠸࡜ࠊᐇ⌧ࡋ࡞࠸࠿ࡶࡋࢀ࡞࠸ࠋ
㸦⏨࣭50 㹼 59

ࣝ࡜ࡣ↔Ⅼࡀࡎࢀ࡚ࡋࡲ࠸ࠊಶูࡢ஦㇟ࡢゎㄝṓ࣭ᅜබ❧኱࣭஦ົ⣔⫋࣭‶㊊ᗘ 3㸧࡟⤊ጞࡋࡓࡢࡣṧᛕ࡛࠶ࡗࡓࠋ୍᪉ࠊධヨࢭࣥ¾࣭⌧ᐇࢆ㋃ࡲ࠼ࡓᨵၿࡀᚲせࡔ࡜ᛮ࠺ࠋ

¾࣭ྛሗ࿌ࡢⓎ⾲ணᐃ᫬㛫ࡣ஦๓࡟࿘▱ࡋ࡚࡯ࡋࢱ࣮ࡢᴗົࡢ኱ኚࡉ࡞࡝ࡀࡼࡃఏࢃࡾࠊᨵࡵ࡚

࠸ࠋᑓ㛛ⓗ⏝ㄒࡀከ⏝ࡉࢀ࡚ࠊ⌮ゎ࡟ⱞࡋࡴሙ

ᩗពࢆ⾲ࡍࡿẼᣢࡕ࡟࡞ࡗࡓࠋ㸦⏨࣭50 㹼 59㠃ࡀ࠶ࡗࡓࠋᴫࡡ᭷┈࡞ෆᐜࡔࡗࡓ࡜ឤࡌࡲࡋ

ṓ࣭⚾❧኱Ꮫ࣭ᩍဨ࣭‶㊊ᗘ 3㸧ࡓࡀࠊࢳࣛࢩ㸦࠾▱ࡽࡏ㸧࡞࡝࡟ࠊሗ࿌ࡢࢱ࢖

¾IRTࠊCBT ࡞࡝ࢆྵࡵ࡚ࠊࡑࢀ࡜᪥ᮏⓗ㢼ᅵࢆ㔜ࢺࣝࡔࡅ࡛࡞ࡃࠊෆᐜࡢᴫ␎࡞࡝ࡶ࠶ࡽ࠿ࡌࡵ

ࡡྜࡏࡓ࡜ࡁࠊᚲࡎࡋࡶ࠺ࡲࡃ࠸࠿࡞࠸౛ࡀከ

グ㍕ࡋ࡚࠾࠸࡚㡬ࡅࡿ࡜ࠊཧຍ⪅ࡶ஦๓࡟ୗㄪ

ࡃฟࡉࢀࠊࡑࢀࢆ࡝࠺ඞ᭹ࡋࠊ࠺ࡲࡃ⤫ྜࡋ࡚࡭ࢆࡋࡓࡾࠊணഛ▱㆑ࢆ௜ࡅ࡚࠸ࡗࡓࡾࡍࡿᑐ

࠸ࡃ࠿ࠊ⪃࠼ࡿ࡜ࡴࡘ࠿ࡋ࠸ၥ㢟࡛ࠊࡇࢀ࠿ࡽ

ᛂࡀ࡜ࢀࠊࡼࡾ⌮ゎࡍࡿࡇ࡜ࡀฟ᮶ࡓ࡜ᛮ࠸ࡲ

ࢸࢫࢺ◊✲⪅ࡀ࡝࠺ゎỴࡋ࡚࠸ࡗࡓࡽࡼ࠸࠿ࠊ

ࡋࡓࠋ㸦ዪ࣭ᅜබ❧኱࣭஦ົ⣔⫋࣭‶㊊ᗘ 2㸧⪃࠼ࡉࡏࡽࢀࡿၥ㢟ࡀከ࠿ࡗࡓࠋ
㸦⏨࣭60 ṓ㹼࣭

¾ ࢭࣥࢱ࣮ഃࡀฟ㢟࡟ࡘ࠸࡚ࡢᝎࡳࢆ࡛ࡁࡿ㝈ࡾ

－130－Ẹ㛫௻ᴗ࣭ᑓ㛛⫋࣭‶㊊ᗘ 4㸧

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४ ࣉ ঩ ၳ!

⊂❧⾜ᨻἲே኱Ꮫධヨࢭࣥࢱ࣮ධᏛ⪅㑅ᢤ◊✲࡟㛵ࡍࡿㄪᰝᐊሗ࿌᭩㸰ࠕ኱Ꮫධヨࡢ᪥ᮏⓗ㢼ᅵࡣኚ࠼ࡽࢀࡿ࠿ࠖ

Ⓨ⾜ᖹᡂ  ᖺ  ᭶  ᪥⦅㞟࣭Ⓨ⾜⊂❧⾜ᨻἲேࠛ -኱Ꮫධヨࢭࣥࢱ࣮

ᮾி㒔┠㯮༊㥖ሙ --㟁ヰ㸸--㸦௦㸧
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