Teaching Practice Report

Implementation of Data-Driven Learning (DDL) ina Course for English Reading and Writing
Aika Miura
Yoshiho Satake

Abstract
This research evaluates the effectiveness of data-driven learning (DDL) in Japanese
university-level English classes. DDL, where learners use corpora to discover
linguistic patterns, was applied in two first-year mandatory classes in English
Reading and Writing at Rikkyo University. The paper reports our educational
practices on the development of the DDL tasks, focusing on the target vocabulary
covered in the assigned coursebooks and the inductive instructions to encourage thestudents to engage in DDL. The qualitative analyses focused on DDL tasks involving
vocabulary, collocations, and grammar. The findings suggest that DDL enhances
lexical comprehension, particularly when learners are guided toward specificlinguistic elements. Tasks on collocations and verb-noun combinations were notably
effective. However, challenges such as the time-intensive nature of DDL and its
inconsistent impact on practical language use were observed. These insights highlightthe need for a balanced DDL task design and further research on optimizing DDL
for diverse learning environments. The study underscores the potential of DDL in
English language teaching and its role in fostering independent learning.
Keywords: Data-driven learning (DDL), English language teaching (ELT), Corpus linguistics

Introduction
Data-driven learning (DDL) is an approach in which learners actively consult corpora
to inductively discover linguistic patterns and usages. As originally advocated by Johns (1991),
this approach utilizes rich real-world language data, empowering learners to notice and deduce
rules of grammar, vocabulary, and semantics, autonomously. The efficacy of DDL has been
corroborated through meta-analytical studies by scholars such as Boulton and Cobb (2017) and
Mizumoto and Chujo (2015), highlighting its pedagogical advantages in language teaching.
In response to the rise of DDL in the field of language teaching, this study aims topromote the implementation of DDL in English language teaching (ELT) in Japan. This aim ispursued through an investigation into the effectiveness of various DDL tasks, which are tailored1

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to align with the Common European Framework of Reference for Languages (CEFR) levels,
to promote the development and subsequent prevalence of pedagogical materials designed toaccommodate learners at varying stages of language proficiency.
The 2023 academic year serves as the foundational phase for our research, during which
diverse DDL tasks targeting different grammatical and lexical items will have been put into
practice. As one of the foundation phase practices, this paper reports on the authors’ DDL
practice conducted at Rikkyo University. This initial stage is pivotal, as it sets the groundworkfor subsequent analysis and material creation. We hope the outcomes of this project will be ofsignificant value to English teachers in Japan seeking options for teaching materials, thereby
contributing to a broader understanding of the role of DDL in language teaching.

Literature Review
DDL is a pedagogical approach wherein learners are immersed in vast amounts ofauthentic usage of the target language in corpora, facilitating their independent navigation anddeduction of linguistic patterns. This approach originated from Johns (1991). Since then, DDL
has been an option in second language (L2) instruction, supported by the evolution of computer
technology and the widespread availability of the Internet, drawing considerable scholarly focus
on using corpora in L2 education.
The efficacy of DDL in enhancing language acquisition has been reported by various
meta-analytic studies (e.g., Boulton & Cobb, 2017; Lee et al., 2019; Mizumoto & Chujo, 2015).
An example of previous empirical research in this domain was conducted by Tono et al. (2014).
Their research scrutinized the impact of consulting the British National Corpus (BNC) on
correcting errors in writing in English as a foreign language (EFL). In this study, 93 university
students engaged in an essay-writing exercise without reference materials, and the authors gave
feedback on two identified errors in each student’s paper. Subsequently, after a three-weekinterval, the students revised the essay, consulting either the BNC or dictionaries. The 188
errors identified were categorized into three major types: omission (where lexical items areomitted), addition (which contains a redundant item), and misformation (where a grammaticalform is misplaced). The results demonstrated that corpus usage significantly enhanced thecorrection of omission and addition errors but was less effective in correcting those categorized
as misformation, suggesting that certain errors are more amenable to correction through corpus
resources than others.
Notwithstanding the demonstrated benefits of DDL, its adoption in ELT in Japan remains
underutilized. While meta-analyses have made clear some of the controversial aspects of DDL(Tono, 2015), the longitudinal research on corpus use conducted in Japanese educational
contexts, which has reported the impacts on learning English (e.g., Satake, 2020; Chujo et al.,2015; Hadley & Charles, 2017), is relatively scant. This paucity of evidence may contribute toteachers’ hesitancy in taking a DDL approach in English classrooms in Japan.
2Implementation of Data-Driven Learning (DDL) in a Course for English Reading and Writing

There is a need for further research that shows the specific benefits of corpus use on L2
learning, potentially persuading a greater number of English teachers in Japan of DDL’s value
and encouraging its integration into their teaching. Thus, this study investigates the effects ofvarious DDL tasks within the framework of English courses where instructors are constrainedby university-wide, unified syllabi with semi-mandatory textbook assignments.

Methods
Participants
The participants in this research were first-year students enrolled in two classes of a
14-week mandatory English course focusing on reading and writing, taught by the authorsof this paper in the spring semester of the 2023 academic year. One class of 21 participants
was designated as Level 2, corresponding to CEFR levels B1 to B2, while the other class,comprising 19 participants, was classified as Level 3, aligning with CEFR levels A2 to B1.
Before performing the DDL practices, the participants received a thorough explanation of theresearch, and their written informed consent was obtained. Only data from those who consented
to participate were included in the analysis for this paper.
Development of Tasks and Corpora Used in DDL
In both Levels 2 and 3 classes, the participants engaged with corpora to perform
DDL tasks designed to facilitate learning vocabulary items featured in the semi-mandated
course textbooks. After completing all the DDL tasks, the authors observed and analyzed theworksheets of the participants in their respective classes. This research primarily focused ona qualitative observation of the effects of various DDL tasks. Consequently, it did not involve
any quantitative validation processes, such as pre- and post-testing.
Level 2 Class
The Level 2 class used the coursebook titled Q: Skills for Success: Reading & Writing
4 (Daise & Norloff, 2020). The second author, who also served as the instructor, developed andimplemented a set of six worksheets to help students learn collocations, prefixes, and suffixes.
Each session gave the participants 15–20 minutes to engage with a DDL task. The participants
were given instructions and encouraged to complete the worksheets in English. When theparticipants were unable to complete a given task within an allocated time, they worked on theunfinished parts as homework outside of class time.
In this class, a corpus tool called Sketch Engine for Language Learning (SKELL)
provided by Lexical Computing CZ s.r.o. (2021) was utilized for DDL activities. The data
sources of this tool contain a varied collection of texts including news, academic papers,
Wikipedia articles, open-source books, web pages, discussion forums, and blogs. This data
is selectively compiled to aid learners of English to access examples providing how language3

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is used in various contexts. SKELL allows users to explore authentic language use throughexample sentences in the Examples page, the Word Sketch page showing co-occurring words,
and thesaurus functions in the page of Similar Words. SKELL enables learners to observe
how words and phrases are used in various contexts, which can aid in understanding nuances
and usage patterns in the target language. SKELL was used for this research due to its userfriendly interface, which requires no registration and is freely accessible. Users can swiftly
receive results by simply typing the target word or phrase, making it an ideal tool for exploring
language patterns in our research.
Level 3 Class
The first author taught the Level 3 class, covering Units 1, 2, and 4 from the coursebook
Q: Skills for Success: Reading & Writing 3 (Ward & Gramer, 2020). We collaboratively
developed eight worksheets focusing on the target vocabulary in the units during the course.
The students received the instructions and engaged in the DDL activities in the classroom (withapproximately 20–30 minutes dedicated to each) and were also assigned some of the worksheets
to complete autonomously outside the classroom. Considering their English proficiency level,
the participants’ mother tongue (Japanese) was used in the instructions and in the worksheets
where they were asked to fill in their findings on lexical behaviors from corpora. This is incontrast to Level 2, where the major instruction language was mostly limited to English.
In this class, SKELL was utilized in five worksheets (i.e., Worksheets 1, 2, 3, 5, and8) as classroom activities and homework assignments submitted via the university’s Learning
Management System, while the BNC—a balanced corpus composed of one hundred million
tokens of spoken and written data of British English, representing a wide variety of genres in
the late 20th century—was used for three worksheets (i.e., Worksheets 4, 6, and 7) in class.
The Rikkyo University Library provides registered users with free access to BNC Online in
the Shogakukan Corpus Network (NetAdvance Inc., 2022) via the university’s network. Theinterface allows users to access concordance lines, frequency information, collocations, andinflectional patterns by searching the target lexical word or phrase. Users can also specify
the part-of-speech information of the target words. In the Shogakukan Corpus Network, thefunction tabs in the BNC are given in Japanese. However, as this service will terminate at theend of March 2024, the university library holds an institutional account for students to freely
access the Sketch Engine, which contains 700 corpora in more than 100 languages, including
the BNC and SKELL, with various advanced corpus tools such as Word Sketch (i.e., collocations
and word combinations), Thesaurus, Word Sketch Difference (i.e., comparing collocations of
two words), Concordance, Wordlist, etc. (Lexical Computing CZ s.r.o., n.d.).

Results and Discussion
This section describes the procedures of DDL activities and reports the participants’4Implementation of Data-Driven Learning (DDL) in a Course for English Reading and Writing

observations of lexical behaviors of the target vocabulary in specified corpora in Level 2 and3 classes. First, we describe two worksheets that required participants to find collocations andlexical meanings of the target vocabulary using several corpora, conducted in the last three
weeks of Level 3, where receptive observations were mainly performed. Then, we summarize
the quantitative and qualitative overview of the Level 2 participants, not only their findings on
lexico-grammatical features, such as nouns, verbs, collocations, prefixes, and suffixes, but also
applications of their findings to their own written products.
DDL for CEFR A1–B1 EFL Learners (Level 3 Class)
The target vocabulary in Level 3 were “appreciate,” “confidence,” and “research (noun)”from Unit 1; “at risk,” “arrange,” and words related to food such as “recipe,” “onions,” “flavor,”“limes,” and “restaurants,” from Unit 2; and “accurate” and “relevant” from Unit 4 (Ward &Gramer, 2020). The basic format of the worksheets using SKELL includes activities such as
searching the target vocabulary in Examples (i.e., observing the word in context), Word Sketch(i.e., finding collocations of the word), and Similar Words (i.e., checking synonymous words).
Table 1 describes Worksheets 7 and 8, which focus on the words “accurate” and“relevant,” given in lessons in the 11th–13th weeks. See Appendix A for the worksheets.
Table 1
Description of DDL Activities in Worksheets 7 and 8
LessonLesson 11

Worksheet
Worksheet 7(25 minutes
in class)

Homework Worksheet 8

Lesson 12

Teacher’s
Feedback on
Worksheet 7

CorpusActivities

BNC• Check the contexts of the target vocabulary (“accurate”
and “relevant”) in the coursebook• Find 10 collocations• Find frequent co-occurring prepositions, write down theexample phrases, and summarize the findings

SKELL• Re-check the contexts in the coursebook• Find collocations with Word Sketch and summarize thefindings• Find synonyms with Similar Words and summarize thefindings• Write reflections on the activities and the findings of the
vocabulary using BNC and SKELL

N/A• Teacher shows the entries of the vocabulary from theLongman Dictionary of Contemporary English Online(English-Japanese)• Teacher shows some students’ findings in the worksheet
as model answers

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Teacher’s
Feedback on
Worksheet 8

SketchEngineTeacher shows some students’ findings in the worksheet asmodel answers
Teacher introduces the Sketch Engine and its output of
Word Sketch Difference between “relevant” and “accurate,”
which visually depicts the co-occurring degrees of thetarget word and collocates

Table 2 summarizes the participants’ findings of the co-occurring prepositions of thetarget vocabulary and identification of the meanings in Japanese after observing the concordancelines (i.e., the target word in contexts) in Worksheet 7. The English translations are given inbrackets.
Table 2
Participants’ Findings and Observations of Lexical Behaviors and Meanings in Worksheet 7
Using the BNC“relevant”
Frequent
prepositions
co-occurringwith thevocabulary

Observed
lexicalmeanings“accurate”• The distance is accurate to within 1 cm at a
• ...relevant to the potential
range of 25 metres.
drama student• The mid-year estimates are accurate for natural• ...relevant for the explanation
change…
of the character• The advance has proved highly accurate in• ...relevant in an industrialised
recognizing...
country• It turned out to be accurate in every respect.

•～ に関する、関連のある(related)• ～に適する、適切である(appropriate)• 妥当である (valid)• 正確である (correct) 正確な情報などを見分けるときの程度として使われる(どの程度の精度か)。(It is used
when identifying the degree of accuracy.) データや図のようなものを表すものが多い。
(The words representing data and diagrams
are frequently collocated.) 結果や情報の前につくことが多い。(It isfrequently followed by words describing
outcomes and information.) t oの後には誤差を表す数値が入る。 (A small
difference or error is placed after “to.”) in, toの後にはevery, decimal など数を表す単語がついている。(After “in” and “to,” words
expressing figures and numbers, such as“every” and “decimal,” are given.)

Table 3 shows the participants’ findings of collocations derived from Word Sketch fromthe SKELL with the identification of the meanings of the vocabulary based on the concordancelines in Worksheet 8.

6Implementation of Data-Driven Learning (DDL) in a Course for English Reading and Writing

Table 3
Participants’ Findings and Observations of Lexical Behaviors and Meanings in Worksheet 8
Using SKELL
Frequent
collocatesand observed
meanings
Words withproperty X“relevant”• 情報: information• 証拠: evidence• 話題、問題: issue, topic, matter• 情報、事実: information, fact
Nouns modified • 法令に関する語: provision,by X
registration, statute•権 限: authority

Modifiers of X

• 医療に関する語 (related to medicalcare): clinically, physiologically,
medically•共 同体に関する語 (related tocommunity): culturally, socially• 特に: particularly, especially“accurate”• 情報: information• 報告: report, statement• 予測、見積もり: prediction, estimate•数 字: figure, percent• 情報、報告: information, report• 測定、診断など医療関連のことで現在のことを表す語 (describing what
is current in terms of medical care):measurement, diagnosis, description• これからのことを予測させる語:
prediction, forecast• 学問に関する語 (related to academicareas): historically, scientifically,
medically, technically•程 度を表す語 (describing the degree):reasonably, fairly, entirely, amazingly,
surprisingly

Table 4 shows the participants’ reflections on a series of activities completed in
Worksheet 8 using the BNC and SKELL. The reflections included their observations of thedifferences between “relevant” and “accurate,” which were unexpectedly provided, andcomprehensive feedback on the DDL activities given in the course. All the comments wereoriginally given in Japanese, but the English translations are only provided below.
The case study on Worksheets 7 and 8 shows that the participants autonomously derived
lexical behaviors and meanings from the given concordance lines in the corpora without theteacher’s explicit instructions. Engaging with abundant examples of the target vocabulary driven
from the data in the corpora provided the participants with an opportunity to cultivate a deeper
understanding of the vocabulary, which was enhanced by internalizing the meaning in theirmother tongue, Japanese, and may not have been achieved from exercises given in the coursebook or consulting dictionaries. However, not all participants performed equally; 40% and 50%of the participants did not manage to complete all of the given exercises in Worksheets 7 and
8, respectively.

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Table 4
Participants’ Reflections on Activities Using the BNC and SKELL
Observed similarities between “accurate” and Observed differences between “accurate” and“relevant”“relevant”• H aving the same and/or similar meanings,• “Relevant” means the results in the past tense,
prepositions, synonyms, and collocations
while “accurate” means the measurement in•D escribing information
present.
• Not used in everyday speech, such as those used
• “ Relevant” refers to specific things, while
in medical or work settings“accurate” is often used with informational• “ Relevant” meaning “related” is close towords.
“accurate,” since it refers to correct information.
Comprehensive feedback on DDL activities• I found it easy to search the vocabulary in the database but difficult to summarize the findings.
• I was impressed to know that the vocabulary was used in so many different contexts and contained
different meanings.
• When I was a junior and high school student, I only learned the meanings of each word, but I cameto realize that it was important to understand the core meanings of the word.

DDL for CEFR B1–B2 EFL Learners (Level 2 Class)
Nouns
The target nouns were “achievement,” “resolve,” and “version” (see Figure B1 in
Appendix B). The participants were asked to write a sentence for each word provided as aprewriting activity. This initial step assessed their baseline understanding and usage of thewords. The participants were instructed to write observations about each word, fostering
deeper engagement with their meanings and usage. The next phase involved researching theverbs commonly associated with these target nouns as objects, with the participants noting any
patterns or insights they discovered.
The participants were also asked to identify three synonyms for each word and write
their findings. Finally, the task concluded with a post-writing exercise in which the participants
wrote a sentence for each word again. This final step evaluated whether the participants’
understanding and use of the words had improved compared to the prewriting phase, thereby
assessing the efficacy of the learning activities undertaken.
An analysis of the data indicated that while some grammatical insights amounted toapproximately 10% from Word Sketch and 20% from Examples, most discoveries were related
to lexical meanings (see Figure 1). Although prior research has reported the effectiveness of
DDL in facilitating learners’ study of collocations (e.g., Saeedakhtar et al., 2020; Vyatkina,2016), in our research, the participants made very few discoveries related to collocations. Thisoutcome highlighted that when the instructor did not specify the focus of discovery and left itto the learners, their attention tended to focus on lexical information.

8

Implementation of Data-Driven Learning (DDL) in a Course for English Reading and Writing

Figure 1
Types and Number of Discoveries Using SKELL: Noun Search
60
50

0
2
0
2

1
1
2
1

4
3
0
2

40
30

33

41

20
10

39

11

4

0
ExamplesgrammarWord Sketch
lexicalgenre

collocation0
Similar words
other

?

There was no significant enhancement in the use of these collocations in written
output, either before or after the intervention, as the students were unable to produce the target
collocations effectively. According to Satake (2022), DDL promotes the retention of unfamiliar
words and enhances the use of familiar ones, suggesting a discrepancy between acquisition andapplication. This finding raises the question of whether the words were entirely unknown to thelearners or known only to a limited extent, with a vague understanding.
The activities designed to deepen lexical comprehension from multiple perspectives
are valuable. However, they are time consuming and pose challenges in their implementation,
which highlights the need to balance the depth of learning and practical classroom constraints.
Verbs
The target verbs included “personify,” “perceive,” “empower,” “align,” “crave,” and“manipulate.” When the focus shifted to verbs as the learning targets, the learning tasks werelargely similar to those employed for the abovementioned tasks, where nouns were the learning
targets (see the Nouns section), with one notable exception: researching nouns commonly usedwith the target verbs. This task was designed to provide a more comprehensive understanding of
the verbs by exploring their contextual usage with specific nouns, thus enhancing the learners’
grasp of practical verb-noun combinations in language applications.
An analysis of learners’ interactions with Examples showed that approximately 35%of the findings pertained to grammatical insights (see Figure 2). It was observed that the tasks
focusing on verbs resulted in more grammatical discoveries compared to those focusing on
nouns (see Figure 1), which could be attributed to how verbs were researched. The target
verbs were highlighted in red and centrally positioned in the concordance lines, making them9

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more conspicuous. This visual prominence might have facilitated a greater awareness andunderstanding of the grammatical structures surrounding these verbs, leading to more frequentidentification of grammatical patterns. However, similar to when the focus was on nouns, mostfindings were predominantly related to lexical meanings, and the participants made very few
discoveries related to collocations (see Figure 2).
Despite the potential for DDL to enhance usage, there was no significant increase in
the use of these verbs in the participants’ writing outputs. Since a notable proficiency wasobserved in the pre-intervention writing tasks in this group of participants, this outcome begs
the question of whether dictionary use contributed to this initial success.
Figure 2
Types and Number of Discoveries Using SKELL: Verb Search
120
100

6
2
0

6
1
0
2

5
0
1

80
60

58

40
20
0

84

90

7

3
Similar words

34
Examplesgram

Word Sketch
lex

genre

collocation

other

?

Collocations
The first task focused on a series of collocations from Daise and Norloff (2020), which
included phrases such as “offer insight into,” “social media influencers,” “show resistanceto,” “personal brand,” “see trends emerge,” “price-conscious,” “social good,” “good value,”“a person’s best interest,” “grab our attention,” “use an app,” “stream a service,” “browse a
website,” “scroll through a Facebook feed,” and “turn off notifications.” The participants chose
six collocations from the above target collocations. They then conducted a task that involved
researching examples of these collocations, transcribing the examples they consulted, and using
these transcribed examples as a basis for composing written texts. The task was implemented
mainly in class, and the results were promising, with almost all students successfully
incorporating these collocations into their written work. The results confirm the task’s potential
as an effective method for promoting the use of known vocabulary within a short timeframe,10Implementation of Data-Driven Learning (DDL) in a Course for English Reading and Writing

making it suitable for classroom instruction. In addition, unlike the learning processes fornouns and verbs (see the Nouns and Verbs sections), the participants could effectively acquire
collocational knowledge when specifically instructed. This finding suggests that appropriate
guidance is crucial for directing learners toward the desired learning objectives.
The second task centered on the “feel + noun” collocation structure (see Figure B2
in Appendix B). The participants investigated six “feel + noun” collocations, transcribed theexamples they referred to, and created their own sentences using these transcribed examples asa reference. Conducted mainly in the classroom, the findings revealed that even learners at theB1–B2 level sometimes struggled to differentiate between nouns and adjectives and referred
to “feel + adjective” collocations (e.g., feel fine), thus highlighting the benefits of specifying
target collocations for instruction. Although there are instances where explicit instruction is
unnecessary in the implementation of DDL (see DDL for CEFR A1–B1 EFL Learners, or Level
3 Class), situations do arise where such instruction is essential. Therefore, it is necessary toobserve learners’ engagement carefully and respond flexibly. On the other hand, the participants
showed an ability to learn the correct use of articles with nouns. The presence or absence ofarticles in collocations following the word “feel” varies depending on the noun used (e.g., feelan urge, feel sympathy). However, it was observed that by consulting examples, participants
could discern between collocations with and without articles. This understanding enabled themto produce correct outputs frequently in their use of these collocations.
Overall, these findings indicate the value of DDL in collocation learning.
Prefixes and Suffixes
The target prefix was “anti,” and the target suffixes were “-ence,” “-ance,” and “-tion.”
A significant finding was that approximately 80% of the students could correctly use thevocabulary with prefixes and suffixes in their English compositions after writing out example
sentences. Approximately 20% of the responses correctly used the researched words, eventhough the participants did not write example sentences. This finding indicates that writingout example sentences of the referenced target phrases may not always be necessary to use
the target phrases correctly. On the other hand, the DDL task was highly time consuming. To
facilitate continuous implementation during classroom sessions, it is necessary to consider
reducing the volume of the task. Doing so would optimize the balance between task efficacy
and time management, ensuring students a more streamlined and effective learning experience.

Conclusion
The implementation and analysis of DDL tasks in English classes, as described in thisstudy, provided significant insights into the potential of DDL in language acquisition. The
study’s findings underscore the effectiveness of DDL in enhancing the understanding of various
language components, such as collocations, verbs, nouns, prefixes, and suffixes, within the11多言語教育実践ジャーナル

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context of ELT in Japan.
A key takeaway from this research is the importance of targeted guidance in DDL tasks
when learners do not properly understand the target vocabulary. When the participants were
explicitly directed toward specific linguistic elements, such as collocations, their learning wasmore focused and effective. This highlights the need for well-structured DDL tasks that alignwith learners’ language proficiency levels and learning objectives.
However, challenges were also noted regarding the time required to complete the DDL
tasks, suggesting a need for optimization in task design that balances the depth of learningwith practical classroom constraints to maintain learner engagement and facilitate continuous
implementation.
Additionally, the study revealed that while DDL effectively promotes lexical
comprehension and grammatical insights, its impact on the practical application of theselearnings, such as writing, can vary. This finding highlights the necessity for further researchand development of DDL methodologies that foster discovery and translate these discoveries
into tangible language skills.
Overall, the research conducted at Rikkyo University demonstrates the potential of
DDL in fostering autonomous, inductive learning among EFL students. The insights gained
from this study contribute significantly to the broader understanding of DDL’s role in language
teaching and offer valuable guidance for English teachers in Japan seeking to incorporate
corpus-based learning into their classrooms. The findings also lay a foundation for further
research, particularly in optimizing DDL tasks for diverse learning environments and exploring
their long-term impacts on language proficiency.

Acknowledgments
This paper was presented at the 49th Conference of the Japan Association for English Corpus
Studies in September 2023. The research was supported by JSPS KAKENHI Grant Number
JP 23K00530.

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Satake, Y. (2020). How error types affect the accuracy of L2 error correction with corpus use.
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https://doi.org/10.1016/j.jslw.2020.100757
Satake, Y. (2022). Eisakubun ni okeru doshi-meishi korokeshon sanshutsu ni taisuru DDL
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Hituzi Syobo.
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https://doi.org/10.1017/S095834401400007X
Vyatkina, N. (2016). Data-driven learning for beginners: The case of German verb-preposition
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Oxford University Press.

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第 4 巻（JOURNAL OF MULTILINGUAL PEDAGOGY AND PRACTICE, VOL. 4）

Appendices
Appendix A: Worksheets for CEFR A1–B1 EFL Learners (Level 3 Class)Figure A1
Worksheet 7
Vocabulary and Grammar Worksheet (VGW) 7
1.

教科書 p. 84 と 85 から、relevant と accurate を使っている例文を書き出してみましょう。

relevant
accurate

p. 84
p. 85
p. 84
p. 85

2.

（小学館コーパスネットワーク）にアクセスしま
立教大学図書館のデータベースから BNC Online（
す。（大学のネットワークからでないとアクセスできません。）

3.

「コロケーション」をクリックし、検索窓 に relevant または accurate を入力します。
上部左側の「relevant と accurate と共によく使用される単語の表が出てきます。1（右隣）の列に表示された単語
はどんなものがあるか（単語以外は除く）、以下に 10 単語を書き出してみましょう。

relevant
accurate
4.

「共起語の品詞」をクリックし、新しく現れたウィンドウの「「前置詞」に☑を入
BNC 上部右側の「「検索条件に追加する」をクリックします。（現れたウィンドウが消えます）。
れ、上部右側の「

5.

「検索」をクリックします。relevant と accurate と共によく使用される前置詞の表が出てきます。1（右隣）の列に表示された前置詞のうち高頻度のものを３件クリックして用例（文、節、語句）を観察して引用してみましょう。また気づいたこと書いてみてください。

前置詞（１）
用例
前置詞（２）
用例
前置詞（３）
用例
気づいたこと前置詞（１）
用例
前置詞（２）
用例
前置詞（３）
用例
気づいたこと

14

relevant

accurate

Implementation of Data-Driven Learning (DDL) in a Course for English Reading and Writing

Figure A2
Worksheet 8
Vocabulary and Grammar Worksheet (VGW) 8 (HOMEWORK)
言語学習支援ツール SKELL (https://skell.sketchengine.eu/#home?lang=en)で relevant と accurate について調べ、理解を深めましょう。検索窓に“relevant”または”“accurate”と入力し、Enter を押します。
Finally, the ad must be accurate. (p. 84)
If you plan to study in a financial area such as accounting, you need to make sure that your work is always
accurate. (p. 85)



Wouldn’t you rather receive ads for products or activities that are relevant to who you are? (p. 84)
Jack’s question at the meeting was not relevant to our discussion of low sales; it was completely off topic.

(1) 検索語を入れ、検索窓の下の、“Word sketch”をクリックし、relevant または accurate の主語（words withproperty X）、副詞（modifiers of X）、名詞（nouns modified by X）になる語について、具体例を数個挙げ、また気づいたことを書きましょう。
主語（words with property X）

relevant

副詞（modifiers of X）
名詞（nouns modified by X）

主語（words with property X）

accurate

副詞（modifiers of X）
名詞（nouns modified by X）(2) 検索窓の下の、“Similar words”をクリックし、relevant および accurate の類義語を数個書き、気づいたことを書きましょう。
relevant の類義語について
accurate の類義語について(3) relevant と accurate について、BNC オンラインや SKELL のデータベースを調べてみましたが、全体的な感想や気づいたことについて、以下に書いてください。

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多言語教育実践ジャーナル

第 4 巻（JOURNAL OF MULTILINGUAL PEDAGOGY AND PRACTICE, VOL. 4）

Appendix B: Worksheets for CEFR B1–B2 EFL Learners (Level 2 Class)Figure B1
Example of Participants’ Completed Noun-Learning Worksheets:RUNVKHHWDFKLHYHPHQWUHVROYHYHUVLRQ                                                  1DPH              :ULWHRQHVHQWHQFHIRUHDFKRIWKHIROORZLQJZRUGVDFKLHYHPHQWUHVROYHYHUVLRQ +HSUDFWLFHVHYHU\GD\WRZDUGVWKHDFKLHYHPHQWRIKLVJRDO +HUHVROYHGWRJRWR$PHULFDWRVWXG\ 7KLVLVDQHZYHUVLRQRIWKHVRQJ
3OHDVH DFFHVV 6.(// KWWSVVNHOOVNHWFKHQJLQHHXKRPH"ODQJ HQ  ZKLFK LV D ODQJXDJHOHDUQLQJVXSSRUWWRRO7\SHWKHWDUJHWZRUGVLQWKHVHDUFKZLQGRZ  :ULWHGRZQRQHWKLQJ\RXQRWLFHDERXWHDFKRIWKHZRUGV ,WPHDQVJHWWLQJWKURXJK ,WPHDQVWRVROYHRUUHFRQVLGHUDQGGHFLGH
 ,WUHSUHVHQWVYDULRXVNLQGVRIWKLQJVRUYLVLRQ &OLFN“Word sketch”DQGORRNXSWKHYHUEVWKDWKDYHWKRVHZRUGVDVREMHFWVDQGZULWH
GRZQZKDW\RXQRWLFH
 7KHVHYHUEVKDYHDFRPPRQLPDJHWKDWVRPHWKLQJFDQEHDFFRPSOLVKHGRUUHOHDVHG
 7KHVHYHUEVUHSUHVHQWQHJDWLYHPHDQLQJVRUFRQIOLFWLQJDSSHDUDQFHV 7KHVHUHSUHVHQWDUDQJHRUOLPLWDWLRQ &OLFN“Similar words”DQGZULWHWKUHHV\QRQ\PVDQGZKDW\RXQRWLFH
 VXFFHVVDFFRPSOLVKPHQWSURJUHVV →7KHVHKDYHSRVLWLYHPHDQLQJDQGLVVWHSSLQJXS
 WHQDFLW\SHUVHYHUDQFHIRUWLWXGH →7KH\H[SUHVVDIHHOLQJRISDWLHQFHDQGVWXEERUQQHVV PRGHOVHWIHDWXUH →7KH\H[SUHVVDIHHOLQJRISDWLHQFHDQGVWXEERUQQHVV :ULWHRQHVHQWHQFHXVLQJHDFKRIWKHWDUJHWZRUGV
 +HKDYHRZQDFKLHYHPHQW
 7KHFRPPLWWHHUHVROYHGWKDWWKHVWHSVKRXOGEHDXWKRUL]HG ,ZDWFKHGWKH(QJOLVK9HUVLRQRIWKHPRYLH

16

Implementation of Data-Driven Learning (DDL) in a Course for English Reading and Writing

Figure B2
Example of Participants’ Completed Worksheets for “feel + noun” Collocations:RUNVKHHW                                           1DPHS9RFDEXODU\6NLOO5HYLHZIHHOQRXQ 名詞 のコロケーションを 6.(// で  つ調べ、参照した用例を書き抜き、書き抜いた用例を参考に作文しましょう。
6.(//
KWWSVVNHOOVNHWFKHQJLQHHXKRPH"ODQJ HQ 用例 7KH\PD\IHHOJXLOWZKHQWKLVLQLWLDWLYHGRHVQRWSURGXFHGHVLUHGUHVXOWV
作文 ,IHHODVHQVHRIJXLOWQRZ 
 用例 6XFKSUHVVXUHZDVZLGHO\IHOWE\PDQ\VFKRROV
作文 ,IHHOSUHVVXUHWRGRSUHVHQWDWLRQ 用例 7KHUHDGHUIHHOVV\PSDWK\DQGFRPSDVVLRQIRUKHU
作文 ,IHHOV\PSDWK\IRUKLP
 用例 ,GLGVRPHOLJKWZRUNDQGIHOWILQH
作文 ,felt fine yesterday because I don’t work much.
 用例 ,IHOWDGHHSORYHIRUWKLVEHDXWLIXOSODFH
作文 ,IHHODJUHDWORYH
 用例 .DWHIHOWDQXUJHWRWHOOKLPWRJHWVWXIIHG
作文 +HIHHODQXUJHWRVWXG\PDWK

17