NAIS Journal

Analyzing the Effects of Learning Error in e-learning
via Mobile Phones
Masayasu Morita ・ Makoto Hirose

2．
Purpose of this Study'company job title') (inside factor). Third, review status is the factor

tests affect learning (Fig.1). We use 'learning environment', 'learningwhich has secondary influence and learners can modify consciously

status' and 'review status' as learning factors. By finding the correla-('rate of reading notes') (Secondary factor).

tion between these factors and 'error', we analyze how 'error' influenc-

This paper analyzes quantitatively the learning effects of using the English educational contents for mobile, 'Eigo de Company' provided byes learning. These factors are categorized as outside factor, inside fac-

ALC Press Inc. The aim of this research is to analyze objectively how 'error' on answering questions affect mobile learning by analyzing quantita-

tor and secondary factor and they are likely to have an impact on learn-tive data from log data. Accuracy in answers are generally used to evaluate learning attainment. However, rates of error can be more applicable asing. Therefore, analyzing these correlations objectively and quantita-

an indicator of learning attainment, because users memorize information better with higher entropy. Evaluating learning attainment with this indica-

tively will enable us to use 'error' to achieve effective learning.

tor is a new approach in mobile learning study.
In this paper, first, the causal relationship of 'error' and learning environment or learning status or review status are statistically explained.
Then how the 'error' influences on learning attainment is evaluated by analyzing quantitative log data. As a result, effective indicator with introduction of 'error' in mobile learning is presented.

of questions per day', 'average time to answer a question' andPurpose of this study is to analyze how 'error' in multiple choicelearning environment

3．
General Description of the Learning Contents
In this research, we analyze the English educational contents formobile phone, of 'Eigo de Company（http://alc.edia.ne.jp）'from ALC

errors in learning

Press Inc. which aims to promote English Proficiency through simulating the work environment in a multinational company. For example,

1.Introduction
Recently e-learning, educational method using information tech-vations for learning were proved from the subjective ratings and thepoints by giving correct answers to English questions. Each set has

nology has gained attention and various related approaches have been

usefulness of mobile phone in the system was indicated. KUNORI

five questions. They will be promoted according to the total points.

done in schools, company training and lifelong learning, etc. E-learn-(2005) had question-and-answer sessions, attendance checks and ex-

Fig.2 indicates the screen of 'Eigo de Company'.

ing is sometimes defined as all educational contents, for instance, CBTchange of opinions on mobile phones during the actual courses in the(Computer Based Training) using such as CD-ROM, WBT (Webuniversity. In his questionnaire surveys, a little less than 80% of stu-

Based Training) which are distributed through IT networks and thedents agreed to the usefulness of mobile phone. The results in these re-real-time delivery of coursework to remote places, etc. However, insearches are important to show the possibility of mobile learning.

other cases, e-learning is limited to education using the network which

However they may include learner's subjectivities and turn to be quali-

has activated e-learning itself. Currently, the latter is often introduced

tative evaluations since they were mainly based on survey question-

as the definition of e-learning and major examples are distance learn-naire.

116

review status

Figure 1. Correlation between Learning and Error

The contents have three functions to sustain motivation for learning.
1) Promotion raises the difficulty level of questions. Then it
will be more difficult to be promoted.
2) The ranking of all users will be displayed. Therefore, it motivates them to compete.
3) After work, the correct answers to questions will be linked toing with special networks used by schools (or companies) and the e-

As the measure of effectiveness in e-learning, the Kirkpatricklearning with the system to deliver or stream the class contents by con-

Evaluation Model (Four Levels of Evaluation) and ROI (e-learning

necting student's and school's (company's) server and PC terminals

White Paper 2005), etc. are used to measure general factors including(personal desktop PC or laptop PC).

the description in Pocket Eijiro (English dictionary). Thus,
they can review them in real time.

introduction of e-learning. Thus, it is difficult to measure the specific4．
Method of Study

The second example for e-learning doesn't have time and spatial

learning effects with them. Evaluation approaches focusing on learn-

4.1 Subjects

restriction and it enables learners to study anywhere anytime. Thus, it

ing effects include survey questionnaire to students and measuring ef-

The registered members in 'Eigo de Company' are the subjects

is reported that it has some effect on training or educating working peo-

fectiveness by specialists. They are qualitative evaluations which re-in this study. Members pay a registration fee and approximately 179

ple (e-learning white paper 2005). However, in the classrooms of high-flect learner (respondent) subjectivity. Thus, it is not suitable to evalu-

yen per month to use this mobile phone service.

er education such as in universities, these restrictions don't exist. There-

ate learning effect with them because learners (respondent) have dif-fore, it is difficult to keep them motivated and it is not that effective.

ferent standards to evaluate each learning effect and it is difficult to ex-

In fact, it was reported that the continuation rate of e-learning by WBTpect the same effect as the result of the same learning (reproducibil-

was about 50% in U.S. and quite a lot of students would drop out (Mo-ity). Therefore, the evaluation of learning effect should be objective

rita 2002). One of the reasons is that most PCs for e-learning don't pro-and quantitative. Evaluating the number of correct answers in exams

vide constant setting to learners. Thus, learners have moderate restric-

and grading papers are often used as objective and quantitative evalua-tions in terms of space and time, and it is difficult to say that they cantion of the learning effect. In order to measure based on objective evalu-

learn anytime anywhere.

ations, it is preferable to use the former such as multiple choice ques-

Figure 2. The Screen of 'Eigo de Company'【objective variable】

4.2 Research Period
We use the log data collected from August 1st, 2004 to May

1rate of error（ err）

31st, 2005.
4.3 Details of Research
As mentioned in the purpose of this study, we analyze and evaluate the correlation between learning and 'error' statistically.

In this situation, mobile phones attract attention as information

tions than the latter method which may include graders' bias. If learn-First, rate of error is chosen as measurement of 'error'. We calcu-

and telecommunications device which can provide constancy to learn-ing evaluation is done through the multiple choice tests, higher accura-late 'rate of error' by dividing total number of mistakes by total number

ers. At the end of March 2004, penetration rate for households of PC

cy rates are considered to show higher learning attainment. But hu-

of questions. In other words, it is the average percentage error during

was 77.8% and for mobile phone it was 91.1%. The penetration rate ofmans tend to learn from 'error' which comes with 'failure' andlearning. In this experiment we analyze using the above-mentioned val-mobile phones was already higher than that of PCs (e-learning white'embarrassment'. Therefore, rates of error can be more or equally appli-ue as the objective variable and several explanatory variables in Table

paper 2005). Besides, its technology has been developed and there iscable as the indicator of learning level like accuracy rates. The prob-

1, and then we analyze the statistical results.

no problem to use general networks with it. As stated above, mobile

lem is that humans tend to hide 'error' and even in anonymous surveys

Review status, learning environment and learning status are ex-phones are effective information and telecommunications devices. To-

some data are suppressed unconscious. Thus, it is necessary to use

planatory variables. Learning environment is the factor which affects

day e-learning with mobile devices i.e. mobile phones are often re-data such as log data which will not include learner subjectivity.

users through interface when they use contents. It is also categorized

ferred to as mobile learning. NAGAI et al. (2005) created the system

In this paper, we measure and analyze how 'error' influences

as an outside factor since the learners basically cannot control the fac-of web discussion board which enabled collaborative learning on bothlearning effect using the English educational contents. This researchtor ('mobile phone model' and 'Speech capability'). Second, learning

PCs and mobile phones. They mentioned the usefulness of this systemutilizes an approach analyzing learning evaluation quantitatively andstatus is the factor relating to time and it affects learners in their sub-based on the subjective ratings by learners. In this case, although theevaluating based on 'error' which has not been presented so far.

consciousness ('average interval of days for playing', 'interval days be-mobile phone had an accessory role to the system, the increased moti-

learning statususers who join the company (registered users) will collect scoring

tween play days', 'average time for playing per day', 'average number

number of mistakes/total
number of questions（0<err<1）【explanatory variable】〈 learning environment 〉
1mobile phone model

I-MODE, J-SKY, EZWEB

2speech capability

yes, no〈 learning status 〉
average interval of days
for playing（ave̲intval）

average number of days between the last and the next
day for playing（day）

standard deviation of interval of days for playing（std̲intval）
average time for playing
per day（second）（day̲ave̲sec）
average number of questions per day（day̲ave̲try）

showing dispersion of number of days between the last
and the next day for playing
total time for playing（ second）/total days for playing（ day）

7

average time to answer aquestion（second）（try̲ave̲sec）

total play time（ second）/total
answered questions（ second）

8

company job title（ level）

job title in company（1<level<10）

3
4
5
6

total of answered questions
for trial/total days for playing（day）〈 review status 〉
9rate of reading notes or
explanation about answers （gloss）

total number of readingnote/total number of answered questions（ 0<gloss<1）

Table 1. Objective Variable and Explanatory Variables in the Experiment117

NAIS Journal

Analyzing the Effects of Learning Error in e-learning
via Mobile Phones
Masayasu Morita ・ Makoto Hirose

2．
Purpose of this Study'company job title') (inside factor). Third, review status is the factor

tests affect learning (Fig.1). We use 'learning environment', 'learningwhich has secondary influence and learners can modify consciously

status' and 'review status' as learning factors. By finding the correla-('rate of reading notes') (Secondary factor).

tion between these factors and 'error', we analyze how 'error' influenc-

This paper analyzes quantitatively the learning effects of using the English educational contents for mobile, 'Eigo de Company' provided byes learning. These factors are categorized as outside factor, inside fac-

ALC Press Inc. The aim of this research is to analyze objectively how 'error' on answering questions affect mobile learning by analyzing quantita-

tor and secondary factor and they are likely to have an impact on learn-tive data from log data. Accuracy in answers are generally used to evaluate learning attainment. However, rates of error can be more applicable asing. Therefore, analyzing these correlations objectively and quantita-

an indicator of learning attainment, because users memorize information better with higher entropy. Evaluating learning attainment with this indica-

tively will enable us to use 'error' to achieve effective learning.

tor is a new approach in mobile learning study.
In this paper, first, the causal relationship of 'error' and learning environment or learning status or review status are statistically explained.
Then how the 'error' influences on learning attainment is evaluated by analyzing quantitative log data. As a result, effective indicator with introduction of 'error' in mobile learning is presented.

of questions per day', 'average time to answer a question' andPurpose of this study is to analyze how 'error' in multiple choicelearning environment

3．
General Description of the Learning Contents
In this research, we analyze the English educational contents formobile phone, of 'Eigo de Company（http://alc.edia.ne.jp）'from ALC

errors in learning

Press Inc. which aims to promote English Proficiency through simulating the work environment in a multinational company. For example,

1.Introduction
Recently e-learning, educational method using information tech-vations for learning were proved from the subjective ratings and thepoints by giving correct answers to English questions. Each set has

nology has gained attention and various related approaches have been

usefulness of mobile phone in the system was indicated. KUNORI

five questions. They will be promoted according to the total points.

done in schools, company training and lifelong learning, etc. E-learn-(2005) had question-and-answer sessions, attendance checks and ex-

Fig.2 indicates the screen of 'Eigo de Company'.

ing is sometimes defined as all educational contents, for instance, CBTchange of opinions on mobile phones during the actual courses in the(Computer Based Training) using such as CD-ROM, WBT (Webuniversity. In his questionnaire surveys, a little less than 80% of stu-

Based Training) which are distributed through IT networks and thedents agreed to the usefulness of mobile phone. The results in these re-real-time delivery of coursework to remote places, etc. However, insearches are important to show the possibility of mobile learning.

other cases, e-learning is limited to education using the network which

However they may include learner's subjectivities and turn to be quali-

has activated e-learning itself. Currently, the latter is often introduced

tative evaluations since they were mainly based on survey question-

as the definition of e-learning and major examples are distance learn-naire.

116

review status

Figure 1. Correlation between Learning and Error

The contents have three functions to sustain motivation for learning.
1) Promotion raises the difficulty level of questions. Then it
will be more difficult to be promoted.
2) The ranking of all users will be displayed. Therefore, it motivates them to compete.
3) After work, the correct answers to questions will be linked toing with special networks used by schools (or companies) and the e-

As the measure of effectiveness in e-learning, the Kirkpatricklearning with the system to deliver or stream the class contents by con-

Evaluation Model (Four Levels of Evaluation) and ROI (e-learning

necting student's and school's (company's) server and PC terminals

White Paper 2005), etc. are used to measure general factors including(personal desktop PC or laptop PC).

the description in Pocket Eijiro (English dictionary). Thus,
they can review them in real time.

introduction of e-learning. Thus, it is difficult to measure the specific4．
Method of Study

The second example for e-learning doesn't have time and spatial

learning effects with them. Evaluation approaches focusing on learn-

4.1 Subjects

restriction and it enables learners to study anywhere anytime. Thus, it

ing effects include survey questionnaire to students and measuring ef-

The registered members in 'Eigo de Company' are the subjects

is reported that it has some effect on training or educating working peo-

fectiveness by specialists. They are qualitative evaluations which re-in this study. Members pay a registration fee and approximately 179

ple (e-learning white paper 2005). However, in the classrooms of high-flect learner (respondent) subjectivity. Thus, it is not suitable to evalu-

yen per month to use this mobile phone service.

er education such as in universities, these restrictions don't exist. There-

ate learning effect with them because learners (respondent) have dif-fore, it is difficult to keep them motivated and it is not that effective.

ferent standards to evaluate each learning effect and it is difficult to ex-

In fact, it was reported that the continuation rate of e-learning by WBTpect the same effect as the result of the same learning (reproducibil-

was about 50% in U.S. and quite a lot of students would drop out (Mo-ity). Therefore, the evaluation of learning effect should be objective

rita 2002). One of the reasons is that most PCs for e-learning don't pro-and quantitative. Evaluating the number of correct answers in exams

vide constant setting to learners. Thus, learners have moderate restric-

and grading papers are often used as objective and quantitative evalua-tions in terms of space and time, and it is difficult to say that they cantion of the learning effect. In order to measure based on objective evalu-

learn anytime anywhere.

ations, it is preferable to use the former such as multiple choice ques-

Figure 2. The Screen of 'Eigo de Company'【objective variable】

4.2 Research Period
We use the log data collected from August 1st, 2004 to May

1rate of error（ err）

31st, 2005.
4.3 Details of Research
As mentioned in the purpose of this study, we analyze and evaluate the correlation between learning and 'error' statistically.

In this situation, mobile phones attract attention as information

tions than the latter method which may include graders' bias. If learn-First, rate of error is chosen as measurement of 'error'. We calcu-

and telecommunications device which can provide constancy to learn-ing evaluation is done through the multiple choice tests, higher accura-late 'rate of error' by dividing total number of mistakes by total number

ers. At the end of March 2004, penetration rate for households of PC

cy rates are considered to show higher learning attainment. But hu-

of questions. In other words, it is the average percentage error during

was 77.8% and for mobile phone it was 91.1%. The penetration rate ofmans tend to learn from 'error' which comes with 'failure' andlearning. In this experiment we analyze using the above-mentioned val-mobile phones was already higher than that of PCs (e-learning white'embarrassment'. Therefore, rates of error can be more or equally appli-ue as the objective variable and several explanatory variables in Table

paper 2005). Besides, its technology has been developed and there iscable as the indicator of learning level like accuracy rates. The prob-

1, and then we analyze the statistical results.

no problem to use general networks with it. As stated above, mobile

lem is that humans tend to hide 'error' and even in anonymous surveys

Review status, learning environment and learning status are ex-phones are effective information and telecommunications devices. To-

some data are suppressed unconscious. Thus, it is necessary to use

planatory variables. Learning environment is the factor which affects

day e-learning with mobile devices i.e. mobile phones are often re-data such as log data which will not include learner subjectivity.

users through interface when they use contents. It is also categorized

ferred to as mobile learning. NAGAI et al. (2005) created the system

In this paper, we measure and analyze how 'error' influences

as an outside factor since the learners basically cannot control the fac-of web discussion board which enabled collaborative learning on bothlearning effect using the English educational contents. This researchtor ('mobile phone model' and 'Speech capability'). Second, learning

PCs and mobile phones. They mentioned the usefulness of this systemutilizes an approach analyzing learning evaluation quantitatively andstatus is the factor relating to time and it affects learners in their sub-based on the subjective ratings by learners. In this case, although theevaluating based on 'error' which has not been presented so far.

consciousness ('average interval of days for playing', 'interval days be-mobile phone had an accessory role to the system, the increased moti-

learning statususers who join the company (registered users) will collect scoring

tween play days', 'average time for playing per day', 'average number

number of mistakes/total
number of questions（0<err<1）【explanatory variable】〈 learning environment 〉
1mobile phone model

I-MODE, J-SKY, EZWEB

2speech capability

yes, no〈 learning status 〉
average interval of days
for playing（ave̲intval）

average number of days between the last and the next
day for playing（day）

standard deviation of interval of days for playing（std̲intval）
average time for playing
per day（second）（day̲ave̲sec）
average number of questions per day（day̲ave̲try）

showing dispersion of number of days between the last
and the next day for playing
total time for playing（ second）/total days for playing（ day）

7

average time to answer aquestion（second）（try̲ave̲sec）

total play time（ second）/total
answered questions（ second）

8

company job title（ level）

job title in company（1<level<10）

3
4
5
6

total of answered questions
for trial/total days for playing（day）〈 review status 〉
9rate of reading notes or
explanation about answers （gloss）

total number of readingnote/total number of answered questions（ 0<gloss<1）

Table 1. Objective Variable and Explanatory Variables in the Experiment117

Analyzing the Effects of Learning Error in e-learning via Mobile Phones

5.1.1. Correlation between 'error' and learning environment
Table 3 shows the correlation ratio of 'rate of error' as objective

objective variable

rate of error

explanatory variablemobile phone model

speech capability

interclass variation（SW ）

9.93

10.06

variable to 'mobile phone model' and 'speech capability' as explanatory

between-class variation（SB ）

0.17

0.00

variables. In addition, the interclass variations in the table show thecorrelation ratio（ n2 ）

0.02

0.00

degree of dispersion within each class (Equation 1), and the between-

5.00

m

Sw=

(Equation 2). The correlation ratio variations are found by calculating
the Equation 3 using the interclass variations and between-class. In ta-

n---- 2ij iand 'speech capability' are 0.02 and 0.00 (weak correlation). Generally,n=2

if correlation ratio is over 0.25, it will be considered as correlated.

Σn（ x -x ）i=1i---- ---- 2iSBS w +S B（2）（3）

of correlation are indicated in Table 4. The result of multiple linear regression analysis is shown in Table 5 (We use Microsoft Excel for all
have the strongest positive correlation with 'error' is 'average intervalof days for playing' (the coefficient of correlation is 0.36). However,
we cannot admit it as the contributing factor because the hazard rate iswith all variables in learning status are not suitable. The applicable regression model is established after we use explanatory variable selection criteria (Equation 4) introduced by UEDA (1997) to choose thevariable which has the strongest positive correlation with 'error'.

objective
variable

62% (0.62) referring its p-value in Table 5. Thus, regression model

explanatory
variablethe calculations in this paper). In Table 4, the factor which seems to

explanatory variablevariablenumber
3
4
5
6
7
8
1

3

4

1.00
0.78
-0.04
0.06
0.05
-0.52

1.00
-0.02
0.02
0.08
-0.41

1.00
0.13
0.72
0.20

1.00
-0.10
-0.10
0.30

1.00
-0.06
-0.06

1.00

0.36

0.28

-0.30

0.08

-0.24

-0.51

job title'. The multiple correlation coefficient (0≦R≦1) shows how

Table for Analysis of Variancevariables, we narrow down to three variables such as 'average number

close it can fall into the regression formula and the value is 0.61. Although it is not so close, the hazard rates for all of p-values are below
5% (0.05) and it is the most applicable model. Next, degree of effectby explanatory variables to objective variables with t-value is shownin Figure 3. 'Average number of questions per day' has a positive sign
and it is proportional to 'rate of error'. In other words, it shows that percentage rate of error will increase if they try more questions. In addi-

coefficientintercept
3
4
5
6
7
8

0.91
0.04
0.04
-0.07
0.23
-0.20
-0.18

Ru=

is inversely proportional to 'rate of error'. This means that the percentage rate of error will be higher if the time to answer is shorter. Morecause 'company job title' also has negative sign.

val of days for playing', 'standard deviation of interval of days for playing' and 'average time for playing per day' have insignificant effect on'error'. Thus, it is possible to say that broad time elements such as total
learning hours of day or the interval days have weak correlation to

5.1.3. Relation between 'error' and review status
The result of simple linear regression analysis is shown in Table

8

1- (1-R 2 )×( n+k+1)
n -k-1intercept
6
7
8

1.00
0.23
-0.25
-0.19

standard
error
0.09
0.04
0.04
0.01

pared to other learning factors. As to learning status, the elements relat-

Table for Analysis of Variancecoefficient

standard
error
0.05297
0.132576t

p-valuerather than as outer factor. Thus, in order to use 'error' effectively inlearning, it is desirable to add inner factor, for instance, questions thatcause 'error'. And it is difficult to say that it is efficient to use 'error' by
relating it to time restriction, restriction of mobile functions and performance after learning.
6．SummaryThis research analyzes quantitatively the learning effect by'error' and it reveals that the factors of learning status affect 'error' morethan learning factors such as learning environment and review status.
If it is possible to learn anytime anywhere like mobile learning, envi-

1.00

ronment has no relation to 'error' in learning. Therefore, mobile learning has the possibility to provide the same learning as e-Learning. It
was found that there is significant correlation between 'error' and learning status and by analyzing more details of the relation may lead tofind key factors to learning. Furthermore, it will help find the fundamental factors to the mobile learning in general.
rate of reading notes or explanation about answer
Figure 4. Correlation of 'Rate to Error' to 'Rate of Reading Notes※ Standardized data

t

0.00
0.62
0.58
0.27
0.00
0.00
0.00

（4）

We would like to thank SPACE ALC，Incorporated for letting
systems.

learning environment
0.01

References
Taichiro UEDA, "Facile Method to Find Out Whether There is a Corre-errors in learning
0.62

0.20

learning statuslearning status

lation", Operation Research Society of Japan July, 1997 (in Japanese).
Noriyasu KUNORI, "An Experiment at the Large-Scale University Lec-

Figure 5. Correlation between Learning and Error

ture by E-Learning via Mobile Phone", Journal of Multimedia Aided
Education Research 2005, Vol.1, No.2, pp145-153, 2005 (in Japanese).

0.61
0.38
0.37
0.79
349.00t

Acknowledgments
us use their English learning contents, 'Eigo de Company' and otherp-value
8.79
0.50
0.55
-1.09
4.80
-3.00
-9.63

Table for Analysis of Variancecoefficient

0.199229
0.039692
0.036909
0.977547
347

intercept-0.01878-0.3546
0.7231033-0.50064-3.77623
0.000187
Table7. Regression Analysis of 'Error' and Review Status

0.62
0.38
0.37
0.79
349.00standard
error
0.10
0.07
0.07
0.07
0.05
0.07
0.02

does not show strong correlation, it has still quite strong influence com-related elements. This means that 'error' affects learning as inner factor

Regression Statistics
multiple correlation coefficient R
multiple determination R2
correction R2
standard error
number of sample

1

Regression Statistics
multiple correlation coefficient R
multiple determination R2
correction R2
standard error
number of sample'error' in terms of learning, whereas focal time elements including total
hours for study have a stronger correlation.

7

Ru: criteria for selecting explanatory variable
R : multiple correlation coefficientn : number of datak : number of explanatory valuable

over, the 'company job title' will rise if 'rate of error' decreases, be-on 'error'. It means that the other explanatory variables 'average inter-6

Although the coefficient of correlation for learning status (0.62)

Table 5. Result of Regression Analysis of 'Error' and Learning Status

tion to this, 'average time to answer a question' has negative sign and it

These three explanatory variables are the most influential factors

very weak correlation and it does not relate to 'error' directly.

ing to content of questions have more influence on learning than time-

Table 4. Coefficient of Correlation of 'Error' to Learning Statusof questions per day', 'average time to answer a question' and 'company

meets the criteria of selecting explanatory variable. As explanatory

118

5

Regression Statistics
multiple correlation R
multiple determination R2
correction R2
standard error
number of sample

Table 6 shows the result of applicable regression model whichthemselves have no direct influence on 'error'. Review status has also

Objective Valuables with T-value

objective variable

able numbers 3-6 in Table 2 are explanatory variables, the coefficientsvariable

Next, when 'rate of error' is set as the objective variable and vari-

Results show that the influential learning factor is learning status. Influence by learning environment is weak and mobile terminals-5.00

Figure 3. Degree of Effect by Explanatory Variables toxij: data within class
xi: mean within class
x: mean in total data

Thus, it is possible to say there is no correlation between them.
5.1.2. Correlation between 'error' and learning status

view status after the experiment.

-15.00m

SB=

ble 3, the correlation ratios of 'rate of error' to 'mobile phone model'

correlations) of 'error', learning environment, learning status and re-company
job title-10.00ΣΣ（ x -x ） （1）i=1 j=1

Figure 5 shows the relation (correlation ratios and coefficient of

0.00

Table 3. Correlation Ratio of 'Error' to Learning Environment

class variations show the degree of class dispersion from the mean

5.2. Discussion

average numberof question
per day
average timeto answer aquestion

10.00

rate of error

5．
Result and Discussion

NAIS Journal

p-value

11.61
5.20
-5.75
-13.32

Table 6. Result of Applicable Regression Analysis

0.00
0.00
0.00
0.00

with 'rate of error' as objective valuable and 'rate of reading notes' as
explanatory variable. And Figure 4 is the scatter plot showing the cor-

Ministry of Economy, Trade and Industry, Japan,"E-learning White Paper, 2005/2006", Ohmsha, 2004 (in Japanese).

relation of 'rate of error' to 'rate of reading notes'.
From the multiple correlation coefficients (0.20) in Table 7, 'rate

Masahiro NAGAI, Takeshi KITAZAWA, Hiroaki KOSHIKAWA, Hiro-of error' and 'rate of reading notes' have very weak correlation. The dis-shi KATO, Kanji AKAHORI "Development and Verification of a For-orderly plotted data in Figure 4 shows that. Generally, either readingnote or explanation about answers is considered as behavior to reduce
rate of error. However, from this result this kind of process does not affect 'rate of error'.

mative Evaluation System Utilizing Mobile Phone for Web-Based Collaborative Learning", Japan Journal of Educational Technology, 28(4)，pp333-342，
2004 (in Japanese).
Masayasu MORITA, "Common Sense about e-Learning", Asahi-shinbunsha, 2002 (in Japanese).

119Analyzing the Effects of Learning Error in e-learning via Mobile Phones

5.1.1. Correlation between 'error' and learning environment
Table 3 shows the correlation ratio of 'rate of error' as objective

objective variable

rate of error

explanatory variablemobile phone model

speech capability

interclass variation（SW ）

9.93

10.06

variable to 'mobile phone model' and 'speech capability' as explanatory

between-class variation（SB ）

0.17

0.00

variables. In addition, the interclass variations in the table show thecorrelation ratio（ n2 ）

0.02

0.00

degree of dispersion within each class (Equation 1), and the between-

5.00

m

Sw=

(Equation 2). The correlation ratio variations are found by calculating
the Equation 3 using the interclass variations and between-class. In ta-

n---- 2ij iand 'speech capability' are 0.02 and 0.00 (weak correlation). Generally,n=2

if correlation ratio is over 0.25, it will be considered as correlated.

Σn（ x -x ）i=1i---- ---- 2iSBS w +S B（2）（3）

of correlation are indicated in Table 4. The result of multiple linear regression analysis is shown in Table 5 (We use Microsoft Excel for all
have the strongest positive correlation with 'error' is 'average intervalof days for playing' (the coefficient of correlation is 0.36). However,
we cannot admit it as the contributing factor because the hazard rate iswith all variables in learning status are not suitable. The applicable regression model is established after we use explanatory variable selection criteria (Equation 4) introduced by UEDA (1997) to choose thevariable which has the strongest positive correlation with 'error'.

objective
variable

62% (0.62) referring its p-value in Table 5. Thus, regression model

explanatory
variablethe calculations in this paper). In Table 4, the factor which seems to

explanatory variablevariablenumber
3
4
5
6
7
8
1

3

4

1.00
0.78
-0.04
0.06
0.05
-0.52

1.00
-0.02
0.02
0.08
-0.41

1.00
0.13
0.72
0.20

1.00
-0.10
-0.10
0.30

1.00
-0.06
-0.06

1.00

0.36

0.28

-0.30

0.08

-0.24

-0.51

job title'. The multiple correlation coefficient (0≦R≦1) shows how

Table for Analysis of Variancevariables, we narrow down to three variables such as 'average number

close it can fall into the regression formula and the value is 0.61. Although it is not so close, the hazard rates for all of p-values are below
5% (0.05) and it is the most applicable model. Next, degree of effectby explanatory variables to objective variables with t-value is shownin Figure 3. 'Average number of questions per day' has a positive sign
and it is proportional to 'rate of error'. In other words, it shows that percentage rate of error will increase if they try more questions. In addi-

coefficientintercept
3
4
5
6
7
8

0.91
0.04
0.04
-0.07
0.23
-0.20
-0.18

Ru=

is inversely proportional to 'rate of error'. This means that the percentage rate of error will be higher if the time to answer is shorter. Morecause 'company job title' also has negative sign.

val of days for playing', 'standard deviation of interval of days for playing' and 'average time for playing per day' have insignificant effect on'error'. Thus, it is possible to say that broad time elements such as total
learning hours of day or the interval days have weak correlation to

5.1.3. Relation between 'error' and review status
The result of simple linear regression analysis is shown in Table

8

1- (1-R 2 )×( n+k+1)
n -k-1intercept
6
7
8

1.00
0.23
-0.25
-0.19

standard
error
0.09
0.04
0.04
0.01

pared to other learning factors. As to learning status, the elements relat-

Table for Analysis of Variancecoefficient

standard
error
0.05297
0.132576t

p-valuerather than as outer factor. Thus, in order to use 'error' effectively inlearning, it is desirable to add inner factor, for instance, questions thatcause 'error'. And it is difficult to say that it is efficient to use 'error' by
relating it to time restriction, restriction of mobile functions and performance after learning.
6．SummaryThis research analyzes quantitatively the learning effect by'error' and it reveals that the factors of learning status affect 'error' morethan learning factors such as learning environment and review status.
If it is possible to learn anytime anywhere like mobile learning, envi-

1.00

ronment has no relation to 'error' in learning. Therefore, mobile learning has the possibility to provide the same learning as e-Learning. It
was found that there is significant correlation between 'error' and learning status and by analyzing more details of the relation may lead tofind key factors to learning. Furthermore, it will help find the fundamental factors to the mobile learning in general.
rate of reading notes or explanation about answer
Figure 4. Correlation of 'Rate to Error' to 'Rate of Reading Notes※ Standardized data

t

0.00
0.62
0.58
0.27
0.00
0.00
0.00

（4）

We would like to thank SPACE ALC，Incorporated for letting
systems.

learning environment
0.01

References
Taichiro UEDA, "Facile Method to Find Out Whether There is a Corre-errors in learning
0.62

0.20

learning statuslearning status

lation", Operation Research Society of Japan July, 1997 (in Japanese).
Noriyasu KUNORI, "An Experiment at the Large-Scale University Lec-

Figure 5. Correlation between Learning and Error

ture by E-Learning via Mobile Phone", Journal of Multimedia Aided
Education Research 2005, Vol.1, No.2, pp145-153, 2005 (in Japanese).

0.61
0.38
0.37
0.79
349.00t

Acknowledgments
us use their English learning contents, 'Eigo de Company' and otherp-value
8.79
0.50
0.55
-1.09
4.80
-3.00
-9.63

Table for Analysis of Variancecoefficient

0.199229
0.039692
0.036909
0.977547
347

intercept-0.01878-0.3546
0.7231033-0.50064-3.77623
0.000187
Table7. Regression Analysis of 'Error' and Review Status

0.62
0.38
0.37
0.79
349.00standard
error
0.10
0.07
0.07
0.07
0.05
0.07
0.02

does not show strong correlation, it has still quite strong influence com-related elements. This means that 'error' affects learning as inner factor

Regression Statistics
multiple correlation coefficient R
multiple determination R2
correction R2
standard error
number of sample

1

Regression Statistics
multiple correlation coefficient R
multiple determination R2
correction R2
standard error
number of sample'error' in terms of learning, whereas focal time elements including total
hours for study have a stronger correlation.

7

Ru: criteria for selecting explanatory variable
R : multiple correlation coefficientn : number of datak : number of explanatory valuable

over, the 'company job title' will rise if 'rate of error' decreases, be-on 'error'. It means that the other explanatory variables 'average inter-6

Although the coefficient of correlation for learning status (0.62)

Table 5. Result of Regression Analysis of 'Error' and Learning Status

tion to this, 'average time to answer a question' has negative sign and it

These three explanatory variables are the most influential factors

very weak correlation and it does not relate to 'error' directly.

ing to content of questions have more influence on learning than time-

Table 4. Coefficient of Correlation of 'Error' to Learning Statusof questions per day', 'average time to answer a question' and 'company

meets the criteria of selecting explanatory variable. As explanatory

118

5

Regression Statistics
multiple correlation R
multiple determination R2
correction R2
standard error
number of sample

Table 6 shows the result of applicable regression model whichthemselves have no direct influence on 'error'. Review status has also

Objective Valuables with T-value

objective variable

able numbers 3-6 in Table 2 are explanatory variables, the coefficientsvariable

Next, when 'rate of error' is set as the objective variable and vari-

Results show that the influential learning factor is learning status. Influence by learning environment is weak and mobile terminals-5.00

Figure 3. Degree of Effect by Explanatory Variables toxij: data within class
xi: mean within class
x: mean in total data

Thus, it is possible to say there is no correlation between them.
5.1.2. Correlation between 'error' and learning status

view status after the experiment.

-15.00m

SB=

ble 3, the correlation ratios of 'rate of error' to 'mobile phone model'

correlations) of 'error', learning environment, learning status and re-company
job title-10.00ΣΣ（ x -x ） （1）i=1 j=1

Figure 5 shows the relation (correlation ratios and coefficient of

0.00

Table 3. Correlation Ratio of 'Error' to Learning Environment

class variations show the degree of class dispersion from the mean

5.2. Discussion

average numberof question
per day
average timeto answer aquestion

10.00

rate of error

5．
Result and Discussion

NAIS Journal

p-value

11.61
5.20
-5.75
-13.32

Table 6. Result of Applicable Regression Analysis

0.00
0.00
0.00
0.00

with 'rate of error' as objective valuable and 'rate of reading notes' as
explanatory variable. And Figure 4 is the scatter plot showing the cor-

Ministry of Economy, Trade and Industry, Japan,"E-learning White Paper, 2005/2006", Ohmsha, 2004 (in Japanese).

relation of 'rate of error' to 'rate of reading notes'.
From the multiple correlation coefficients (0.20) in Table 7, 'rate

Masahiro NAGAI, Takeshi KITAZAWA, Hiroaki KOSHIKAWA, Hiro-of error' and 'rate of reading notes' have very weak correlation. The dis-shi KATO, Kanji AKAHORI "Development and Verification of a For-orderly plotted data in Figure 4 shows that. Generally, either readingnote or explanation about answers is considered as behavior to reduce
rate of error. However, from this result this kind of process does not affect 'rate of error'.

mative Evaluation System Utilizing Mobile Phone for Web-Based Collaborative Learning", Japan Journal of Educational Technology, 28(4)，pp333-342，
2004 (in Japanese).
Masayasu MORITA, "Common Sense about e-Learning", Asahi-shinbunsha, 2002 (in Japanese).

119