International Journal of Educational Media and Technology
2021, Vol. 15, No. 1 pp.56-66

Factors Affecting University Students' At-home Learning
during the COVID-19 Pandemic-Implications for a Student Support SystemTakeshi Matsuda
Tokyo Metropolitan University, Japan
mat@tmu.ac.jp
Yuki Watanabe
Tokyo University of Science, Japan
wat@rs.tus.ac.jp
Katsusuke Shigeta
Hokkaido University, Japanshige@iic.hokudai.ac.jp
Nobuhiko Kondo
Tokyo Metropolitan University, Japankondo@tmu.ac.jp
Hiroshi KatoThe Open University of Japan, Japanhkato@ouj.ac.jp
The purpose of this study is to collect data that can be useful in designing systems that support the out-of-classlearning of undergraduate students in Japanese universities. The authors investigated students' at-home learning
activities during the COVID-19 pandemic and tried to provide implications for designing out-of-class learning
support systems for such students. Two surveys were conducted in June and October 2020, in which students
recorded all their learning activities for an entire week. After which, they were asked to complete a questionnaire.
Both surveys showed year-wise differences; that is, first-year students had more supportive aspects than senior
students. An examination of the influence of self-directed learning readiness scale factors showed that factors
concerning learning skills affected learning time in most categories; this finding has important implications for
system development.

Keywords: COVID-19, Self-Directed Learning Readiness Scale (SDLRS), Student
Support System, Student Year

Introduction
The COVID-19 pandemic forced universities worldwide to transition to remote teaching and learning modules,
and Japanese universities were no exception. As the pandemic worsened, students’ learning environments
shifted away from university campuses. According to MEXT (2020, p. 1), 83.9% of all Japanese universities
conducted online classes by July 2020. Many students began attending classes from home using web
conferencing systems, such as Zoom. Aside from topics related to accessing financial aid and renting laptop
computers and Wi-Fi routers, only a few reports have discussed best learning-support-related practices forstudents in remote learning environments as of the beginning of 2021 when this paper was being written (Meiji
University 2020; Ochanomizu University 2020; Tohoku University 2020). While the Association for the
Advancement of Computing in Education in the United States published a number of case studies ontechnological responses to COVID-19, learning support systems for students were not addressed (Fertig et al.
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2020).
Due to the rapid spread of infection, the transition to online lectures began abruptly as an emergency measureduring the pandemic. Universities therefore prioritized financial support and the technicalities of implementing
online lessons. While this can be considered a reasonable course of action, most universities still lack theresources to understand students’ learning situations and develop appropriate support methods. As a mutant
strain of the highly infectious new coronavirus has been reported, and a cure for COVID-19 is yet to be found,the current outlook remains uncertain; thus, students may still have to depend on home learning for some time
to come.
The authors previously developed a student enrollment support system using institutional research data fromtheir university (Matsuda et al. 2020a; Matsuda et al. 2015). In the fiscal year 2020, they had planned to develop
a support system for academic decision-making, such as choosing a seminar and studying abroad. However,considering the pandemic situation, they felt there was a need to survey students to assess their home-basedlearning situations.

Materials and Methods
PurposeThe purpose of this study is to collect data that can be used to design systems that support out-of-class learningand to provide implications and hypotheses for their development. In addition, the system is to be developed
based on the assumption that students’ self-directed learning (SDL) readiness affects out-of-class learning. To
achieve this objective, the current study investigated Japanese undergraduate students' at-home learning
activities during the COVID-19 pandemic. In other words, we do not intend to create a generalized student
model, but rather, a prototype for a useful system. Therefore, we aimed to collect data that could be used in
the prototype data design. More precisely, this research aimed to narrow down "data to be inputted" based on
the actual situation of the system user.

Subjects
Study data were collected through two surveys. The first was conducted in June 2020 and the second in Octoberof the same year. The June survey was designed to collect data about the learning experiences of 30
undergraduate students at four universities (A, B, C, and D) during the COVID-19 pandemic. It was sent out
via e-mail mid-June, when more than a month had passed since classes had completely transitioned to an online
format. The authors recruited students who were representative of different years in degree programs. Fourthyear students who spend most of their time in job hunting and research activities, and less time in lectures, were
excluded. Participation in the survey was voluntary.
The October survey was conducted during the second week of the month. The recruitment method andquestion items for this survey were almost the same as those in the June survey. Of the 52 respondents, 20 had
also participated in the June survey (Table 1).

Table 1
Survey participants
University

1st Year
October152
6 (3)*
43 (1)6
24 (4)June
A (Public)
B (National)
C (Private)
D (Private)
Total

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2nd Year
June October
8
8 (3)
4 (3)
3
11

12 (6)

3rd Year
June
October11
14 (8)
213

2 (2)16 (10)

June
19
2
7
2
30

Total
October
37 (11)10 (6)2 (2)3 (1)
52 (20)57.

International Journal of Educational Media and Technology
2021, Vol. 15, No. 1 pp.56-66*Numbers in parentheses indicate those who participated in the June survey.

Methods
The surveys were conducted over a seven-day period from June 13 to 19 and October 12 to 19, 2020. Students
entered all their learning activities into an Excel spreadsheet for both surveys throughout one week before they
were asked to complete a questionnaire. The June survey questionnaire contained five parts: 1) learning habits
and orientation, 2) course selection criteria, 3) self-directed learning readiness scale (SDLRS), 4) adult career
maturity scale, and 5) online learning benefits and improvement points. The October survey replaced Question
4 from the June survey with a question on the changes that occurred during the second semester of the year.
The question items in the current research focused on the SDLRS because it is required for learning activities
and is an important part of acquiring college credits, as explained in the authors’ previous study on enrollment
advising (Matsuda et al. 2020a). Based on the SDLRS, which was developed by Guglielmino (1998) as ameasurement method, this paper presents information regarding unit availability using the Japanese version
developed by Matsuura et al. (2003). The Japanese SDLRS comprises 58 items, and seven factors were extracted
from their research. Participants answered each question using a five-point Likert scale (“Almost never true forme” to “Almost always true for me”). In this study, we used a subset of the Japanese SDLRS selected by
Matsuda et al. (2014) and utilized in subsequent studies, such as Matsuda et al. (2015, 2020a). A total of 16
items (Table 2) were extracted from one to three items with high factor loadings from the original question
items that corresponded to each factor.
To design an advice system for their individual learning situations, this study classified students according toseveral criteria. Each category was analyzed by considering useful support-oriented characteristics based onfour classification criteria in the first survey: year, SDL readiness, learning that is unrelated to university courses,
and learning planning habits. The authors compared the students’ learning records with their questionnaireanswers.
Matsuda et al. (2020b), who reported the first survey findings, stated that participants’ year and SDL readiness
affected other variables, particularly learning that was unrelated to their classes (Table 3). The reasons for thisdifference could be explained as follows. With many students performing laboratory work during their thirdyear, the number of required credits decreases and the share of specialized subjects simultaneously increases.
Furthermore, third-year students focus on finding employment or enrolling in graduate school. Consequently,
as the year progresses, SDL readiness for love of learning, responsibility, and study skills increases. However, it
is possible that third-year students' self-efficacy and expectations for the future, which first-year students
experience because of many elective subjects and a wider range of future possibilities, tend to decline. With
regard to SDLRS-related values, factors other than factors 4 (inquisitive mind) and 3 (acceptance of
responsibility in learning), which included factors 1 (love of learning), 2 (ability to use basic study and problemsolving skills), 5 (initiative and independence in learning), 6 (self-efficacy), and 7 (positive orientation to the
future) may have influenced learning time that was unrelated to classes.

Table 2SDLRS items in the questionnaire
Factor
1. Love of Learning
2. Ability to Use Basic
Study Skills andProblem-Solving Skills
3. Acceptance ofResponsibility in

Item※
1. I love to learn.
12. The more I learn, the more exciting the world becomes.
13. Learning is fun.
2. *I do not work very well on my own.
3. I can learn things on my own better than most people.
16. I can learn effectively both in a classroom situation and on my own.
4. Only I am truly responsible for what I learn.
6. *Even if I do not learn a given content, I do not hold any responsibility

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2021, Vol. 15, No. 1 pp.56-66
FactorLearning

Item※
for it.
15. Only I am responsible for my learning.
11. *I do not like dealing with questions where there is no single rightanswer.
14. *I always prefer well-known ways of learning to novel learning methods.
5. If there is something I have decided to learn, I can find time for it nomatter how busy I am.
7. I can learn almost everything that I need to know.
8. I have a great deal of curiosity about everything.
9. I am good at thinking of unusual ways to do things.
10. I like to think about my future.

4. Inquisitive Mind
5. Initiative andIndependence inLearning
6. Self-Efficacy

7. Positive Orientation to
the Future※The number in front of the question item indicates the question order.
* Reverse items

Table 3
Average learning hours based on year
Year
First
SecondThird

Total Time35h 47m
37h 29m
41h 47m

Per session
1h 18m1h 32m1h 38mUnrelated to courses
2h 35m6h 32m
9h 49mConsidering the functions of supportive systems for student learning, the target of support should not be
activities that tackle learning during class hours and class tasks. In most cases, students carry out such learning
based on the instructions of the teacher in charge of each class. In short, the system should target factors that
influence decision-making for the number of classes selected based on certain standards and choosing ways toengage in any learning that is not causally related to the courses. Therefore, the October survey focused on
students’ year-related differences and SDL readiness, as explained in the Results section.

Results
Table 4 presents the overall results of the October survey. Compared to the June survey, there was a decreasein the average number of learning sessions, total learning time per week, and learning time unrelated to courses,
while the average duration per session and number of registered courses did not change or only increasedslightly.
Year-wise differences were widely observed in both surveys. As the academic year progressed, learning time
per session, learning time unrelated to the lesson, the proportion of students with learning planning habits, andthe proportion of students who agreed that “learning is fun” increased. Among students in all years, those in
their first year spent the greatest number of hours on learning and took the most number of courses. They
were also the largest group, which suggests that they could not learn well (Factor 2). Furthermore, they showed
lower learning initiative and independence than second- and third-year students (Factor 5) and did not think ofthemselves as being very self-efficacious (Factor 6). (Tables 5 and 6; Figure 1).

Table 4Overall results of the October survey (N=52)
Indicator
Number of learning sessions
Total learning time
Duration per session

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Total
1,125
1,672h 56m

Average*21.63 (26.67)
32h 10m (39h 0m)
1h 29m (1h 27m)

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Time unrelated to courses
283h 55m
Number of registered courses
672
SDLRS
Q2. I do not work very well on my own.
Q13. Learning is fun.
* Average: per student except for “Duration per session"
Numbers in parentheses indicate the participants in the first survey.

5h 27m (7h 10m)
12.92 (12.03)
2.81 (3.31)
3.96 (3.83)

Table 5
Average value based on year
Year

Learning time
Out-of-class Unrelated to course

Total
First
Second and Third

33h 28m
31h 03m

13h 41m
17h 44m

2h 59m7h 34mNumber oflearning
sessions
23.50
20.04

Number ofcourses
15.29
10.89

Table 6
Average SDL readiness based on year
Year
F1First (A)
3.81
Second and Third (B)4.26
Difference (B-A)
0.46
Reversed items have been processed.

F2
3.04
3.21
0.17

F4
2.23
2.45
0.22

F5
3.02
3.30
0.28

19

Second and Third

Year

F3
4.46
4.39
-0.07

0%

20%

F7
3.71
3.68
-0.03

9

8

First

F6
3.21
3.59
0.38

16
40%

60%

80%

100%

Student Ratio
have

do not have

Figure 1. Students with or without planning habits

Table 7
Correlation between the SDLRS and learning time
SDLRS
Total
Q.13 Learning is fun.
Factor 1. Love of Learning
Q.16 I can learn effectively both in a classroom situation and
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0.252
0.187
0.239

Learning Time
Week
Out-of-end
class
.282*
.382**
0.177
.321*
0.150
.372**

Unrelatedto course
0.243
0.189
.312*
60

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International Journal of Educational Media and Technology
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SDLRS
Total
on my own.
Factor 2. Ability to Use Basic Study and Problem.288*
Solving Skills
Q.15 Only I am responsible for my learning.
0.270
Factor 3. Acceptance of Responsibility in Learning
0.240
Factor 4. Inquisitive Mind
-0.002
Q.5 If there is something I have decided to learn, I can find.324*
time for it, no matter how busy I am.
Factor 5. Initiative and Independence in Learning.298*
Q.8 I have a great deal of curiosity about everything.
0.162
Factor 6. Self-Efficacy
0.072
Factor 7 (= Q. 10). Positive Orientation to the Future
0.251
Spearman: *p<.05, **p<.01; Reversed items have been processed.

Learning Time
Week
Out-of-end
class

Unrelatedto course

0.270.427**

.334*

0.077
0.182
0.042
0.186

0.214
0.194
0.152
.347*

0.215
0.159
0.169
0.015

0.237
.350*
0.134
.369**

.292*
0.127
0.140
.299*

0.089
0.051
0.086
-0.010

Next, we examined the influence of SDL readiness on each categorized learning time. The examination of therank correlation (Spearman) between the SDLRS and categorized learning time indicated significant
correlations between cumulative learning time, out-of-class learning time, and learning time unrelated to courses(Table 7). The characteristic correlation for each factor showed that the correlation between each factor andlearning time tended to be different.
Factor 2 was the only one correlated with three of the four learning time categories: learning time unrelated tocourses, learning time outside the class, and total learning time. A scatter plot for the mean value of Factor 2
and the distribution of learning time outside class and unrelated to class showed that there was a highcorrelation between Factor 2 and out-of-class learning time (Figures 2 and 3).
Factor 5 was correlated with total learning time and out-of-class learning time. Question 5 showed a particularlyhigh correlation.
Factor 7 indicated a correlation between weekend and out-of-class study time. Factors 3, 4, and 6 did notcorrelate significantly with the categorized learning time. Of the questions forming these three factors, only
Question 8 (Factor 6) correlated with weekend learning time. No significant correlation was detected for theremaining items.
60:00

Time (hh:mm)

48:00
36:00
24:00
12:00
0:00
1

2

3

4

5

Value of F2
Figure 2. Value of Factor 2 of the SDLRS (horizontal) and out-of-class learning time (vertical)
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33:36

Time (hh:mm)

28:48
24:00
19:12
14:24
9:36
4:48
0:00
1

2

3

4

5

Value of F2
Figure 3. Value of Factor 2 of the SDLRS (horizontal) and time unrelated to courses (vertical)
Time (hh:mm)
60:00
48:00
36:00
24:00
12:00
0:00
-10

-5

0

5

10

15

Change in Value
Figure 4. Increase or decrease of total value of the SDLRS (horizontal) and out-of-class learning time(vertical)

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Time (hh:mm)

9:36
4:48
0:00
19:12
14:24
9:36
4:48
0:00
-10

-5

0

5

10

15

Change in Value
Figure 5. Increase or decrease of total value of the SDLRS (horizontal) and learning time unrelated to course(vertical)
As mentioned earlier, 20 of the 52 student participants from the October survey also participated in the June
survey. We confirmed the changes in these students' overall SDLRS scores; the total SDLRS value of 12
students increased, while that of seven students decreased. Figure 4 shows the relationship between theincrease or decrease in SDLRS value and out-of-class study time. Students with lower SDLRS values(surrounded by the red dashed line) tended to have shorter out-of-class study time. Furthermore, the SDLRS
values of students who did not record study time unrelated to class were concentrated in groups that showeda decrease or hardly showed any increase (Figure 5).

Discussion
Findings of the October survey
Based on the findings of the June survey, it could be expected that the difference in students’ years would affectstudy time and SDL readiness, even in October. This could be because the 2020 class of first-year students
could not experience various learning styles at the university due to the prolonged pandemic situation during
the survey period. Under such circumstances, many of them seemingly focused on their lessons and tasks, andlearned passively without much planning, as indicated by SDLRS Factors 1 and 5. There may have been little
room to foster love for and independence in learning.
The correlations of individual factors showed that, first, Factor 1 affected out-of-class learning time but did
not significantly affect learning time unrelated to lessons. In short, students with high scores for this factor
were more likely to engage in lesson-related learning, but less likely to extend their reach to any learning thatwas unrelated to their courses.
Second, Factor 2 affected learning time in most categories. This finding is convincing considering the unending
pandemic situation and the fact that this was the first time many students had to continue studying at home forover half a year. Thus, even if the factor values related to learning attitudes and motivation were high, it could
be inferred that it is difficult for first-year students to induce actual learning if readiness in finding ways to learn
in an unknown environment is low.
Of the three factors that did not show a significant correlation with learning time, Factor 3 could be statisticallyexplained. Its mean values (4.46, in the first year; 4.39, in the second and third years) showed that most of thestudents responded with 4 and 5 to the questions that form this factor. Therefore, it was, in fact, difficult for
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the correlation with other variables to be significant.
On the other hand, the reasons why Factors 4 and 6 did not affect learning time differed from those for Factor
3’s lack of effect. Even if the SDL readiness indicated by Factors 4 and 6 was high, it did not lead to any
attempts for it to be implemented in the form of out-of-class learning in situations where interactions between
students in the classroom, on campus, and in actual group learning were restricted because such students did
not know how to make the most of their qualities.
Factor 7 showed a similar tendency to that of Factor 1, but it notably had the greatest effect on weekend
learning time among all factors. Thus, students who thought more positively about their future spent more
hours learning on weekends, which is a useful finding when considering the direction of system-provided
support.
Finally, the number of classes taken, which was greatly influenced by the year, showed an interesting result.
Both surveys indicated a weak negative correlation between the number of classes taken and learning time
unrelated to the class (r=-.25, p=.183 in June; r=-.21, p=.145 in October [based on Pearson]). Initially, we
thought this finding would be attributable to the fact that students who took more lessons had to study more
often and were likely less able to handle other studies; however, no such phenomenon was observed.
As Figure 6 shows, the number of learning sessions per week of students without any unrelated course learning
peaked at around 30 regardless of the number of courses taken. Furthermore, since there was a weak negative
correlation between the number of lessons and all SDLRS factors, support for SDL readiness could increase
out-of-class learning, especially learning time not related to lessons.

No. of learning sessions

60
50
40
30
20
10
0
0

5

10

15

20

25

30

35

No. of registered classes

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No. of learning sessions

60
50
40
30
20
10
0
0

5

10

15

20

25

30

35

30

35

No. of registered classes

No. of learning sessions

60
50
40
30
20
10
0
0

5

10

15

20

25

No. of registered classes
Figure 6. Number of classes registered (horizontal) and number of learning sessions per student (vertical)
Sessions of all students, students with no time unrelated to courses, and students with time
unrelated to courses (from top to bottom)

Implications for the prototype design
The following hypotheses can be made regarding system development: First, first-year students require more
support than students in the succeeding years. Specifically, those who stop planning and concentrate on earning
credits once they are in university should receive more attention. While not having any learning time that isunrelated to course work is not detrimental for a student, more support for increasing SDL readiness is needed
since low SDL readiness is one cause of poor academic performance.
It can be inferred that Factor 2 of the SDLRS had the most significant effect on learning time, which is anespecially important implication for system development. This finding was more attributable to the fact thatthe learning skills included in Factor 2 made it easier to give specific advice and confirm its effect, comparedto the love for learning included in Factor 1 and the self-efficacy included in Factor 6. Therefore, with regards
to the development of a learning support system, it is important to prioritize Factor 2, that is, the function ofpromoting out-of-class learning by supporting learning methods. Concurrently, showing the future caused by

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each kind of learning could enhance the features of Factor 7. Moreover, this study suggests that it would be
beneficial to develop the functions to improve situations where the curiosity-related potential of students withhigh values for Factors 4 and 6 is not being demonstrated.
In the school year 2021, face-to-face lessons will resume in earnest at universities in large cities in Japan, so wewill continue to collect data on learning times and the SDLRS, as well as develop prototypes utilizing data fromthe two surveys.

Acknowledgements

This work was supported by JSPS KAKENHI Grant-in-Aid (Grant Number 19H01717). We would like tothank Editage (www.editage.com) for English language editing. We are also grateful to all students who
participated in the survey.

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