Learning behavioral patterns of students with varying performance in a high school mathematics course using an e-book system

Abstract

Traditional textbooks are progressively being replaced by e-book systems, which are also being utilized more commonly in K-12 education. The study investigated learning behavioral patterns in a seven-week high school mathematics course using an e-book system. In this study, learning data from the BookRoll system was analyzed with lag sequential analysis to examine learning behavioral patterns, learning strategies, and the differences between students with different performances. The results of the learning behavior patterns of all students confirmed the usage of rehearsal and elaboration strategies. However, it demonstrated the lack of using metacognitive strategies in the e-book learning process. Additionally, the results also revealed different learning patterns among students with different learning performances. Students with decreased performance tended to use shallow cognitive processing strategies, while students with increased performance used deeper learning strategies, such as integrating information from the previous and next pages to highlight learning contents. Regarding the strategy usage of students with unchanged performance, students in the unchanged low and middle performance groups tended to utilize the re-reading strategy, while students in the unchanged high performance group utilized the elaboration strategy. Notably, students with increased performance employed fewer learning behavioral patterns than decreased performance students. The behavioral patterns of students with increased performance were more efficient and effective.

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Learning behavioral patterns of students with
varying performance in a high school
mathematics course using an e-book system
Xuewang Geng 1 *, Li Chen 2 , Yufan Xu 1 , Hiroaki Ogata 3 , Atsushi Shimada 2 and Masanori Yamada 1,4
*Correspondence:
geng@mark-lab.net
Graduate School of Human-
Environment Studies,
Kyushu University,
744 Motooka, Nishi-ku, Fukuoka,
819-0395 Fukuoka, Japan
Full list of author information is
available at the end of the article
Abstract
Traditional textbooks are progressively being replaced by e-book systems, which are
also being utilized more commonly in K–12 education. The study investigated
learning behavioral patterns in a seven-week high school mathematics course using
an e-book system. In this study, learning data from the BookRoll system was
analyzed with lag sequential analysis to examine learning behavioral patterns,
learning strategies, and the differences between students with different
performances. The results of the learning behavior patterns of all students
confirmed the usage of rehearsal and elaboration strategies. However, it
demonstrated the lack of using metacognitive strategies in the e-book learning
process. Additionally, the results also revealed different learning patterns among
students with different learning performances. Students with decreased
performance tended to use shallow cognitive processing strategies, while students
with increased performance used deeper learning strategies, such as integrating
information from the previous and next pages to highlight learning contents.
Regarding the strategy usage of students with unchanged performance, students in
the unchanged low and middle performance groups tended to utilize the re-reading
strategy, while students in the unchanged high performance group utilized the
elaboration strategy. Notably, students with increased performance employed
fewer learning behavioral patterns than decreased performance students. The
behavioral patterns of students with increased performance were more efficient
and effective.
Keywords: Behavioral pattern, Lag-sequential analysis, E-book system, High school
education, Learning analytics, Self-regulated learning, Learning sequence
Introduction
Advances in information and communication technologies are creating new opportunities
in high school education, and they have the potential to employ new devices, such as iPads,

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 2 of 23
to provide students with new learning experiences (Henderson & Yeow, 2012). E-book
systems are gradually replacing traditional textbooks, as they offer numerous
functionalities that are unavailable in traditional textbooks and allow students to interact
with learning content at any time and place (Turel & Sanal, 2018; Zarzour et al., 2020). In
addition, e-books systems provide an alternative to textbooks to support the classroom
learning process (Embong et al., 2012). Unlike textbooks, e-book systems are equipped
with additional features that support learning activities and improve the student learning
process. E-book systems allow self-regulated learning (SRL) well-integrated with
technology in the form of e-books and are an opportunity for students to become
independent learners (Susantini et al., 2021). SRL is an active learning process involving
cognitive, metacognitive, and behavioral engagement, including planning learning goals,
choosing appropriate learning strategies, and regulating and monitoring learning strategies
(Schunk, 2008; Susantini et al., 2021; Zimmerman, 1989). Previous research has
demonstrated several advantages of adopting e-book systems with SRL. Chen and Su (2019)
reported that students using an e-book system showed significant self-regulated learning
and self-efficacy improvements. Hwang and Lai (2017) found that the use of an e-book
system increased students’ self-efficacy and learning achievement and was more effective
for students with lower self-efficacy in elementary school.
E-book systems provide learning logs that are collected during the learning process.
Learning analytics (LA) can be employed to understand learning behaviors and processes
in e-book systems. SRL is one of the theoretical foundations of LA used to explain learning
activities and generate feedback (Viberg et al., 2020). A growing number of studies have
illustrated the potential benefits of LA as a method of examining student SRL behavior in
online learning environments, such as e-book systems (Viberg et al., 2020). For instance,
Chen and Su (2019) collected the reading behaviors and analyzed the improvement in self-
regulated learning and self-efficacy of college students who used e-books. Chen et al. (2021)
employed classifiers to predict academic achievement based on college students’ reading
behavior in an e-book system. So far, however, few studies have been available on taking
into account the sequential nature of the behaviors, analyzing only the frequency of
learning behaviors (e.g., Chen & Su, 2019; Chen et al., 2019; Chen et al., 2021; Ogata et
al., 2017). It has been stated by Roll and Winne (2015) that SRL is not a learning activity,
but rather a process of continuous learning. The temporal nature of learning should not be
disregarded in the analysis of behavioral patterns and learning processes utilizing LA (Fan
et al., 2021). As an LA method to determine the sequence between two actions, lag
sequential analysis (Bakeman & Gottman, 1988) reveals whether the probability that one
action will occur after another is statistically significant, based on the temporal nature of
learning. Lag sequential analysis inspects the performance of SRL processes over time,
focusing on the sequence of learned behaviors and considering the relationship of

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 3 of 23
behavioral transitions to identify temporal differences in learning behaviors. Lag sequential
analyses allow for identification and comparison of students’ use of e-book systems, that
is, their learning behavioral patterns, and effective SRL. This study aimed to investigate
and analyze the behavioral patterns and SRL strategies of high school students using
e-book systems by employing lag sequential analyses.
Literature review
E-book systems in high school education
The presentation of content using computing technologies in a format similar to printed
books is referred to as an e-book (Smeets & Bus, 2012; Zhang et al., 2020). Readers can
access digital content through e-books anytime and anywhere using mobile devices (Turel
& Sanal, 2018; Zarzour et al., 2020). Furthermore, with the increasing use of multimedia
and communication technologies in education, e-book systems with enhanced functions
beyond traditional books have emerged. For instance, BookRoll is an e-book system that
allows students to read and highlight digital textbooks used in lectures, along with
augmented functions, such as bookmarking, taking notes, and searching (Ogata et al., 2015).
As such, e-book systems offer students the possibility of SRL, allowing them to be active
learners. Some previous studies have demonstrated that the augmented functions of e-book
systems improve students’ learning outcomes, motivation, and self-efficacy and reduce
learning anxiety (Chen & Su, 2019; Chen et al., 2019; Hwang & Lai, 2017; Turel & Sanal,
2018).
Due to the advantages of e-books, such as flexibility of content design and Internet
accessibility (Yamada et al., 2017), their application in university settings has been rapidly
growing (Chen et al., 2019; Chen et al., 2020; Shimada et al., 2019; Zarzour et al., 2020).
Previous research on e-books has focused on undergraduate students, while few empirical
studies have investigated the use of e-books in K–12 education (Huang et al., 2012). Tang
(2021) reviewed 79 published articles related to e-books from 2010 to 2019, and the results
reported a gradual increase in investigations at the elementary and secondary levels from
2015, however, mainly focused on motivation and satisfaction. For example, Hwang et al.
(2017) developed a concept mapping-based e-book system, and explored the impact of e-
book on middle school students’ motivation. Tang (2021) also noted that more advanced
learning functions of e-book systems can potentially bring new learning experiences and
performance for learners. Thus, the usage patterns of e-book systems with a variety of
learning functions and their impact on learning performance remain unclear, especially in
K–12 education. Furthermore, some studies on e-book systems were limited by the short
duration of the experiments, such as Yin et al. (2017), who investigated the behavioral
patterns of graduate students using an e-book system to read academic papers with an

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 4 of 23
experimental period of 1.5 hours. As SRL is continuous rather than short-term (Winne &
Nesbit, 2009), the present study conducted a seven-week experiment in high school.
Learning analytics for e-book learning behaviors
Furthermore, e-books differ from traditional paper-based textbooks in that e-books allow
for the collection of student learning logs. For instance, BookRoll records students’
activities, such as turning to the previous and next pages, highlighting, and taking notes, in
the server databases (Ogata et al., 2015; Yamada et al., 2017). Therefore, researchers and
teachers can utilize LA to understand learning behaviors using e-books. LA can measure,
collect, analyze, and report data regarding learners and their contexts to understand
learning processes (Learning Analytics & Knowledge, 2011). LA can be utilized for
research on student behavior modelling, performance prediction, dropout prediction,
improvement assessment, and recommendation of resources (Papamitsiou & Economides,
2014). Furthermore, LA utilizing student e-book learning logs can identify students’
learning levels, help improve learning materials, determine at-risk students, and predict
final grades (Yamada et al., 2017). Several studies have reported the benefits of applying
LA to research on e-book learning behaviors. Geng et al. (2020) revealed that e-book
learning behaviors related to rehearsal strategies, such as bookmarking and highlighting,
affected learning outcomes. Zarzour et al. (2020) investigated Facebook-based e-book
learning behaviors and found significant differences in liking, commenting, and sharing
behaviors among students with different levels of engagement.
SRL is closely related to cognition, metacognition, motivation, and behavior
(Zimmerman, 1989). Cognition and metacognition involve the ability of the learner to plan,
monitor, regulate, and evaluate learning. Students can use e-books to conduct SRL
activities, such as planning (setting learning goals and planning learning strategies prior to
reading e-books), monitoring (highlighting content to mark mastered and less understood
text and using the memo function to summarize learning content), regulation (adjusting
learning strategies based on the results of monitoring), and evaluation of the achievement
of learning goals and effectiveness of learning strategies using the quiz function. LA can
identify learning behavioral patterns based on e-book logs in order to understand students’
SRL strategy utilization. As such, LA avoids the necessity of surveys, the labor-consuming
complexity of observation, and the inaccuracy of learner self-reports (Chen & Li, 2021;
Winne & Nesbit, 2009). Chen and Li (2021) utilized LA to examine the behavioral patterns
of online learning and found that students engaged in SRL using strategies such as
rehearsing, repeating, evaluating, and searching. LA focuses on learning behavioral
patterns, providing an opportunity to analyze SRL to build learning models and
instructional design (Carthy et al., 2014).

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 5 of 23
Learning strategies used by students are often related to learning performance during
independent study and lectures (Chen et al., 2020). Broadbent and Poon (2015) revealed
that SRL strategies were significantly correlated with online learning performance.
Furthermore, Wang et al. (2013) demonstrated that the utilization of SRL strategies
predicted high learning performance. Therefore, it is critical to understand the learning
behavior patterns and learning strategies, such as e-book use, of students with different
learning performances. Previous research indicates that learning behavioral patterns and
strategies of students with higher and lower learning performance may differ (Yamada et
al., 2018). However, few studies utilized LA to explore the learning behavioral patterns of
high school students with different learning performances using e-books. Lag sequential
analysis addresses the limitation of focusing on the frequency of behaviors and
psychological data and investigates the behavioral patterns and strategies of students during
SRL. Therefore, this study aimed to investigate the learning behavioral patterns of high
school students utilizing e-books through a lag sequential analysis and to explore the
differences in behavioral patterns and learning strategies of students with different
performances. This study posed the following research questions (RQs):
RQ1: What were the learning behavioral patterns of students using the e-book system?
RQ2: What were the differences in learning behavioral patterns using the e-book system
among students with different performances?
Methodology
Course and participants
Eighty 10th-grade students from a Japanese high school participated in this study. The
study was conducted during a seven-week mathematics course, which focused on quadratic
functions, the law of sines and cosines, and other geometric knowledge. There were five
lectures per week, each session lasting 50 minutes. Each participant was provided with an
iPad and an Apple pencil for learning in class. Participants accessed the e-book system
using their own smartphones and computers for learning outside of class. All participants
learned and mastered the use of iPads before the experiment.
Experimental procedure
At the beginning of the study, participants were given a pre-test to check their existing
mathematical knowledge. This study utilized the BookRoll e-book system. Teachers
instructed the participants on BookRoll use to ensure that each participant could master the
operation of the iPad and the functions of BookRoll. During the course, teachers uploaded
digital learning materials and textbooks to BookRoll, of which participants could access
before lectures. The digital learning material mainly included supplementary reading

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 6 of 23
material provided by the teachers, such as explanations and examples of knowledge points.
The quizzes were administered via BookRoll’s quizzes function and paper test sheets. Each
participant logged into BookRoll using their own account for learning activities. In class,
participants were allowed to use BookRoll to read textbooks and materials and make
annotation, turn pages, add bookmarked text and handwritten memos, view teacher-
recommended learning content, answer teacher-prepared quizzes, and search for materials
and memos (Figure 1). After the class, participants were allowed to preview and review
materials posted on BookRoll.
Participants studied the learning materials using BookRoll. As shown in Figure 1,
participants could turn pages, return to previous pages, jump to pages, bookmark pages,
mark important or difficult content, and attach memos. In addition, participants could use
BookRoll to answer quizzes and add handwritten or text notes based on lectures. Outside
the classroom, BookRoll provided participants with extended knowledge on relevant topics,
while bookmarking and search functions aided participants in locating content and notes
for quicker preview and review. The course lasted seven weeks. At the end of the course,
participants took a post-test on the course content.
Data collection and analysis
Pre- and post-test scores and behaviors of the participants while using the BookRoll system
were recorded. Concerning the tests, based on the 10th-grade mathematics curriculum, the
questions of the pre-test covered rational numbers, irrational numbers, monomials, and
quadratic equations; the post-test consists of 30 questions related to factorization, quadratic
Fig. 1 The main interface of BookRoll

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 7 of 23
function, quadratic inequality, etc. The tests were designed by the teachers, and the perfect
score was 100. As the two tests differed in content and difficulty, pre- and post-test scores
were divided into high, low, and intermediate groups based on the maximum value minus
standard deviation, minimum value plus standard deviation, and the scores between these
values. Changes between pre- and post-test scores in the high, intermediate, and low groups
are shown in Figure 2. Based on these changes, students were divided into three groups:
increased performance (IP), unchanged performance (UP), and decreased performance
(DP). Students in the IP group belonged to the low and medium performance groups in the
pre-test, and their performances improved to the intermediate or high score groups in the
post-test (represented by the black line in Figure 2). Students in the UP group had no
change in pre- and post-test performance (represented by the grey line in Figure 2).
Students in the DP group were in the high or intermediate performance group in the pre-
test and dropped to the intermediate or low performance group in the post-test (represented
by the dotted line in Figure 2). The IP, UP, and DP groups included 5, 51, and 24 students,
respectively.
Participants’ behaviors while using BookRoll were automatically stored in the database,
which comprised 22 learning behaviors. A total of 149,369 records were collected. To
answer RQ1, behavior patterns of participants were examined using lag sequential analyses
(Bakeman & Gottman, 1988). A total of 304 behavioral sequences were obtained. In
addition, adjusted residuals were calculated, indicating that a value of 1.96 or higher was
considered a significant sequence at the 5% significance level. RQ2 was examined by
comparing the learning behaviors and behavioral patterns of the IP, UP, and DP groups.
Fig. 2 Changes in pre- and post-test scores in the high, intermediate, and low performance
groups

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 8 of 23
Table 1 Learning behavior codes in BookRoll
Code
Learning behavior
Code
Learning behavior
AHMM
Adding a handwritten memo
CLOSE
Closing read material
NEXT
Turning to the next page
QA
Answering a quiz
AMK
Adding a marker
BMKJ
Using a bookmark to jump to a
page of the material
UHMM
Undoing a handwritten memo
ABMK
Adding a bookmark to the
material
PREV
Turning to the previous page
DBMK
Deleting an added bookmark
OPEN
Opening and accessing material
CRC
Clicking the recommendation
button to see learning content
recommended by the teacher
DMK
Deleting a marker
DMM
Deleting a memo
PJ
Selecting a page and jumping to
it
SEARCH
Searching for memos and
learning content
CHMM
Deleting a handwritten memo
MMJ
Jumping to a memo from
search results
QAC
Correctly answered a quiz
SJ
Jumping to content from
search results
AMM
Adding a memo
CMM
Changing the previous memo
Results
Learning behavioral pattern
Table 2 shows the frequency and percentage of learning behaviors of all participants. The
most frequent behavior was adding a handwritten memo (AHMM), which accounted for
48.32% of all learning behaviors. The rates of turning to the next page (NEXT), adding a
marker (AMK), undoing a handwriting memo (UHMM), turning to the previous page
(PREV), opening material (OPEN), and deleting a marker (DMK) were 9.02%, 8.80%,
5.76%, 5.27%, 3.84%, and 3.68%, respectively, among all learning behaviors. Deleting a
memo (DMM), searching for memos and learning content (SEARCH), jumping to memos
from search results (MMJ), and jumping to material content from search results (SJ)
accounted for less than 0.01% of all learning behaviors. Lag sequential analyses (LSA,
Bakeman & Gottman, 1988) were conducted to explore the behavioral patterns of students
using BookRoll for learning. A part of the adjusted residual table indicating results of the
seven high-frequency behaviors obtained through the lag sequential analyses is shown in
Table 3. Adjusted residual values above 1.96 indicated that the occurrence of behavior
transformation sequences was significant. Among the 484 generated behavior
transformation sequences, 103 sequences were significant at the 0.05 level. Behavior
transformation sequences with over 100 occurrences were visualized, and the behavior
transformation diagram is shown in Figure 3.

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 9 of 23
Table 2 Frequency and percentage of participants’ learning behaviors
Code
Frequency
Percentage %
AHMM
72181
48
.324%
NEXT
28411
19
.021%
AMK
13148
8
.802%
UHMM
8597
5
.756%
PREV
7878
5
.274%
OPEN
5729
3
.835%
DMK
5498
3
.681%
PJ
2741
1
.835%
CHMM
1323
0
.886%
QAC
1129
0
.756%
AMM
768
0
.514%
CMM
424
0
.284%
CLOSE
354
0
.237%
QA
288
0
.193%
BMKJ
212
0
.142%
ABMK
189
0
.127%
DBMK
154
0
.103%
CRC
144
0
.096%
DMM
133
0
.089%
SEARCH
39
0
.026%
MMJ
26
0
.017%
SJ
3
0
.002%
Table 3 Adjusted residual table of participants’ learning behavior sequences (part of the full table)
AMK
AHMM
DMK
NEXT
OPEN
PREV
UHMM
AMK
244
.773*
-114
.995
66
.526*
-24
.821
-0
.504
-17
.35
-29
.522
AHMM
-115
.935
345
.323*
-72
.961
-180
.495
-70
.206
-87
.771
-43
.698
DMK
52
.522*
-72
.315
224
.664*
-25
.978
-3
.818
-12
.727
-18
.378
NEXT
-12
.049
-176
.797
-29
.742
249
.961*
21
.907*
48
.212*
-45
.795
OPEN
-20
.482
-70
.076
-14
.429
61
.453*
82
.046*
-17
.893
-18
.65
PREV
-8
.606
-85
.874
-12
.032
25
.491*
23
.22*
180
.248*
-22
.073
UHMM
-29
.211
-48
.168
-18
.558
-45
.795
-15
.566
-22
.295
275
.358*
*p<0.05
There were 16 nodes and 34 arrows, which represented 24 learning behaviors in 34
behavioral transformation sequences (Figure 3). The direction of the arrow in the
behavioral transition diagram denotes the direction of the transformation, and numbers
written on the lines are the adjusted residuals. The behavioral transition diagram was
divided into five areas based on BookRoll functions: reading material, annotation,
highlighting, bookmarks, and answering quizzes. The reading material area revealed that
NEXT, PREV, OPEN, and PJ were sequentially correlated, and there was a two-way
transition relationship between these four behaviors (PREV⇄NEXT⇄OPEN⇄PJ, reading
multiple materials thoroughly, the following is labeled as “QuitAndRead-multiple” , the

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 10 of 23
following is labeled as “ReadNew”). The behavioral pattern of OPEN→PJ→PREV and
the cycle of three behavioral transitions (purposeful opening of new materials),
OPEN→NEXT→CLOSE→OPEN (confirmation of the content of multiple new materials,
the following is labeled as “QuitAndRead-new”), were observed. For annotation, the
learning behavioral patterns of UHMM→CHMM⇄AHMM (revision of note-taking, the
following is labeled as “ReviseNote”) and AHMM→AMM (writing annotations after note-
taking, the following is labeled as “NoteToAnnotation”) were found. However, there was
no transition between AMM and CMM (revision of annotations). Furthermore, the other
three areas showed significant transformation of behaviors, such as AMK⇄DMK,
QA⇄QAC, and ABMK→DBMK. Interestingly, AMK→AMM→NEXT (turning to the
next page after highlighting and annotation) and AMK→AMM→OPEN (opening new
material after highlighting and annotation) behavioral transition patterns were observed,
indicating that students highlighted and annotated parts of the content before engaging with
reading materials.
Comparisons of learning behavioral patterns among students with different
performances
The frequency and percentage of learning behaviors of students in the DP and IP groups
are shown in Appendix A. The result indicated that the percentage of AHMM and QAC
learning behaviors was higher for students in the IP group compared with those in the DP
Fig. 3 Behavioral transition diagram

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 11 of 23
group and all students. To analyze the correlation between performance improvement and
reduction and differences in learning behavior, the Mann-Whitney U-test was conducted
on the IP and DP groups (shown in Appendix B). The results revealed that the IP group
significantly used some learning behaviors more frequently than the DP group: changing
memo (CMM: U=23.5, p<0.05), answering the quiz correctly (QAC: U=25, p<0.05), and
adding memo (AMM: U=27.5, p<0.1).
The adjusted residual tables of the learning behavior sequences for the IP and DP groups
are presented in Tables 4 and 5, respectively. The behavioral transition diagrams with
occurrences over 100 for the two groups are shown in Figure 4 and Figure 5, respectively.
The DP group had 12 learning behaviors and 21 behavioral transition sequences, while the
IP group had seven learning behaviors and 12 behavioral transition sequences. In other
words, the IP group had fewer significant behavioral transformation sequences than the DP
group. Participants in the DP group demonstrated patterns related to highlighting
(AMK⇄DMK), annotation (CHMM⇄AHMM→AMM), and material reading
(PREV⇄NEXT⇄OPEN,OPEN→PJ). Only patterns of material reading
(PREV⇄NEXT⇄OPEN) were found in the IP group. Interestingly, although there were
fewer behavioral transitions in the IP group than in the DP group, the NEXT→AMK
sequence observed in the IP group was not revealed in the DP group.
Table 4 Adjusted residual table for learning behavioral sequences in the DP group (part of the full
table)
AMK
AHMM
DMK
NEXT
OPEN
PREV
UHMM
AMK
122
.74*
-55
.745
29
.759*
-18
.807
-2
.571
-11
.849
-13
.785
AHMM
-56
.247
180
.965*
-35
.887
-91
.926
-32
.616
-43
.879
-15
.172
DMK
23
.785*
-35
.384
116
.712*
-18
.804
-3
.087
-8
.53
-8
.863
NEXT
-12
.331
-90
.094
-20
.052
127
.26*
7
.446*
17
.801*
-22
.572
OPEN
-12
.406
-32
.85
-8
.602
28
.984*
35
.914*
-10
.652
-8
.481
PREV
-8
.414
-42
.732
-8
.033
6
.208*
8
.861*
96
.344*
-10
.474
UHMM
-13
.366
-17
.46
-8
.743
-22
.469
-6
.899
-10
.791
146
.466*
*p<0.05
Table 5 Adjusted residual table for learning behavioral sequences in the IP group (part of the full
table)
AMK
AHMM
DMK
NEXT
OPEN
PREV
UHMM
AMK
75
.187*
-31
.715
27
.658*
-0
.854
2
.758*
-1
.377
-6
.29
AHMM
-32
.398
113
.582*
-16
.923
-64
.146
-28
.53
-33
.308
-26
.281
DMK
21
.28*
-16
.764
49
.731*
-1
.429
2
.729*
-0
.853
-2
.964
NEXT
2
.877*
-62
.519
-1
.877
78
.554*
14
.024*
19
.277*
-12
.181
OPEN
-2
.597
-28
.503
-2
.015
27
.701*
25
.763*
-4
.671
-5
.449
PREV
2
.754*
-33
.171
0
.301
11
.021*
10
.683*
57
.392*
-6
.411
UHMM
-6
.29
-26
.91
-3
.29
-12
.279
-5
.039
-6
.589
93
.961*
*p<0.05

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 12 of 23
To provide more comprehensive insight into the behavioral patterns of learners for each
learning outcome and to evaluate behavioral patterns, the behavioral transition diagrams of
learners in UP were also plotted based on the adjusted residuals determined by LSA. LSA
was performed on students in the UP group, which refers to those who maintained their
academic performance. The students were categorized as low to low (3 students), medium
to medium (46 students), and high to high (2 students) groups according to their pre-test
and post-test, and their behavioral transition diagrams are shown in Figure 6, Figure 7 and
Figure 8. The middle-to-middle group demonstrated a higher count of behavioral transition
sequences as compared to both the high-to-high and low-to-low groups. The transition
Fig. 4 Behavioral transition diagram for the DP group
Fig. 5 Behavioral transition diagram for the IP group

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 13 of 23
Fig. 6 Behavioral transition diagram for the low-to-low group of the UP group
Fig. 7 Behavioral transition diagram for the middle-to-middle group of the UP group
Fig. 8 Behavioral transition diagram for the high-to-high group of the UP group

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 14 of 23
sequences of both the low-to-low group and the high-to-high group are related to the seven
behaviors, namely AMK, DMK, NEXT, OPEN, PREV, AHMM, and UHMM. The
high-to-high group showed a unidirectional behavioral pattern for reading materials
(OPEN→NEXT→PREV), whereas the low-to-low group demonstrated a bidirectional
behavioral sequence (OPEN⇄NEXT⇄PREV). Concerning the middle-to-middle group’s
learning behaviors in the reading materials, in addition to the OPEN⇄NEXT⇄PREV
sequence, the behavioral pattern of OPEN→PJ→PERV, which uses the page jump to
quickly locate the content to be read, was also shown. As one of the differences between
the low-to-low and high-to-high groups, the behavioral patterns of adding and deleting
handwritten notes (AHMM⇄CHMM) was only demonstrated in the middle-to-middle
group. Furthermore, the most striking result from the behavioral transition diagrams is that
none of the three UP groups showed the pattern of adding highlight behaviors to the process
of reading the material in the IP group (NEXT→AMK).
Discussion
The first research question aimed to determine the learning behavior model approach for
all participants when learning with BookRoll. Learning behavioral patterns of students
while using BookRoll were indicated by the results of the lag sequential analysis and the
behavioral transition diagram.
Searching and jumping to search results (SEARCH, MMJ, and SJ), which had a lower
frequency, showed no significant behavioral transition, indicating that students did not use
search strategies to locate learning content. Coordinating information sources and finding
locations in the e-book system is an effective strategy for regulating SRL (Azevedo &
Cromley, 2004). “ReviseNote” and “NoteToAnnotation” behaviors illustrated that students
took notes to expand their knowledge. This finding indicates that, when performing
cognitive activities, students stopped at the stage of expanding knowledge and did not take
the next step of locating information. It demonstrates the insufficiency of SRL activities
among high school students using BookRoll and indicates the necessity of improving
BookRoll and providing instructor guidance to support SRL. Numerous studies have
shown the potential for further development of BookRoll to support SRL. Concerning the
enhancement of metacognition, for instance, Flanagan et al. (2018) created an
automatically produced content model based on the textbooks in BookRoll to assist
students in understanding the connections between knowledge fast. Additionally, the
advancement of LA also opens up previously unexplored prospects for SRL based on
digital e-book systems. The design and implementation of BookRoll-based dashboards
demonstrate that the dashboard facilitates monitoring the learners’ current learning
situation, indicates the following learning contents, and also provides instructional clues
for teachers (e.g., Chen & Su, 2019; Chen et al., 2019; Majumdar et al., 2021). Employing

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 15 of 23
tracking learning data allows students to obtain personalized scaffolding to optimize the
learning process when necessary in SRL (e.g., Lim et al., 2023). Moreover, the results
revealed that students not only repeatedly read one material for confirmation but also
purposefully opened additional materials for reading and confirmation (e.g., “ReadNew”,
“QuitAndRead-new”, “QuitAndRead-multiple”). This result suggests that students used
rehearsal learning strategies, that is, repeatedly studying the same content, and elaboration
strategies, which fused new information with existing information in order to learn new
material (Broadbent & Poon, 2015). As such, the participants employed both surface and
high-level cognitive strategies. This behavioral pattern was not observed in studies on other
e-book systems, where there was no bidirectional transition between the behaviors of
turning to the next and previous pages (Yin et al., 2017; Zarzour et al., 2020). This could
be due to the short duration of experiments conducted in previous studies, which were 1.5
hours (Zarzour et al., 2020) and 4 days (Yin et al., 2017). As SRL is a continuous learning
process, it is difficult to explore it completely through short-term learning activities (Winne
& Nesbit, 2009).
Bookmarking and quiz answering behaviors did not have any significant behavioral
sequences with the reading material. On the other hand, sequential behavioral patterns
(AMK→AMM→NEXT and AMK→AMM→OPEN, continuing reading backward after
highlighting and annotating) were found between the behaviors of highlighting, annotation,
and reading material activities. The results indicated that students first marked the learning
content and then summarized or commented on it, followed by turning to the next page of
the material or opening new material. However, this learning behavioral pattern did not use
metacognitive strategies. Learners who use metacognitive strategies tend to be confused
about the material and consciously go to the previous page to aid their understanding (e.g.,
AMK→AMM→PERV was not significant). Therefore, this study analyzed learning
behavioral patterns of the e-book system employing LA and presented students’ utilization
of SRL strategies.
To answer Research Question 2, students were divided into 3 groups. Based on pre- and
post-test results, five students with increased performance and 24 students with decreased
performance were identified. This study focused on investigating the learning behaviors of
students in the increased and decreased performance groups in order to determine the
differences in behavioral frequency. The results revealed that students with increased
performance more frequently added and changed annotations and answered quizzes
correctly than students with decreased performance. The positive impact of the addition
and modification of annotations on learning materials on student engagement and learning
performance has been reported in several literatures (Chen et al., 2021; Majumdar et al.,
2021; Wakefield et al., 2018). For instance, Chen et al. (2021) investigated the e-book
reading behaviors of 100 first-year undergraduates, and indicated that annotation function

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 16 of 23
was a significantly positive correlation with academic performance. Adding and changing
annotations tends to be closely related to organizational strategies, unlike highlighting,
which requires more powerful cognitive and metacognitive processing to help understand
summary content (Broadbent & Poon, 2015). Therefore, students need to have a sufficient
comprehension of the materials to add and modify annotations on the e-book system.
Nesbit et al. (2006) argues that highlighting selected text is a surface learning due to the
process of text selection only, requiring less cognitive processing; the use of annotations is
a deeper learning method that integrates relevant information and links the text to previous
knowledge in a way that often requires more cognitive processing. The frequency of
correctly answering quizzes verified that the learning performance of the increased
performance group was better than that of the decreased group.
Lag sequential analyses and behavioral transition diagrams revealed that students with
increased performance employed fewer learning behavioral patterns than those with
decreased performance. In other words, the behavioral patterns of students with increased
performance were more efficient and effective in terms of learning performance.
Furthermore, difference in the sequence of behavioral transitions between the two groups
demonstrated that the behaviors of increased performance students repeatedly performed
the same behavior mostly, such as AHMM→AHMM (adding handwritten notes multiple
times). In contrast, students with decreased performance showed many transitions between
different behaviors, such as AMK⇄DMK (repeatedly adding and removing highlighting)
and CHMM⇄AHMM (repeatedly adding and deleting handwritten notes). For instance,
the learning behavioral pattern of highlighting in the DP group illustrated that learners
simply repeated the behaviors of highlighting and deleting highlights. This finding could
be due to students marking content that they did not understand without cognitive learning.
It was confirmed in interviews with the teacher. Students tended to find answers to content
they did not understand and seldom engaged in SRL activities, such as judging and
evaluating based on learning goals. Likewise, deleting highlights, students felt that they
achieved understanding and did not engage in SRL evaluation activities. Therefore, the
bidirectional sequence between different behaviors of students in the DP group was likely
a surface processing strategy (Bernacki et al., 2012), indicating that students with increased
performance had more efficient and effective learning behavioral patterns.
Moreover, there was no transition between learning behavior areas, such as reading
materials and annotation, in the DP group; however, there were PREV⇄NEXT→AMK
(adding highlights during the reading of new material) and OPEN⇄NEXT→AMK (add
highlighting during repeated reading of material) sequences in the IP group. This
demonstrated that students in the IP group read the material before highlighting the
annotated text. This behavioral pattern differed from repetitive highlighting and deletions,
which is the first step toward achieving a deeper learning strategy (Leutner et al., 2007).

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 17 of 23
Leutner et al. (2007) stated that the highlighting behavioral pattern requires identifying and
focusing on important information, integrating existing information, and processing that
information in working memory in order to successfully achieve the goal of SRL strategies.
Concerning the learning behavioral patterns of the UP group, this study found that
students who remained in the high-performance group had different reading patterns
compared to those who remained in the low- and middle-performance group. Specifically,
the high-to-high students made linking new information to previous pages as they
sequentially read the materials (OPEN→NEXT→PREV). In contrast, the students who
remained unchanged in the low and middle groups were more likely to repeat the reading
materials (NEXT⇄PREV). It indicates that the high-to-high group employed more
elaboration strategies, whereas the low-to-low group and the middle-to-middle group
employed re-reading strategies. The finding is probably due to differences in the prior
knowledge levels of the students, as the high-to-high groups of students had higher prior
knowledge levels and were, therefore, better able to establish links between prior
knowledge (Glogger et al., 2012). On the other hand, due to the relative insufficiency of
previous knowledge, the middle-to-middle group and low-to-low group might read
repeatedly to help them remember and understand. In fact, students may not be able to
locate the difficulties and key points of their own knowledge in re-reading. Re-reading
strategies give students an incorrect sense that they are reading effectively to facilitate
learning (Miyatsu et al., 2018). However, the re-reading strategy can be made more
effective by using highlight behaviors of the meta-recognition strategy to mark out key
content in the materials and stimulate students’ thinking about knowledge (Miyatsu et al.,
2018; Rawson et al., 2000). This was supported by the IP group’s PREV⇄NEXT→AMK
behavioral pattern which is underlining the text after reading the materials. The students in
the IP group did not just reread the materials mechanically. Instead, they engaged in
metacognitive processing after reading, further identified important information in the
materials in greater depth, and their performance on the post-test improved. Furthermore,
it is noteworthy that the middle-to-middle students reveal similar sequences of learning
behavioral transitions to those in the DP group, particularly in relation to handwritten note-
taking behaviors.
Furthermore, it is noteworthy that the middle-to-middle students reveal similar sequences
of learning behavioral transitions to those in the DP group, particularly in relation to
handwritten note-taking behaviors (AHMM⇄CHMM). The frequent addition and deletion
of handwritten notes were observed to be caused by students always recording the teacher’s
narration and blackboard writing verbatim. Due to the focus on recording, it was difficult
to determine the suitable locations of pages in the materials to take notes, resulting in
repeated additions and deletions. Similar to the process of annotation, the activity of note-
taking also requires information positioning to ensure the relevance of the teacher’s

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 18 of 23
narration, blackboard writing, and learning materials. Note-taking is commonly considered
a deeper method of information processing and a productive approach to learning (Nesbit
et al., 2006). Miyatsu et al. (2018) have argued that the act of copying material verbatim
during note-taking by students is a shallow method of processing information, which does
not improve learning outcomes more than simply not taking notes. Therefore, it is essential
to provide support for students in information positioning to improve their materials
reading more effectively. For example, providing students with structured note formats to
assist students in effectively locating information. Kauffman et al. (2011) examined the
impact of three note formats, including conventional, outline, and matrix on 119 students,
and the results demonstrated that outline and matrix notes can assist students in retrieving
information and enhancing their performance.
The present findings are significant in two major aspects. First, this study applied the LA
approach to visualize the learning behavioral patterns of students using an e-book system,
addressing the labor-consuming complexity of observation and inaccuracy of learner self-
reports regarding SRL. Second, this study addressed the limitations of some existing
studies, such as the inability to identify specific learning behavioral patterns. For instance,
Geng et al. (2020) only analyzed the relationship between the learning behaviors using
e-book systems and learning performance without considering the significant temporal
sequential behavior transitions.
Conclusion and future works
This study investigated learning behavioral patterns in a seven-week high school
mathematics course using an e-book system. This study used the BookRoll e-book system
to support students’ learning and collect learning logs. The learning behavioral patterns of
students while using BookRoll were identified through lag sequential analyses. The results
of the analysis revealed that the use of search strategies was not significant using the
e-book system and that the functions of bookmarks and quiz responses did not effectively
correlate with the reading material for all students. The usages of rehearsal and elaboration
strategies were confirmed in the e-book learning behaviors of all students. In addition, this
study also employed LA to investigate behavioral differences and learning strategies
among students with different learning performances. The results indicated that the
students with increased learning performance utilized more efficient and effective
behavioral patterns. Especially, students with decreased performance employed surface
processing strategies of repeatedly adding and deleting highlights, while students with
increased performance read the material before highlighting the contents. Students with
unchanged performance relied on different levels of prior knowledge and had different
learning behavioral patterns. Students in the unchanged low and middle performance
groups were found to adopt the same re-reading strategies as the students with decreased

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 19 of 23
performance, while students in the unchanged high group were found to employ more
elaboration strategies. Moreover, the present study also highlighted that e-book systems
should further support cognitive and metacognitive activities, such as providing structured
note formats and adding dashboard functions to assist students in monitoring and reflecting
on their learning process.
There were a few limitations with this study. Future studies should interview students
and collect student psychological data, such as the Motivated Strategies for Learning
Questionnaire (MSLQ; Pintrich & Groot, 1990), to validate the findings of the present
study. Considering that the present study only examined students with different learning
performances, further research should be undertaken to investigate students classified
under different dimensions, such as different SRL motivations. In addition, further research
is required to investigate how SRL strategies can be supported in high schools. This study
used the definition of increased performance and decreased performance based on changes
in performance groups. As such, direct changes in academic performance could not be
analyzed. Future studies should consider that the pre-test is at the same level of difficulty
as the post-test. Moreover, this study included only the 10th-grade mathematics curriculum.
Therefore, further research is needed to analyze additional subjects.
Appendix A
Table 6 The frequency and percentage of learning behaviors in the DP and IP groups
Code
DP group (n=24)
IP group (n=5)
Frequency
Percentage %
Frequency
Percentage %
AHMM
16133
41.31%
9952
62.64%
NEXT
9254
23.69%
2160
13.60%
AMK
4054
10.38%
611
3.85%
UHMM
1592
4.08%
931
5.86%
PREV
2559
6.55%
668
4.20%
OPEN
1672
4.28%
527
3.32%
DMK
1765
4.52%
172
1.08%
PJ
818
2.09%
83
0.52%
CHMM
282
0.72%
62
0.39%
QAC
235
0.60%
299
1.88%
AMM
191
0.49%
89
0.56%
CMM
98
0.25%
57
0.36%
CLOSE
153
0.39%
4
0.03%
QA
44
0.11%
21
0.13%
BMKJ
14
0.04%
136
0.86%
ABMK
43
0.11%
46
0.29%
DBMK
36
0.09%
28
0.18%
CRC
34
0.09%
24
0.15%
DMM
59
0.15%
9
0.06%
SEARCH
12
0.03%
5
0.03%
MMJ
9
0.02%
2
0.01%
SJ
1
0.00%
2
0.01%

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 20 of 23
Appendix B
Table 7 Results of the Mann Whitney U-test for learning behaviors of the IP and DP groups
Code
DP group (n=24)
IP group (n=5)
U
p
Mean
SD
Mean
SD
ABMK
1
.79
2
.41
9
.20
12
.19
44
0.382
AMK
168
.92
94
.64
122
.20
136
.55
37
.5
0.201
AMM
7
.96
10
.64
17
.80
15
.96
27
.5†
0.059
AHMM
672
.21
538
.92
1990
.40
2365
.65
41
0.295
BMKJ
0
.58
1
.72
27
.20
51
.84
42
0.323
CMM
4
.08
6
.54
11
.40
12
.44
23
.5*
0.032
CHMM
11
.75
11
.49
12
.40
9
.40
52
0.674
CRC
1
.42
3
.48
4
.80
4
.15
25
*
0.044
CLOSE
6
.38
14
.49
0
.80
1
.79
52
.5
0.674
DBMK
1
.50
2
.47
5
.60
6
.66
40
0.270
DMK
73
.54
63
.67
34
.40
30
.99
34
0.145
DMM
2
.46
5
.47
1
.80
2
.17
55
0.801
MMJ
0
.38
1
.24
0
.40
0
.89
57
0.889
NEXT
385
.58
269
.72
432
.00
324
.77
60
1.000
OPEN
69
.67
51
.81
105
.40
65
.49
41
0.295
PJ
34
.08
23
.58
16
.60
14
.15
32
.5
0.114
PREV
106
.63
101
.71
133
.60
97
.17
46
0.448
QA
1
.83
4
.11
4
.20
6
.69
35
.5
0.162
QAC
9
.79
21
.39
59
.80
72
.30
23
*
0.032
SEARCH
0
.50
0
.98
1
.00
2
.24
58
0.933
SJ
0
.04
0
.20
0
.40
0
.89
50
0.594
UHMM
66
.33
71
.78
186
.20
189
.68
34
.5
0.145
†p<0.1, * p<0.05
Abbreviations
SRL: Self-regulated learning; LA: Learning analytics; IP: Increased performance group; UP: Unchanged performance
group; DP: Decreased performance group; LSA: Lag sequential analysis.
Acknowledgements
We would like to thank F High School and Fukuoka City Board of Education for their cooperation in this research.
Authors’ contributions
Xuewang Geng and Masanori Yamada designed this research overall. Xuewang Geng and Masanori Yamada were
engaged in analysis method of this study. Hiroaki Ogata and Atsushi Shimada developed and deployed the learning
analytics platform. Xuewang Geng, Li Chen, Yufan Xu and Masanori Yamada advised the improvement of the
instructional design. Masanori Yamada supervised this research. All authors read and approved the final manuscript.
Authors’ information
Xuewang Geng is a doctoral student at the Graduate School of Human-Environment Studies, Kyushu University, Japan.
He received his master’s degree in education from Kyushu University in 2020. His research interests include
augmented reality, mobile learning, cognitive load, and learning analytics.
Li Chen is an assistant professor at the Faculty of Information Science and Electrical Engineering, Kyushu University ,
Japan. She received her Ph.D. in education from Kyushu University in 2021. Her research interests include learning
analytics, self-regulated learning, and collaborative problem-solving in STEM education.
Yufan Xu received his master’s degree in education from Kyushu University, Japan in 2021. His research interests
include social presence, data visualization, and learning analytics.
Hiroaki Ogata is a professor at the Academic Center for Computing and Media Studies, and the Graduate School of
Informatics, Kyoto University, Japan. His research includes learning analytics, educational data science, evidence-
informed education, CSCL, CSCW, and CALL.

Geng et al. Research and Practice in Technology Enhanced Learning (2024) 19:11 Page 21 of 23
Atsushi Shimada received his DE degree from Kyushu University, Japan in 2007. He is a professor at the Faculty of
Information Science and Electrical Engineering, Kyushu University, Japan. His current research interests include
learning analytics, pattern recognition, media processing, and image processing.
Masanori Yamada is a professor in the Data -Driven Innovation Initiative and the Graduate School of Human-
Environment Studies at Kyushu University, Japan. He is engaged in learning analytics research, in particular, the
relationship between learning behaviors and psychological factors such as self -regulated learning.
Funding
This research was supported in part by JST AIP Grant No. JPMJCR19U1, JSPS KAKENHI JP21K18134, JP21KK0184,
JP22H00552, and the Cross-Ministerial Strategic Innovation Promotion Program of the Cabinet Office.
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
Declarations
Competing interests
No conflict of interest.
Author details
1 Graduate School of Human-Environment Studies, Kyushu University, Fukuoka, Japan
2 Faculty of Information Science & Electrical Engineering, Kyushu University, Fukuoka, Japan
3 Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan
4 Data-Driven Innovation Initiative, Kyushu University, Fukuoka, Japan
Note: Mr. Yufan Xu’s affiliation refers to his affiliation at the time of submission.
Received: 8 June 2022 Accepted: 2 July 2023
Published online: 1 January 2024 (Online First: 18 July 2023)
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