=Paper=
{{Paper
|id=Vol-2876/paper3
|storemode=property
|title=Modelling Student Behavior in Synchronous Online Learning during the COVID-19 Pandemic
|pdfUrl=https://ceur-ws.org/Vol-2876/paper3.pdf
|volume=Vol-2876
|authors=Gianni Fenu,Roberta Galici
|dblpUrl=https://dblp.org/rec/conf/wsdm/FenuG21
}}
==Modelling Student Behavior in Synchronous Online Learning during the COVID-19 Pandemic ==
https://ceur-ws.org/Vol-2876/paper3.pdf
Modelling Student Behavior in Synchronous Online
Learning during the COVID-19 Pandemic
Gianni Fenu, Roberta Galici
Department of Mathematics and Computer Science, University of Cagliari, V. Ospedale 72, 09124 Cagliari, Italy
Abstract
The outbreak of COVID-19 has significantly impacted on education, training, and mobility opportunities
provided to learners, teachers, and educators. In response to this situation, synchronous online learn-
ing has been massively adopted in universities and, therefore, the analysis of student behavior in this
context is becoming essential. However, the literature has mainly tackled student behavior modelling in
asynchronous online learning (e.g., interactions with pre-recorded videos), making how students learn
in synchronous online learning so far under-explored. Grounding on the experience on online learning
at the University of Cagliari, this paper proposes a preliminary study on student participation in syn-
chronous lessons, covering more than 80 courses, and identifies patterns and strategies of engagement
in classes over the semester. Then, by means of clustering techniques applied to data from more than
25,000 students, we model fine-grained behavioral strategies and discuss how they are related with the
student’s experience across courses and whether planning elements (e.g., the hour of the day a lesson
is delivered) influence their level of participation. We expect that this study will support the educa-
tional stakeholders with preliminary data-driven informed decisions and pose the basis for data-driven
personalization for students and teachers involved in online synchronous learning.
Keywords
E-Learning, Engagement, Clustering, Learning Behavior, Student Modelling, Synchronous Learning.
1. Introduction
The Covid-19 pandemic has forced schools and universities to move face-to-face learning and
teaching in classrooms to online environments. The term online learning refers to educational
strategies that rely on the use of multimedia and Internet technologies for learning and teach-
ing. For instance, this strategy has been originally adopted to support students who cannot
physically attend lessons in classroom. Recent literature has focused on providing guidelines
on and discussing benefits and limitations of online learning, mainly identifying asynchronous
and synchronous strategies and addressing questions such as when, why and how to use these
two educational delivering modes [1]. Specifically, synchronous learning refers to learning ex-
periences where a group of participants is engaged at the same time, either in the same physical
location (e.g., a classroom) or the same online environment (e.g., a web conference room), and
can interact among each other in real time. Conversely, asynchronous learning refers to the
L2D’21: First International Workshop on Enabling Data-Driven Decisions from Learning on the Web, March 12, 2021,
Jerusalem, IL
" fenu@unica.it (G. Fenu); roberta.galici@unica.it (R. Galici)
0000-0003-4668-2476 (G. Fenu); 0000-0002-1492-3514 (R. Galici)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
strategies where instructors and students are not engaged in the learning process at the same
time (e.g., interactions with pre-recorded videos or on-demand online exams) [2].
Given the increasing attention on synchronous and asynchronous online learning during
the last years [3, 4], analyzing how students behave in these contexts becomes of paramount
importance for several aspects, such as improving teaching strategies, sizing technological in-
frastructures, assessing and predicting student engagement, preventing students from losing
interest and possibly dropping out, and finally improving the student’s learning outcomes. It
has been proved that asynchronous learning benefits online students by providing them with
the flexibility they need and giving them more time to reflect on the course content. However,
all those characteristics that distinguish a face-to-face lesson are missing in asynchronous con-
texts (e.g., the opportunity of asking questions and the feeling of being part of a class). There-
fore, synchronous lessons are usually preferred while moving these face-to-face online.
Tracking students’ behavior in both delivering modes is producing a huge amount of data
that can open new ways for better understanding how students learn and, therefore, analyze
data coming these contexts is becoming an unprecedented opportunity for the research and
educational research communities. A vast amount of studies on asynchronous online learning
has been carried out in literature [5, 6, 7, 8, 9, 10], mainly dealing with the analysis of click-
streams to predict whether a student will drop out of an online course [11, 12] or the final grade
a student will receive [13, 14]. Synchronous online learning is conversely an under-explored
domain and, given its growing adoption, there is an urgent need of analyzing how students
learn and how their behavior can be modelled under this educational modality as well [15].
In this paper, we analyze how students interact with a synchronous online learning platform
in all the courses delivered by a university over a semester. For this purpose, we first collect
and pre-process student’s interactions in the form of entry-exit records from lesson rooms.
Then, we apply clustering techniques to model behavioral patterns shared between students
at faculty and course level. Finally, we identify and interpret the clusters, depicting their main
characteristics. More precisely, we will answer to the following research questions:
1. How does the level of student’s participation in courses change over a semester?
2. Which are the principal participation patterns at faculty and course level?
3. Does the hour of the day a course is delivered influence the level of participation?
The rest of this paper is organized as follows: Section 2 describes related work. Section 3
introduces the educational context and the dataset considered in this paper, while Section 4
provides a preliminary analysis of the students’ participation level, the participation strategy
modelling, and their analysis over the semester. 5, we represent the discussion, implications
and limitations of our work. Finally, Section 6 provides insights for future work.
2. Related Work
Our research lies at the intersection between studies on the analysis of synchronous learning
from an educational science perspective and those on educational data mining in the context
of online learning. To better contextualize our work, therefore, we discuss representative prior
work in these two fields and identify how we differ from them.
2.1. Synchronous Online Learning
Synchronous online learning is one of the most widely adopted methods by teachers due to
several reasons, defined as a real-time, instructor-led online learning experience where partic-
ipants are connected at the same time and communicate with each other [16]. For instance,
students can ask questions and get answers in real-time, as the lesson is live, and the instruc-
tor can assess and shape students’ understanding in real-time, adjusting his/her spoken words
accordingly. Moreover, students feel an increased sense of the instructor actually being there
and instructors can facilitate workshop-style classes and run breakout group activities, live
chats or office hours allow for real-time interaction (e.g., a conversation and synchronous ses-
sions provide a schedule to help students who struggle with task initiation to stay on track).
To better understand the benefits of synchronous online learning, for instance, Francescucci
et al. [17, 18] investigated the effects of a novel online synchronous course format, based on
virtual, interactive, real-time, instructor-led rooms, on students’ learning outcomes and their
level of engagement, compared to a fully face-to-face format in classroom. Similarly, Kohnke
and Moorhouse [19] analyzed whether students’ perceptions about communities of inquiry
constructs reflect their behaviours in the synchronous online learning settings and in what
ways the interrelationships between constructs differ in terms of students’ perceptions and
behaviours. The widespread adoption of synchronous online courses has become more and
more evident during the last year, as demonstrated by the recent studies conducted by [20, 21],
whose goal was to evaluate learner’s behavior in synchronous online continuing education lec-
tures during the COVID-19 pandemic. Some high schools found themselves in crisis to manage
online learning [22]. Indeed, it has become clearer that the education system is susceptible to
external dangers [23]. Feldman [24] addresses student assessment during this pandemic on how
districts can legislate unbiased and evenhanded grading policies based on these recommenda-
tions; (i) pandemic related anxiety will have negative effects on student academic performance,
(ii) academic performance of students might be affect by racial, economic and resource differ-
ences, and (iii) the larger parts of instructors were not effectively ready to deliver high-quality
instruction remotely.
2.2. Student Behavior Modelling
Educational data mining deals with the development of methods for exploring the increasingly
large-scale data obtained in educational settings [25, 26, 27, 28, 29]. Recent studies modelled
student’s behavioral patterns in terms of their participation in online initiatives (e.g., forums,
courses) and investigated the existence of relationships between students’ behavioral patterns
and their learning performance, assuming that different habits of online students will lead to
different levels of learning achievement in students [30]. Some studies focused their attention
on analyzing server logs, to construct knowledge on typical patterns of online learning behav-
iors, to further discover the unique advantages of data mining techniques to support dynamic
online instruction, and to build a predictive model for online learning [31]. In [32], data mining
techniques were applied to extracted LMS server logs to analyze and compare student online
learning behaviors between peer-moderated and teacher-moderated groups in an undergradu-
ate online collaborative project-based learning course in Taiwan. The authors in [33] compared
the performance of various clustering and classification algorithms applied on the same educa-
tional dataset. In [34], the authors provided an unexplored review of educational data mining
based on clustering with respect to teaching and learning processes. In [35, 36], a clustering
approach to partition students into different groups based on their learning behavior has been
presented. Furthermore, the authors presented the personalized e-learning system architec-
ture used to detect and adapt teaching contents according to the students’ level of knowledge.
Others, such as [37, 38], used the K-Means clustering algorithm to model behavioral patterns
of passing and failing students and others built effective indicators of students’ performance in
degree programmes. Our work differs from prior work in terms of educational context, given
that we tackle synchronous and not asynchronous online learning, while we rely on similar
techniques to model behavior in this emerging context (e.g., K-Means). We argue that behav-
ioral patterns are highly dependent from the educational context, so our study brings novel
observations and contributions to better understand the synchronous context.
3. Educational Context and Dataset
Our work considers data coming from synchronous learning delivered by an Italian university.
Specifically, the University of Cagliari has chosen to provide its lesson-related services through
Adobe Connect1 , an e-learning platform where students can access to virtual rooms by means
of computers, tablets and smartphones. For each student and each day of a given university
lesson, we analyzed their participation level based on the amount of lesson time attended by
the student (i.e., remained connected to virtual room). Before describing our approach, we
present the context, the university structure, and the data collected from Adobe Connect.
University Structure Description. More than 25,000 students have been covered by our
study, across the lessons of each degree programme. Six faculties have been involved, namely
Biology and Pharmacy, Engineering and Architecture, Medicine and Surgery, Science, Economic,
Law and Political Sciences, and Humanities. Figure 1 shows the number of degree programmes
per faculty, for a total of 89 degree courses. Each faculty nominates a person for coordinating
the lessons planning across programmes, booking then the virtual rooms in Adobe Connect.
Online Educational Infrastructure and Delivering. Each lesson has been delivered in an
Adobe Connect virtual room, which provides tools and services for teleconference, e-learning
sessions, and collaborative content creation and delivering. Each virtual room is a permanent
online space an instructor can use to deliver lessons to students. In the context of the university
we consider, more than 50 virtual rooms have been created, and each room was assigned to a
lesson of a specific course at a given time slot, similarly to classrooms assignments in a face-to-
face context. Then, each instructor is assigned to a virtual room on some fixed time slots across
weeks. Students enrolled in a specific course can login using their Adobe Connect account and
enter the virtual room associated to the desired lesson. As soon as the instructor starts the
lesson, students can take part in it. During the lesson, they can virtually raise the hand, write
messages in a chat, and ask the teacher to activate the video and audio in case they want to
answer to some questions or ask questions and then participate in the discussion. Figure 2
1
https://www.adobe.com/products/adobeconnect.html
Figure 1: Bachelor’s and Master’s degree programmes per faculty covered by our study (89 in total).
Figure 2: Schema of the online synchronous environment considered in this study.
provides a schematic representation of the Adobe Connect environment.
Data Collection Processes and Format. For each lesson, the date and time of entry and
exit of students from the virtual room, the content of the chat, and the reactions and raised
hands have been traced. However, for privacy reasons, this paper only deals with the analysis
of entry-exit data. This data is structured in folders, following a hierarchical structure aligned
with the faculty–degree-course structure of a university. Therefore, the main folder includes
six sub-folders, one per faculty. Inside each sub-folder, one Excel file with multiple sheets
pertaining to the entry-exit student’s records is stored for each course in that faculty. Each
sheet includes entry-exit records for a specific lesson of that course. Table 1 provides an
example of how a sheet is structured. We collected a total of 6,296 sheets across faculties and
degree programmes, with 684 sheets for the Faculty of Biology and Pharmacy, 1,320 sheets for
the Faculty of Engineering and Architecture, 947 sheets for the Faculty of Medicine, 760 sheets
for the Faculty of Science, 1,362 sheets for the Faculty of Economic, Law and Political Sciences,
and 1,223 sheets for the Faculty of Humanities (around 464.5 MB of data). This data covers an
entire semester, from March to June 2020.
Example of a
Attribute Description registered user
(Guest)
The id of the transcription and it is unique
transcript-id 4445626093
for each line
The id related to that session. When a
teacher closes the classroom and ends the
asset-id 4445465743
meeting, another one with a different id is
generated at the next login
sco-id The unique id of the virtual classroom 4211809190
The unique id of the registered user. For guest 3727616656
principal-id
logins this field is empty (-)
student@gmail.com
login It is the username and for guests it is empty
(-)
It is the name field, which is also present 2019_20/40/12345 John Smith
session-name
for guests (Guest Name)
It is name of the virtual classroom. Now it
sco-name has changed its name and it is called CdL Letters - Room 3
"training object"
date-created Is the timestamp of entry into the classroom 2020-11-10 08:37:50
2020-11-10 08:38:02
date-end It is the timestamp of leaving the classroom
(-)
It is the registered username field and it is not 2019_20/40/12345 John Smith
participant-name
present for guests (-)
answered-survey If a survey is proposed, indicate who answered 0
Table 1
Example of data recorded for a lesson of a given course.
4. Student Behavior Characterization
In this section, guided by three research questions, we will analyze student participation pat-
terns throughout the semester, how student groups are related to the courses and if there is a
correlation between student participation and class schedule.
Participation Level. In this section, we aim to investigate the participation level of students
at faculty level in terms of the average number of students who attended courses in that fac-
ulty, to understand whether the trend of the participation level changes over the semester. As
mentioned in Section 3, Adobe Connect collects entry-exit logs in forms of CSV files. For each
CSV pertaining to a given lesson of a course, we counted the number of students present in
each lesson. Cases in which someone from the university staff logged in, empty fields and
duplicates were excluded (e.g., some students, due to connection problems, logged in several
times, for this reason a single access for each student was considered, not counting the other
attempts). This procedure was repeated for all the lessons pertaining to a given faculty, aver-
aging the number of students present in a lesson on a given day across all the lessons delivered
for that faculty on that day. Figure 3 shows the participation level over the semester for the six
graphs considered faculties. It should be noted that we cannot compare the extent to which
Figure 3: Average number of students per lesson for the six faculties considered in this study.
the student participate in the lessons across faculties, given that each faculty has a different
number of students. We are thus interested in the general participation trend in each faculty,
individually. For each plot, the time span in which the lessons have been held is represented
on the x-axis, ranging from mid-March to early June. The average of the number of students is
indicated in the y-axis. All plots are characterized by a strong initial interest, but over time this
interest tends to decrease. This decline is very present in three faculties in particular, namely
Medicine and Surgery, Economic, Law and Political Science, and Humanities. Science is one
of the few that tends to maintain a constant student average, but even here, there is a decline
as the months go by2 . Based on these patterns we understood that there is a need for an adap-
tive load based on the needs of the varying faculties. This kind of graphs have been generated
and analyzed mainly to give support to students and teachers. In particular, if we focus our
attention on trends, it is immediately clear that over time, fewer and fewer students connect
to the platform. This fact can help teachers to make the lessons more interactive and perhaps
increase the attention threshold, trying to limit as much as possible the drop in students over
the months. While these plots point out on the possibility of adapting the technological infras-
tructure based on the changing participation level over time, it does not tell us to what extent
these patterns vary for each course in a given faculty. Therefore, the following section analyzes
the participation patterns at course level.
Group Modelling. In this section, we are interested in investigating whether there exist core
participation patterns in each course and how these patterns vary across courses. To this end,
due to the amount of courses included in the dataset and to better shape our analysis, we
focused only on the courses of a specific faculty and a specific degree programme, namely
2
Please, note that the peaks down in the plots are associated to lessons that were planned but not given then.
This highlights the need for a more fine-grained analysis, we left as a future work.
Science and its Bachelor’s Degree in Computer Science. Specifically, we have analyzed the second
semester, a period between March and June 2020, including 9 courses (4 courses of the first
year, 4 courses of the second year and 1 course of the first year). Given the large number
of faculties and consequently of courses, we have selected a degree course with significant
patterns that can also be found in other courses. We leave pattern analysis in other courses
besides computer science courses to future work. For all the courses in this Bachelor’s Degree,
all the Excel files with the corresponding entry-exit records have been examined. For each
course and lesson in that course, we measure the amount of minutes a student was connected
to that lesson during that day, i.e. how long that particular student was connected to the
platform. Therefore, for each student in a given course, we computed a N-dimensional vector
(where N is the total number of lessons in that course) and each cell contains the amount of
minutes that student was connected into the platform for that lesson. Then, we computed the
pairwise distances across vectors and feed the pairwise-distance vectors (one per student) of
all the students in that course to a K-Means clustering algorithm. In order to identify the right
number of clusters, we applied the Elbow method, using the Silhouette score as a measure of
the quality of the clusters. Figure 4 shows the centroids of the clusters identified through the
applied methodology for each considered course. It can be observed that, for almost all the
courses, two main clusters of students have been identified and these two clusters are well-
balanced in terms of the number of students they include. One cluster refers to the students
who constantly follow the course over the semester, while the second cluster identifies students
whose level of participation decreases over time. This finding is particularly important as a first
point for shaping adaptive interventions to students who would need to be motivated along the
course. Interestingly, the course in the plot (d) was characterized by three main clusters. For
this course, there is a clear subset of students who do not engage with the course at all, from its
beginning. Observing this also highlights the need of multiple level of adaptive interventions3 .
Considering the participation patterns with respect to the hour of the day a course is delivered,
we observed that there is a relation between the slope of the curves for the cluster who lose
engagement and the time of the day, for the courses delivered on the same year of study. This
would be important for the future planning of the lessons schedule.
5. Implications and Limitations
There are some limitations on our work, but these do not compromise reliability of this pre-
liminary study. We only used the entry and exit data of the students’ connection, without
considering the intermediate intervals, so we do not manage cases where a student may have
been connected for a short time and have made many intermediate accesses, without perhaps
following the lesson for a certain period. Furthermore, due to privacy constraints, no addi-
tional information from the students was considered, e.g., all student interventions via chat or
3
It should be noted that the line associated to each cluster has sometimes a peak towards zero, e.g., in plot (a) in
Figure 4 for the lesson with ID 3. By carefully observing the data, it is possible to notice that for that lesson only two
students were present. For the peak with the lesson with ID 7, the students connected for a maximum of 30 minutes.
This could mean that the platform had problems and the connection was interrupted; another alternative would be
that the teacher assigned an exercise and so students no longer needed to stay online. Similar circumstances were
present for the other courses.
(a) 1st Year (b) 2nd Year (c) 1st Year
9.00 - 10.40 a.m. (Mon) 9.00 - 10.40 a.m. (Wed) 9.00 - 10.40 a.m. (Tue - Thu)
(d) 1st Year (e) 1st Year (f) 3rd Year
11.00 - 12.40 a.m. (Tue - Thu) 11.00 - 12.40 a.m. (Thu) 11.00 - 12.40 a.m. (Mon - Tue)
(g) 2nd Year (h) 2nd Year (i) 2nd Year
3.00 - 4.40 p.m. (Mon - Wed) 3.00 - 4.40 p.m. (Thu - Fri) 3.00 - 4.40 p.m. (Tue)
Figure 4: Centroids of the clusters identified for the nine considered courses.
system interactions (raised hand) were not included in this study. A limited period was taken
into consideration, given that the data refers to three months of lessons (i.e., a semester). This
means that, taking into account a longer period of time, different findings could emerge, so it is
our task to extend the work and analyze longer periods of time. The process has not considered
issues associated with connection problems. This can cause, as we have seen previously, some
problems on the analysis, in particular on the entry-exit records. It is not clear to us if users
who connected for short periods, less than 20 minutes, decided to leave the lesson or if they
had connection problems and were unable to reconnect shortly. Lastly, we used the K-Means
algorithm, but other techniques can be adopted to model the data, so it will be our future task
to try to model the data using other techniques and make comparisons.
6. Conclusions and Future Work
In this paper we analyze how students interact through the use of Adobe Connect, a syn-
chronous online learning platform, by means of data extracted from the platform logs. Then,
we modelled the students’ behavior, measuring the participation of students for each lesson
across an entire semester on all the courses and faculties of a university. Based on the results:
• The workload associated to the technological infrastructure varies across the semester,
with the first period subjected to higher workloads. This finding can be used to adapt
the computational resources associated to the platform across time.
• Half of the students often exhibited an strong decrease in attention along the semester,
with the majority of them following regularly at beginning, then decreasing consider-
ably. In some degree programs, this behavior is more evident.
• The patterns exhibited in the analyzed data open up to adaptive interventions strategies,
such as notifying the teacher in case there are students who are loosing engagement or
who have stopped following, in order to understand the reasons and try to keep them
engaged.
• There is a relation between the participation level and the time of the day the courses
are delivered, with a more rapid decrease in participation for courses delivered earlier in
the day. This would be an important element to consider for the future planning of the
lessons schedule.
Our work opens up a wide range of future research directions. We plan to enrich the amount
of data analyzed, gradually inserting all the logs relating to the lessons that are done at the
moment and those done in the first half of 2020 (September-December). Furthermore, we will
extend our analysis to other approaches, taking into account chats, student reactions, raised
hands. It would be also interesting to make a comparison with students’ participation in face-
to-face courses in the pre-pandemic period and explore whether the behavioral patterns are
similar to those we showed in this paper. Finally, our findings can be of help for teachers to
understand the students’ participation level during their lessons, given that it is important that
a teacher is aware of how many students are present and how they follow the lessons.
Acknowledgments
Roberta Galici gratefully acknowledges the University of Cagliari for the financial support of
her PhD scholarship.
References
[1] S. Hrastinski, Asynchronous and synchronous e-learning, Educause quarterly 31 (2008)
51–55.
[2] A. T. Peterson, P. N. Beymer, R. T. Putnam, Synchronous and asynchronous discussions:
Effects on cooperation, belonging, and affect., Online Learning 22 (2018) 7–25.
[3] G. Fenu, M. Marras, Leveraging continuous multi-modal authentication for access control
in mobile cloud environments, in: New Trends in Image Analysis and Processing - ICIAP
2017 - ICIAP International Workshops, volume 10590 of Lecture Notes in Computer Science,
Springer, 2017, pp. 331–342.
[4] S. Barra, M. Marras, G. Fenu, Continuous authentication on smartphone by means of
periocular and virtual keystroke, in: Network and System Security - 12th International
Conference, NSS 2018, volume 11058 of Lecture Notes in Computer Science, Springer, 2018,
pp. 212–220.
[5] F. Yang, F. W. Li, Study on student performance estimation, student progress analysis, and
student potential prediction based on data mining, Computers & Education 123 (2018) 97–
108.
[6] M. Cantabella, R. Martínez-España, B. Ayuso, J. A. Yáñez, A. Muñoz, Analysis of stu-
dent behavior in learning management systems through a big data framework, Future
Generation Computer Systems 90 (2019) 262–272.
[7] T. Lubicz-Nawrocka, K. Bunting, Student perceptions of teaching excellence: an analysis
of student-led teaching award nomination data, Teaching in Higher Education 24 (2019)
63–80.
[8] Y. Wang, W. M. White, E. Andersen, Pathviewer: Visualizing pathways through student
data, in: Proceedings of the 2017 CHI Conference on Human Factors in Computing Sys-
tems, 2017, pp. 960–964.
[9] P. Prinsloo, S. Slade, M. Khalil, Student data privacy in moocs: A sentiment analysis,
Distance Education 40 (2019) 395–413.
[10] L. R. Amir, I. Tanti, D. A. Maharani, Y. S. Wimardhani, V. Julia, B. Sulijaya, R. Puspitawati,
Student perspective of classroom and distance learning during covid-19 pandemic in the
undergraduate dental study program universitas indonesia, BMC medical education 20
(2020) 1–8.
[11] C. G. Brinton, S. Buccapatnam, M. Chiang, H. V. Poor, Mining mooc clickstreams: Video-
watching behavior vs. in-video quiz performance, IEEE Transactions on Signal Processing
64 (2016) 3677–3692.
[12] G. Kőrösi, R. Farkas, Mooc performance prediction by deep learning from raw click-
stream data, in: International Conference on Advances in Computing and Data Sciences,
Springer, 2020, pp. 474–485.
[13] J. L. Harvey, S. A. Kumar, A practical model for educators to predict student performance
in k-12 education using machine learning, in: 2019 IEEE Symposium Series on Computa-
tional Intelligence (SSCI), IEEE, 2019, pp. 3004–3011.
[14] R. I. Rashu, N. Haq, R. M. Rahman, Data mining approaches to predict final grade by over-
coming class imbalance problem, in: 2014 17th International Conference on Computer
and Information Technology (ICCIT), IEEE, 2014, pp. 14–19.
[15] Q. Cao, T. E. Griffin, X. Bai, The importance of synchronous interaction for student sat-
isfaction with course web sites, Journal of Information Systems Education 20 (2009) 331.
[16] D. Leeder, H. Wharrad, T. BChir, Beyond institutional boundaries: reusable learning ob-
jects for multi- professional education (2002).
[17] A. Francescucci, L. Rohani, Exclusively synchronous online (viri) learning: The impact
on student performance and engagement outcomes, Journal of marketing Education 41
(2019) 60–69.
[18] A. Francescucci, M. Foster, The viri (virtual, interactive, real-time, instructor-led) class-
room: The impact of blended synchronous online courses on student performance, en-
gagement, and satisfaction., Canadian Journal of Higher Education 43 (2013) 78–91.
[19] Ü. Çakıroğlu, S. Kılıç, Understanding community in synchronous online learning: do
perceptions match behaviours?, Open Learning: The Journal of Open, Distance and e-
Learning 35 (2020) 105–121.
[20] X. Yang, D. Li, X. Liu, J. Tan, Learner behaviors in synchronous online prosthodontic
education during the 2020 covid-19 pandemic, The Journal of prosthetic dentistry (2020).
[21] T. C. Shoepe, J. F. McManus, S. E. August, N. L. Mattos, T. C. Vollucci, P. R. Sparks, Instruc-
tor prompts and student engagement in synchronous online nutrition classes, American
Journal of Distance Education 34 (2020) 194–210.
[22] O. B. Adedoyin, E. Soykan, Covid-19 pandemic and online learning: the challenges and
opportunities, Interactive Learning Environments (2020) 1–13.
[23] A. Bozkurt, R. C. Sharma, Emergency remote teaching in a time of global crisis due to
coronavirus pandemic, Asian Journal of Distance Education 15 (2020) i–vi.
[24] J. Feldman, To grade or not to grade, Educational Leadership 77 (2020) 43–46.
[25] O. Scheuer, B. M. McLaren, Educational Data Mining, Springer US, Boston, MA,
2012, pp. 1075–1079. URL: https://doi.org/10.1007/978-1-4419-1428-6_618. doi:10.1007/
978-1-4419-1428-6_618.
[26] D. Dessì, M. Dragoni, G. Fenu, M. Marras, D. R. Recupero, Evaluating neural word em-
beddings created from online course reviews for sentiment analysis, in: Proceedings of
the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019, ACM, 2019, pp.
2124–2127.
[27] L. Boratto, G. Fenu, M. Marras, Connecting user and item perspectives in popularity
debiasing for collaborative recommendation, Inf. Process. Manag. 58 (2021) 102387.
[28] D. Dessì, G. Fenu, M. Marras, D. R. Recupero, Leveraging cognitive computing for multi-
class classification of e-learning videos, in: The Semantic Web: ESWC 2017 Satellite
Events - ESWC 2017 Satellite Events, Portorož, volume 10577 of Lecture Notes in Computer
Science, Springer, 2017, pp. 21–25.
[29] G. Fenu, M. Marras, M. Meles, A learning analytics tool for usability assessment in moodle
environments, Journal of e-Learning and Knowledge Society 13 (2017).
[30] J. Byun, D. Pennington, J. Cardenas, S. Dutta, J. Kirwan, Understanding student behaviors
in online classroom: data scientific approach, in: 2014 IEEE International Congress on
Big Data, IEEE, 2014, pp. 802–803.
[31] J.-L. Hung, K. Zhang, Revealing online learning behaviors and activity patterns and mak-
ing predictions with data mining techniques in online teaching, MERLOT Journal of
Online Learning and Teaching (2008).
[32] J.-L. Hung, S. M. Crooks, Examining online learning patterns with data mining techniques
in peer-moderated and teacher-moderated courses, Journal of Educational Computing
Research 40 (2009) 183–210.
[33] R. Ahuja, A. Jha, R. Maurya, R. Srivastava, Analysis of educational data mining, in:
Harmony Search and Nature Inspired Optimization Algorithms, Springer, 2019, pp. 897–
907.
[34] M. W. Rodrigues, S. Isotani, L. E. Zarate, Educational data mining: A review of evaluation
process in the e-learning, Telematics and Informatics 35 (2018) 1701–1717.
[35] S. Kausar, X. Huahu, I. Hussain, Z. Wenhao, M. Zahid, Integration of data mining cluster-
ing approach in the personalized e-learning system, IEEE Access 6 (2018) 72724–72734.
[36] D. Ding, J. Li, H. Wang, Z. Liang, Student behavior clustering method based on campus big
data, in: 2017 13th International Conference on Computational Intelligence and Security
(CIS), IEEE, 2017, pp. 500–503.
[37] M. Durairaj, C. Vijitha, Educational data mining for prediction of student performance
using clustering algorithms, International Journal of Computer Science and Information
Technologies 5 (2014) 5987–5991.
[38] R. Asif, A. Merceron, S. A. Ali, N. G. Haider, Analyzing undergraduate students’ perfor-
mance using educational data mining, Computers & Education 113 (2017) 177–194.