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How Online Collaborative Learning leads to improved Online Learning Experience in Higher Education

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We are facing an exciting and challenging time for collaborative learning in higher education. During the corona pandemic, educational institutions such as universities are forced to intensify online teaching, as face-to-face teaching is neither possible nor permitted to the usual extent. This situation represents a challenge but also an unintended opportunity in higher education, as educators now must take the step into the digital transformation and gain extensive experiences in digitizing their teaching. This requires reflection on teaching methods and collaborative capabilities offered by social media. Our aim is to derive recommendations for improved online learning experiences. To achieve this goal, we are researching the following question: Which learning objectives from Bloom's Taxonomy can best be achieved via online collaboration? Online collaboration has played an essential role in research and practice since the end of the 1980s. A current definition describes collaboration as the work of two or more individuals on shared material, deliberately planned to achieve a common group objective. To achieve this group objective, communication, coordination, and cooperation of the actors involved are necessary. There is also extensive research on the use of online collaboration in the field of teaching, which we can draw on. Online collaborative learning describes an internet-technology supported pedagogical process that encourages students to discuss information and problems from different perspectives, and to elaborate and refine their understanding to re-and co-construct (new) knowledge or to solve a problem. Based on a quantitative online survey of 1.080 students from 44 German universities, this study demonstrates that online collaborative learning in groups can lead to improved individual learning experience and learning results but also can require more time and effort than with online learning without collaborative activities. If online teaching is combined with collaborative learning, advantages from traditional offline classroom settings can be transferred to online teaching.
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How Online Collaborative Learning leads to improved Online
Learning Experience in Higher Education
Tobias Knopf, Stefan Stumpp and Daniel Michelis
Anhalt University of Applied Sciences, Bernburg, Germany
tobias.knopf@hs-anhalt.de
stefan.stumpp@hs-anhalt.de
daniel.michelis@hs-anhalt.de
DOI: 10.34190/ESM.21.010
Abstract: We are facing an exciting and challenging time for collaborative learning in higher education. During the corona
pandemic, educational institutions such as universities are forced to intensify online teaching, as face-to-face teaching is
neither possible nor permitted to the usual extent. This situation represents a challenge but also an unintended
opportunity in higher education, as educators now must take the step into the digital transformation and gain extensive
experiences in digitizing their teaching. This requires reflection on teaching methods and collaborative capabilities offered
by social media. Our aim is to derive recommendations for improved online learning experiences. To achieve this goal, we
are researching the following question: Which learning objectives from Bloom’s Taxonomy can best be achieved via online
collaboration? Online collaboration has played an essential role in research and practice since the end of the 1980s. A
current definition describes collaboration as the work of two or more individuals on shared material, deliberately planned
to achieve a common group objective. To achieve this group objective, communication, coordination, and cooperation of
the actors involved are necessary. There is also extensive research on the use of online collaboration in the field of
teaching, which we can draw on. Online collaborative learning describes an internet-technology supported pedagogical
process that encourages students to discuss information and problems from different perspectives, and to elaborate and
refine their understanding to re- and co-construct (new) knowledge or to solve a problem. Based on a quantitative online
survey of 1.080 students from 44 German universities, this study demonstrates that online collaborative learning in groups
can lead to improved individual learning experience and learning results but also can require more time and effort than
with online learning without collaborative activities. If online teaching is combined with collaborative learning, advantages
from traditional offline classroom settings can be transferred to online teaching.
Keywords: Online Collaboration, Online Collaborative Learning, E-Learning, Covid 19, Learning Experience
1. Online Teaching in Pandemic Situation
Education is a cornerstone of our society. Important past research helped define education effectiveness in the
traditional offline classroom setting (Young, 2006). For several years, institutions of higher education have
increasingly embraced online education (Kim and Bonk, 2006). This development is promoted by technological
progress (McBrien et al., 2009). In 2020, the Covid 19 pandemic catalyzed online education nearly overnight:
millions of students and educators across the globe were forced to teach online under pandemic conditions
(Valverde-Berrocoso et al., 2020). This situation represents a challenge but also an unintended opportunity in
higher education, as educators now must take the step into digital transformation and gain extensive
experience in digitizing their teaching.
Online teaching in institutions of higher education can be described as the process of educating students via
the internet with various online-based media, such as video calls, webinars, online learning platforms and
social media (Mishra et al., 2020). Online learning, on the other hand, can be defined as “learning experiences
in synchronous or asynchronous environments using different devices (e.g., mobile phones, laptops, etc.) with
internet access. In these environments, students can be anywhere (independent) to learn and interact with
instructors and other students” (Singh and Thurman, 2019). Also, online learning is defined as “a tool that can
make the teachinglearning process more student-centred, more innovative, and even more flexible”
(Dhawan, 2020). Research in online teaching and online learning most often addresses the keywords MOOC
(massive open online courses), e-Learning, virtual teaching-learning strategies, and interactive learning
environments (Valverde-Berrocoso et al., 2020).
Several research studies have covered effective pedagogical strategies for online teaching. Young (2006), for
example, pointed out that effective online teaching can be realized by adapting to student needs, providing
meaningful examples, motivating students to do their best, facilitating the course effectively, delivering a
valuable course, communicating effectively, and showing concern for student learning. In addition, both
advantages and disadvantages for online teaching and learning were identified. The advantages cited are
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Tobias Knopf, Stefan Stumpp and Daniel Michelis
flexibility of time and place, high reach, access to a wide range of courses and content, and direct feedback.
The disadvantages are an unequal distribution of ICT (Information and Communication Technology)
infrastructure, the different quality of online teaching, digital illegality, digital divide, and technology costs &
obsolescence (Dhawan, 2020).
The pandemic situation and the associated changeover to digital semesters so far have clearly demonstrated
the need for support for educators and students for online teaching and learning. In this paper, we focus on
improving the quality of online teachisng by explicitly engaging collaborative learning with the aim of
increasing learning success among students. Our research objective is to derive recommendations for
improved online learning experiences. To achieve this goal, we are researching the following question:
RQ: Which learning objectives from Blooms Taxonomy can best be achieved via online collaboration?
To answer this research question, we have structured this paper as follows: chapter 2, the theoretical part,
deals with Bloom’s Taxonomy of educational objectives in digital environments and online collaborative
learning. Bloom’s Taxonomy presents different levels of learning objective achievements, which we would like
to examine in the light of online collaborative learning. In the third chapter we explain our research
methodology, a quantitative online survey of 1080 students from 44 German universities. In the fourth chapter
we present our results, the fifth chapter contains limitations and future work. In the final chapter 6 we come
to our conclusion.
2. Theoretical Background
2.1 Bloom’s Taxonomy of educational objectives in digital environments
Bloom’s Taxonomy of Educational Objectives was established in 1956 and represents a classification of major
educational objectives from “lower order thinking skills” to “higher order thinking skills”. For this purpose,
Bloom defined the dimensions of knowledge, comprehension, application, analysis, synthesis, and evaluation
(Bloom et al., 1956). It is a taxonomy of learning objectives in the cognitive learning field (knowledge and
intellectual skills). In a further development and empirical foundation of this approach Anderson and
Krathwohl are considering six cognitive process dimensions in their revision of Bloom’s taxonomy (Anderson
and Krathwohl, 2001). These dimensions are shown in figure 1.
The most basic learning objective of students is their ability to recall facts and basic concepts, like definitions
of words and repeating their meaning. Anderson and Krathwohl alter Bloom’s least cognitive rigour needing
dimension to remember. Understand is the next step in the cognitive process requiring the students to explain
those remembered facts and concepts. Answering questions and solving problems, by applying the understood
information in new situations, is the third dimension of the taxonomy. At the fourth level of the process
students must analyze information by drawing connections among ideas. The next cognitive level is taking
apart information, to justify a stand or decision. Students can evaluate, if they can make accurate assessments
of ideas and concepts to find effective solutions and justify the decision for it. The most cognitive rigour is
applied by students that Create new or original work, for example by designing a poster or writing a report.
Figure 1: Revision of Bloom’s Taxonomy of educational objectives (Anderson and Krathwohl, 2001)
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Bloom’s Taxonomy is also suitable as an analysis framework for online teaching. Halawi et al. (2009) evaluated
an exploratory study of an e-learning approach based on Bloom’s Taxonomy and have shown that e-learning
can be an effective learning approach. Based on Bloom’s Taxonomy, Murray et al. (2014) found that online
learning in a hybrid course design produces similar learning outcomes as compared to the traditional offline
classroom course. Bloom’s Taxonomy is also used to highlight various approaches to further improve learning
success in the pure context of online teaching. It has been shown that the use of visual media (a combination
of picture, video and numerical content) may lead to better results (Santosh et al. 2021). Abuhassna et al.
(2020) prove that collaboration is one of several factors that are influencing students’ academic achievements
and satisfaction with using online learning platforms. Teaching methods featuring online collaborative learning
are increasingly adopted in higher education institutions.
2.2 Online Collaborative Learning with Social Media
Online collaboration has played an essential role in research and practice since the end of the 1980s. A current
definition describes collaboration as the work of two or more individuals on shared material, deliberately
planned to achieve a common group objective. To achieve this group objective, communication, coordination,
and cooperation of the actors involved are necessary (Leimeister, 2014, p. 8). There is also extensive research
on the use of online collaboration in the field of teaching, which we can draw on. Referring to Yücel and Usluel
(2016), online collaborative learning describes an internet-technology supported pedagogical process that
encourages students to discuss information and problems from different perspectives, and to elaborate and
refine their understanding to re- and co-construct (new) knowledge or to solve a problem.
Past research on online collaborative learning has shown that “the reality of online collaborative learning is
disappointing” (Reeves et al. 2004). It was already evident that more collaborative groups produced better
quality projects, but ineffective communication seemed to be a major challenge to online collaboration
(Thompson and Ku, 2006). In addition, there is an impact of social aspects, like the development of a
community, the social roles of teachers and students and the creation of online presence on online teaching
and learning (Wallace, 2003). However, the evolution of technology is putting online collaborative learning in
higher institutions on a new foundation. Among other things, modern learning environments offer the
possibility to offer learning materials in different media and discuss them online in groups, whereby interactive
media leads to best performance and an active learning atmosphere (Wang et al., 2020). In addition, a positive
correlation was found between online collaborative learning and the students’ sense of community, which
increases information flow, cooperation, support, and a sense of commitment toward group goals among
student groups (Chatterjee and Correia, 2020).
Social media is a possible channel for the transmission of knowledge between communities and learners,
which might lead to better performance by learners (Al-Rahmi and Zeki, 2017). Allen (2011) says that success
with social media in higher education probably depends on exploring and validating students’ choices of the
tools to hand, with which they are comfortable and familiar and that make sense for the task. Social media
applications that enhance collaborative learning are in particular tools for asynchronous interaction (e.g. blogs,
messengers), for synchronous interaction, debate, discussion and online interaction (e.g. chat, messengers,
video conferencing) and for collaborative text production and work (e.g. wiki, cloud office suites) (Cavazos-
Olson et al., 2012, Mondahl and Razmerita, 2014, Al-Samarraie and Saeed, 2018). Ansari and Khan (2020)
revealed that social media used for collaborative learning had a significant impact on interactivity, which also
has an impact on students’ engagement and academic performance.
The explicit added values of online collaborative learning compared to classic online learning without
collaborative elements have not yet been considered quantitatively enough. Our main research question
therefore is, if there is a significant difference between the achieved learning objectives (using the revised
Bloom’s Taxonomy) of online courses with and without online collaborative learning.
3. Quantitative Online Survey and Hypotheses Development
To answer this study’s research question, a quantitative online survey was conducted in October 2020. The
sample for the conducted online survey questionnaire consists of 1080 (adjusted data set N=1078) randomly
selected students from 44 German universities. Assuming that there are currently 2,891,049 students in
Germany (Destatis, 2020) and that we calculate with a confidence level of 95% and a margin of error of 5%, the
data set is representative of students in Germany. The questionnaire analyzes the students’ perception of
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online teaching and offline teaching courses, the perceived impact of online collaborative learning and if this
collaboration is benefiting the achievement of learning objectives following the revisited taxonomy of Bloom.
The respondents’ perception of online and offline teaching courses is operationalized by a symmetric
differential from -2 to +2, measuring the students’ ability to follow the educator’s explanations and to learn
individually/independently, the motivation to get a good grade, the enjoyment of the course, the amount of
time needed for studying, and the perceived stress level. Those items are also applied to measure the
perception of online collaborative learning, except for the respondents’ ability to follow the educator’s
explanations and to learn individually/independently, due to the nature of group work not having an educator
and individual learning being absent. Instead of both questions, students are asked if they can learn and
acquire knowledge better or worse with online collaborative learning. This question forms a transition for
operationalizing the learning objectives using Bloom’s taxonomy. The taxonomy distinguishes between the
cognitive dimensions: remember (DV1), understand (DV2), apply (DV3), analyse (DV4), evaluate (DV5) and
create (DV6). The dimensions correspond to the dependent variables which are influenced by the inclusion of
online collaborative learning into the course (IV1) (see table 1).
Table 1: Indicator, manipulator, and measurement level (learning objectives)
Dependent variable
Indicator
Measurement level
DV1
Remember
Evaluation of achieved learning
objectives in all revised Bloom’s
taxonomy
dimensions
Ordinal scale, from 1 (strongly agree)
to 5 (strongly disagree)
DV2
Understand
DV3
Apply
DV4
Analyse
DV5
Evaluate
DV6
Create
Independent variable
Manipulator
Measurement level
IV1
Online collaborative
learning
Course uses online collaborative learning
Nominal scale, values: “Yes” and “No”.
To measure the value of dependent variables, the students have been asked to think about their previous
academic term’s online course that they liked best, and rate statements about this online course (term’s may
vary depending on university but include spring/summer 2020). The indicators for the dependent variables, i.e.
the corresponding cognitive dimensions, are given by the evaluation of the subject in a Likert scale from 1
(strongly agree) to 5 (strongly disagree). All statements are designed in a way that indication of agreement
matches better achievement of learning objectives. Thus, a lower score implies a better performance. The
independent variable, the inclusion of online collaborative learning, is determined by asking the respondents,
if this online course included collaborative learning and can assume the nominal scaled values “yes” and “no”.
This research assumes that the achievement of learning objectives will be improved by including collaborative
learning into an online course. The following hypothesis is therefore applied:
H: Measured mean value of learning objectives score better (lower) for online courses with collaborative
learning.
This hypothesis must be specified regarding the cognitive dimensions of Bloom’s Taxonomy to determine the
performance of the individual dimensions. The resulting hypotheses for the respective dimensions are shown
in table 2.
Table 2: Online collaborative learning hypotheses
Hypotheses
H1
Measured mean value for Remember scores better (lower) for online courses with collaborative learning.
H2
Measured mean value for Understand scores better (lower) for online courses with collaborative learning.
H3
Measured mean value for Apply scores better (lower) for online courses with collaborative learning.
H4
Measured mean value for Analyse scores better (lower) for online courses with collaborative learning.
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Hypotheses
H5
Measured mean value for Evaluate scores better (lower) for online courses with collaborative learning.
H6
Measured mean value for Create scores better (lower) for online courses with collaborative learning.
The hypotheses presented were addressed in the survey with the help of various questions that reflect the
respective learning dimensions according to Bloom’s Taxonomy. The results can be seen in figure 4. Before
that, we compared online and offline teaching in general and asked participants about their experiences.
4. Online Collaborative Learning improves Online Teaching
Participants of the online survey questionnaire were asked to rate their perception of online and offline
teaching in general for courses of their last academic term.
The results demonstrate that students may prefer offline teaching about following educator’s explanations, as
they tend to be better able to do so in offline teaching courses (mean of 0.24 in a symmetric differential from -
2 to +2). On the other hand, students participating in the survey favour online teaching (mean of -0.37 in the
same differential scale) for their individual/independent preparation and follow-up of the teaching unit
provided by the educator. While the advantages and disadvantages of online teaching are more or less on a
par in those two aspects of in-class and individual learning experience, online teaching’s biggest challenges are
motivation and enjoyment. Motivation to achieve good learning results tends to be more challenging in online
teaching scenarios (mean of 0.31). With a mean of 0.43, students also voted offline teaching to be more
enjoyable. A marginal advantage of online teaching is seen by the respondents in the topic of study time
(mean of -0.06). Lastly, the participants of the survey perceive a lower stress level (mean of -0.15).
Figure 2: Students’ perception of online and offline teaching courses
Overall, offline teaching is better perceived than online teaching. One reason for this result could be the
fundamental characteristics of online teaching. However, it could also be due to the actual implementation of
online teaching by educators. In particular, since educators may have had to change their teaching methods in
a rather timely manner and against their own preferences.
As already clarified, implementing online collaborative learning as a form of group work could potentially
influence the acceptance of online teaching courses (Chatterjee and Correia, 2020). Based on this assumption,
we asked participants to evaluate their last academic terms online collaborative learning experience. To be
able to contextualize the results with the identified disadvantages of online teaching, similar semantic
differentials have been constructed.
Overall, students perceived the implementation of online collaborative learning in their courses to be greatly
beneficial for their learning experience (see figure 3). Only in the aspect of the amount of time needed to
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study, proved that collaborative learning is a strain on students (mean of -0.39). In addition, there is almost no
influence of collaborative learning measured for the students’ stress level (mean of -0.03). In the perception of
the participants, online collaboration has a positive effect on their ability to learn and acquire knowledge
(mean of -0.29).
Figure 3: Students’ perception of online collaborative earning
While motivation and enjoyment has been ascertained as a drawback for online teaching courses in general,
with means of -0.27 (motivation) and -0.42 (enjoyment), the implementation of online collaborative learning in
an online course is beneficial for the learning experience. Thus, collaborative Learning has the potential to
improve online teaching.
The primary goal of teaching is to maximize students’ achievement of learning objectives. As derived, Bloom’s
Taxonomy offers a suitable approach to assess this metric. In direct comparison of students’ success,
measured in achievement of learning objectives in online courses, we can show that collaborative learning is
consistently beneficial in each cognitive dimension of Bloom’s Taxonomy. Figure 4 supports this claim.
Figure 4: Achieved learning objectives of online courses
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In online courses that include collaborative learning, students can better remember facts and concepts (mean
difference md=-0.12), understand and explain those ideas and concepts (md=-0.13), apply this knowledge to
solve problems (md=-0.19), use this knowledge to analyze and differentiate between ideas (md=-0.16),
evaluate facts and ideas (md=-0.13) and lastly create new ideas, or products (md=-0.17). Table 3 compares the
parameters sample size (N), mean, standard deviation (SD) and standard error of mean (SEM) for the scales in
both groups (with/without online collaborative learning).
Table 3: Comparison of scale means, standard deviations, and standard error of mean
Scale
Without Online Collaborative Learning
With Online Collaborative Learning
N
Mean
SD
SEM
N
Mean
SD
SEM
Remember
682
2.33
1.021
0.039
396
2.20
0.983
0.049
Understand
682
2.30
0.978
0.037
396
2.17
0.932
0.047
Apply
682
2.36
0.970
0.037
396
2.17
0.947
0.048
Analyse
682
2.28
0.914
0.035
396
2.12
0.846
0.043
Evaluate
682
2.25
0.913
0.035
396
2.12
0.874
0.044
Create
682
2.54
1.055
0.040
396
2.37
1.051
0.053
To verify the significance of these results, a t-test for independent samples was carried out. To determine
whether there is homogeneity of variance in the samples, Levene’s test for equality of variances was also
performed. Levene’s test showed unequal variances for the scales: remember, understand, analyse and
evaluate. As the standard t-test assumes compared sample means to be normally distributed with equal
variance, Welch’s t-test was conducted to address this issue. Welch’s t-test is designed for unequal sample
distribution variance, but the assumption of sample distribution normality is maintained. Table 4 shows the
results for Levene’s test and the t-test, with necessary adjustments for scales with unequal variances by
application of Welch’s t-test.
Table 4: Levene test for equality of variances and t-test for independent samples
Scale
Levene test
t-test
F
P
T
df
P
(1)
P
(2)
MD
SED
95% CI
Remember
5.35
0.021
-1.98
851.09
0.024
0.048
-0.12
0.06
-0.25
0.00
Understand
5.77
0.017
-2.23
858.22
0.013
0.026
-0.13
0.06
-0.25
-0.02
Apply
2.46
0.117
-3.06
1076.00
0.001
0.002
-0.19
0.06
-0.31
-0.07
Analyse
9.73
0.002
-2.98
878.37
0.002
0.003
-0.16
0.06
-0.27
-0.06
Evaluate
4.41
0.036
-2.41
855.06
0.008
0.016
-0.13
0.06
-0.25
-0.02
Create
0.05
0.824
-2.51
1076.00
0.006
0.012
-0.17
0.07
-0.30
-0.04
These results prove that German students participating in our online survey questionnaire attest online
courses with included online collaborative learning to perform significantly (P(1) < 0.05) better (lower means,
negative mean difference), than online courses without it in respect of achievement of learning objectives.
5. Limitations and Future Work
The results of this study reflect the findings of a literature research and a quantitative online survey. A
limitation of the present study arises from the fact that participants were surveyed about their last academic
term. The students’ ability to remember, which certainly varies considerably from student to student, is
therefore a decisive factor for the quality of the data. Furthermore, the period students have to recall differs
slightly, due to differences in academic calendars of German universities. We are not able to comprehend how
accurately the respective participants remember their experiences in the courses. Since the sample was
collected from 44 German universities, the approaches, commitment, and implementation of digital teaching
may be very heterogeneous. The number of participants per university also differs, and so does the individual
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impact of a university. Ultimately, it can be assumed that educators in particular have a major influence on the
implementation of digital teaching and online collaborative learning and thus also on the experiences of their
students. In the case of collaborative learning, the students’ experience is also dependent on group size and
composition.
One might assume that collaborative learning, whether online or offline, could improve the online and offline
learning experience. As this research focused on online collaborative learning in the context of online teaching
and online learning, this assumption cannot be confirmed or falsified. However, it has been shown that the
strengths of online collaboration work particularly well in areas where online teaching has weaknesses.
This study was able to give a useful insight into online collaborative learning as a component of teaching at
German universities and proved beneficial for the achievement of learning objectives. Nevertheless, the
findings should be confirmed by experiments under more homogeneous conditions (for instance, via A/B
testing). Furthermore, a development of digital teaching and collaborative learning over time is of interest.
Thus, developments in digital teaching and the influence of a pandemic paradigm can be shown.
6. Practical Implications and Conclusion
Our results contribute to the future design of online teaching and learning in institutions of higher education.
Online collaborative learning can be implemented in practical teaching, for example, by embedding online
group work in teaching, defining group objectives, and stimulating group discussions. Various video
conferencing tools enable the creation of breakout sessions to enhance adequate group work. Accompanying
this, it is important to provide individual group feedback by the educator. Research also shows that a media
mix is of great importance (e. g. video and podcasts, text elements, dynamic and interactive elements, and
social media).
The findings show that students see different advantages in online teaching as well as in offline teaching. The
perceived advantages of online teaching are improved individual learning and lower stress levels. Advantages
of offline teaching are that the educator’s explanations can be followed better, higher motivation of students
to achieve good grades and higher enjoyment of offline courses. If online teaching is combined with
collaborative learning (online group work elements), the advantages from traditional offline classroom settings
(e.g. motivation and enjoyment of student interaction) can be transferred to online teaching, too. According to
our findings, online group work usually takes place in groups of 2-5 people and improves the perceived
learning success and knowledge acquisition. In addition, students have more fun. However, it was also found
that online collaborative learning is more time-consuming. Our results demonstrate that online collaborative
learning can promote significantly better achievement of learning objectives along Bloom’s Taxonomy. It can
be assumed that hybrid teaching, a combination of online and offline teaching as well as collaborative learning,
can accomplish the best possible results for the students’ achievement of learning objectives. Nevertheless,
learning success depends on the educator’s individual design and way of teaching.
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... For example, a facilitator guiding an online asynchronous discussion has the potential to develop students' critical thinking and reflection, which deepens their understanding of the content (Martin & Bolliger, 2018). Other examples of online engagement strategies include introducing learning activities related to communication, collaboration, discovery and search for information, active listening, and activities that require the direct involvement of students (Knopf et al., 2021;Myers et al., 2014). Understanding what drives learners to engage in online learning activities is important. ...
... Our findings on behavioural engagement are supported by Knopf et al. (2021) who also reported that behavioral engagement is characterized by students' interaction with classmates, teamwork, collaborative and soft skills. Moreover, Bali and Liu (2018) argued that online soft skills are likely to be improved in inclusive and supportive online learning environments. ...
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... The authors indicated motivation, positive self-perception of digital literacy, and self-regulation as the crucial factors for the success of online learning engagement. Other studies also found that a high level of engagement is deeply related to: (i) the students' interaction with classmates, teamwork, collaborative and soft skills [18]; (ii) students' motivation [19][20][21]; (iii) emotional support, positive attitude and previous online experience [22,23]. Therefore, the literature suggested to us that it is fundamental for teachers to find strategies and methods to increase students' involvement and their motivation. ...
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