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Paper—Inuencing Factors of Learners’ Cognitive Engagement in an Online Learning Environment…
Inuencing Factors of Learners’ Cognitive Engagement
in an Online Learning Environment
A PST Model
https://doi.org/10.3991/ijet.v17i17.33851
Lin Lin1, Junyi Wang2(*), Xianyun Meng3
1Department of Engineering Management, Henan Technical College of Construction,
Zhengzhou, China
2Department of Management, University of York, York, United Kingdom
3Zhumadian Cigarette Factory of China Tobacco Henna Industrial. CO, LTD,
Zhumadian, China
jw3342@york.ac.uk
Abstract—This study used the Pedagogical Affordance-Social Affordance-
Technical Affordance (PST) model as basis in designing a questionnaire to inves-
tigate the inuencing factors of learners’ cognitive engagement in an online learn-
ing environment. Moreover, the inuencing degrees of educational, social, and
technological affordances on learners’ cognitive engagement in an online learn-
ing environment were estimated. Research results demonstrated that the overall
Cronbach’s α of the questionnaire was 0.883, KMO was 0.859, and cumulative
variance interpretation rate after rotation was 79.199%. Thus, the designed
questionnaire has very good reliability and validity. Educational affordance
can signicantly improve learners’ supercial and deep learning engagements.
Social and technological affordances can signicantly increase learners’ super-
cial learning engagement, but they cannot signicantly increase deep learning
engagement. Online learning contact time has signicant differences under the
1% and 10% levels. Research results can provide some references to explore
the relationship between the PST model and cognitive engagement and improve
the overall affordance of an online learning environment.
Keywords—online learning environment, learners, cognitive engagement,
PST model, technological affordance, educational affordance
1 Introduction
In the “Internet + education” era, the integration degree of information technology
and education is increasing continuously. The inuence of the COVID-19 pandemic
has prompted China to comprehensively implement large-scale online teaching mode,
shifting from the traditional classroom teaching mode centered on the teaching activ-
ities of teachers to the independent online learning centered on learners. In existing
teaching scenes, there are increasing categories of information technologies and con-
siderably diversied methods. Technological intervention and technological-enriched
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environment have become an inevitable learning development trend. At present,
integration with information technologies, such as could computing, big data, Internet of
Things (IoT), and articial intelligence, has reconstructed new states of different indus-
tries and further promoted the application of technologies in education. Applications
of technologies in the education ecology promote the transformation of educational
concepts and innovation of teaching modes. With the enriching degree of technologies
and continuous improvement of intelligence degree, digitalization, electronization, and
hardware in a classroom learning environment will become ordinate states in the future.
Flipped classroom and smart education based on the cloud platform, microlectures,
and mobile terminals have been widely applied in classroom environment teaching.
In recent years, the gradual penetration of technologies in the education eld has
demonstrated that teaching in a technology-rich environment can signicantly promote
student engagement. Adverse effects of negative factors (e.g., distraction, unsatisfying
learning outcome) on learner engagement can be relieved signicantly by enhancing
the online learning environment.
Given that online learning lacks face-to-face communication between teachers and
students and has a weak sense of immediacy, learning engagement of learners to online
learning is not satisfying in China. During online teaching classes, some students appear
to be engaged in e online learning, but they neither enter into deep learning nor adopt
corresponding learning strategies. This situation has immense hidden dangers to stu-
dents’ individual development and also has signicant inuences on the overall teach-
ing effect. The learning state of learners is difcult to determine in a timely manner and
real engagement state is challenging to master because online learning characterized by
autonomy, self-assistance, and convenience. The quality of online learning outcomes
is typically related directly with the quality of students’ course mastery. Learning
engagement is a major prediction index of the learning performances of students and
an important reference element to the quality of school education. Numerous studies
have demonstrated that the learning engagement of university students is benecial to
improving their lifelong learning ability, facilitating their considerable progresses in
higher-order thinking abilities, and encouraging them to turn in good academic per-
formances. On the one hand, high-level learning engagement can motivate learners
to comprehensively realize self-regulation, self-management, and self-promotion, and
increase self-efcacy. On the other hand, such an engagement can strengthen high-or-
der thinking abilities, thereby enabling students to realize effective deep learning.
High-efciency and accurate improvement of learning engagement of university
students to online learning is an essential requirement of China’s higher education in
the current background of the COVID-19 pandemic. Lastly, such an enhancement is
also an important inuencing factor in improving online learning performances.
2 Theoretical basis and hypotheses development
2.1 Theoretical basis
Astin, A. W. [1] proposed student involvement theory and advocated the import-
ant role of “involvement” in the learning process of students. Physiological and
psychological engagements to learning process are important elements of student
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involvement. Student involvement theory emphasizes that the primary mission of higher
education is to set a learning environment that encourages the positive involvement of
students, promotes their cognitive development, and results in high-efciency learning
achievements. The major opinion of this theory is that learning engagement refers to
the effort and physical power that students input to form learning experiences. Learn-
ing engagement is a continuous process, and different students may show different
degrees of engagement state. In a specic context, the same students show different
degrees of engagement to different tasks and at different times. Degree of engagement
can be measured comprehensively in terms of quality and quantity. The acquired learn-
ing and development of students from the education process are directly related with
their engagement. Implementation effects of education policies are determined by their
degree of learning engagement. Therefore, student involvement theory was the basis of
the basic theory for questionnaire design in this study.
2.2 Hypotheses development
With the continuous integration of information technologies in the curricula, the
role of such technologies in educational teaching has become increasingly prominent.
However, apart from promoting teaching, technologies also bring instability in online
teaching quality. One important reason is that the components of an online learning
environment are extremely complicated and have important inuences on cognitive
engagement. Many studies have discussed the inuencing factors of cognitive engage-
ment in an online learning environment. Richardson, J. C., et al. [2] explained that with
the increasing acquired experiences in online learning, students have assumed more
responsibilities for their studies. Research conclusions have indicated positive inu-
ences on how online courses and designers organize online courses. Greene, B. A., et al.
[3] found that perception is positively related with signicant cognition and that learn-
ing objective is positively related with perception ability. DeBacker, T. K., et al. [4]
determined that achievement objectives, which are set by learners, may inuence their
cognitive engagement. Taylor, B. M., et al. [5] demonstrated that good teaching meth-
ods of teachers (e.g., representation, guidance) could improve learners’ higher-order
thinking. These methods could comprehensively promote the reading growth of stu-
dents and improve their cognitive engagement in literacy learning to the maximum
extent. Zhu, E. [6] determined that teacher–student interaction has important inu-
ences on students’ cognitive engagement. Ben-Eliyahu, A., et al. [7] discovered that
self-efcacy is positively related with overall involvement and the objective orientation
of performance method is positively related with behavioral cognitive engagement.
Park, S., et al. [8] believed that motivation support is a key factor that determines
the successful online distance learning experiences of university students. The results
demonstrated that teachers, tutors, and designers of online courses attach considerable
attention to the motivation characteristics of students; and they are major inuencing
factors of students’ cognitive engagement. Chang, Y., et al. [9] explained that self-ef-
cacy and peer support perception have signicantly positive inuences on cognitive
engagement. Autonomous motivation can mediate the inuences of peer support per-
ception on cognitive engagement. Walker, C. O., et al. [10] deemed that self-efcacy,
internal motivation, and academic identity are three major inuencing aspects of
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cognitive engagement. Mallawaarachchi, V., et al. [11] presented the successful use
of such a learner support tool, and developed to be used in bioinformatics teaching
and research. Tang, C. M., et al. [12] demonstrated that good digital literacy is the
prerequisite for students to achieve effective learning in a blended learning environ-
ment. Shamatha, J. H., et al. [13] found that when classroom activities are guided by
components of an effective learning environment, students are likely to develop context
and transferable understanding. Gonzalez, G. R., et al. [14] believed that success in a
learning-oriented educational concept in the marketing curriculum is determined by
creating an effective learning environment. Consequently, creating an effective learn-
ing environment is conducive to improving the academic performance level of learn-
ers. Delialioglu, O., et al. [15] demonstrated that real interactive learning activities,
team cooperation, and personalized learning of students play important roles in blended
learning courses. Preciado-Babb, A. P., et al. [16] believed that students’ use of mobile
technologies could signicantly improve their learning engagement. Schols, M. [17]
found four factors that encourage teachers and educators to participate in technological
learning. Yan, Y., et al. [18] indicated that cognitive engagement of learners can be
promoted through game-based teaching mode.
Kirschner, P., et al. [19] proposed a relatively classical PST model. He was con-
vinced that a technically supported effective learning environment will be equipped
with educational, social, and technological affordances. Educational affordance refers
to the characteristic of a learning environment or technical tools that determines
whether a learning activity can be implemented or how to be implemented in a given
educational context. Social affordance is a characteristic that the learning environment
perceived by learners or technical tools can promote the social interaction of students.
Technological affordance refers to the usability of a learning environment or technical
tools. Therefore, online learning environment was expressed by educational, social,
and technological affordances in this study. Accordingly, the inuences of the three
affordances on learning engagements, including supercial and deep learning engage-
ments, were analyzed.
Hence, the following hypotheses were proposed:
H1: In online learning, educational affordance can signicantly improve learners’
supercial learning engagement.
H2: In online learning, social affordance can signicantly improve learners’
supercial learning engagement.
H3: In online learning, technological affordance can signicantly improve learners’
supercial learning engagement.
H4: In online learning, educational affordance can signicantly improve learners’
deep learning engagement.
H5: In online learning, social affordance can signicantly improve learners’ deep
learning engagement.
H6: In online learning, technological affordance can signicantly improve learners’
deep learning engagement.
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3 Methodology
3.1 Questionnaire design
A questionnaire of Inuencing Factors of Learner’s Cognitive Engagement in the
Online Learning Environment was designed. It covers the following two aspects. Part I
covers four questions on gender, subject, postgraduate grade, and online learning con-
tact time. Part II is the core part of the questionnaire. Given that cognitive engagement
of learners in an online learning environment involves relatively complicated inu-
encing factors, the literature review and Kirschner, P., et al. [19] indicated that a
technically supported effective learning environment is equipped with educational,
social, and technological affordances. The three aspects were measured by four, ve,
and four questions, respectively. Cognitive engagement includes deep and supercial
engagements, which were measured by three questions in the questionnaire of Greene,
B. A., et al. [20]. All problems used a seven-point Likert scale.
3.2 Respondents
This study initially completed a pre-survey in the school of the author. The question-
naire was corrected and enhanced according to feedback information, and evaluated by
inviting experts in the education technology eld. Eventually, the formal questionnaire
was formed. Postgraduate students in a university in Zhengzhou City, Henan Province
were investigated using the formal questionnaire. This university has invested con-
siderable capital for teachers’ special online teaching training since the start of the
COVID-19 pandemic, thereby improving their online teaching abilities. Moreover, the
university purchased an online teaching platform that enables teachers to use several
online teaching modes, such as live and recorded broadcasting. To acquire effective
survey data in a limited time range, the questionnaire was formulated on the website of
www.wjx.cn, which is an extensively used online investigation tool in China, and a QR
code was generated. The questionnaire was mainly sent through the working WeChat
group of faculty members in postgraduate school. Course teachers transferred it to all
postgraduate students in the university. A total of 235 questionnaires were collected,
among which 185 valid ones were collected after questionnaires with missing infor-
mation and the same answers to all questions were excluded. Effective collection rate
was 78.72%. Basic information of the respondents are as follows. For gender, there
were 100 males (54.05%) and 85 females (45.95%). For subject, there were 33 from
Engineering Science (17.84%), 35 from Science (18.92%), 74 from Economics (40%),
and 43 from Management Science (23.24%). For postgraduate grades, there were 49
rst-year graduate students (26.49%), 80 second-year graduate students (43.24%),
and 56 third-year graduate students (30.27%). For online learning contact time, there
were 15 students who have engaged in online learning for under 0.5 year (8.11%),
8 students for 0.5–1 year (4.32%), 29 students for 1–2 years (15.68%), 42 students
for 2–3 years (22.7%), and 91 students for over 3 years (49.19%). Evidently, gen-
der ratio was relatively balanced. Given that the university emphasizes on economic
management, the proportions of Economics and Management Science were relatively
high. Grade distribution of postgraduate students was relatively reasonable. Given that
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postgraduate students were chosen as respondents, nearly 50% of them have engaged
in online learning for over 3 years, indicating that they have very good online learning
experiences.
4 Results analysis and discussion
4.1 Reliability and validity test
Reliability refers to the stability and consistency of questionnaire survey results when
using the same method to the same respondents. That is, reliability reects whether a
measuring tool can accurately measure the tested object or variable accurately. Higher
reliability indicates that the scale is more stable. This study applied Cronbach’s α in the
reliability test. Results are shown in Table 1.
Table 1. Reliability test results
Variable Types Names of Variables Codes of
Questions Cronbach’s αCronbach’s α
Independent
variables
Educational
affordance A1–A4 0.880
0.883
Social affordance B1–B5 0.909
Technological
affordance C1–C4 0.900
Dependent
variables
Supercial learning
engagement Y1-1–Y1-3 0.928
Deep learning
engagement Y2-1–Y2-3 0.749
Reliability of the collected data was tested using SPSS25.0. Evidently, Cronbach’s α
of specic variables is above 0.7 and the overall Cronbach’s α is 0.883 (>0.8). This
result indicates that research data has very high reliability quality and the questionnaire
has good quality. Moreover, the result interprets comprehensively that the designed
online learning questionnaire has very good reliability and can be used for further deep
analysis.
Validity, or known as effectiveness, expresses the degree that measurement results
of questions in the questionnaire can accurately reect response contents. Higher
validity indicates a higher agreement between the measuring results and correspond-
ing investigation contents. Structural validity test on this study’s data was performed
using SPSS25.0. Structural validity refers to the relationship between the structure
reected by the measuring results and measuring values. The structural validity of
a questionnaire is often expressed by factor analysis results. Results are shown in
Tables 2 and 3.
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Table 2. Validity test results
KMO value 0.855
Bartlett sphericity test
Approximate chi-square 2711.460
df 171
P value 0.000
Table 2 shows that the KMO value is 0.859 and the corresponding P value is 0.000
(<0.01). This result indicates that the collected questionnaire survey data are very
appropriate for factor analysis.
Table 3. Factor analysis results
No. of
Questions
Initial Eigenvalues Extracted Quadratic Sum
Total Variation % Accumulation % Total Variation % Accumultion %
A2 4.026 21.189 54.227 4.026 21.189 54.227
A3 2.441 12.846 67.073 2.441 12.846 67.073
A4 1.19 6.261 73.334 1.19 6.261 73.334
B1 1.114 5.865 79.199 1.114 5.865 79.199
B2 0.595 3.133 82.332
B3 0.525 2.764 85.096
B4 0.509 2.677 87.773
B5 0.382 2.012 89.785
C1 0.319 1.679 91.464
C2 0.253 1.333 92.797
C3 0.239 1.258 94.055
C4 0.217 1.141 95.197
Y1-1 0.191 1.007 96.204
Y1-2 0.179 0.941 97.144
Y1-3 0.172 0.905 98.049
Y2-1 0.154 0.809 98.858
Y2-2 0.119 0.628 99.486
Y2-3 0.098 0.514 100
Table 3 shows that the cumulative variance interpretation rate after rotation is
79.199%, which is considerably above 50%. This result reveals that the research items
involving inuencing factors could be extracted effectively.
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4.2 Linear regression
Table 4. Regression results of the inuencing factors of supercial learning engagement
Variables Regression
Coefcients
Standard
Deviation T-Values
Constants 3.4235*** 0.5337 6.41
Educational affordance 0.2258* 0.1166 1.94
Social affordance 0.2410*** 0.0869 2.78
Technological
affordance
0.2148** 0.0950 2.26
Note: ***, **, * indicate signicance at 1% level, 5% level, and 10% level, respectively.
Table 5. Regression results of the inuencing factors of deep learning engagement
Variables Regression
Coefcients
Standard
Deviation T-Values
Constants 3.6170*** 0.4837 7.48
Educational
affordance
0.2219** 0.1057 2.10
Social affordance 0.1278 0.0788 1.62
Technological
affordance
0.1289 0.0861 1.50
Note: ***, **, * indicate signicance at 1% level, 5% level, and 10% level, respectively.
Tables 4 and 5 show the following results.
(1) H1 is true: In online learning, educational affordance can signicantly improve
learners’ supercial learning engagement. The reasons are explained as follows. For
educational affordance, teachers realize teaching objectives comprehensively by pro-
ciently using methods more appropriate for online learning. At present, teacher groups
from universities have relatively high information literacy and they are familiar with
online teaching. In a specic online teaching environment, they can complete a spe-
cic teaching process effectively. In online teaching activities, they can realize the full
teaching process by using various teaching tools based on information communication
and appropriate teaching methods for online learning. For example, teachers use case
discussion, team cooperation learning, inquiry-based learning, and role play during
online learning. These methods can signicantly improve learners’ internal motiva-
tion, encourage them to enhance their learning motivation, and increase their supercial
learning engagement.
(2) H2 is true. In online learning, social affordance can signicantly improve learn-
ers’ supercial learning engagement. Given that learners have different cultural and
education backgrounds in an online learning environment, they are encouraged to
communicate with teachers and peers when encountering problems and present strong
demands for platform use and online emotional communication. Online learning plat-
forms can completely support synchronous or heterogeneous communications through
interaction affordance of information communication tools, as well as strengthen
peer-peer interactions and student-teacher interactions. Moreover, online learners can
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interact mutually through information sharing, team cooperation, problem discus-
sion, and theme lecture. Teachers play an important role in these learning processes,
specically by guiding and managing the learning activities of students.
(3) H3 is true. In online learning, technological affordance can signicantly improve
learners’ supercial learning engagement. The reasons are explained as follows. With
the continuous development of education informatization, technological affordance
plays an irreplaceable role in the construction of an information teaching environment.
Only good technological affordance can build a good environment for learners and
teachers, thereby enabling the smooth promotion of teaching activities. At present,
technological affordance of most online learning platforms is characterized for easy
learning, simple structure, and convenience. In teaching activities, most online learning
platforms allow learners to upload or download learning resources in various forms,
showing strong compatibility and high sharing degree of network resources. Mean-
while, various interfaces of online learning have excellent visual sense and strong
attraction, which are easily accepted and used prociently by learners. Online learning
platforms are easy and convenient, thereby enhancing the interest of learners in these
platforms. Therefore, supercial engagement of learners is relatively evident.
(4) H4 is true. In online learning, educational affordance can signicantly improve
learners’ deep learning engagement. In the knowledge construction process, learners
can share personal information and publish their own opinions through the online
interaction link, thereby further solidifying knowledge. Students can also use knowl-
edge MindMap to construct a knowledge system by connecting key knowledge points.
In solving a specic problem, teachers can present the problem through various vid-
eos or short lms on the online platform. Students discuss the problem by searching
information on the Internet and using communication tools. Teachers design and imple-
ment various effective teaching methods by using information tools to enhance interest
on knowledge and encourage students to pursue diversied learning activities. With
respect to the learning objective, online learning platforms can support various learn-
ing activities. Moreover, information tools can enrich knowledge, make knowledge
visual, and enhance the learning interest of students, thereby helping them considerably
comprehend deep knowledge.
(5) H5 is false. In online learning, social affordance cannot signicantly improve
learners’ deep learning engagement. This result can be explained as follows. Although
an increasing number of online learning platforms provide free and full communication
environment for learners, social affordance reects the information communication
ability between learners and teachers and between different learners. However, there
is no face-to-face communication between students and teachers and among different
students in an online learning environment. Hence, there is no sense of immediacy.
Consequently, deep engagements, such as free discussion, complicated problem solving
model, higher-order thinking ability training, and operation skill internalization, are not
enhanced signicantly.
(6) H6 is false. In online learning, technological affordance cannot signicantly
improve learners’ deep learning engagement. Although technological affordance can
promote learners’ deep learning engagement, the regression coefcient is not signicant.
This outcome can be interpreted as follows. Regions with strong technological
affordance can realize synchronous or heterogeneous communication, enhance
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emotional communication among learners, and provide strong technological support to
various types of network teaching interaction. However, after learners become familiar
with such a learning mode, their interest wanes and their curiosity for this interaction
mode loses gradually with the passage of time. Consequently, the possibility for deep
reection based on good platforms decreases. Moreover, deep learning proposes a high
requirement on learners’ self-study. Learners have to spend some time in higher-order
thinking mode, such as reection and exploration of solving complicated problems,
which mainly relies on knowledge accumulation and experiences of learners.
4.3 Difference analysis
Table 6. Differences in inuences of online learning contact time on supercial
and deep learning engagements
Learning
Engagement
Types
Online Learning Contact Time
(mean ± standard deviation) F P
1.0
(n = 15)
2.0
(n = 8)
3.0
(n = 29)
4.0
(n = 42)
5.0
(n = 91)
Supercial
engagement
3.82±1.12 5.63±1.07 4.63±1.37 4.44±1.18 4.35±1.03 3.676 0.007***
Deep
engagement
4.57±0.97 5.06±1.06 4.43±0.81 4.22±0.88 4.87±0.79 2.187 0.072*
Note: ***, **, * indicate signicance at 1% level, 5% level, and 10% level, respectively.
Table 6 shows that inuences of online learning contact time on supercial learning
engagement have evident differences at the 1% level. Learners who engage in online
learning for 0.5–1 year show the highest supercial learning engagement. The reason
is that they need some time to be familiar with online learning mode in the beginning,
but they will be more familiar with it after half a year, thereby increasing supercial
learning engagement to the evident peak. After 1 year of engagement, learners may
master online learning skills better and they may choose on-hook, nding someone to
study for them, and other misuses. All of these factors can decrease learners’ super-
cial learning engagement annually. This conclusion also inspires universities to focus
substantially on the problem that learners are easily bored with online learning and
emphasize on curriculum resources development and emotional communication during
online teaching to maintain learners’ supercial learning engagement at a high level for
a long period.
Inuences of online learning contact time on deep learning engagement have evident
differences at under the 10% level. Deep learning engagement of learners reaches the
peak after engaging in online learning for 0.5–1 year, showing the same variation charac-
teristics with supercial learning engagement. As online learning contact time exceeds
3 years, learners’ deep learning engagement increases again owing to the following rea-
sons. Deep learning engagement mainly comes from learners’ endogenous motivation.
After they gain familiarity with the online learning mode, they can exert considerable
effort and time to reect on deep problems, deep interaction with teachers, and reec-
tion on higher-order thinking problems. Therefore, their deep engagement is increased
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signicantly. This conclusion also inspires online course teachers to provide some chal-
lenging learning resources to students who are good at online learning and provide stu-
dents who have engaged in online learning for over 3 years additional learning contents
for reection, team cooperation, group discussion, and free development. Moreover,
teachers will formulate more personalized teaching schemes for students to maintain a
high-level of deep engagement.
5 Conclusions
Information technologies, such as big data and IoT, facilitate the enhancement of an
online learning environment. Online learning has become one of the learning modes are
accepted by learners. Strong education informatization technological intervention and
technology-rich environment enrich the teaching environment. The PST model is used
as basis to estimate the inuencing degrees of educational, social, and technological
affordances of an online learning environment on learners’ cognitive engagement.
Research results indicate that the overall Cronbach’s α, KMO value, and cumulative
variance interpretation rate after rotation are 0.883, 0.859, and 79.199%, respectively.
This result proves that the designed questionnaire has very good reliability and validity.
Educational affordance can signicantly increase learners’ supercial and deep learning
engagements. Social and technological affordances can signicantly increase learn-
ers’ supercial learning engagement but cannot signicantly increase deep learning
engagement. Inuences of online learning contact time on supercial and deep learning
engagements show signicant differences at the 1% and 10% levels. Centered at social
sampling in a more extensive scope, further deep studies of the inuences of other
independent or mediating variables on cognitive engagement of learners are needed in
the future.
6 References
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http://www.i-jet.org
Paper—Inuencing Factors of Learners’ Cognitive Engagement in an Online Learning Environment…
7 Authors
Lin Lin, Bachelor of management, is a lecturer at Department of Engineering
management, Henan technical college of construction. Her research interests focus on
engineering management and teaching research. (linlin@hnjs.edu.cn).
Junyi Wang, Master of Science, is a merit accounting and nancial management
graduate from the Department of Management, University of York. Her research
interests are in management and education.
Xianyun Meng, Bachelor of management, is a engineer of Engineering
management, Zhumadian Cigarette Factory of China Tobacco Henna Industrial. CO,
LTD. Her research interests focus on engineering management and teaching research.
(mxyun123@gmail.com).
Article submitted 2022-06-03. Resubmitted 2022-07-05. Final acceptance 2022-07-07. Final version
published as submitted by the authors.
iJET ‒ Vol. 17, No. 17, 2022
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