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Learning behavior, digital platforms for learning and its impact on university student’s motivations and knowledge development

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Background Learning digital technologies in higher education is a process of knowledge generation, and the rapid growth of technology in education has a significant impact on students’ learning behaviors, motivation, and knowledge development. Pakistan’s remarkable technological breakthrough has increased in the education field. Study objectives The study focuses on estimating students’ learning behaviors, identifying the positive influence of educational apps on digital learning platforms, and analyzing their impact on students’ motivation and knowledge development. Materials and methods According to the study’s objectives, a questionnaire survey was conducted to gather the primary data. The participants were students of universities in Lahore city of Pakistan. For this study, the sample size was N = 300, carefully chosen using the purposive sampling technique. Of the respondents, there were 146 male and 154 female students, and the sample consisted of individuals aged 25–35 years. Smart-PLS-Bootstrapping, T-Values (PLS) 3.2.9 and the structural equation model (SEM) were applied to get the appropriate outcomes from the proposed study framework. Results SEM analysis results shows that all proposed hypotheses [Animated Movies (AM) –> Student Motivation (SM), Educational Apps (EA) –> Knowledge Development (KD), Learning Behavior (LB) –> Animated Movies, Learning Behavior –> Educational Apps, Learning Behavior –> Knowledge Development, Learning Behavior –> Virtual Classrooms (VCr), Virtual Classrooms –> Knowledge Development, Virtual Classrooms –> Student Motivation] are confirmed while Learning Behavior –> Student Motivation is not confirmed. Conclusion This study found that digital learning platforms significantly impact students’ learning and what motivates them to learn. The study also found that using educational apps and virtual classrooms more often helps students learn more and be more motivated to learn.
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fpsyg-13-933974 November 18, 2022 Time: 5:19 # 1
TYPE Original Research
PUBLISHED 23 November 2022
DOI 10.3389/fpsyg.2022.933974
OPEN ACCESS
EDITED BY
Manzoor Ahmed Hashmani,
University of Technology Petronas,
Malaysia
REVIEWED BY
Muhammad Afzaal,
Shanghai International Studies
University, China
Behzad Anwar,
University of Gujrat, Pakistan
*CORRESPONDENCE
Xu Qingyu
qyxu@suda.edu.cn
SPECIALTY SECTION
This article was submitted to
Educational Psychology,
a section of the journal
Frontiers in Psychology
RECEIVED 24 May 2022
ACCEPTED 27 October 2022
PUBLISHED 23 November 2022
CITATION
Noor U, Younas M, Saleh Aldayel H,
Menhas R and Qingyu X (2022)
Learning behavior, digital platforms
for learning and its impact on
university student’s motivations
and knowledge development.
Front. Psychol. 13:933974.
doi: 10.3389/fpsyg.2022.933974
COPYRIGHT
© 2022 Noor, Younas, Saleh Aldayel,
Menhas and Qingyu. This is an
open-access article distributed under
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academic practice. No use, distribution
or reproduction is permitted which
does not comply with these terms.
Learning behavior, digital
platforms for learning and its
impact on university student’s
motivations and knowledge
development
Uzma Noor1, Muhammad Younas1, Hessah Saleh Aldayel2,
Rashid Menhas3and Xu Qingyu1*
1School of Education, Soochow University, Suzhou, China, 2Department of English Language Skills,
King Saud University, Riyadh, Saudi Arabia, 3Research Center of Sport and Social Sciences, Soochow
University, Suzhou, China
Background: Learning digital technologies in higher education is a process
of knowledge generation, and the rapid growth of technology in education
has a significant impact on students’ learning behaviors, motivation, and
knowledge development. Pakistan’s remarkable technological breakthrough
has increased in the education field.
Study objectives: The study focuses on estimating students’ learning
behaviors, identifying the positive influence of educational apps on digital
learning platforms, and analyzing their impact on students’ motivation and
knowledge development.
Materials and methods: According to the study’s objectives, a questionnaire
survey was conducted to gather the primary data. The participants were
students of universities in Lahore city of Pakistan. For this study, the sample
size was N= 300, carefully chosen using the purposive sampling technique.
Of the respondents, there were 146 male and 154 female students, and the
sample consisted of individuals aged 25–35 years. Smart-PLS-Bootstrapping,
T-Values (PLS) 3.2.9 and the structural equation model (SEM) were applied to
get the appropriate outcomes from the proposed study framework.
Results: SEM analysis results shows that all proposed hypotheses
[Animated Movies (AM) >Student Motivation (SM), Educational
Apps (EA) >Knowledge Development (KD), Learning Behavior
(LB) >Animated Movies, Learning Behavior >Educational Apps,
Learning Behavior >Knowledge Development, Learning Behavior
>Virtual Classrooms (VCr), Virtual Classrooms >Knowledge
Development, Virtual Classrooms >Student Motivation] are confirmed
while Learning Behavior >Student Motivation is not confirmed.
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Conclusion: This study found that digital learning platforms significantly
impact students’ learning and what motivates them to learn. The study also
found that using educational apps and virtual classrooms more often helps
students learn more and be more motivated to learn.
KEYWORDS
learning behavior, digital platforms, technological applications, student motivation,
knowledge development
Introduction
This study examines the use of digital technology in higher
education and the learning process of knowledge creation.
Web technology is becoming more critical in many areas of
Pakistan, including education. The rapid rise of Web technology
in teaching and its effect on how people learn encourages
students to use technology in the classroom. Interactive teaching
tools may enhance the students’ active learning habits. The
polytechnic university of the Philippines surveyed students’
attitudes about online and remote learning, motivation, and
learning practices (Avila et al.,2021;Shieh and Hsieh,2021).
Recent studies compare direct lectures, interaction, and self-
efficacy as determinants of online motivation and satisfaction
in the COVID-19 pandemic scenario (Rahman et al.,2021).
The research explores students’ learning behaviors and academic
achievement in a comparative examination of certain faculty
strata and gender and the impact of technological applications,
objectives, and time flexibility on students’ digital learning
behaviors (Farid et al.,2014;Zhang,2021). The researchers
looked at what factors influenced student learning online during
the COVID-19 pandemic to conclude that all educational
institutions, teachers, and students need to embrace technology
and hone their digital literacy to keep up with the latest global
trends and realities in education (Qiuhan et al.,2020;Younas
et al.,2022a).
Web conferencing in education helps both educators and
students achieve their critical goals of creating the ideal learning
environment for kids and balancing life and teaching tasks
for educators. Following the definition of online learning, an
overview of recent pedagogical strategies has been used in online
learning environments (Hartnett,2016). It is important to note
that students’ self-efficacy in computer and online learning,
perceived utility, and simplicity of use are essential success
criteria in online learning environments (Barclay et al.,2018).
The critical challenges for current information technology
integrated education are creating educational activities for
digital learning and deploying technology tools flexibly. Higher
education institutions may use web conferencing to improve
instructors’ online access and create new teaching and learning
opportunities (Lin and Chen,2017;Adipat,2021). The research
evaluates the usefulness of e-learning technologies in teaching
public institutions in the United Arab Emirates, focusing
on course administration and interaction with the students’
information systems (Sandybayev,2020). Passey and Higgins
(2011), Sousa and Rocha (2018) Studies focus on digital learning
and motivation among higher education students using an open
education platform. These studies focus on current insights
on learning outcomes resulting from the usage of learning
platforms in schools by learners, students, and instructors.
Younas et al. (2022b) indicate how homeschooled English
language learners feel about different homeschooling platforms
and apps, formulate learning principles, and analyze the broader
implications of real-world learning. Vorwerk and Engenhart-
Cabillic (2022), Long et al. (2021) discuss how students felt
about a newly established digital instructional program, which
included an interactive e-book and short learning videos on
a YouTube channel. It looks at the connection between how
people learn and how well they do in a course, and the flaws
in the blended learning mode, which is a big help in improving
the quality of education. Finally, the essential components
of excellent education are digital platforms for learning and
their influence on student motivation and knowledge growth.
Digital platforms for learning have unique properties that
assist the long-term development of education. The present
study explores the learning behaviors and how digital learning
impacts students’ motivation and study circles among confident
objectives: (1) to estimate the learning behavior of students; (2)
to identify the positive influence of educational apps on digital
learning platforms; and (3) to analyze the impact of digital
learning on students’ motivation and knowledge development.
Statement of the study
This study aimed to determine the attractiveness of digital
learning platforms and their impact on learning behaviors
and knowledge development. The study illustrates that using
interactive components of e-learning boosts the motivation
of undergraduate students for the learning process (Abou El-
Seoud et al.,2014). The research examines students’ self-efficacy,
attitudes, confidence in utilizing technology, instructional
tactics, the capacity to monitor and assess educational results,
and student motivations (Hongsuchon et al.,2022). Game-based
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learning outcomes were examined, including academic success,
problem-solving, critical thinking, and student attitudes and
behaviors (Yu et al.,2021). The study looks at the self-
regulated learning (SRL) techniques that students use in massive
open online courses, emphasizing how learners’ reasons for
participating impact their behavior and use of SRL tactics
(Littlejohn et al.,2016). According to the research, the social
regulation-based online learning strategy improves students’
learning outcomes and motivation (Hwang et al.,2021). As a
result, the study indicated that widespread usage of educational
apps and virtual classrooms provides better performance in
motivating students and knowledge development. The proposed
relationships of study variables have been shown in Figure 1,
and the hypotheses are given below.
Literature review
Digital platforms influence on
improving students’ learning behavior
Lai et al. (2016) examined an online training platform that
offered pedagogical rationales for self-directed learning and
a strategic foundation for aligning technology choices with
learning objectives and processes. According to Faridah et al.
(2020), the objective of the research is to quantify students’
interest in and engagement with digital learning and investigate
how students’ interest in and engagement with digital learning
changed throughout the COVID-19 epidemic. Singh et al.
(2021) attempt to explain the epochal correlations between the
constructs and examine how information might be utilized to
increase student acceptance of digital collaboration platforms.
Aduba and Mayowa-Adebara’s (2022) study examines how
internet platforms were used during the lockdown and (Luo
et al.,2019) interpersonal connections have a good influence
on the learners’ experience, strengthening their desire to
utilize the e-learning platform. Cui et al. (2022) highlight
the benefits of combining autonomy with structural assistance
and the importance of instructors in a web-based inquiry
learning environment. Kang and Zhang (2020) suggest that
online education is becoming more popular globally because it
engages and motivates students. Panigrahi et al. (2018) reported
that technology is being used around the globe to improve
education, minimizing the time and space issues associated with
conventional learning. The survey results of Moroccan EFL
university students are presented on the influence of digital
technology on learning behavior and reading motivation. Most
students use digital resources for learning and entertainment
(Larhmaid et al.,2019).
Digital platforms’ impact on students’
motivation
Learning motivation is critical for success in online learning,
particularly for K-12 students, and the impacts of K-12 students’
perceived presence and technical acceptability in online learning
(Zuo et al.,2021). Abou El-Seoud et al. (2014) research shows
that interactive e-learning features increase undergraduate
students’ interest in learning. Sandybayev (2020) examined how
digital technology may help culturally diverse students succeed
in school by interviewing 46 students from different academic
programs. Flipped teaching may encourage underachieving
students to study acting and improve their learning efficacy and
psychological needs through self-determination theory. (Chou
et al.,2021;Chiu,2022) studies examined student engagement
in online learning and its effectson students learning outcomes.
The study aims to determine if there is a strong correlation
between e-learning and student motivation in higher education
and the increasing use of e-learning (Harandi,2015). It is more
FIGURE 1
The relationship between different variables of the study.
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important than ever to figure out how to create electronic
learning resources that cater to student motivation and make
learning easier (Song and Bonk,2016), and the study’s goal was
to see how the design of a virtual learning environment affects
adult learners’ motivation at work (Bashshar,2017).
Digital platforms’ impact on students’
knowledge development
Recent studies show that the quick shift from traditional
to digital learning has harmed students’ intrinsic and extrinsic
motivation (Gustiani,2020). Education-related departments
strive to positively cultivate students’ professional information
knowledge and skills (Sun and Pan,2021). Rippa and Secundo’s
(2019) work adds to the burgeoning notion of digital academic
entrepreneurship, and Díaz-Noguera et al.’s (2022) study gives
a development model of students’ adaptive capabilities to the
digital revolution in university education. The study finds the
essential abilities and information from studies that reinforce
each other and help people learn more deeply (Makani et al.,
2016). This research examined how tourism and hospitality
students see how sharing economic platforms help them learn
and develop their beliefs and attitudes (Horng et al.,2022).
According to the findings, which looked at students’ motivation
for online learning, most students said they were engaged in
class and had strong time management skills while taking online
courses (Cabansag et al.,2020).
Theoretical framework
Social cognitive theory (SCT) stresses learning from
the social environment (Schunk and Usher,2012). A key
feature of SCT is its emphasis on external and internal
social reinforcement. In the social climate, SCT stresses the
unique way humans learn and maintain behavior. These
earlier experiences build attitudes and expectancies, which
decide whether or not a person engages in a particular
activity. People engage in diverse vicarious, symbolic, and
self-regulatory processes to build a feeling of agency. In
addition to objectives and self-evaluations of achievement, key
motivators include values, peer comparisons, and self-efficacy.
People establish and track objectives. Progress perception
boosts self-efficacy and motivation. People operate according
to their principles and seek the desired results. Other people’s
learning and goal achievement might be compared. Self-
efficacy influences task choices, effort, perseverance, and
accomplishment. Depending on their social surroundings,
humans might be proactive, engaged, or estranged (Ryan
and Deci,2000). To this end, social–contextual elements that
encourage or impede self-motivation and healthy psychological
development have been studied. Specific variables, such as
enhancing or reducing intrinsic motivation, self-regulation, and
wellbeing were studied.
Hypotheses of the study
H1: Learning behavior (LB) is positively associated with
watching animated movies (AMs).
H2: Student’s motivation (SM) is positively
influenced by AM and LB.
H3: LB is positively impacted SM through virtual
classroom (VCr).
H4: LB positively influences the use of
educational apps (EAs).
H5: Knowledge development (KD) is positively
influenced by VCr and LB.
H6: KD is positively influenced by the use of EAs and LB.
Research method
Study locale and population
The present study has been conducted in Lahore, the capital
of Punjab Province in Pakistan and is exploratory. The online
survey was used to gather primary data according to the research
goals. The research goal was explained to all survey participants
and their agreement was obtained. The researchers did a quality
check while the data were being gathered. All the people who
participated in the study were entirely voluntary, and they were
told their information would only be used for research purposes.
Sampling
The sample size for this research was N= 300, which was
chosen using purposive sampling. The criteria mentioned were
students with four majors (such as social sciences, psychology,
management studies, and education) who participated in this
study and had different educational backgrounds. Finally,
75 students were selected from each primary subject, and
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participants of different genders and age groups participated
in this study (Liébana-Presa et al.,2020). The information
gathered during data collection was divided into categories
depending on the frequency and percentages of each question
in demographics, and Table 1 summarizes the findings. There
were 146 male and 154 female students among the responders.
Eighty-three respondents were under 25 years, 129 were
between the ages of 25 and 30 years, and 88 were between
the ages of 31 and 35 years. Similarly, for the question,
29 respondents belonged with a doctoral degree, 107 were
from master’s degree, 95 were from bachelor’s degree, and
69 respondents had an associate degree. Similarly, students
with four different significant studies (such as social sciences,
psychology, management studies, and education) participated in
this study, and 75 male and female students were chosen from
each major equally to get unbiased data (please see Table 1).
Data collection process
The survey’s approach was employed to gather data, and
a questionnaire on learning behavior and the influence of
students’ motivation and knowledge growth was created. It
was built on a five-point Likert scale, with one indicating
strong agreement and five indicating extreme disagreement,
and the scale was modified based on previous studies (DeVries
et al.,2018). Dependent on the availability of participants,
questionnaires were used to gather data, and the data for this
research were gathered at Pakistani universities. Respondents
filled out these surveys, and the demographic employed in
this research included university students attending courses
on learning practices, digital platforms, and their effects on
knowledge acquisition. A survey was performed to gather
primary data according to the research goals. The research goal
was explained to all survey participants and their agreement was
TABLE 1 Demographic information of participants.
Demographic summary Category Frequency/Percentage
Gender Male 146 (48.66%)
Female 154 (51.33%)
Age 20–25 83 (27.66%)
25–30 129 (43%)
30–35 88 (29.33%)
Qualifications Doctoral 29 (9.66%)
Master 107 (35.67%)
Bachelor 95 (31.67%)
Associate 69 (23%)
Study fields Social sciences 75 (25%)
Psychology 75 (25%)
Management 75 (25%)
Education 75 (25%)
obtained. The researchers did a quality check while the data
were being gathered. The survey participants’ anonymity and
the confidentiality of the received information were guaranteed.
While getting informed consent, it was clear that all data would
only be used for research.
Operationalization of study variables
The questionnaires included six variables to gather data,
and 23 items were included in the questionnaires. The study’s
conceptual framework contained one independent variable
(such as learning behavior), three mediators (namely animated
movies, virtual classrooms, and educational Apps), and two
dependent variables (such as student motivation and knowledge
development). Some previous studies contain one independent
variable, three or four moderators, and one or two dependent
variables (Lin and Chen,2017;DeVries et al.,2018).
Demographic information
Considering the previous research (Salkind,2010), the
study contains gender, age, qualifications, and study fields
as demographic factors. All self-reported variables were
categorized by gender (male or female), age (between 20 and
35 years), qualification (namely doctoral, master, associate, and
bachelor), and study major (such as social sciences, psychology,
management studies, and education).
Digital learning impact
Learner motivation and learning outcomes are better in
digital education than in traditional education. Motivating
oneself to learn has a significant favorable impact on one’s
learning ability. To determine the elements impacting students’
happiness and performance in online courses and their
interaction (Gopal et al.,2021). The digital learning impact on
students was assessed by using the Likert-scale questions about
AM, VCRs, and EAs.
Students’ motivation and knowledge
development
Student motivation plays a pivotal role in knowledge
development during learning with technological aspects.
Developing student motivation is a challenging but vital part
of teaching that instructors must address. This research aimed
to see how technology affects students’ drive to learn and
remember new material (Granito and Chernobilsky,2012;
Yarborough and Fedesco,2020). The survey participants were
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assessed through motivation and knowledge development
questions.
Results
Smart PLS-Bootstrapping, T-Values (PLS) 3.0 and structural
equation modeling (SEM) were used to examine the model,
including internal consistency reliability, convergent validity,
and discriminant validity as examples of indicator loadings
(Hair et al.,2019). With the help of SEM, the Smart-PLS study
strategy is a robust, scalable, and cutting-edge approach to
creating a substantial statistical model (Abbas et al.,2019a). PLS-
SEM looks at complicated models with both observable and
latent parts. It may be able to give SEM results with different
levels of structural complexity, such as higher-order structures
that often solve problems with multicollinearity and look into
the measurement and structural models (Ringle et al.,2015;
Sharif et al.,2021).
Internal consistency reliability
The Internal consistency reliability (ICR) was implemented
to assess the consistency of findings across indicators. The
present technique reported Cronbach’s alpha and composite
reliability (CR). ICR values should range from 0 to 1. Cronbachs
alpha and Cronbach’s coefficient of determination (CR) should
be more than 0.700. Cronbach’s alpha and Cronbachs CR reports
are shown in Table 2. All constructs have a good Cronbach’s
alpha, and their CR values meet or exceed what is needed.
Animated movies had a Cronbach’s alpha of 0.726 and a CR
of 0.846, while educational apps had an alpha of 0.767 and a CR
of 0.734. Knowledge development possessed an alpha of 0.774
and a CR of 0.869. Learning behavior had an Alpha of 0.714 and
a CR of 0.755. Students’ motivation possessed an alpha of 0.869
and a CR of 0.906. Finally, virtual classrooms obtained an alpha
of 0.791 and a CR of 0.864.
Variance inflation factor
The prediction skills of the structural model were tested
as part of the evaluation. However, the collinearity value
should be indicated before providing the structural model by
reporting the variance inflation factor (VIF) values. Notably,
the predictors/mediators were assessed for the collinearity of
animated movies, educational apps, and virtual classrooms
as mediators of learning behavior, student motivation, and
knowledge development, respectively (Hair et al.,2019). VIF
levels should be less than three; values greater than three are
generally associated with multicollinearity issues. According to
the data analysis, all VIFs are less than three. As a result,
collinearity is not a concern in this study’s model.
TABLE 2 Reflective indicator loadings, internal consistency reliability,
and convergent validity.
Constructs Items Loadings VIF Alpha CR AVE
Animated movies AMs_1 0.898 1.956 0.726 0.846 0.650
AMs_2 0.855 1.773
AMs_3 0.644 1.230
Educational apps EAs_1 0.723 1.076 0.767 0.734 0.580
EAs_2 0.607 1.098
EAs_3 0.742 1.105
Knowledge development KD_1 0.799 1.460 0.774 0.869 0.689
KD_2 0.845 1.722
KD_3 0.846 1.680
Learning behavior LB_1 0.533 1.143 0.714 0.755 0.587
LB_2 0.562 1.236
LB_3 0.536 1.182
LB_4 0.803 1.280
LB_5 0.635 1.285
Students motivation SM_1 0.752 1.701 0.869 0.906 0.659
SM_2 0.872 2.808
SM_3 0.739 1.826
SM_4 0.806 1.953
SM_5 0.878 2.700
Virtual classrooms VCr_1 0.861 2.046 0.791 0.864 0.621
VCr_2 0.849 1.952
VCr_3 0.854 1.914
VCr _4 0.541 1.210
AMs, animated movies; EAs, educational apps; KD, knowledge development; LB,
learning behavior; SM, student motivation; VCr, virtual classrooms.
Convergent validity
Convergent validity is a subtopic of construct validity in
which tests with the same or comparable constructs should be
substantially connected. The average variance derived from this
research is used to calculate the convergent validity average
variance extracted (AVE). The AVE was calculated using Smart
PLS 3.0. According to the methodology, AVE values should
be 0.500 or higher, explaining 50% or more of the variation
(Table 2). All constructs had AVE values of more than 0.500,
indicating that they presented more than half of the variation.
Animated movies’ AVE value was 0.650, educational apps’ AVE
value was 0.580, knowledge development’s learning behavior
was 0.587, student motivation’s AVE value was 0.659, and virtual
classrooms were 0.621.
Loading indicators
Figure 2 shows the factor loadings acquired by PLS-
SEM to confirm the validity. The loadings of reflective
indicators attained in SEM should be more than 0.500, and all
loadings should be greater than 0.500 based on the calculation.
“Animated Movies (0.898, 0.855, 0.644), Educational Apps
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FIGURE 2
PLS-structural equation modeling (SEM) results.
(0.723, 0.607, 0.742), Knowledge Development (0.799, 0.845,
0.846), Learning Behavior (0.533, 0.562, 0.536, 0.803, 0.635),
Students Motivation (0.752, 0.872, 0.739, 0.806, 0.876), and
Virtual Classrooms (0.861, 0.849, 0.854, 0.541).”
Discriminant validity
Discriminant validity (DV) showed how to quantify
constructs that were conceptually unrelated to one another.
Discriminant validation seeks to show any discriminating
evidence based on all components’ dissimilarities (Campbell
and Fiske,1959). The overlap of measurements on each other
is used to assess discriminant validity (please see Table 3).
Comparing the square root of a factor’s AVE values with the
correlation between constructs might indicate DV. AVE values
should be greater than correlations (Campbell and Fiske,1959).
Table 2 shows that the AVE square root is more significant than
correlation values, which indicates good assessment.
Model fit summary
This work’s model fitness was assessed using Standardized-
root-mean-square-residual (SRMR), normed fit index (NFI),
and Chi-square (X2). It is a measure of model fitness that
compares observed covariance to hypothesized matrices (Chen,
2007;Brown,2015). The SRMR value must be less than
or equal to 0.08 to be considered acceptable. Results show
that the predicted SRMR value of 0.079 is a satisfactory
model fit for the standardized root mean square residual.
An NFI score of 0.475 and a X2value of 2868.490 (please
see Table 4) indicate that the two datasets are statistically
insignificant.
PLS-bootstrapping, T-values
The import of all straight effects was assessed for
the structural model by examining the path coefficients,
TABLE 3 Discriminant validity (N= 300).
AMs EAs KD LB SM VCr
Animated movies 0.806
Educational apps 0.669 0.693
Knowledge development 0.705 0.698 0.830
Learning behaviors 0.630 0.463 0.631 0.622
Students motivation 0.880 0.737 0.743 0.579 0.812
Virtual classrooms 0.754 0.698 0.752 0.543 0.753 0.788
AMs, animated movies; EAs, educational apps; KD, knowledge development; LB,
learning behavior; SM, student motivation; VCr, virtual classroom.
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TABLE 4 Model fit summary.
Statistical tests
SRMR 0.079
d_ULS 8.380
d_G 2.319
X22868.490
NFI 0.475
SRMR, standardized-root-mean-square-residual; d_ULS, unweighted least squares
discrepancy, d_G, geodesic discrepancy; X2, chi-square, NFI, normed fit index.
T-statistics, and p-value. We computed the data through
a bootstrapping procedure. The bootstrapping computation
results are presented in Table and Figure, with the Table
informing the hypotheses, relationship, path, T-value, and
p-value. Figure 3 illustrates the T-value and loading value of the
path lines during the bootstrapping process.
The hypotheses’ statistical significance was assessed using a
standard beta calculation. We can see how much the dependent
component may vary from the independent factor using the
beta number. Each association’s standardized beta (β) value was
determined following the predicted study model (Table 5). High
and significant beta (β) values indicate that endogenous latent
variables have a strong influence. T-statistics were utilized to
validate the significance of the beta value for each route in
the experiment. The significance level of putative associations
was assessed and evaluated using the beta (β) value acquired
TABLE 5 Path, T-value, and P-value.
H Relationships Path (β) T-value P-value Decision
H1 AMs –> SM 0.716 13.092 0.001 Confirmed
H2 EAs –> KD 0.292 4.696 0.001 Confirmed
H3 LB –> Ams 0.63 15.771 0.001 Confirmed
H4 LB –> Eas 0.463 8.221 0.001 Confirmed
H5 LB –> KD 0.28 6.415 0.001 Confirmed
H6 LB –> SM 0.017 0.537 0.592 Not-Confirmed
H7 LB –> VCr 0.543 12.206 0.001 Confirmed
H8 VCr –> KD 0.396 5.751 0.001 Confirmed
H9 VCs –> SM 0.204 3.551 0.001 Confirmed
AMs, animated movies; EAs, educational apps; KD, knowledge development; LB,
learning behavior, SM, student motivation, VCr, virtual classrooms.
using the bootstrapping approach. The structural model’s
hypothesized connections are shown in all beta (β) values in
Figure 3,Table 5, respectively, to illustrate the link between
T-values and observed variables (see Figure 3).
The maximum T-value was attained by the path between
facilities and research activities (t= 15.771), while the last
value was the association between practical activities and
employability (t= 0.537). All hypotheses projected in this
study were supported. In detail, H1 was reported to be
significant in influencing students’ motivation (β= 0.716;
t= 13.092; p<0.001) and knowledge development. H2 reveals
that educational apps have a significant impact (β= 0.292;
FIGURE 3
PLS bootstraping.
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Noor et al. 10.3389/fpsyg.2022.933974
t= 4.696; p<0.001) on knowledge development. H3 was also
supported where learning behavior is significantly predicted by
animated movies (β= 0.630; t= 15.771; p<0.001). Similarly,
the significant role of learning behavior in educational apps
(H4) was also reported (β= 0.463; t= 8.221; p<0.001).
Learning behavior is also a significant predictor for knowledge
development, H5 (β= 0.280; t= 6.415; p<0.001). The result of
PLS-SEM supports H6 because there is a direct effect of learning
behavior on student motivation (β= 0.017; t= 0.537; p<0.592).
H7 is also supported as learning behavior is significantly
predicted by virtual classrooms (β= 0.543; t= 12.206; p<0.001).
Finally, the findings also support hypotheses 8 and 9. Positive
relationships also emerged between virtual classrooms and
knowledge development (β= 0.396; t= 5.751; p<0.001).
Virtual classrooms are also known to be a significant predictor
of students’ motivation (β= 0.204; t= 3.551; p<0.001).
Discussion
It was determined that e-learning was successful and
investigated major antecedents of e-learning success in the
COVID-19 pandemic (Elshaer and Sobaih,2022;Jaoua et al.,
2022). The major motivators for the likely continuance of
videoconferencing as a supplement to face-to-face tutorials
at Spanish institutions (Infante-Moro et al.,2021). Sequential
analysis is used to assess the impact of student motivation on
online reading behavior and the perceived online social presence
in an online course (Tao,2009;Sun et al.,2018). According to
the result of the SEM model, these hypotheses (H1: Animated
Movies >Student Motivation, β= 0.716; t= 13.092; p<0.001;
H2: Educational Apps >Knowledge Development, β= 0.292;
t= 4.696; p<0.001; H3: Learning Behavior >Animated
Movies, β= 0.630; t= 15.771; p<0.001; H4: Learning Behavior
>Educational Apps, β= 0.463; t= 8.221; p<0.001; H5:
Learning Behavior >Knowledge Development, β= 0.280;
t= 6.415; p<0.001) are confirmed. The result of H6 indicates
that (Learning Behavior >Student Motivation, β= 0.017;
t= 0.537; p<0.592) there is no direct effect of learning
behavior on student motivation. The approach suggests three
characteristics to enhance students’ ability to adapt to digital
change in university teaching: motives, digital pedagogy, and
student autonomy (Hwang et al.,2015;Díaz-Noguera et al.,
2022). Since information and computer technology (ICT) skills
are becoming more important everywhere, especially in the
workplace, one of the main goals of colleges has been to
teach students how to deal with problems (Bond et al.,2018;
Muhammad et al.,2020). Previous research examines the effect
of enhancing overall wellness during the COVID-19 epidemic
and university students’ dangerous online activity (Peng et al.,
2022).
In education, digital transformation is a long-term process
that has become a pressing issue. Studies indicate a positive
association between openness to experience and creativity and
a mediation function for intrinsic drive and creative process
participation in this relationship (Tan et al.,2019;Bogdandy
et al.,2020). The study analyzed teacher opinions on how
technology affects student academic behavior and performance
in a blended learning environment and also looked at students’
behavioral intentions (motivation) for adopting online learning
technologies (Chen et al.,2002;McHone,2020). The analysis
results show that these hypotheses (H7: Learning Behavior >
Virtual Classrooms, β= 0.543; t= 12.206; p<0.001: H8: Virtual
Classrooms >Knowledge Development, β= 0.396; t= 5.751;
p<0.001; H9: Virtual Classrooms >Student Motivation,
β= 0.204; t= 3.551; p<0.001) are confirmed. To construct
an efficient 21st century classroom that fits the requirements of
students, a modern teacher must consider a student’s drive to
study and the effect of technology has on inclusionary education
(Granito and Chernobilsky,2012;Kizilcec and Schneider,2015).
There has never been a study that looked specifically at how
university students’ learning attitudes are affected by social
media’s good and bad features (Abbas et al.,2019b).
Online education is rapidly developing due to a growing
demand for higher and continuing education. Yet, many online
students fail to fulfill their educational objectives due to the
lack of face-to-face interaction (Zheng et al.,2015;Kizilcec
et al.,2020). Learning behavior is also a significant predictor
of knowledge development in H5 (β= 0.280; t= 6.415;
p= 0.000). H6 results show that there is a direct effect of
learning behavior on student motivation (β= 0.017; t= 0.537;
p= 0.592). Students’ willingness to attend online courses
was predicted using the Motivation Orientation Scale and the
Unified Theory of User Acceptance of Technology (Cullum,
2016). The capacity of students to persevere and get high
marks when taking online classes is compared with their ability
to do so while taking face-to-face courses (Xu and Jaggars,
2013). Investigations were carried out to determine how strong
the link between online learning and students’ motivation
was among the participants and whether there was a direct
correlation between student views of their motivation to read
and their accomplishments in a blended learning environment
(Minda,2020;Miller,2021). Researchers wanted to determine
how e-learning activities interacted with such characteristics as
gender, maturity level, field of study, geographic location, and
grade level (Thomas,2020). As the world becomes more digital,
it is important to define information literacy and investigate
studies on game-based learning (Press,2017). This research
found that digital platforms significantly influence learning
behavior and affect students’ motivation and knowledge growth.
Learner motivation and learning outcomes are greater in digital
education than in traditional education.
Conclusion
With the growing usage of digital platforms for learning,
the introduction of various technology applications, and
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Noor et al. 10.3389/fpsyg.2022.933974
their influence on learning behavior, student motivation, and
knowledge acquisition, the whole domain of learning and
education in Pakistan and throughout the globe has altered. This
study also examines students’ studying habits in light of digital
learning’s impact on education. Intentions for digital learning
are more vivid in students who participate in digital learning.
Motivating oneself to study has a substantial favorable effect
on one’s learning ability. Furthermore, goal-setting behavior
and social pressures have increased students’ digital learning
practices. These results have been a significant step forward
in creating knowledge and improving student learning. The
findings of this study have a wide range of ramifications for
future academics and organizations that want to replicate this
research in various places with their resources. New research
paths like these may benefit significantly from the use of these.
Study limitations
There are several limitations regarding the current study.
The respondent must be 20 + years old university student. There
are a lot of digital platforms for academic learning. Some of them
are freely available and some of them are paid or subscription-
based. Most universities have paid or subscription-based access
to their students’ digital platforms of academic learning. Only
those university students can participate in the study survey
that uses their universities’ available digital platforms. Purposive
sampling was used as part of the non-probability sampling
(NPS) technique since it best suited the study’s goals and
objectives. The conclusions of this research cannot be extended
to the whole population since it is difficult to repeat the results
of the purposeful sample.
Data availability statement
The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed
and approved by School of Education, Soochow University. The
patients/participants provided their written informed consent to
participate in this study.
Author contributions
XQ is the principal investigator. UN and MY
collected/analyzed data and wrote the manuscript. RM designed
the study model and hypothesis and contributed to the
discussion section. HS contributed to drafting the literature
review, discussion, and overall editing of the manuscript.
All authors contributed to the article and approved the
submitted version.
Funding
This project research was supported by study on the
Construction Quality of Research Groups in Universities in
China, the National Office for Education Sciences Planning
China. Project No: BIA200166.
Acknowledgments
We thank all the researchers for data collection and
processing and acknowledge our study’s survey participants.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
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This study evaluated how tourism and hospitality students' perceptions of how they utilize sharing economic platforms enhance their learning and change the values and attitudes of sustainability. A total of 567 students participated in this study, and a multiple mediation-moderation approach was adopted. In the mediation effects, the results show that sharing an economic platform as a foundational attribute indirectly influences students' values and attitudes via sustainable behaviour development (e.g., innovative, supportive and eco-behaviour). Furthermore, in the moderation effects, digital learning is a critical moderating factor that influences sustainable behaviour to students’ effort expectancy and values for sustainability.