sustainability

Article
Does the Impact of Technology Sustain Students’ Satisfaction,
Academic and Functional Performance: An Analysis via
Interactive and Self-Regulated Learning?
Muhammad Qasim Memon 1,2 , Yu Lu 1, * , Abdul Rehman Memon 3 , Aasma Memon 4 , Parveen Munshi 5
and Syed Farman Ali Shah 6

1 Advanced Innovation Center for Future Education, Faculty of Education, Beijing Normal University,
Beijing 100875, China; memon_kasim@usms.edu.pk
2 Department of Information and Computing, University of Sufism and Modern Sciences,
Bhitshah 70140, Sindh, Pakistan
3 Department of Chemical Engineering, Mehran University of Engineering & Technology,
Jamshoro 76062, Sindh, Pakistan; enxarm@gmail.com
4 School of Economics and Management, Beijing University of Technology, Beijing 100021, China;
kaasma.bjut@gmail.com
5 Department of Education, University of Sufism and Modern Sciences, Bhitshah 70140, Sindh, Pakistan;
vc@usms.edu.pk
6 Department of Business Administration, University of Sufism and Modern Sciences,
Bhitshah 70140, Sindh, Pakistan; registrar@usms.edu.pk
* Correspondence: luyu@bnu.edu.cn

Citation: Memon, M.Q.; Lu, Y.; Abstract: High-quality academic outcomes are required for students’ educational attainment and
Memon, A.R.; Memon, A.; Munshi, P.; promote their desire to learn. However, not all educational sectors boast of the same, leading students
Shah, S.F.A. Does the Impact of to attain inferior outcome performances. The current study examines the impact of technology on
Technology Sustain Students’ student satisfaction, academic, and functional performance via the mediating factors of interactive and
Satisfaction, Academic and
self-regulated learning. However, existing works focused less on technology and more on psychological
Functional Performance: An Analysis
learning factors, rendering mere acceptance of technology, proved to be useless. The present research
via Interactive and Self-Regulated
investigates such mediators with existing technology resources and their impact on students’ overall
Learning? Sustainability 2022, 14,
7226. https://doi.org/10.3390/
growth. Research hypotheses are tested through structural equation modeling and applied to the data
su14127226 collected from 302 respondents via a structured questionnaire. In addition, the present study considers
the collection of each student’s data across different universities, colleges, vocational and education
Academic Editors: Danial Hooshyar,
institutions, mainly where students are involved in/using the technology when it comes to satisfaction,
Michael D. Kickmeier-Rust and Nour
academic, and functional performance. The results indicated that the impact of technology via interactive
El Mawas
learning has a significant influence on students’ satisfaction (β = 0.238, p < 0.05), academic performance
Received: 1 April 2022 (β = 0.194, p < 0.05), and functional performance (β = 0.188, p < 0.05). It is also noted that the impact of
Accepted: 27 May 2022 technology via self-regulated learning has positively contributed to satisfaction, academic, and functional
Published: 13 June 2022 performance. Our findings support the hypothesis and encourage students’ adaptability, engagement,
Publisher’s Note: MDPI stays neutral and behavioral interactions stimulating the performance outcomes. The performance outcome of this
with regard to jurisdictional claims in research presents valuable information for decision-makers to articulate sustainable strategies and tactics
published maps and institutional affil- in educational sectors.
iations.
Keywords: technology education; digital learning; technology engagement; interactive learning;
academic performance; functional performance; structure equation modelling; self-regulated learning

Copyright: © 2022 by the authors.

Licensee MDPI, Basel, Switzerland.
This article is an open access article
1. Introduction
distributed under the terms and
conditions of the Creative Commons In the interim, dawn of capitalism, educational sectors have been reformed and
Attribution (CC BY) license (https:// evolved in several uncertain ways. However, the momentum of such reforms in the present
creativecommons.org/licenses/by/ day and age is diverse. The fast growth of technology has contributed to eliminating
4.0/). distances among people to ease the learning process; hence, educational sectors are certainly

Sustainability 2022, 14, 7226. https://doi.org/10.3390/su14127226 https://www.mdpi.com/journal/sustainability

Sustainability 2022, 14, 7226 2 of 19

motivated by the capacity and efficacy of digital learning [1]. For example, a study was
conducted on 299 undergraduate students with a 71-item survey, which showed that 25% of
participants have problems with disruptions from technology. Consequently, incorporating
technology without a strategic methodology causes an onerous outcome than otherwise [2].
At the same time, a study suggested that educational sectors must be familiar that it is not
about the trappings of adopting technology only [3]. Still, enactment and validation via
training and learning strategies are also essential.
Digital learning leads to an era in which artificial intelligence (AI) has become a
central element in our lives. AI in engineering and technology does not demand practical,
technical knowledge and skills, but “creatively-focused technology fluency” (CFTF) [4].
Moreover, creativity has developed both individual competencies and intrinsic motivation,
and it is a recognized construct in technology-enhanced learning [5]. However, there are
still challenges: how to teach creativity via different learning factors in digital learning.
Creativity infuses students with the desire to learn, be successful, and, maybe above
all, attempt something different. Although including such learning factors is a good
step towards student achievement, such as (1) adaptive learning [6], (2) self-regulated
learning (SRL) [7], (3) online learning (eLearning) [8], (4) mobile learning (mLearning) [9],
(5) interactive learning [10], (6) badging and gamification [11], (7) blended learning [12],
and (8) virtual reality [13]. Literature in this regard confers that interactive learning and
self-regulated learning are key learning factors [1]. It is noted that the selection of a smaller
number of learning factors and their delineation to a student is a significant concern, which
can lead to unresponsive and undesirable student’ behavior [1,2].
On the contrary, acceptance of technology by educational institutions towards the
cause of students’ development, who are the subjects/respondents, is considered an es-
sential part of higher education [14]. Educators are already conscious of the role that
educational climate help creates among students, which plays a vital role in student sat-
isfaction, motivation, and academic and social attainment [15]. Many studies have been
conducted on technology acceptance in the last two decades, considering the differences in
learning orientations or styles [16].
For instance, the factors that can enhance the computer-based assessments of in-class
and outside-class computer training. The research was considered a class of some 400 with
direct and current experience using computer-based training and assessment for course
credit [17]. However, academics are limited as to what kind of ‘educational environment’
they can create per se. They also have to make the best use of the technology for every
student, regardless of their satisfaction with technology, engagement, motivation, and
learning styles in person and virtually [18].
Alternatively, there may be a need to study how students engage with that technology—
what role the impact of technology plays in terms of performance features such as students’
satisfaction, academic, and functional performance? However, a study has been prejudiced
with the performance features merely with educational attainment rather than social [19,20].
Overall, there is only one factor of digital learning than hybrid factors compared to our present
research model as mentioned in earlier reports [1,18]. For the most part, we also noticed that
previous studies were not intended to teach the students per se; instead, they are probably
more about the use of technologies around learning [21].
Thereby, we present two methodological contributions to technology acceptance with
the adoption of digital learning. First, the Measurement Invariance (MI) study suggests a
within-study using the Common Factor Analysis (CFA) [22]. Second, the demonstration of
the within-study MI of the Unified Theory of Acceptance and Use of Technology (UTAUT)
is validated [23]. In particular, MI is the primary concern in different social and behavioral
studies when the sample size includes several populations [24]. Within-study MI is used
to group items/variables that differ within unique research, i.e., response count from an
individual study contains different categorized item levels [25]. For instance, respondents
pertaining highest and lowest educational backgrounds are considered in this research.
Sustainability 2022, 14, 7226 3 of 19

Underpinning the aim of the present study as it sheds light on learning and teaching.
It ultimately has a clear motivation for teachers by assessing the impact of technology
via digital learning in the classrooms. However, technology exists everywhere, wherein
academics can choose to use it with the students—or not. It’s their decision and not
the decision of the students. For instance, most academics provide black or whiteboard
learning, which is familiar with most universities; whether students (or staff) like it, they are
all forced to use it [26]. Regardless of their preferences and willingness or how much they
engage with the technologies, students (and lecturers) will have to use them [27]. Therefore,
our concern in the present research is not restricted to focusing on how well the students
tend to engage with the technology only. However, the factors, such as interactive and
self-regulated learning, which act as mediators, contribute to successful academic and social
attainment outcomes. In this regard, the current research aims to harvest contributions
such as:
• This research examines technology acceptance using learning factors of digital learning.
• This study presents an empirical analysis to observe the relationship of technology
between self-regulated and interactive learning.
• Students’ engagement with the technology via the mediating role of interactive
and self-regulated learning can improve their satisfaction and academic and func-
tional performance.
We used five other constructs (interactive learning, self-regulated learning, satisfaction,
academic and functional performance) to establish the within-study MI as per the UTAUT
model. Engagement should be the primary concern since engaging the student is both-
ersome and faced with continuous interruptions because the more students are engaged,
the more they learn [28]. The content offered in educational settings has rich importance
and instant worth to students to ensure students are getting full attention [29]. Therefore,
we refer to ‘technology engagement,’ defined as students in the classroom indulging in a
deeper understanding of topics that interest them, working together, and boosting their
learning in digital knowledge.
On the contrary, the satisfaction construct is a short-term attitude, and it is assessed
using students’ educational experience, services, and facilities [30]. The academic per-
formance construct involves intellectual level, personality, motivation, skills, interests,
and the teacher—student relationship [31]. Functional performance refers to applying
academic skills in a cumbersome number of methods and various settings [32]. Functional
performance can be perceived in how the student is involved in routine daily activities,
including communication, activeness, behavior, and social skills. It also consists of daily
routine academic and social activities that influence the students’ performance. Hence, it is
not restricted to academics only, but contains other concerns associated with the general
curriculum standards.
A self-regulated construct is defined as the student’s ability to understand and
control the learning environment, including self-monitoring, self-instruction, and goal-
oriented [33]. Interactive learning is learning that requires student participation via a set of
activities, including group discussions and digital learning [34].
Existing studies have explored two existing models, such as TAM (Technology Accep-
tance Model) [35], UTAUT as prior technical knowledge, and game-based learning [11],
respectively. Since the UTAUT model follows, the constructs reported by [23] are method-
ologically restricted. Within this perspective, this study has been guided by the research
questions (RQ) as follows:
RQ1: How do the students engage with technology via interactive/self-regulated
learning to sustain their satisfaction and academic and functional performance?
RQ2: What role does engaging in/using the technology play in academic and social
attainment via composite learning factors?
Sustainability 2022, 14, 7226 4 of 19

1.1. Preliminaries
Higher education sectors have been capitalizing on assets using Information and Com-
munication Technologies for Developments (ICT4D) to provide education attainment [36].
Further, previous works emphasized digital learning that helps to escalate the possibility
of learning, which encompasses the different learning factors mentioned earlier [6–13].
In this paper, we considered the adoption of digital learning using technology involves
two theories: (1) TAM and (2) UTAUT. In contrast, these two theories have remained the
primary concern and adopted in recent works [17,18]. Whereas, diffusion of technology
appraised in the opposite direction from a developed country into a developing coun-
try [36]. Consequently, technology adoption is indiscriminating; thus, it is not generalized
to developing countries. Therefore, contributing factors and espousal in understanding the
technology is still thought-provoking in developing countries [37].
In particular, students in the classroom consider the technology using interactive and
self-regulated learning; they allow an exact infrastructure such as a rich mental framework
to recognize moral and social responsibilities [38]. For instance, eLearning and mLearning
have a strong link with academic research. However, students’ acquaintance related to
mLearning may vary since they do not have the same level of perception or interest. Educa-
tional sectors persist in dissimilarities in the context of interests across various cultures and
historical circumstances [17]. Therefore, this study provides one of the functional perfor-
mance outcomes of social attainment following the different cultural issues [32]. Constructs
and hypotheses are combined to explore the learning factors (interactive and self-regulated
learning) and present a correlation between implementation and performance outcomes in
educational settings [39].
H1. Students engaging with the technology significantly affect their academic performance and satisfaction.
H2. Students engaging with the technology significantly influence their functional performance.

1.2. Learning Factors: Interactive and Self-Regulated Learning

Several factors influence a student’s learning like movement, repetition, feedback,
stress, and emotions. Novel developments have occurred in recent years via digital learn-
ing, but typical issues always plod on. For instance, the attenuation rate of online learning
is 75% [40]. Since researchers have recommended a clear motivation, they showed that
students’ satisfaction and academic performance reflect perseverance [41]. With this un-
derstanding, a student engaging in/using technology receives satisfaction and achieves a
successful outcome as a lower drop-out ratio via interactive and self-regulated learning [42].
As a result, self-regulated and interactive learning are reasonable in computation, which is
a simple and effective way to improve perseverance. Therefore,
H3. Self-regulated learning has a significant direct effect on students’ academic performance
and satisfaction.
H4. Self-regulated learning has a significant direct influence on students’ functional performance.
H5. Interactive learning directly affects students’ satisfaction, academic and functional performance.

1.3. Students Engaging in Technology via Interactive and Self-Regulated Learning

According to the contradictory situation, the authors indicated that technology re-
lies on learning factors to convey communication and content [43,44]. While adhering
to the impact of technology might have been neglected. In this regard, the investigation
covered the theoretical gap entitled “How user-interface interaction affects the intention
to accomplish a task” [45], wherein the authors claimed that learners who interact with
the technology are expected to assist or impede other interactions. Similarly, researchers
urged toward software for handling interaction affected satisfaction, student-to-student
interaction, learning outcome, and academic achievement. However, researchers discov-
ered that the factors such as support and availability prophesied education attainment
Sustainability 2022, 14, 7226 5 of 19

via self-regulated learning [46]. Therefore, we posit that students engaging in technology
influence their performances via composite learning factors such as self-regulated and
interactive learning [47].
H6. Students engaging in technology via self-regulated learning significantly affect academic performance.
H7. Students engaging in technology via self-regulated learning contributed positively and signifi-
cantly to satisfaction and functional performance.
H8. Students engaging in technology via interactive learning contributed significantly to satisfac-
tion, academic and functional performance.
It is noted that the interface is a mediating factor in the mainstream educational
settings among all the interactions [48]. Further, it is also indicated that some interactions
rely on students’ ability to engage with technology. Therefore, we infer that interactive and
self-regulated learning mediates between technology and student satisfaction, academic,
functional performance.
H9. Self-regulated learning has a positive and significant mediating role between technology and
satisfaction, academic performance, and functional performance.
H10. Interactive learning has a positive and significant mediating role between technology and
satisfaction, academic performance, and functional performance.
The rest of the paper is organized as follows: First, we illustrate the hypothesized
model and its validation in an isolated structure model and direct and indirect relations
among constructs. Second, we present the discussion according to existing works, limita-
tions, and future works of the current study. Finally, concluding remarks are imparted.

2. Research Model and Methodology

Hypothesized model of this study is presented in Figure 1. Technology engagement
plays an independent variable, where mediating factors (interactive and self-regulated
Sustainability 2022, 14, x FOR PEER REVIEW 6 of 19
learning) mediate the relations between independent and dependent variables (academic
performance, functional performance, and students’ satisfaction).

Interactive Functional
Learning Performance

Technology Academic
Engagement Performance

Self-Regulated
Learning Satisfaction

Figure
Figure 1.
1. Hypothesized
Hypothesized model.
model.

2.1. Sample
2.1. SampleSelection
Selection and
and Data
Data Analysis
Analysis
The targeted
The targeted respondents
respondentsininthis research
this researchareare
included across
included different
across countries/continents,
different countries/con-
thus validating
tinents, MI within-study;
thus validating students from
MI within-study; China and
students Pakistan
from Chinaareandconsidered
Pakistancountry-wise.
are consid-
At the same time, the rest of the respondents are categorized continent-wise (see Figure
ered country-wise. At the same time, the rest of the respondents are categorized continent- 2a). Since
the selection of respondents was not biased to a country/continent; therefore, we prefer
wise (see Figure 2a). Since the selection of respondents was not biased to a country/conti- to include
responses from different cultural and geographical backgrounds. The respondents
nent; therefore, we prefer to include responses from different cultural and geographical received diverse
responses, and we collected a sample size of 302 based on probability-based
backgrounds. The respondents received diverse responses, and we collected a sample size sampling formula
of 302 based on probability-based sampling formula using a margin of error (5%) and a
confidence interval of 95%. Demographically, 51.66 were female and 48.34 male, where
the age of respondents was categorized in different levels (see Figure 2b). The data was
collected through a structured questionnaire (see Supplementary Materials for a question-
 2.1. Sample Selection and Data Analysis
The targeted respondents in this research are included across different countries/con-
tinents, thus validating MI within-study; students from China and Pakistan are consid-
ered country-wise. At the same time, the rest of the respondents are categorized continent-
Sustainability 2022, 14, 7226 wise (see Figure 2a). Since the selection of respondents was not biased to a country/conti-
6 of 19
nent; therefore, we prefer to include responses from different cultural and geographical
backgrounds. The respondents received diverse responses, and we collected a sample size
of 302 based on probability-based sampling formula using a margin of error (5%) and a
using a margin of error (5%) and a confidence interval of 95%. Demographically, 51.66 were female
confidence interval of 95%. Demographically, 51.66 were female and 48.34 male, where
and 48.34 male, where the age of respondents was categorized in different levels (see Figure 2b).
the age of respondents was categorized in different levels (see Figure 2b). The data was
The data was collected through a structured questionnaire (see Supplementary Materials for a
collected through a structured questionnaire (see Supplementary Materials for a question-
questionnaire sample). The respondents’ information, who participated in this research, such as
naire sample). The respondents’ information, who participated in this research, such as
educational background and field of interest, is shown in Figure 3a,b), respectively.
educational background and field of interest, is shown in Figure 3a,b), respectively.

Sustainability 2022, 14, x FOR PEER REVIEW 7 of 19

Figure 2. (a) Respondents from different Ethnic groups, and (b) age of respondents.
Figure 2. (a) Respondents from different Ethnic groups, and (b) age of respondents.

Figure
Figure 3. 3.
(a)(a) Educationbackground
Education backgroundof
ofrespondents,
respondents, and
and (b)
(b)field
fieldofofinterest
interestofofrespondents.
respondents.

2.2.2.2.
Measures
Measures
Technology
Technologyengagement
engagement contains
contains composite measures,such
composite measures, suchasasthat
that first
first phase
phase of of
items, and
items, andweweanalyzed
analyzedthethestudent
student learning expertiseinindigital
learning expertise digitallearning
learning with
with technology
technology
engagement
engagement[23]. [23].The
Theessence
essence ofof computer
computer andandtechnology
technologyengagement
engagement viavia self-efficacy,
self-efficacy,
academic/workexpectancy,
academic/work expectancy,and andbehavioral
behavioralintention
intention is
is assumed
assumed as learners’
learners’ perception.
perception. It
It shows
shows that that assessment
assessment related
related to technology
to technology engagement
engagement with
with self-regulated
self-regulated andand inter-
interactive
active learning
learning appearedappeared
to be an to be an adequate
adequate supportsupport when forming
when forming the learner’s
the learner’s ability.ability.
We refer
to We
the refer to the investigation,
investigation, which indicated
which indicated that learning
that learning factorsfactors via eLearning
via eLearning events
events need
need comprehensive learning with technology to predict academic
comprehensive learning with technology to predict academic performance [21]. performance [21].
Similarly,
Similarly, thethe authors
authors indicatedthat
indicated thatstudent
studentperception
perception through
through interactive
interactive learning
learning is
is based on the expectancy-value theory that stimulates the
based on the expectancy-value theory that stimulates the student’s academic student’s academic perfor-
performance
mance and satisfaction [49]. Consequently, we used 14 items in our present
and satisfaction [49]. Consequently, we used 14 items in our present structured question- structured
questionnaires (somewhat revised with the previous research [49]) that were previously
naires (somewhat revised with the previous research [49]) that were previously tested and
tested and validated using technology-based learning for higher education [50]. On the
validated using technology-based learning for higher education [50]. On the other hand,
other hand, the remaining 15 items are related to academic performance (6 items), satis-
the remaining 15 items are related to academic performance (6 items), satisfaction (5 items),
faction (5 items), and functional performance (4 items), respectively.
and functional performance (4 items), respectively.
2.3. Descriptive Statistics
Descriptive statistics of the total sample (see Table 1) present the mean value, stand-
ard deviation (S.D), and normality of the constructs. Interactive learning has the highest
mean value, while functional performance has the lowest. Similarly, interactive learning
Sustainability 2022, 14, 7226 7 of 19

2.3. Descriptive Statistics

Descriptive statistics of the total sample (see Table 1) present the mean value, standard
deviation (S.D), and normality of the constructs. Interactive learning has the highest mean
value, while functional performance has the lowest. Similarly, interactive learning has the
highest S.D, while functional performance has the lowest. The mean value of interactive
learning and functional performance is low due to the respondents included in the sample
data associated with high/low socio-economic countries (i.e., Asian, African, and American
countries). The skewness measures the degree of lopsidedness in the frequency distribution.
In contrast, kurtosis measures the degree of tailedness in the frequency distribution. The
data used in this study are standard, as all factors have their skewness, and kurtosis values
are lower than ±2, as suggested in existing literature [51].

Table 1. Descriptive statistic.

N Minimum Maximum Mean Standard Deviation Skewness Kurtosis

TechnologyEngag 302 3.00 5.00 3.5276 0.42375 0.188 −0.198
SelfRegLearning 302 3.00 5.00 3.6464 0.44792 −0.134 −0.182
InteractiveLearning 302 3.00 5.00 3.6854 0.45305 0.057 0.125
AcademicPerform 302 3.00 5.00 3.1621 0.37378 −0.068 1.602
Satisfaction 302 3.00 4.00 3.1530 0.39110 0.197 −1.546
FunctionalPerform 302 3.00 4.00 3.14 0.35291 −1.325 0.199

2.4. Common Method Bias (CMB)

In the cross-sectional data, CMB impacts the performances [52]. We performed Har-
mon’s one-factor test to analyze the problem of CMB. We retrieved six factors with eigen-
values greater than 1. The first factor was examined at only 26.71% variance, less than 50%.
Therefore, it is confirmed that there is no issue with CMB in the data.

2.5. Confirmatory Factor Analysis (CFA)

CFA is performed (see Figure 4) to check the items’ validity, reliability, and loadings.
The values in CFA are tested following the model fitness using estimated values as
suggested by [53,54]. Such that CMIN/DF is 2.049, which is less than 3. GFI = 0.90,
AGFI = 0.87, NFI = 0.90 and TLI = 0.94 are in the acceptable range and show satisfac-
tory model fit. RMR = 0.011 and RMSEA =0.055 provided satisfactory values are less
than 0.08.

2.6. Model Validity and Reliability

We considered Average Variance Extracted and Composite Reliability (CR) to evaluate
the model validity and reliability. A measure has a good quality in survey research, and
one sign of quality is the internal consistency of items. Internal consistency is how the
individual items constitute a test that correlates with one another or the total. Reliability is
computed using Cronbach’s Alpha [55], CR and AVE [56] as follows:

∑ik=1 λ2ij
AVEj = (1)
∑ik=1 λ2ij + ∑ik=1 ε ij
 2
∑ik=0 λ2ij
CR =  2 (2)
∑ik=0 λ2ij + ∑ik= j ε ij
 2.5. Confirmatory Factor Analysis (CFA)
CFA is performed (see Figure 4) to check the items’ validity, reliability, and loadings.
The values in CFA are tested following the model fitness using estimated values as sug-
Sustainability 2022, 14, 7226 gested by [53,54]. Such that CMIN/DF is 2.049, which is less than 3. GFI = 0.90, AGFI8 of = 19
0.87, NFI = 0.90 and TLI = 0.94 are in the acceptable range and show satisfactory model fit.
RMR = 0.011 and RMSEA =0.055 provided satisfactory values are less than 0.08.

Figure 4. Measurement model.

Figure 4. Measurement model.

2.6. Model
Table 2Validity
showsand Reliabilityvalidity, discriminant validity, and reliability. All the items
convergent
are significantly loaded (p < 0.01) with their respective factors, as shown in the column
“Estimates” on the left side. Convergent validity obtained satisfactory values (above 0.50)
√
as suggested by [53,54]. Discriminant validity (also known as AVE) retrieved fair values
(above 0.70) for all the items [56]. Composite reliability (CR) also assures the internal
consistency of the factors as these factors contain CR values above 0.70 [54]. In addition,
Cronbach α provided an acceptable range of values above 0.70 [54].

Table 2. Standardized factor loading, validity, and reliability.

Sum of Squared √
Variables and Items Estimates AVE AVE CR Cronbach α
Loadings ()
te1 <— TechEngag 0.801 ***
te2 <— TechEngag 0.675 ***
te3 <— TechEngag 0.864 ***
3.534 0.589 0.767 0.895 0.899
te4 <— TechEngag 0.707 ***
te5 <— TechEngag 0.731 ***
te6 <— TechEngag 0.81 ***
Sustainability 2022, 14, 7226 9 of 19

Table 2. Cont.

Sum of Squared √
Variables and Items Estimates AVE AVE CR Cronbach α
Loadings ()
srl1 <— SelfRegLearn 0.696 ***
srl2 <— SelfRegLearn 0.827 ***
srl3 <— SelfRegLearn 0.845 *** 3.16 0.632 0.795 0.895 0.908
srl4 <— SelfRegLearn 0.849 ***
srl5 <— SelfRegLearn 0.745 ***
il1 <— InterLearn 0.89 ***
il2 <— InterLearn 0.65 *** 2.021 0.674 0.821 0.859 0.844
il3 <— InterLearn 0.898 ***
ap1 <— AcadPerform 0.69 ***
ap2 <— AcadPerform 0.809 ***
ap3 <— AcadPerform 0.725 ***
3.208 0.534 0.731 0.873 0.877
ap4 <— AcadPerform 0.771 ***
ap5 <— AcadPerform 0.633 ***
ap6 <— AcadPerform 0.746 ***
sa1 <— Satisfac 0.57 ***
sa2 <— Satisfac 0.739 ***
sa3 <— Satisfac 0.872 *** 2.686 0.537 0.733 0.8507 0.846
sa4 <— Satisfac 0.698 ***
sa5 <— Satisfac 0.753 ***
fp1 <— FuncPerform 0.702 ***
fp2 <— FuncPerform 0.861 ***
2.418 0.605 0.777 0.858 0.854
fp3 <— FuncPerform 0.683 ***
fp4 <— FuncPerform 0.847 ***
Note: *** p value (0.001). C.R = Composite reliability, AVE = Average variance extracted.

2.7. Correlation
We performed the Pearson correlation (see Table 3) in SPSS v.21 to test the rela-
tions between the variables. We found a significant relation of technology engagement
with self-regulated learning (r = 0.226, p < 0.05), with interactive learning (r = 0.292,
p < 0.05), with academic performance (r = 0.451, p < 0.05), and with satisfaction (r = 0.217,
p < 0.05). However, technology engagement is negatively related to functional performance
(r = −0.08, p > 0.05), whereas self-regulated learning has significant relationship with
interactive learning (r = 0.659, p < 0.05), academic performance (r = 0.308, p < 0.05), sat-
isfaction (r = 0.218, p < 0.05) and functional performance (r = 0.119, p < 0.05). Similarly,
interactive learning has significant relationship with academic performance (r = 0.351,
p < 0.05), satisfaction (r = 0.320, p < 0.05), and functional performance (r = 0.211, p < 0.05).

Table 3. Correlation coefficients.

TechnologyEngag SelfRegLearning InteractiveLearning AcademicPerform Satisfaction FunctionalPerform

TechnologyEngag 1
SelfRegLearning 0.226 ** 1
InteractiveLearning 0.292 ** 0.659 ** 1
AcademicPerform 0.451 ** 0.308 ** 0.351 ** 1
Satisfaction 0.217 ** 0.218 ** 0.320 ** 0.091 1
FunctionalPerform −0.008 0.119 * 0.211 ** 0.061 −0.037 1
** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).

2.8. Structural Models

We first established the direct relations in a separate structural model to express the
outcome and exploration of technology engagement via self-regulated and interactive
learning. Structural model 1 (see Figure 5a) depicts hypothesis testing via direct relation
of technology engagement significantly affects satisfaction and academic performance. In
Sustainability 2022, 14, 7226 10 of 19

addition, performance has an outcome on academic performance and satisfaction (see

Table 4; Structural Model 1), such as (β = 0.199 p < 0.05), (β = 0.393, p < 0.05), which fully
supported H1, respectively. Conversely, technology engagement does not significantly af-
fect functional performance, which has not supported H2. Structure model 2 (see Figure 5b)
shows that self-regulated learning also significantly influences satisfaction and academic
performance. The performance outcome of self-regulated learning (see Table 4; Struc-
tural Model 2) on satisfaction (β = 0.187, p < 0.05), and academic performance (β = 0.248,
Sustainability 2022, 14, x FOR PEER REVIEW
p < 0.05), which fully supported H3, respectively. However, self-regulated learning11hasof not
19
significantly influenced functional performance, not supporting H4.

Figure
Figure5.5.(a)
(a)Hypothesis
Hypothesistesting:
testing:direct
directeffects
effectsofoftechnology
technologyengagement,
engagement,and
and(b)
(b)hypothesis
hypothesistesting:
testing:
directs effects of self-regulated learning.
directs effects of self-regulated learning.

Table
Table4.4.Results
Resultsperformances
performancesof
ofStructural
StructuralModel
Model1,1,22and
and3.
3.

Structure ModelStructure
1 Model 1 Estimate
Estimate C.R.C.R. PP
Satisfaction <---
Satisfaction Education Education
<— −−0.042
0.042 −0.994
−0.994 0.320
0.320
Satisfaction <---
Satisfaction EthnicGroup
<— EthnicGroup 0.002
0.002 0.071
0.071 0.943
0.943
AcademicPerform <---
AcademicPerform Education Education
<— −−0.008
0.008 −0.210
−0.210 0.833
0.833
AcademicPerform <---
AcademicPerform EthnicGroup
<— EthnicGroup 0.011
0.011 0.544
0.544 0.586
0.586
AcademicPerform <---
AcademicPerform <—Major Major −−0.011
0.011 −0.554
−0.554 0.580
0.580
FunctionalPerform <---
FunctionalPerform <—Major Major 0.014
0.014 0.626
0.626 0.532
0.532
FunctionalPerform <---
FunctionalPerform EthnicGroup
<— EthnicGroup 0.015
0.015 0.747
0.747 0.455
0.455
Satisfaction <---
Satisfaction <— Age Age 0.055
0.055 1.489
1.489 0.136
0.136
FunctionalPerform <---
FunctionalPerform <— Age Age −−0.013
0.013 −0.379
−0.379 0.704
0.704
Satisfaction <---
Satisfaction <—Major Major 0.012
0.012 0.518
0.518 0.604
0.604
FunctionalPerform <---
FunctionalPerform
Education Education
<— −−0.001
0.001
−0.023
−0.023 0.982
0.982
AcademicPerform <---
AcademicPerform <—
Age Age
0.021
0.021
0.643
0.643
0.521
0.521
Satisfaction <--- TechnologyEngag 0.199 3.839 ***
Satisfaction <— TechnologyEngag 0.199 3.839 ***
FunctionalPerform <--- TechnologyEngag 0.000 −0.005 0.996
FunctionalPerform <— TechnologyEngag 0.000 −0.005 0.996
AcademicPerform <--- TechnologyEngag 0.393 8.687 ***
AcademicPerform <— TechnologyEngag 0.393 8.687 ***
Structure Model 2 Estimate C.R. P
Satisfaction <--- Education −0.046 −1.078 0.281
Satisfaction <--- EthnicGroup −0.007 −0.296 0.768
AcademicPerform <--- Education −0.013 −0.324 0.746
AcademicPerform <--- EthnicGroup −0.004 −0.192 0.848
AcademicPerform <--- Major −0.015 −0.694 0.488
Sustainability 2022, 14, 7226 11 of 19

Table 4. Cont.

Structure Model 2 Estimate C.R. P

Satisfaction <— Education −0.046 −1.078 0.281
Satisfaction <— EthnicGroup −0.007 −0.296 0.768
AcademicPerform <— Education −0.013 −0.324 0.746
AcademicPerform <— EthnicGroup −0.004 −0.192 0.848
AcademicPerform <— Major −0.015 −0.694 0.488
FunctionalPerform <— Major 0.018 0.841 0.400
FunctionalPerform <— EthnicGroup 0.014 0.689 0.491
Satisfaction <— Age 0.053 1.438 0.151
FunctionalPerform <— Age −0.019 −0.548 0.583
Satisfaction <— Major 0.013 0.550 0.582
FunctionalPerform <— Education −0.003 −0.068 0.946
AcademicPerform <— Age 0.024 0.687 0.492
Satisfaction <— SelfRegLearning 0.187 3.828 ***
FunctionalPerform <— SelfRegLearning 0.104 2.319 0.020
AcademicPerform <— SelfRegLearning 0.248 5.436 ***
Structure Model 3 Estimate C.R. P
Satisfaction <— Education −0.028 −0.685 0.493
Satisfaction <— EthnicGroup −0.006 −0.277 0.782
AcademicPerform <— Education 0.006 0.161 0.872
AcademicPerform <— EthnicGroup −0.003 −0.132 0.895
AcademicPerform <— Major −0.019 −0.872 0.383
FunctionalPerform <— Major 0.018 0.848 0.396
FunctionalPerform <— EthnicGroup 0.014 0.709 0.478
Satisfaction <— Age 0.040 1.098 0.272
FunctionalPerform <— Age −0.028 −0.841 0.400
Satisfaction <— Major 0.012 0.513 0.608
FunctionalPerform <— Education 0.008 0.213 0.831
AcademicPerform <— Age 0.012 0.364 0.716
Satisfaction <— InteractiveLearning 0.272 5.782 ***
FunctionalPerform <— InteractiveLearning 0.172 3.942 ***
AcademicPerform <— InteractiveLearning 0.283 6.367 ***
*** denotes the significance level for a given hypothesis test is a value for which a P-value less than or equal to is
considered statistically significant. Typical values for are 0.1.

Similarly, direct relation of interactive learning has significant effect on satisfaction,

academic performance, and functional performance as shown in structural model 3 (see
Figure 6a). The performance outcome of interactive learning (see Table 4; Structural
Model 3) on satisfaction (β = 0.272, p < 0.05), academic performance (β = 0.172, p < 0.05),
and functional performance (β = 0.283, p < 0.05), which fully supported H5, respectively.
 or equal to is considered statistically significant. Typical values for are 0.1.

Similarly, direct relation of interactive learning has significant effect on satisfaction,

academic performance, and functional performance as shown in structural model 3 (see
Sustainability 2022, 14, 7226
Figure 6a). The performance outcome of interactive learning (see Table 4; Structural 12 Model
of 19
3) on satisfaction (β = 0.272, p < 0.05), academic performance (β = 0.172, p < 0.05), and func-
tional performance (β = 0.283, p < 0.05), which fully supported H5, respectively.

Sustainability 2022, 14, x FOR PEER REVIEW

Figure 6. (a) Hypothesis testing: direct effects of interactive learning, and (b) hypothesis13 of 19
testing:
Figure 6. (a) Hypothesis testing: direct effects of interactive learning, and (b) hypothesis testing:
indirect effects of technology engagement via self-regulated learning.
indirect effects of technology engagement via self-regulated learning.

Structural
Structuralmodels
models4 and
4 and 5 (see Figures
5 (see 6b and
Figures 6b 7a)
andshows that student
7a) shows having having
that student direct relation
direct
to technology
relation engagement
to technology via self-regulated
engagement and interactive
via self-regulated learning contributed
and interactive positively
learning contributed
to satisfaction,
positively academic, and
to satisfaction, functional
academic, andperformance. In particular, In
functional performance. result performances
particular, (see
result per-
Table 5; Structure Model 4) indicated that technology engagement via self-regulated
formances (see Table 5; Structure Model 4) indicated that technology engagement via self- learning
has a significant
regulated influence
learning only on academic
has a significant influence performance (β = 0.177,
only on academic p < 0.05), (β
performance which fullyp
= 0.177,
supported H6. fully
< 0.05), which Technology
supportedengagement via self-regulated
H6. Technology engagement learning has positivelylearning
via self-regulated contributed
has
to satisfaction
positively (β = 0.154)toand
contributed functional(βperformance
satisfaction = 0.154) and (βfunctional
= 0.109), partially supporting
performance H7. In
(β = 0.109),
contrast,
partiallyresult performances
supporting of technology
H7. In contrast, resultengagement
performances via interactive learning
of technology (see Tablevia
engagement 5;
Structure Model 5) found significant influence on satisfaction (β = 0.238, p <
interactive learning (see Table 5; Structure Model 5) found significant influence on satis- 0.05), academic
performance
faction (β = (β 0.238, p < p0.05),
= 0.194, < 0.05), and functional
academic performance
performance (β = 0.188,
(β = 0.194, p < 0.05),
p < 0.05), andwhich fully
functional
supported H8.
performance (β = 0.188, p < 0.05), which fully supported H8.

Figure 7.
Figure 7. (a)
(a) Hypothesis
Hypothesis testing:
testing: indirect
indirect effects
effects of
of technology
technology engagement
engagement via
via interactive
interactive learning,
learning,
and (b) hypothesis testing: direct and indirect effects of technology engagement via self-regulated
and (b) hypothesis testing: direct and indirect effects of technology engagement via self-regulated
and interactive
and interactive learning.
learning.

Structural Model 6 (see Figure 7b) shows the mediating role of self-regulated and
interactive learning between technology engagement and student performance outcome
(i.e., satisfaction, academic and functional performance). The result performances (see Ta-
ble 6) present the direct and indirect effect of technology engagement via self-regulated
learning on satisfaction (β = 0.006, p > 0.05), academic performance (β = 0.110, p < 0.05),
functional performance (β = −0.020, p > 0.05), which has partially supported H9. In con-
trast, the direct and indirect effect of technology engagement via interactive learning on
satisfaction, academic, and functional performance is significant, such as (β = 0.273, p <
Sustainability 2022, 14, 7226 13 of 19

Table 5. Results performances of Structural Model 4 and 5.

Structure Model 4 Estimate CR P

SelfRegLearning <— TechnologyEngag 0.239 4.026 ***
Satisfaction <— Education −0.045 −1.074 0.283
Satisfaction <— EthnicGroup −0.001 −0.063 0.950
AcademicPerform <— Education −0.011 −0.301 0.764
AcademicPerform <— EthnicGroup 0.007 0.380 0.704
AcademicPerform <— Major −0.005 −0.258 0.797
FunctionalPerform <— Major 0.017 0.809 0.418
FunctionalPerform <— EthnicGroup 0.013 0.651 0.515
Satisfaction <— Age 0.048 1.323 0.186
FunctionalPerform <— Age −0.018 −0.527 0.598
Satisfaction <— Major 0.018 0.761 0.447
FunctionalPerform <— Education −0.003 −0.072 0.943
AcademicPerform <— Age 0.013 0.408 0.683
Satisfaction <— TechnologyEngag 0.165 3.147 0.002
FunctionalPerform <— TechnologyEngag −0.024 −0.499 0.618
AcademicPerform <— TechnologyEngag 0.354 7.829 ***
AcademicPerform <— SelfRegLearning 0.177 4.132 ***
Satisfaction <— SelfRegLearning 0.154 3.118 0.002
FunctionalPerform <— SelfRegLearning 0.109 2.366 0.018
Structure Model 5 Estimate CR P
InteractiveLearning <— TechnologyEngag 0.313 5.306 ***
Satisfaction <— Education −0.030 −0.729 0.466
Satisfaction <— EthnicGroup −0.002 −0.098 0.922
AcademicPerform <— Education 0.002 0.064 0.949
AcademicPerform <— EthnicGroup 0.008 0.403 0.687
AcademicPerform <— Major −0.009 −0.434 0.664
FunctionalPerform <— Major 0.016 0.768 0.442
FunctionalPerform <— EthnicGroup 0.012 0.623 0.533
Satisfaction <— Age 0.037 1.044 0.296
FunctionalPerform <— Age −0.027 −0.812 0.417
Satisfaction <— Major 0.016 0.688 0.492
FunctionalPerform <— Education 0.009 0.232 0.817
AcademicPerform <— Age 0.006 0.200 0.842
Satisfaction <— TechnologyEngag 0.126 2.427 0.015
FunctionalPerform <— TechnologyEngag −0.057 −1.169 0.242
AcademicPerform <— TechnologyEngag 0.334 7.300 ***
AcademicPerform <— InteractiveLearning 0.194 4.522 ***
Satisfaction <— InteractiveLearning 0.238 4.892 ***
FunctionalPerform <— InteractiveLearning 0.188 4.111 ***
*** denotes the significance level for a given hypothesis test is a value for which a P-value less than or equal to is
considered statistically significant. Typical values for are 0.1.
Sustainability 2022, 14, 7226 14 of 19

Structural Model 6 (see Figure 7b) shows the mediating role of self-regulated and
interactive learning between technology engagement and student performance outcome
(i.e., satisfaction, academic and functional performance). The result performances (see Table 6)
present the direct and indirect effect of technology engagement via self-regulated learning
on satisfaction (β = 0.006, p > 0.05), academic performance (β = 0.110, p < 0.05), functional
performance (β = −0.020, p > 0.05), which has partially supported H9. In contrast, the direct
and indirect effect of technology engagement via interactive learning on satisfaction, academic,
and functional performance is significant, such as (β = 0.273, p < 0.05), (β = 0.168, p < 0.05),
and (β = 0.253, p < 0.05), respectively, which fully supported H10.

Table 6. Direct and indirect effects of technology engagement: Structure model 6.

Hypothesis Direct Effect Indirect Effect Total Effect

Satisfaction ← Technology engagement 0.137 (0.019) 0.081(0.000) 0.218 (0.001)
Academic performance ← Technology engagement 0.381 (0.001) 0.074(0.000) 0.455 (0.001)
Functional performance ← Technology engagement −0.068 (0.247) 0.069(0.000) 0.002 (0.991)
Satisfaction ← Self-regulated learning 0.006 (0.871) - 0.006 (0.871)
Academic performance ← Self-regulated learning 0.110 (0.049) - 0.110 (0.049)
Functional performance ← Self-regulated learning −0.020 (0.773) - −0.020 (0.773)
Academic performance ← Interactive learning 0.168 (0.022) - 0.168 (0.022)
Satisfaction ← Interactive learning 0.273 (0.004) - 0.273 (0.004)
Functional performance ← Interactive learning 0.253 (0.002) - 0.253 (0.002)
Academic performance ← Age 0.017 (0.883) - 0.017 (0.883)
Satisfaction ← Age 0.090 (0.424) - 0.090 (0.424)
Functional performance ← Age −0.072 (0.411) - −0.072 (0.411)
Academic performance ← Education −0.005 (0.968) - −0.005 (0.968)
Satisfaction ← Education −0.064 (0.545) - −0.064 (0.545)
Functional performance ← Education 0.023 (0.771) - 0.023 (0.771)
Academic performance ← Ethnic group 0.018 (0.740) - 0.018 (0.740)
Satisfaction ← Ethnic group −0.005 (0.934) - −0.005 (0.934)
Functional performance ← Ethnic group 0.035 (0.613) - 0.035 (0.613)
Academic performance ← Major −0.016 (0.739) - −0.016 (0.739)
Satisfaction ← Major 0.038 (0.440) - 0.038 (0.440)
Functional performance ← Major 0.042 (0.515) - 0.042 (0.515)

3. Discussion
The proposed research investigates the use of technology with the mediating role of
interactive and self-regulated learning that sustains satisfaction, academic and functional
performance. The performances are validated in student outcomes, affirming academic
and social attainment retention. Previous studies were restricted to a single learning
factor (such as mLearning/self-regulated learning/eLearning), which is not adequate
for students in higher education [46]. However, it was concluded that sole emphasis on
technology usage is misleading [42]. Our primitive concern was to mediate the relation of
technology engagement via digital learning. Technology engagement via self-regulated
and interactive learning as mediators generates different performance features: (a) between
technology engagement and satisfaction, (b) between technology engagement and academic
performance, and (c) between technology engagement and functional performance.
Moreover, our purpose was to investigate such mediators with existing technology
resources to complete a student’s degree/course. However, existing works focused less on
technology, and psychological learning factors were prioritized, rendering mere acceptance
of technology. For instance, research followed TAM and UTAUT models and focused on
eLearning only [49]. Additionally, a study introduced a scale for measuring the distance
between students and learning technology by focusing merely on online distance learn-
ing [5]. A study prejudiced that students emphasize eLearning tools canvas, Blackboard,
and WebCT [17]. Furthermore, a recent study also conflicted with the idea, and it transpired
that the sole focus of technology via a learning factor is misleading [42]. Consequently, we
Sustainability 2022, 14, 7226 15 of 19

confirmed the belief that students with more than one learning factor in educational sectors
can enhance their learning abilities and improve successful outcomes [57–59].
In response to research questions RQ 1 and 2, which evolve three main contributions
in comparison to intellectualizing technology engagement with learning factors of digital
learning, which are as follows:
First, our study confers direct assistance to academic and social attainment from the
perspective of student outcomes. This conclusion is linked with the fiction that technology
engagement can establish a broader conception than characteristically implemented in digi-
tal learning [46]. The number of students endorsing engagement with technology inflicted
a weak indicator of academic attainment due to insufficient learning skills [21]. Therefore,
this study magnifies data samples of students from different countries/continents of higher
education to validate within-study MI. Our findings suggested that composite learning
factors could mediate the relationship between technology engagement and students’ per-
formance outcomes. In other words, technology engagement itself is a sign of student
motivation, and it is an inspiration rather than the usage of technology that predicts student
success, as concluded in the recent works [60,61]. However, the relationship between tech-
nology engagement and satisfaction, academic performance, and functional performance
are detained when the model’s inspirations exist. Hence, results signify that anticipation of
technology engagement with learning factors generates a unique contribution in this study
toward student success.
Second, the present study conducted an approach for technology engagement progres-
sively in conjunction with one another, instead emphasizing a mere resource of technology
engagement in isolation. Moreover, the intrinsic value of pedagogical factors is raised in
educational sectors [15]. For instance, indicators such as clickers, course blogs, keypads,
and discussion boards are not considered substantial in student success [43]. As a result,
student involvement and ease of access to technology include social media groups for
a successful outcome. Each group found significant student success indicators, includ-
ing student-centric mobile apps, problem-solving using gamification, flipped classroom,
conducting assignments via blogs/podcasts, analyzing reading skills via recording and
playback, and visual representation [9–13].
Third, this research has demonstrated a deeper assessment of technology engagement
adhering to its opportunities and insights. The evaluation of academic students was
necessary because the focus is to increase the number of universities rather than the
number of students in a university [18]. Still, the level of students’ education, ethnic
group, the field of interest, and different age groups is considered in this research during
data collection. The sample size has a significant estimate, which renders approximate
performance outcomes for students. The performance outcome of this research presents
valuable information for decision-makers to articulate sustainable strategies and tactics in
educational sectors.
Overall, results following our sample evaluations suggested that students with higher
education obtain higher outcomes than those with a less educational background, as
reported elsewhere [47]. We added the gender differences, which affect indices of measure-
ment significantly. Additionally, our sample data targeted from one country to another or
even within the same country, depending on cities or rural areas.

Limitations and Future Research

We used cross-sectional data in the current study, which inflicted CMB and non-
response bias. Researchers in this regard can use longitudinal data to overcome this
problem and explore good understandings. We followed self-reported information re-
sulting in CMB. Researchers could implement experiments in the classroom with respect
to the current study to explore beneficial insights for further implications in developing
countries for higher education. This research is verified in developing countries that may
not give fruitful results due to their institutional arrangement. Therefore, researchers from
developing/developed countries in the prospect of policymakers are encouraged to test the
Sustainability 2022, 14, 7226 16 of 19

model in different environmental settings. Moreover, this research is limited to mediating

the role of self-regulated and interactive learning between technology engagement and
student performance factors. However, other learning factors of digital learning such as
adaptive learning, mobile learning, eLearning and blended learning, and technological
capabilities can be tested as mediators.

4. Conclusions
This research is conducted to assess students’ academic satisfaction and functional
performances. Indeed, the impact of technology via self-regulated and interactive learning
has revived little attention in education and social attainment. Our research overcame
the gap by observing the intervening role of self-regulated and interactive learning. The
performance features are associated with academic performance, and students engaging
significantly in face-to-face education or technologies necessarily reflect the cultural envi-
ronment in which they are socialized. Personal, social, and cultural stories shape students’
engagement. The curricula, learning activities, and technological means used to stimulate
student engagement are presented in social, religious, and cultural contexts that define
acceptable and valued arguments. In this research, these groups in the structured ques-
tionnaire seek success by participating in activities that develop the skills and dispositions
necessary to excel in the cultural and social environment.
Additionally, academic commitment and success are described in one cultural and
social environment that may differ from one another. Still, a commitment to learning is
complex when viewed through socio-cultural lenses. Interactive/self-regulated learning,
and mainly construct (i.e., technology engagement), is empathetic and subject to variation,
even depending on the age of development.
Experimental validation of our model was verified using structured questionnaires
from 302 respondents from different universities in developing countries. Hypotheses
were tested in AMOS using structural equation modeling and analysis performed in SPSS
(v.21). First, we empirically validated the primary model validated. Later, we established
the direct and indirect relations in the isolated structural model, such that the hypothesis
was found significant while H2 and H4 were not significant. Our findings suggest that
sufficient technology resources could expressively contribute to satisfaction and academic
performance, but significantly affect functional performance. At the same time, educational
sectors were found informal with students’ functional performance via interactive and
self-regulated learning.
The present research validated the performances in terms of student outcome, which
affirm the retention of academic and social attainment. It was concluded that sole emphasis
on technology usage is misleading. Our primitive concern was to mediate the relation of
technology engagement via digital learning. Consequently, technology engagement via
self-regulated and interactive learning as mediators generates performances: (a) between
technology engagement and satisfaction, (b) between technology engagement and academic
performance, and (c) between technology engagement and functional performance. Our
research also suggests practitioners and administrators emphasize students’ engagement
with the technology effectively by dint of learning factors in digital learning.

Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/su14127226/s1, questionnaire sample.
Author Contributions: Conceptualization, M.Q.M. and Y.L.; methodology, M.Q.M., P.M. and A.M.;
software, M.Q.M.; validation, A.M., M.Q.M. and Y.L.; formal analysis, Y.L., A.R.M., P.M. and S.F.A.S.;
investigation, M.Q.M. and Y.L.; resources, Y.L.; data curation, A.M.; writing—original draft prepa-
ration, M.Q.M.; writing—review and editing, M.Q.M., A.R.M. and S.F.A.S.; visualization, P.M.;
supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and
agreed to the published version of the manuscript.
Funding: Open Project of the State Key Laboratory of Cognitive Intelligence (No. iED2021-M007),
and Fundamental Research Funds for the Central Universities.
Sustainability 2022, 14, 7226 17 of 19

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The work in this paper is supported by Open Project of the State Key Laboratory of
Cognitive Intelligence (No. iED2021-M007), and Fundamental Research Funds for the Central Universities.
Conflicts of Interest: The authors declare no conflict of interest.

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