1. Introduction
In the interim, dawn of capitalism, educational sectors have been reformed and evolved in several uncertain ways. However, the momentum of such reforms in the present day and age is diverse. The fast growth of technology has contributed to eliminating distances among people to ease the learning process; hence, educational sectors are certainly 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 essential 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 satisfaction, 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.
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 functional 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 bothersome 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 performance 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 Acceptance Model) [
35], UTAUT as prior technical knowledge, and game-based learning [
11], respectively. Since the UTAUT model follows, the constructs reported by [
23] are methodologically 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?
1.1. Preliminaries
Higher education sectors have been capitalizing on assets using Information and Communication 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,
7,
8,
9,
10,
11,
12,
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 country [
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. Educational sectors persist in dissimilarities in the context of interests across various cultures and historical circumstances [
17]. Therefore, this study provides one of the functional performance 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 learning, 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 understanding, 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 relies 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 discovered that the factors such as support and availability prophesied education attainment 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 significantly to satisfaction and functional performance.
H8. Students engaging in technology via interactive learning contributed significantly to satisfaction, 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, limitations, and future works of the current study. Finally, concluding remarks are imparted.
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 learning [
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 confirmed the belief that students with more than one learning factor in educational sectors can enhance their learning abilities and improve successful outcomes [
57,
58,
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 digital 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’ performance 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 technology 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 progressively 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, including 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,
10,
11,
12,
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 measurement 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 resulting 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 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 environment 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 questionnaire 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.