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A Two-Staged SEM: Artificial Neural Network Approach for Understanding and Predicting the Factors of Students' Satisfaction with Emergency Remote Teaching

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Title: A Two-Staged SEM: Artificial Neural Network Approach for Understanding and Predicting the Factors of Students' Satisfaction with Emergency Remote Teaching
Language: English
Authors: Anupma Sangwan, Anurag Sangwan, Anju Sangwan, Poonam Punia (ORCID 0000-0002-3560-2859)
Source: Educational Technology Research and Development. 2024 72(2):1249-1286.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 38
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Distance Education, Student Satisfaction, Self Efficacy, Internet, Interaction, Foreign Countries, Universities, Learning Strategies, Self Management, Student Attitudes, College Students, Electronic Learning, Predictor Variables
Geographic Terms: India
DOI: 10.1007/s11423-023-10335-9
ISSN: 1042-1629
1556-6501
Abstract: This study seeks to address knowledge gaps regarding the role of self-regulated learning as a mediator in the relationship between interactions, internet self-efficacy, and student satisfaction. We conducted a survey of 1590 students from north Indian universities about their level of satisfaction, self-regulated learning, internet self-efficacy, and different interactions (learner-learner interaction, learner-content interaction, and learner-instructor interaction) during emergency remote teaching. By employing a two-stage SEM-ANN approach, this study contributes to methodological advancements and provides a comprehensive analysis of complex relationships. According to the findings, the identified factors are significant predictors of students' satisfaction with online education in synchronous settings. Our research also shows that self-regulated learning fully mediates the effect of internet self-efficacy on student satisfaction during emergency remote teaching. This suggests that internet self-efficacy alone may not guarantee student satisfaction unless accompanied by self-regulated learning skills.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1424604
Database: ERIC
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  Value: <anid>AN0177624875;etr01apr.24;2024Jun05.04:48;v2.2.500</anid> <title id="AN0177624875-1">A two-staged SEM: artificial neural network approach for understanding and predicting the factors of students' satisfaction with emergency remote teaching </title> <p>This study seeks to address knowledge gaps regarding the role of self-regulated learning as a mediator in the relationship between interactions, internet self-efficacy, and student satisfaction. We conducted a survey of 1590 students from north Indian universities about their level of satisfaction, self-regulated learning, internet self-efficacy, and different interactions (learner-learner interaction, learner-content interaction, and learner-instructor interaction) during emergency remote teaching. By employing a two-stage SEM-ANN approach, this study contributes to methodological advancements and provides a comprehensive analysis of complex relationships. According to the findings, the identified factors are significant predictors of students' satisfaction with online education in synchronous settings. Our research also shows that self-regulated learning fully mediates the effect of internet self-efficacy on student satisfaction during emergency remote teaching. This suggests that internet self-efficacy alone may not guarantee student satisfaction unless accompanied by self-regulated learning skills.</p> <p>Keywords: Learner-learner interaction; Learner-content interaction,; Earner-instructor interaction; Internet self-efficacy; Self-regulated learning; Satisfaction; Emergency remote teaching</p> <p>Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11423-023-10335-9.</p> <hd id="AN0177624875-2">Introduction</hd> <p>Emergency remote teaching refers to the abrupt shift in educational practices and methodologies that occurs when traditional face-to-face instruction is no longer possible due to unforeseen circumstances, such as natural disasters, pandemics, and other emergencies. This involves swiftly adopting alternative teaching methods that heavily depend on digital technologies and online platforms for remote learning purposes. "Emergency remote teaching" has emerged as a popular alternative term during this pandemic that online education scholars and practitioners have used to distinguish it from online education (Hodges et al., [<reflink idref="bib56" id="ref1">56</reflink>]). This online emergency remote teaching (ERT) is different from online distance education as it emerged as an obligation during this pandemic (Bozkurt & Sharma, [<reflink idref="bib18" id="ref2">18</reflink>]). It entails using entirely remote teaching instead of face-to-face, blender, or hybrid courses and returning to the earlier format once the crisis or emergency has passed (Hodges et al., [<reflink idref="bib56" id="ref3">56</reflink>]). In response to the global outbreak of coronavirus, educational institutions around the world were required to shut down in mid-March 2020 (Muthuprasad et al., [<reflink idref="bib89" id="ref4">89</reflink>]). The purpose of these closures was to mitigate the spread of the virus. Due to the indefinite nature of this closure, educational institutions had no choice but to explore alternative methods of instruction (Kuo et al., [<reflink idref="bib71" id="ref5">71</reflink>]). Teachers and technology staff alike were unprepared for such a digital shift to an online mode of instruction as a result of this emergency (Lin & Johnson, [<reflink idref="bib79" id="ref6">79</reflink>]). Therefore, the distinctive challenges and adaptations demanded by this particular form of teaching necessitate further research in this area.</p> <p>Online education and distance education have been extensively studied, resulting in a vast corpus of research and writing. The research shows that the potential for online education has increased in tandem with the internet's growth over the past decade (Muilenburg & Berge, [<reflink idref="bib88" id="ref7">88</reflink>]). Still, literature has reported mixed results on the effectiveness of online education, especially when compared with intense activity-based classroom education (Garrison, [<reflink idref="bib43" id="ref8">43</reflink>]; Sangwan et al., [<reflink idref="bib106" id="ref9">106</reflink>]; Weller, [<reflink idref="bib121" id="ref10">121</reflink>]). Additionally, details on how online education will be implemented and the effects it will have are still being debated (Zhang et al., [<reflink idref="bib131" id="ref11">131</reflink>]). Online learning has both benefits and drawbacks (Coman et al., [<reflink idref="bib31" id="ref12">31</reflink>]). The advantages include cost and time savings, safety, convenience (Muthuprasad et al., [<reflink idref="bib89" id="ref13">89</reflink>]), and increased participation (Doyumğaç et al., [<reflink idref="bib35" id="ref14">35</reflink>]; Khan et al., [<reflink idref="bib66" id="ref15">66</reflink>]). Distraction, reduced focus, workload, technological issues, internet connectivity (Muthuprasad et al., [<reflink idref="bib89" id="ref16">89</reflink>]), and insufficient assistance from instructors and colleagues are potential downsides of online education (Maqableh & Alia, [<reflink idref="bib86" id="ref17">86</reflink>]). The literature indicates that there has been extensive research on online education, but there remains a significant gap in the understanding of emergency remote teaching. The rapid expansion of online education and the unprecedented shift towards emergency remote teaching have prompted a surge in research examining student satisfaction in these contexts. Recent studies have shed light on various aspects like academic performance (Iglesias-Pradas et al., [<reflink idref="bib58" id="ref18">58</reflink>]), student engagement (Su et al., [<reflink idref="bib113" id="ref19">113</reflink>]), learning experiences (Shin & Hickey, [<reflink idref="bib108" id="ref20">108</reflink>]), stress (Obermeier et al., [<reflink idref="bib92" id="ref21">92</reflink>]), opportunities and challenges of remote teaching (Ferri et al., [<reflink idref="bib40" id="ref22">40</reflink>]), teachers' opinions (Potyrała et al., [<reflink idref="bib97" id="ref23">97</reflink>]), parents' experiences and perspectives (Shin & Hickey, [<reflink idref="bib108" id="ref24">108</reflink>]). Among several important factors in understanding online educational success, student satisfaction with online learning is critical (Fogerson, [<reflink idref="bib41" id="ref25">41</reflink>]; Hamdan et al., [<reflink idref="bib53" id="ref26">53</reflink>]). It significantly impacts the success of e-learning and improves the e-learning system's quality (Yekefallah et al., [<reflink idref="bib128" id="ref27">128</reflink>]). However, few studies have been undertaken on the satisfaction of learners with emergency remote teaching (Adekannbi & Ipadeola, [<reflink idref="bib1" id="ref28">1</reflink>]; Erragcha & Babay, [<reflink idref="bib38" id="ref29">38</reflink>]; Kovačević et al., [<reflink idref="bib68" id="ref30">68</reflink>]; Natarajan & Joseph, [<reflink idref="bib91" id="ref31">91</reflink>]; Quispe & Alecchi, [<reflink idref="bib100" id="ref32">100</reflink>]; Rodriguez-Rivero et al., [<reflink idref="bib105" id="ref33">105</reflink>]; Wilhelm et al., [<reflink idref="bib123" id="ref34">123</reflink>]).</p> <p>Other factors, such as different types of interactions, internet self-efficacy, and self-regulation, also influence student satisfaction. Although individual studies have explored the relationships between interactions, internet self-efficacy, self-regulated learning, and student satisfaction (Ahoto et al., [<reflink idref="bib3" id="ref35">3</reflink>]; Ali & Mirza, [<reflink idref="bib6" id="ref36">6</reflink>]; Bailey, [<reflink idref="bib11" id="ref37">11</reflink>]; Hamdan et al., [<reflink idref="bib53" id="ref38">53</reflink>]; Turley et al., [<reflink idref="bib117" id="ref39">117</reflink>]), there remains a research gap regarding the mediating role of self-regulated learning in this context. To address this gap, the current study employs a two-stage SEM-ANN approach. This methodology has recently gained attention for its ability to capture complex relationships and non-linear dynamics. By integrating SEM and ANN, this approach allows for a comprehensive analysis of the relationships between variables and the mediating role of self-regulated learning in the context of student satisfaction in online education. By identifying the factors that influence student satisfaction and the mediating role of self-regulated learning, interventions and strategies can be developed to enhance students' engagement, motivation, and overall satisfaction in online education. This research work is mainly guided by the following research questions:</p> <p></p> <ulist> <item> What is the impact of different types of interactions on student satisfaction during emergency remote teaching?</item> <p></p> <item> To what extent does internet self-efficacy predict students' engagement and success in online education?</item> <p></p> <item> How does the level of self-regulated learning skills impact students' satisfaction with emergency remote teaching?</item> <p></p> <item> To what extent does self-regulated learning mediate the relationship between interactions, internet self-efficacy, and student satisfaction in online education?</item> </ulist> <p>The results of this study have practical implications for educators and policymakers responsible for designing effective online learning environments. By identifying the various factors that influence student satisfaction while acknowledging how self-regulated learning serves as a mediator between these factors. It becomes possible to develop interventions and strategies aimed at enhancing students' engagement level and motivation levels. As well as overall satisfaction with regard to their experience with emergency remote teaching. As such this research represents a contribution towards the ongoing endeavor to improve the quality and effectiveness of online education, thereby guaranteeing students' positive learning experiences in our digital era.</p> <hd id="AN0177624875-3">Literature review and theoretical development</hd> <p></p> <hd id="AN0177624875-4">Students' satisfaction</hd> <p>Students' satisfaction reflects their perceptions of learning experiences (Guo, [<reflink idref="bib47" id="ref40">47</reflink>]) and is a critical measure in any program evaluation. In addition, students who report higher satisfaction are more committed to their learning (Yavuzalp & Bahcivan, [<reflink idref="bib127" id="ref41">127</reflink>]). According to research, providing learners with a satisfying experience (in terms of communication with the instructor, administrative, and technical support) can help maintain and improve retention (Fein & Logan, [<reflink idref="bib39" id="ref42">39</reflink>]). Additionally, student satisfaction is associated with higher academic achievement and a higher likelihood of performing well in the course (Pike, [<reflink idref="bib96" id="ref43">96</reflink>]). Factors like institutions' involvement, support, and functioning of ICT and teachers' technological and methodological preparation are key determinants of students' satisfaction with e-learning (Maciaszczyk et al., [<reflink idref="bib83" id="ref44">83</reflink>]). Lin and Wang ([<reflink idref="bib80" id="ref45">80</reflink>]) reported that the difference in technology and the characteristics of teachers, students, and courses also influence student satisfaction levels.</p> <p>Despite this, there are still significant differences in how students perceive online learning experiences during their studies (Muilenburg & Berge, [<reflink idref="bib88" id="ref46">88</reflink>]). When students have negative perceptions of their past, present, and future online learning experiences, they are more likely to drop out (Carr, [<reflink idref="bib21" id="ref47">21</reflink>]), have low motivation to learn (Maltby & Whittle, [<reflink idref="bib85" id="ref48">85</reflink>]), and have low student satisfaction with their educational experience. There is a significant relationship between e-learning satisfaction, gender, and history of attending online classes before the coronavirus outbreak. Female students, as well as students who had previously taken online classes prior to COVID-19, were more satisfied with e-learning (Yekefallah et al., [<reflink idref="bib128" id="ref49">128</reflink>]). Hence, gender and hours spent online may influence students' satisfaction levels, which has been further explored in the present work to gain insights into gender and online engagement perspectives in online learning.</p> <p>Teachers' prompt responses and feedback significantly impact the learners' satisfaction (Arbaugh, [<reflink idref="bib9" id="ref50">9</reflink>]). Learner-learner interaction and learner-teacher interaction influence learner satisfaction (Walker & Fraser, [<reflink idref="bib118" id="ref51">118</reflink>]). However, these results are not applicable to all students, in all scenarios, or at all times of the year (Muilenburg & Berge, [<reflink idref="bib88" id="ref52">88</reflink>]). The COVID-19 pandemic prompted adjustments, in the way teaching is conducted online including changes in communication platforms and activities. These adaptations have had an impact, on students' satisfaction levels. Google Classroom, LMS (Moodle) and Google Hangout were rated as effective tools for instruction delivery. Students also appreciated multimodality in instruction (Almusharraf & Khahro, [<reflink idref="bib7" id="ref53">7</reflink>]). Still, some authors reported students' dissatisfaction with e-learning (Subramanian et al., [<reflink idref="bib114" id="ref54">114</reflink>]; Yekefallah et al., [<reflink idref="bib128" id="ref55">128</reflink>]). Kaur et al. ([<reflink idref="bib64" id="ref56">64</reflink>]) found that online classes effectively improved communication, skill development, and understanding through recorded classes, question sessions, professional career grooming, and assignment submission. Although students found it less effective in four areas: convenience, interaction level, individual learning needs, and balancing practical and theoretical knowledge. As per the findings of Maqableh and Alia ([<reflink idref="bib86" id="ref57">86</reflink>]), distraction, reduced concentration, psychological issues, and management issues are the most important factors contributing to student dissatisfaction with online learning.</p> <p>Online education can encounter difficulties due to the following factors: weak online teaching infrastructure, inexperienced teachers, information gaps, and a complex environment at home (Zhang et al., [<reflink idref="bib131" id="ref58">131</reflink>]). The main factors influencing the user and learner-user experience in online education are technical issues that lead to student dissatisfaction and significantly affect the efficiency and quality of teaching (Chen et al., [<reflink idref="bib26" id="ref59">26</reflink>]). During this pandemic, students dealt with a variety of issues, such as depression and anxiety, poor internet connectivity, and an unfavorable study environment at home (Kapasia et al., [<reflink idref="bib63" id="ref60">63</reflink>]). They recommended that universities and colleges should create an education continuity plan to ensure continued online learning and funding to improve the educational system. Therefore, the gains and losses of implementing online education should be carefully examined and studied.</p> <p>Many authors investigated factors influencing student satisfaction with e-learning or blended learning (Aldhahi et al., [<reflink idref="bib5" id="ref61">5</reflink>]; Gomezelj & Čivre, [<reflink idref="bib45" id="ref62">45</reflink>]; Kuo et al., [<reflink idref="bib71" id="ref63">71</reflink>]; Phan & Dang, [<reflink idref="bib95" id="ref64">95</reflink>]; Wong & Fong, [<reflink idref="bib124" id="ref65">124</reflink>]). Different combinations of these factors were studied to determine their correlation, with or ability to predict student satisfaction, in online learning environments (Artino, [<reflink idref="bib10" id="ref66">10</reflink>]; Reinhart et al., [<reflink idref="bib103" id="ref67">103</reflink>]). According to the findings of several studies, student satisfaction is related to various factors such as motivation, cognitive engagement (Adekannbi & Ipadeola, [<reflink idref="bib1" id="ref68">1</reflink>]), interaction, kinds of support, student autonomy, technology, self-efficacy, and self-regulation (Artino, [<reflink idref="bib10" id="ref69">10</reflink>]; Robles, [<reflink idref="bib104" id="ref70">104</reflink>]). Kuo ([<reflink idref="bib69" id="ref71">69</reflink>]) assessed the relationship between internet self-efficacy, interactions (learner-learner interaction, learner-content interaction and learner-instructor interaction), self-regulation and student satisfaction in distance education courses. In general, the use of e-learning in India effectively improved communication at the time of COVID-19 as it gave more freedom to interact with teachers and fellow students (Khan et al., [<reflink idref="bib66" id="ref72">66</reflink>]). However, many new problems arose in this new form of online education due to carrying out online education during this pandemic (Chen et al., [<reflink idref="bib26" id="ref73">26</reflink>]). Nowadays, this new form of online education is referred to as emergency remote teaching (ERT). It is a temporary shift with the primary objective of recreating an educational ecosystem with limited resources and less planning. It is not wise to compare it with online learning (Hodges et al., [<reflink idref="bib56" id="ref74">56</reflink>]). Self-regulated learning, internet self-efficacy, learner-content interaction, and learner-learner interaction were important factors in students' satisfaction with online education (Hamdan et al., [<reflink idref="bib53" id="ref75">53</reflink>]). The present study focuses on three factors: learner interactions (learner-learner interaction, learner-content interaction and learner-instructor interaction), internet self-efficacy, and self-regulated learning. Types of interaction, which include student-content, student–student, and student-instructor, are among the most extensive bodies of research in online learning (Hodges et al., [<reflink idref="bib56" id="ref76">56</reflink>]). These three factors have been considered important predictors of student satisfaction (Kuo, [<reflink idref="bib69" id="ref77">69</reflink>]). This paper investigates user satisfaction with synchronous online education platforms during the COVID-19 situation, which differs from the focus on learner-user satisfaction under normal conditions. Zoom and Google Meet were the preferred modes of instruction.</p> <hd id="AN0177624875-5">Interactions</hd> <p>Interaction is a complex concept that has been identified as an essential component in all forms of education (Kuo et al. [<reflink idref="bib71" id="ref78">71</reflink>]). Moore ([<reflink idref="bib87" id="ref79">87</reflink>]) presented three key constituents of interactions: learner-instructor interaction, learner-learner interaction, and learner-content interaction. Learner-instructor interaction is two-way communication and can happen in several ways (Moore, [<reflink idref="bib87" id="ref80">87</reflink>]). It includes guidance, support, motivation, evaluation, feedback etc. Students who communicate with their instructor easily showed more satisfaction (Bray et al., [<reflink idref="bib19" id="ref81">19</reflink>]). Heinemann ([<reflink idref="bib55" id="ref82">55</reflink>]) described three categories of interactions between students and teachers: organizational, social, and intellectual. These three types of student–teacher interactions have impacted cognitive and affective learning outcomes in online learning environments. Although only a few studies have looked into the effects of student–teacher interaction on student satisfaction, more research is needed (Kuo et al., [<reflink idref="bib72" id="ref83">72</reflink>]). Bailey ([<reflink idref="bib11" id="ref84">11</reflink>]) found that as individuals increase their social presence, their interactions with others also increase, leading to positive expectations of academic outcomes.</p> <p>Learner-learner interaction specifies the communication between learners to exchange ideas and give feedback to each other (Kuo et al., [<reflink idref="bib72" id="ref85">72</reflink>]). It keeps students motivated and helps in a better and deeper understanding of the content (Kuo, [<reflink idref="bib69" id="ref86">69</reflink>]). In the absence of this type of interaction, students may feel isolated, leading to frustration and overload (Coman et al., [<reflink idref="bib31" id="ref87">31</reflink>]). On the flip side, some findings suggest that students who prefer not to interact with their peers are more satisfied with online courses or that student-learner interaction does not affect student satisfaction. It could be because students may need both encouragement and help to interact online in a "low context" setting where information about age, gender, social status, and the interests of others is less important (Bray et al., [<reflink idref="bib19" id="ref88">19</reflink>]).</p> <p>Moore ([<reflink idref="bib87" id="ref89">87</reflink>]) defined learner-content interaction as a one-way process where students elaborate and reflect on the subject matter or course content. In contrast to traditional classroom learning, online educational settings allow learners to interact with the content through various technologies, including synchronous and asynchronous modes (Kuo, [<reflink idref="bib69" id="ref90">69</reflink>]). Learner-content interaction is essential for learners' knowledge construction and engagement in effective and efficient learning (Xiao, [<reflink idref="bib126" id="ref91">126</reflink>]). It is an important factor in promoting learners' satisfaction (Bray et al., [<reflink idref="bib19" id="ref92">19</reflink>]; Keeler, [<reflink idref="bib65" id="ref93">65</reflink>]).</p> <p>Moore ([<reflink idref="bib87" id="ref94">87</reflink>]) explained three types of interaction in distance education: learner-content, learner-instructor, and learner-learner interaction. There have been numerous attempts to extend this framework. Anderson and Garrison ([<reflink idref="bib8" id="ref95">8</reflink>]) proposed adding three more types of interaction to Moore's framework: teacher-teacher, teacher-content, and content-content. Jung et al. ([<reflink idref="bib62" id="ref96">62</reflink>]) proposed three types of interaction: academic, collaborative, and social in web-based learning. Despite these efforts, Moore's classification remains the most accepted framework for research studies (Xiao, [<reflink idref="bib126" id="ref97">126</reflink>]). Because of the popularity of Moore's classification and its appropriateness for the study, it has been taken up in the present study. However, due to mixed results, there is no clear answer to which three types of interaction best predict student satisfaction (Kuo, [<reflink idref="bib69" id="ref98">69</reflink>]).</p> <p>Thus, the hypotheses proposed are:</p> <hd id="AN0177624875-6">H1</hd> <p>Learner-instructor interaction positively influences student satisfaction with emergency remote teaching.</p> <hd id="AN0177624875-7">H2</hd> <p>Learner-learner interaction positively influences student satisfaction with emergency remote teaching.</p> <hd id="AN0177624875-8">H3</hd> <p>Learner-content interaction positively influences student satisfaction with emergency remote teaching.</p> <hd id="AN0177624875-9">Internet self-efficacy</hd> <p>Albert Bandura coined the term "self-efficacy" and defined it as a particular set of beliefs determining how well one can carry out a plan of action in future situations. It is not the actual abilities or skills, but a person's self-assessment of their abilities and skills (Bandura, [<reflink idref="bib12" id="ref99">12</reflink>]). Perceived self-efficacy aids in acquiring knowledge and skills (Bandura, [<reflink idref="bib13" id="ref100">13</reflink>]). The belief in one's ability to plan and carry out the internet actions required to achieve specific goals is referred to as internet self-efficacy (Eastin & LaRose, [<reflink idref="bib36" id="ref101">36</reflink>]). Internet experience has a positive relationship with student satisfaction (Lim, [<reflink idref="bib78" id="ref102">78</reflink>]) and internet self-efficacy plays an important role in student satisfaction (Ahoto et al., [<reflink idref="bib3" id="ref103">3</reflink>]). Internet self-efficacy is considered a significant factor in predicting student satisfaction with online education (Chang et al., [<reflink idref="bib23" id="ref104">23</reflink>]). Students' performance is predicted by internet self-efficacy (Joo et al., [<reflink idref="bib60" id="ref105">60</reflink>]; Thompson et al., [<reflink idref="bib116" id="ref106">116</reflink>]). However, few studies reported that internet self-efficacy did not predict student satisfaction (Puzziferro, [<reflink idref="bib99" id="ref107">99</reflink>]; Robles, [<reflink idref="bib104" id="ref108">104</reflink>]). The results of studies investigating the relationship between internet self-efficacy and student performance were inconsistent (Kuo, [<reflink idref="bib69" id="ref109">69</reflink>]). It may be possible that in earlier studies, students who were opting for online education had a certain level of ability to use the internet (Kuo, [<reflink idref="bib69" id="ref110">69</reflink>]). However, in the case of this emergency shift where every student, irrespective of their previous ability, has involuntarily joined ERT, the state of affairs may be different. In order to confirm the link between internet self-efficacy and student satisfaction, more research is required. Thus, the hypothesis proposed is:</p> <hd id="AN0177624875-10">H4</hd> <p>Internet self-efficacy positively influences student satisfaction with emergency remote teaching.</p> <hd id="AN0177624875-11">Self-regulated learning</hd> <p>Several researchers have described the concept of self-regulated learning differently; however, the core concept underpinning it is the same. It indicates motivation and learning strategies adopted by the student to achieve learning objectives. The degree to which students actively participate in metacognitive, motivational, and behavioural learning is defined as self-regulated learning (Zimmermann & Schunk, [<reflink idref="bib132" id="ref111">132</reflink>]). It is an essential factor in academic achievement (Zimmermann & Schunk, [<reflink idref="bib132" id="ref112">132</reflink>]), and a low level of self-regulated learning may lead to failure or insufficient effort (Schunk, [<reflink idref="bib107" id="ref113">107</reflink>]). It positively influences student satisfaction (Yavuzalp & Bahcivan, [<reflink idref="bib127" id="ref114">127</reflink>]). Compared to a traditional classroom, online education demands more discipline and effort from students, implying that the ability to use self-regulatory skills becomes more important and necessary (Kuo, [<reflink idref="bib69" id="ref115">69</reflink>]). Various studies examined the impact of self-regulated learning (Bell, [<reflink idref="bib15" id="ref116">15</reflink>]; Yukselturk & Bulut, [<reflink idref="bib129" id="ref117">129</reflink>]); however, very few studies have been conducted to examine its impact on student satisfaction (Kuo, [<reflink idref="bib69" id="ref118">69</reflink>]). As a result, more research is required to confirm the link between self-regulated learning and satisfaction. Thus, the hypothesis proposed is:</p> <hd id="AN0177624875-12">H5</hd> <p>Self-regulated learning positively influences student satisfaction with emergency remote teaching.</p> <hd id="AN0177624875-13">Mediation</hd> <p>The mediation role of self-regulated learning has been explored in the relationship of learner-learner interaction, learner-instructor contact, and internet self-efficacy with student satisfaction with ERT. Mediation is used to see how or why an independent variable influences an outcome (Gunzler et al., [<reflink idref="bib46" id="ref119">46</reflink>]). The mediating role of self-regulated learning has been explored earlier (Barzegar, [<reflink idref="bib14" id="ref120">14</reflink>]; Chen & Wu, [<reflink idref="bib25" id="ref121">25</reflink>]; Jansen et al., [<reflink idref="bib59" id="ref122">59</reflink>]). The importance of self-regulated learning is evident in online and offline learning settings (Artino, [<reflink idref="bib10" id="ref123">10</reflink>]; Schunk, [<reflink idref="bib107" id="ref124">107</reflink>]). Mediator analysis of self-regulated learning can provide a clue as to how the indirect effect of any intervention flows through self-regulated learning (Jansen et al., [<reflink idref="bib59" id="ref125">59</reflink>]). Another reason for studying the mediation effect is that mediating variables become the basis for forming many psychological theories (MacKinnon et al., [<reflink idref="bib84" id="ref126">84</reflink>]), which assist in explaining human ideas, behaviors, and emotions along with predicting future human actions. Additionally, as mentioned earlier, previous studies have reported inconsistent results concerning the relationship between interaction types and internet self-efficacy with student satisfaction; this allows the possibility that some additional measurement and latent variables mediate these relationships.</p> <p>Nevertheless, to the best of our knowledge, no studies have tested the mediating role of self-regulated learning in the present perspective. Statistical mediation analysis did not support the mediating role of self-regulated learning in the relationship between learner-content interaction and student satisfaction. As a result, the following hypotheses have been proposed to understand the role of self-regulated learning as a mediator in the relationships between learner-learner interaction, learner-instructor interaction, internet self-efficacy, and student satisfaction with ERT.</p> <hd id="AN0177624875-14">H6</hd> <p>Self-regulated learning mediates the relationship between learner-learner interaction and student satisfaction with ERT.</p> <hd id="AN0177624875-15">H7</hd> <p>Self-regulated learning mediates the relationship between learner-instructor interaction and student satisfaction with ERT.</p> <hd id="AN0177624875-16">H8</hd> <p>Self-regulated learning mediates the relationship between internet self-efficacy and student satisfaction with ERT.</p> <p>Building on this, online user data have been collected to identify factors that influence learner-user satisfaction and develop a structural model that can more accurately reflect the relationship between various factors related to satisfaction with online communication platforms used during the epidemic. Literature suggests that few studies examined student satisfaction with online learning in the Indian context during the pandemic. Since online education is still in its infancy in developing nations like India, this research can add to our little understanding of how well college students in developing countries are prepared and responsive to emergency distance learning. Identifying the most important factors influencing student satisfaction raises significant concerns for educators and the entire research program can be run to address these issues. Henceforth, a subsequent theoretical framework is presented. Our study used two-staged structural equation modeling-artificial neural networks to analyze and develop the following model that defines the relationship between various constructs and satisfaction.Fig. 1.</p> <p>Graph: Fig. 1 Hypothesized model</p> <p>Many studies have been conducted on the factors that lead to student satisfaction in online learning contexts, such as self-motivation, learning style, internet self-efficacy, instructor knowledge, course structure, and academic self-efficacy (Eom et al., [<reflink idref="bib37" id="ref127">37</reflink>]; Joo et al., [<reflink idref="bib60" id="ref128">60</reflink>]; Kuo et al., [<reflink idref="bib71" id="ref129">71</reflink>]; Yavuzalp & Bahcivan, [<reflink idref="bib127" id="ref130">127</reflink>]), but few attempts have been made in this new normal of online education. Prior research has generally relied on multiple regressions with a disjointed unit of analysis. This work examines the predictive importance of different types of interactions, self-regulated learning, and internet self-efficacy in determining student satisfaction with ERT. The influence of demographic and background variables has been investigated in order to gain a better understanding of the subject. We created and tested a new model to identify predictors of students' online learning satisfaction.</p> <hd id="AN0177624875-17">Methods and materials</hd> <p></p> <hd id="AN0177624875-18">Design of the study</hd> <p>The study used a quantitative approach and a cross-sectional survey. A structured questionnaire was created in the form of a Google form, and the link was sent through WhatsApp and emailed to thousands of students. The data did not contain any duplicate entries or missing values because the Google form was designed in such a way that only one attempt was allowed and only a completely filled form could be submitted. A total of 1598 respondents filled out the questionnaire, and after cleaning, 1590 responses were used for further analysis. Only eight respondents were deleted as outliers, which could be explained by the fact that the survey was only open to those who were willing to participate. The detailed demographic profile of the sample is presented in Table 1.</p> <p>Table 1 Demographic detail of the sample (N = 1590)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" /><th align="left"><p>Frequencies (f)</p></th><th align="left"><p>Percentage (%)</p></th></tr></thead><tbody><tr><td align="left" rowspan="2"><p>Gender</p></td><td align="left"><p>Female</p></td><td char="." align="char"><p>897</p></td><td char="." align="char"><p>56.4</p></td></tr><tr><td align="left"><p>Male</p></td><td char="." align="char"><p>693</p></td><td char="." align="char"><p>43.6</p></td></tr><tr><td align="left" rowspan="3"><p>Age</p></td><td align="left"><p>15–20 years</p></td><td char="." align="char"><p>674</p></td><td char="." align="char"><p>42.4</p></td></tr><tr><td align="left"><p>20–25 years</p></td><td char="." align="char"><p>885</p></td><td char="." align="char"><p>55.7</p></td></tr><tr><td align="left"><p>More than 25 years</p></td><td char="." align="char"><p>31</p></td><td char="." align="char"><p>1.9</p></td></tr><tr><td align="left" rowspan="2"><p>Location</p></td><td align="left"><p>Urban area</p></td><td char="." align="char"><p>783</p></td><td char="." align="char"><p>49.2</p></td></tr><tr><td align="left"><p>Rural area</p></td><td char="." align="char"><p>807</p></td><td char="." align="char"><p>50.8</p></td></tr><tr><td align="left" rowspan="4"><p>Hours of study per week</p></td><td align="left"><p>Less than 5 h</p></td><td char="." align="char"><p>373</p></td><td char="." align="char"><p>23.5</p></td></tr><tr><td align="left"><p>6–10 h</p></td><td char="." align="char"><p>435</p></td><td char="." align="char"><p>27.4</p></td></tr><tr><td align="left"><p>11–15 h</p></td><td char="." align="char"><p>314</p></td><td char="." align="char"><p>19.7</p></td></tr><tr><td align="left"><p>More than 16 h</p></td><td char="." align="char"><p>468</p></td><td char="." align="char"><p>29.4</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0177624875-19">Participants</hd> <p>The study sample consists of higher education students studying at undergraduate and postgraduate levels in colleges and universities in North India. Out of 1590 respondents, 56.4% were females and 43.6% were males. Most respondents were between 20 and 25 years old (55.7%) and 15–20 years old (42.4%): only about 1.9% were more than 25 years old. 29.4% spent more than 16 h per week, 19.7% spent 11–15 h, 27.4% spent 6–10 h, and 23.5% spent less than 5 h online. The sample represented almost equally urban (49.2%) and rural students (50.8%) in the study.</p> <hd id="AN0177624875-20">Measures</hd> <p>The survey instrument was adapted from the study used by Kuo ([<reflink idref="bib69" id="ref131">69</reflink>]). This study included undergraduate and postgraduate students attending exclusively online classes. <emph>"Learner Interaction, Internet Self-Efficacy, Self-Regulated Learning and Satisfaction Survey"</emph> included statements related to six variables: learner-learner interaction, learner-content interaction, learner-instructor interaction, internet self-efficacy, self-regulated learning, and student satisfaction. Since the scope of the instrument was limited to online distance education courses, some of the statements were modified as per the requirement of the present study. The content validity of the adapted survey was determined by getting it vetted by three experts, and only those items that received unanimous approval were retained. A pilot study of 416 respondents was conducted to establish reliability for each variable. Table 2 shows that the item-excluded Cronbach's value for all the items was lower than the overall alpha value for three variables: learner-learner interaction, learner-instructor interaction and satisfaction. However, a total of four items (learner-content interaction (one item), internet self-efficacy (one item), and self-regulated learning (two items) had an 'item-excluded alpha value' higher than the 'overall alpha value'. These four items were not deleted from the survey because deleting items based on the index 'coefficient alpha if item deleted' can be misleading in scale construction, and they have less than the 2% of variation above all item values (Raykov, [<reflink idref="bib102" id="ref132">102</reflink>]).</p> <p>Table 2 Cronbach's Alpha values (N = 415)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" colspan="2"><p>Learner-learner interaction</p></th><th align="left" colspan="2"><p>Learner-instructor interaction</p></th><th align="left" colspan="2"><p>Learner-content interaction</p></th><th align="left" colspan="2"><p>Internet self-efficacy</p></th><th align="left" colspan="2"><p>Self-regulated learning</p></th><th align="left" colspan="2"><p>Satisfaction</p></th></tr><tr><th align="left"><p>Items</p></th><th align="left"><p>Α</p></th><th align="left"><p>items</p></th><th align="left"><p>α</p></th><th align="left"><p>Items</p></th><th align="left"><p>Α</p></th><th align="left"><p>Items</p></th><th align="left"><p>Α</p></th><th align="left"><p>Items</p></th><th align="left"><p>Α</p></th><th align="left"><p>Items</p></th><th align="left"><p>α</p></th></tr></thead><tbody><tr><td align="left"><p>All items</p></td><td char="." align="char"><p>0.8814</p></td><td align="left"><p>All items</p></td><td char="." align="char"><p>0.8355</p></td><td align="left"><p>All items</p></td><td char="." align="char"><p>0.8987</p></td><td align="left"><p>All items</p></td><td char="." align="char"><p>0.9618</p></td><td align="left"><p>All items</p></td><td char="." align="char"><p>0.8484</p></td><td align="left"><p>All items</p></td><td char="." align="char"><p>0.9255</p></td></tr><tr><td align="left"><p>LL_1 excluded</p></td><td char="." align="char"><p>0.8729</p></td><td align="left"><p>LI_5 excluded</p></td><td char="." align="char"><p>0.8263</p></td><td align="left"><p>LC_4 excluded</p></td><td char="." align="char"><p>0.9012</p></td><td align="left"><p>ISE_8 excluded</p></td><td char="." align="char"><p>0.9621</p></td><td align="left"><p>SLR_8 excluded</p></td><td char="." align="char"><p>0.8675</p></td><td align="left"><p>S_4 excluded</p></td><td char="." align="char"><p>0.9169</p></td></tr><tr><td align="left"><p>LL_2 excluded</p></td><td char="." align="char"><p>0.8682</p></td><td align="left"><p>LI_3 excluded</p></td><td char="." align="char"><p>0.8254</p></td><td align="left"><p>LC_1 excluded</p></td><td char="." align="char"><p>0.8641</p></td><td align="left"><p>ISE_1 excluded</p></td><td char="." align="char"><p>0.955</p></td><td align="left"><p>SLR_1 excluded</p></td><td char="." align="char"><p>0.8652</p></td><td align="left"><p>S_5 excluded</p></td><td char="." align="char"><p>0.9128</p></td></tr><tr><td align="left"><p>LL_3 excluded</p></td><td char="." align="char"><p>0.8678</p></td><td align="left"><p>LI_1 excluded</p></td><td char="." align="char"><p>0.8066</p></td><td align="left"><p>LC_3 excluded</p></td><td char="." align="char"><p>0.8579</p></td><td align="left"><p>ISE_7 excluded</p></td><td char="." align="char"><p>0.9565</p></td><td align="left"><p>SLR_9 excluded</p></td><td char="." align="char"><p>0.8366</p></td><td align="left"><p>S_3 excluded</p></td><td char="." align="char"><p>0.9106</p></td></tr><tr><td align="left"><p>LL_6 excluded</p></td><td char="." align="char"><p>0.8669</p></td><td align="left"><p>LI_2 excluded</p></td><td char="." align="char"><p>0.802</p></td><td align="left"><p>LC_2 excluded</p></td><td char="." align="char"><p>0.8519</p></td><td align="left"><p>ISE_5 excluded</p></td><td char="." align="char"><p>0.9557</p></td><td align="left"><p>SLR_5 excluded</p></td><td char="." align="char"><p>0.8363</p></td><td align="left"><p>S_2 excluded</p></td><td char="." align="char"><p>0.9024</p></td></tr><tr><td align="left"><p>LL_8 excluded</p></td><td char="." align="char"><p>0.8663</p></td><td align="left"><p>LI_4 excluded</p></td><td char="." align="char"><p>0.7954</p></td><td align="left" /><td char="." align="char" /><td align="left"><p>ISE_6 excluded</p></td><td char="." align="char"><p>0.9577</p></td><td align="left"><p>SLR_7 excluded</p></td><td char="." align="char"><p>0.8348</p></td><td align="left"><p>S_1 excluded</p></td><td char="." align="char"><p>0.8994</p></td></tr><tr><td align="left"><p>LL_4 excluded</p></td><td char="." align="char"><p>0.8648</p></td><td align="left"><p>LI_6 excluded</p></td><td char="." align="char"><p>0.7949</p></td><td align="left" /><td char="." align="char" /><td align="left"><p>ISE_2 excluded</p></td><td char="." align="char"><p>0.9534</p></td><td align="left"><p>SLR_2 excluded</p></td><td char="." align="char"><p>0.8326</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left"><p>LL_7 excluded</p></td><td char="." align="char"><p>0.8637</p></td><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left"><p>ISE_4 excluded</p></td><td char="." align="char"><p>0.9588</p></td><td align="left"><p>SLR_4 excluded</p></td><td char="." align="char"><p>0.8291</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left"><p>LL_5 excluded</p></td><td char="." align="char"><p>0.8631</p></td><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left"><p>ISE_3 excluded</p></td><td char="." align="char"><p>0.9535</p></td><td align="left"><p>SLR_12 excluded</p></td><td char="." align="char"><p>0.8286</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left"><p>SLR_3 excluded</p></td><td char="." align="char"><p>0.8282</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left"><p>SLR_10 excluded</p></td><td char="." align="char"><p>0.8274</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left"><p>SLR_11 excluded</p></td><td char="." align="char"><p>0.8259</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left" /><td char="." align="char" /><td align="left"><p>SLR_6 excluded</p></td><td char="." align="char"><p>0.8223</p></td><td align="left" /><td char="." align="char" /></tr></tbody></table> </ephtml> </p> <p>Moreover, the measurement model analysis presented in Tables 3 and 4 provided reliability and validity evidence for all the constructs. Reliability results for learner-learner interaction (0.88), learner-instructor interaction (0.84), learner-content interaction (0.90), internet self-efficacy (0.96), self-regulated learning (0.84) and student satisfaction (0.93) reveal that the survey holds good reliability (Bland & Altman, [<reflink idref="bib17" id="ref133">17</reflink>]; Hundleby & Nunnally, [<reflink idref="bib57" id="ref134">57</reflink>]). Items related to learner interaction and satisfaction are scored on a five-point Likert scale, whereas items related to internet self-efficacy and self-regulated learning are scored on a seven-point Likert scale. Other information, such as student demographic profiles and hours per week spent online was also collected.</p> <p>Table 3 Factor loading, reliability and validity (for reflective constructs)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Construct</p></th><th align="left"><p>Items</p></th><th align="left"><p>Factor loading</p></th><th align="left"><p>Cronbach's alpha</p></th><th align="left"><p>rho_A</p></th><th align="left"><p>Composite reliability</p></th><th align="left"><p>Average variance extracted</p></th></tr></thead><tbody><tr><td align="left" rowspan="8"><p>Learner-Learner Interaction</p></td><td align="left"><p>LL_1</p></td><td char="." align="char"><p>0.707</p></td><td char="." align="char" rowspan="4"><p>0.896</p></td><td char="." align="char" rowspan="4"><p>0.898</p></td><td char="." align="char" rowspan="4"><p>0.917</p></td><td char="." align="char" rowspan="4"><p>0.579</p></td></tr><tr><td align="left"><p>LL_2</p></td><td char="." align="char"><p>0.665</p></td></tr><tr><td align="left"><p>LL_3</p></td><td char="." align="char"><p>0.718</p></td></tr><tr><td align="left"><p>LL_4</p></td><td char="." align="char"><p>0.708</p></td></tr><tr><td align="left"><p>LL_5</p></td><td char="." align="char"><p>0.768</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>LL_6</p></td><td char="." align="char"><p>0.653</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>LL_7</p></td><td char="." align="char"><p>0.786</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>LL_8</p></td><td char="." align="char"><p>0.749</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" rowspan="6"><p>Learner-Instructor Interaction</p></td><td align="left"><p>LI_1</p></td><td char="." align="char"><p>0.77</p></td><td char="." align="char" rowspan="3"><p>0.857</p></td><td char="." align="char" rowspan="3"><p>0.864</p></td><td char="." align="char" rowspan="3"><p>0.894</p></td><td char="." align="char" rowspan="3"><p>0.585</p></td></tr><tr><td align="left"><p>LI_2</p></td><td char="." align="char"><p>0.765</p></td></tr><tr><td align="left"><p>LI_3</p></td><td char="." align="char"><p>0.736</p></td></tr><tr><td align="left"><p>LI_4</p></td><td char="." align="char"><p>0.705</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>LI_5</p></td><td char="." align="char"><p>0.536</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>LI_6</p></td><td char="." align="char"><p>0.723</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" rowspan="4"><p>Learner-Content Interaction</p></td><td align="left"><p>LC_1</p></td><td char="." align="char"><p>0.819</p></td><td char="." align="char" rowspan="4"><p>0.90</p></td><td char="." align="char" rowspan="4"><p>0.906</p></td><td char="." align="char" rowspan="4"><p>0.931</p></td><td char="." align="char" rowspan="3"><p>0.771</p></td></tr><tr><td align="left"><p>LC_2</p></td><td char="." align="char"><p>0.907</p></td></tr><tr><td align="left"><p>LC_3</p></td><td char="." align="char"><p>0.859</p></td></tr><tr><td align="left"><p>LC_4</p></td><td char="." align="char"><p>0.747</p></td><td char="." align="char" /></tr><tr><td align="left" rowspan="5"><p>Internet Self-efficacy</p></td><td align="left"><p>ISE_4</p></td><td char="." align="char"><p>0.672</p></td><td char="." align="char" rowspan="5"><p>0.94</p></td><td char="." align="char" rowspan="5"><p>0.964</p></td><td char="." align="char" rowspan="5"><p>0.954</p></td><td char="." align="char" rowspan="3"><p>0.804</p></td></tr><tr><td align="left"><p>ISE_5</p></td><td char="." align="char"><p>0.778</p></td></tr><tr><td align="left"><p>ISE_6</p></td><td char="." align="char"><p>0.718</p></td></tr><tr><td align="left"><p>ISE_7</p></td><td char="." align="char"><p>0.971</p></td><td char="." align="char" /></tr><tr><td align="left"><p>ISE_8</p></td><td char="." align="char"><p>1.122</p></td><td char="." align="char" /></tr><tr><td align="left" rowspan="5"><p>Satisfaction</p></td><td align="left"><p>S_1</p></td><td char="." align="char"><p>0.903</p></td><td char="." align="char" rowspan="3"><p>0.936</p></td><td char="." align="char" rowspan="3"><p>0.938</p></td><td char="." align="char" rowspan="3"><p>0.951</p></td><td char="." align="char" rowspan="2"><p>0.796</p></td></tr><tr><td align="left"><p>S_2</p></td><td char="." align="char"><p>0.91</p></td></tr><tr><td align="left"><p>S_3</p></td><td char="." align="char"><p>0.859</p></td><td char="." align="char" /></tr><tr><td align="left"><p>S_4</p></td><td char="." align="char"><p>0.856</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>S_5</p></td><td char="." align="char"><p>0.786</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Table 4 Result of formative measurement model for self-regulated learning</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Indicators</p></th><th align="left"><p>VIF</p></th><th align="left"><p>Outer Weight</p></th><th align="left"><p>T-statistics</p></th><th align="left"><p>p-value</p></th><th align="left"><p>95% confidence interval</p></th><th align="left"><p>Outer Loading</p></th></tr></thead><tbody><tr><td align="left"><p>SLR_2</p></td><td char="." align="char"><p>1.527</p></td><td char="." align="char"><p>0.398</p></td><td char="." align="char"><p>7.379</p></td><td align="left"><p>0</p></td><td align="left"><p>[0.295, 0.508]</p></td><td char="." align="char"><p>0.797</p></td></tr><tr><td align="left"><p>SLR_3</p></td><td char="." align="char"><p>1.672</p></td><td char="." align="char"><p>0.137</p></td><td char="." align="char"><p>2.346</p></td><td align="left"><p>0.019</p></td><td align="left"><p>[0.021, 0.251]</p></td><td char="." align="char"><p>0.649</p></td></tr><tr><td align="left"><p>SLR_4</p></td><td char="." align="char"><p>1.721</p></td><td char="." align="char"><p>-0.032</p></td><td char="." align="char"><p>0.517</p></td><td align="left"><p>0.606</p></td><td align="left"><p>[− 0.152, 0.094]</p></td><td char="." align="char"><p>0.549</p></td></tr><tr><td align="left"><p>SLR_5</p></td><td char="." align="char"><p>1.645</p></td><td char="." align="char"><p>0.095</p></td><td char="." align="char"><p>1.664</p></td><td align="left"><p>0.096</p></td><td align="left"><p>[− 0.017, 0.207]</p></td><td char="." align="char"><p>0.574</p></td></tr><tr><td align="left"><p>SLR_6</p></td><td char="." align="char"><p>1.822</p></td><td char="." align="char"><p>0.259</p></td><td char="." align="char"><p>4.241</p></td><td align="left"><p>0</p></td><td align="left"><p>[0.14, 0.383]</p></td><td char="." align="char"><p>0.755</p></td></tr><tr><td align="left"><p>SLR_9</p></td><td char="." align="char"><p>1.537</p></td><td char="." align="char"><p>-0.022</p></td><td char="." align="char"><p>0.375</p></td><td align="left"><p>0.707</p></td><td align="left"><p>[− 0.137, 0.095]</p></td><td char="." align="char"><p>0.498</p></td></tr><tr><td align="left"><p>SLR_10</p></td><td char="." align="char"><p>1.781</p></td><td char="." align="char"><p>-0.023</p></td><td char="." align="char"><p>0.36</p></td><td align="left"><p>0.719</p></td><td align="left"><p>[− 0.148, 0.10]</p></td><td char="." align="char"><p>0.566</p></td></tr><tr><td align="left"><p>SLR_11</p></td><td char="." align="char"><p>1.718</p></td><td char="." align="char"><p>0.42</p></td><td char="." align="char"><p>6.83</p></td><td align="left"><p>0</p></td><td align="left"><p>[0.3, 0.54]</p></td><td char="." align="char"><p>0.809</p></td></tr><tr><td align="left"><p>SLR_12</p></td><td char="." align="char"><p>1.584</p></td><td char="." align="char"><p>0.074</p></td><td char="." align="char"><p>1.282</p></td><td align="left"><p>0.2</p></td><td align="left"><p>[− 0.036, 0.186]</p></td><td char="." align="char"><p>0.614</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0177624875-21">Data analysis</hd> <p>The first section of the result includes descriptive results, and PLS-SEM results have been reported in the second section. Descriptive and comparative analysis was carried out using Jamovi, which is free and open software. Partial Least Squares Structural Equation Modeling (PLS-SEM) is rapidly emerging as a prominent method in social research, and many authors have found it to be more appropriate for tackling complex problems that CB-SEM cannot (Albahri et al., [<reflink idref="bib4" id="ref135">4</reflink>]). PLS-SEM also has predictive benefits, such as providing information about the model's coefficient of determination and predictive relevance (Hair et al., [<reflink idref="bib51" id="ref136">51</reflink>]). In the last section of results, artificial neural network (ANN) results are presented. The two-step SEM-ANN approach was used to offset the limitations of these two analyses when performed alone. In particular, SEM is not suitable for providing non-linear and non-compensatory relationships among constructs (Albahri et al., [<reflink idref="bib4" id="ref137">4</reflink>]), while ANN cannot test hypotheses. By merging SEM with ANN, both linear and non-linear relationships between variables can be captured, and predictor variables can be ranked based on their importance in the model (Zabukovšek et al., [<reflink idref="bib130" id="ref138">130</reflink>]). The artificial neural network section discusses more details on the justification and benefits of using a dual SEM-ANN approach. IBM SPSS Neural Network has been used because of its ability to explore subtle or hidden patterns in data. Smart PLS 3.3.5 software has been used for PLS-SEM Analysis since it can handle formative constructs, complex models, mediation, and focus on prediction. Before performing data analysis, data cleaning, and assumption tests like the Shapiro–Wilk test and Levene's test were carried out. Common method bias is a well-documented phenomenon that can influence the relationships between variables and rigour research; therefore, it is important to check if there is any threat of having common method bias in the data (Jordan & Troth, [<reflink idref="bib61" id="ref139">61</reflink>]).</p> <hd id="AN0177624875-22">Results</hd> <p></p> <hd id="AN0177624875-23">Assessing common method bias (CMB)</hd> <p>This study collected data using one survey questionnaire, and information regarding all the endogenous and exogenous variables was collected simultaneously; hence, there may be chances of having common method bias. It is a prerequisite to address CMB before doing further analysis. Data should be free from CMB to arrive at a meaningful and reliable conclusion. All participants were informed that there was no right or wrong answer to a question/statement. They were also assured that their identities would not be divulged at any cost, and they should give their candid responses. Harman's single-factor test is one of the most commonly used approaches for testing CMB (Jordan & Troth, [<reflink idref="bib61" id="ref140">61</reflink>]), wherein exploratory factor analysis (EFA) loads all items of each variable/construct on a single factor (Fuller et al., [<reflink idref="bib42" id="ref141">42</reflink>]). Harman's single factor test results indicated that single factor explicated just 30.45% of the overall variance, which is far less than 50%; hence there is no threat of common bias.</p> <hd id="AN0177624875-24">Assessing multivariate assumptions</hd> <p>Before performing multivariate analyses, multivariate assumptions were validated. Multicollinearity assumptions for all the variables were tested using the correlation matrix presented in Table 5. Multicollinearity is a problem that arises due to the high correlation between explanatory variables. It reduces the statistical significance of independent variables ("The Problem of Multicollinearity," [<reflink idref="bib115" id="ref142">115</reflink>]) and can result in the overfitting of the model. The examination of correlations reveals that none of the variables are highly correlated (r < 0.80); hence multicollinearity does not exist between variables. The Mardia test for skewness and kurtosis indicated (b<subs>skewness</subs> = 148.0, p = 0; b<subs>kurtosis</subs> = 2603.011, p = 0) that the dataset does not hold multivariate normal distribution. Most of the researchers consider that if the values of skewness and kurtosis are above the range of -1 to 1, then it is indicative of a highly non-normal distribution (Lei & Lomax, [<reflink idref="bib74" id="ref143">74</reflink>]). Further, the literature suggests that tests for multi-normality are overly sensitive hence, univariate normality should also be determined (Koh, [<reflink idref="bib67" id="ref144">67</reflink>]). The univariate normality of data was tested by calculating skewness and kurtosis values for each latent variable with the help of WebPower. Skewness and kurtosis values for internet self-efficacy (Skewness = − 0.046, Kurtosis = − 0.742), learner-content interaction (Skewness = − 0.213, Kurtosis = − 3.464), learner-instructor (Skewness = − 0.655, Kurtosis = − 10.669), learner-learner interaction (Skewness = − 0.563, Kurtosis = − 0.9172), satisfaction (Skewness = 0.045, Kurtosis = 0.727) and self-regulated learning (Skewness = − 0.196, Kurtosis = − 3.202) demonstrated that univariate normality assumptions hold true for all the variables except for learner-instructor interaction and learner-learner interaction. Because, in order to prove a normal univariate distribution, values for asymmetry and kurtosis should be between − 2 and + 2 (George & Mallery, [<reflink idref="bib44" id="ref145">44</reflink>]). Hence, PLS-SEM, a soft modeling technique, is a better choice in this situation than CB-SEM as data follows a non-normal distribution (Lowry & Gaskin, [<reflink idref="bib82" id="ref146">82</reflink>]).</p> <p>Table 5 Correlation matrix</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>L-L</p></th><th align="left"><p>L-I</p></th><th align="left"><p>L-C</p></th><th align="left"><p>ISE</p></th><th align="left"><p>SLR</p></th><th align="left"><p>S</p></th></tr></thead><tbody><tr><td align="left"><p>L-L</p></td><td align="left"><p>–</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>L-I</p></td><td align="left"><p>0.711*</p></td><td align="left"><p>–</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>L-C</p></td><td align="left"><p>0.625*</p></td><td align="left"><p>0.590*</p></td><td align="left"><p>–</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>ISE</p></td><td align="left"><p>0.255*</p></td><td align="left"><p>0.269*</p></td><td align="left"><p>0.307*</p></td><td align="left"><p>–</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>SLR</p></td><td align="left"><p>0.410*</p></td><td align="left"><p>0.464*</p></td><td align="left"><p>0.390*</p></td><td align="left"><p>0.317*</p></td><td align="left"><p>–</p></td><td align="left" /></tr><tr><td align="left"><p>S</p></td><td align="left"><p>0.550*</p></td><td align="left"><p>0.522*</p></td><td align="left"><p>0.765*</p></td><td align="left"><p>0.240*</p></td><td align="left"><p>0.350*</p></td><td align="left"><p>–</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>L-L</emph> learner-learner interaction, <emph>L-I</emph> learner-instructor interaction, <emph>L-C</emph> learner-content interaction, <emph>ISE</emph> internet Self-efficacy, <emph>SLR</emph> = self-regulated learning, <emph>S</emph> satisfaction</p> <hd id="AN0177624875-25">Descriptive results</hd> <p>The aim of studying gender differences is to identify the existence of multifaceted perspectives in online learning. Table 6 shows that male and female students have reported significant differences in learner-learner interaction (t = 4.08, p < 0.001) and learner-instructor interaction (t = 4.97, p < 0.001). It indicates that females showed better learner-learner interaction and learner-instructor interaction than male students. Similarly, gender also made a significant difference in the self-regulated learning level (t = 7.46, p < 0.001) and internet self-efficacy (t = 2.091, p = 0.037). However, no significant difference was observed on the basis of gender in learner-content interaction (t = 1.612, p = 0.107), and satisfaction level (t = 0.613, p = 0.540).</p> <p>Table 6 Descriptive statistics and t-test of gender</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="3" /><th align="left" colspan="4"><p>Gender</p></th><th align="left" rowspan="3"><p>t-test</p></th><th align="left" rowspan="3"><p>p</p></th><th align="left" rowspan="3"><p>Effect size</p></th></tr><tr><th align="left" colspan="2"><p>Male</p></th><th align="left" colspan="2"><p>Female</p></th></tr><tr><th align="left"><p>M</p></th><th align="left"><p>SD</p></th><th align="left"><p>M</p></th><th align="left"><p>SD</p></th></tr></thead><tbody><tr><td align="left"><p>Learner-learner interaction</p></td><td char="." align="char"><p>24.9</p></td><td char="." align="char"><p>6.61</p></td><td char="." align="char"><p>26.2</p></td><td char="." align="char"><p>6.04</p></td><td char="." align="char"><p>4.08</p></td><td char="." align="char"><p> <.001</p></td><td char="." align="char"><p>0.2067</p></td></tr><tr><td align="left"><p>Learner-instructor interaction</p></td><td char="." align="char"><p>19.9</p></td><td char="." align="char"><p>4.53</p></td><td char="." align="char"><p>21.0</p></td><td char="." align="char"><p>4.09</p></td><td char="." align="char"><p>4.979</p></td><td char="." align="char"><p> <.001</p></td><td char="." align="char"><p>0.2518</p></td></tr><tr><td align="left"><p>Learner-content interaction</p></td><td char="." align="char"><p>11.7</p></td><td char="." align="char"><p>4.25</p></td><td char="." align="char"><p>12.1</p></td><td char="." align="char"><p>3.93</p></td><td char="." align="char"><p>1.612</p></td><td char="." align="char"><p>0.107</p></td><td char="." align="char"><p>0.0815</p></td></tr><tr><td align="left"><p>Internet self-efficacy</p></td><td char="." align="char"><p>32.2</p></td><td char="." align="char"><p>13.52</p></td><td char="." align="char"><p>30.9</p></td><td char="." align="char"><p>12.08</p></td><td char="." align="char"><p>2.091</p></td><td char="." align="char"><p>0.037</p></td><td char="." align="char"><p>0.1058</p></td></tr><tr><td align="left"><p>Self-regulated learning</p></td><td char="." align="char"><p>53.6</p></td><td char="." align="char"><p>10.71</p></td><td char="." align="char"><p>57.3</p></td><td char="." align="char"><p>9.23</p></td><td char="." align="char"><p>7.465</p></td><td char="." align="char"><p> <.001</p></td><td char="." align="char"><p>0.3775</p></td></tr><tr><td align="left"><p>Satisfaction</p></td><td char="." align="char"><p>13.6</p></td><td char="." align="char"><p>5.73</p></td><td char="." align="char"><p>13.8</p></td><td char="." align="char"><p>5.19</p></td><td char="." align="char"><p>0.613</p></td><td char="." align="char"><p>0.540</p></td><td char="." align="char"><p>0.0310</p></td></tr></tbody></table> </ephtml> </p> <p>Results presented in Table 7 indicated that the location of students does not make any significant difference in the learner-learner interaction, learner-instructor interaction, learner-content interaction, and satisfaction from online education. However, urban students have reflected significantly better internet self-efficacy (t = 4.31, p < 0.001) and self-regulated learning (t = 2.009, p = 0.045) than rural students.</p> <p>Table 7 Descriptive statistics t-test of location</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="3" /><th align="left" colspan="4"><p>Location</p></th><th align="left" rowspan="3"><p>t-test</p></th><th align="left" rowspan="3"><p>P</p></th><th align="left" rowspan="3"><p>Effect size</p></th></tr><tr><th align="left" colspan="2"><p>Rural</p></th><th align="left" colspan="2"><p>Urban</p></th></tr><tr><th align="left"><p>M</p></th><th align="left"><p>SD</p></th><th align="left"><p>M</p></th><th align="left"><p>SD</p></th></tr></thead><tbody><tr><td align="left"><p>Learner-learner interaction</p></td><td char="." align="char"><p>25.8</p></td><td char="." align="char"><p>6.33</p></td><td char="." align="char"><p>25.6</p></td><td char="." align="char"><p>6.33</p></td><td char="." align="char"><p>0.59</p></td><td char="." align="char"><p>0.533</p></td><td char="." align="char"><p>0.0298</p></td></tr><tr><td align="left"><p>Learner-instructor interaction</p></td><td char="." align="char"><p>20.4</p></td><td char="." align="char"><p>4.48</p></td><td char="." align="char"><p>20.7</p></td><td char="." align="char"><p>4.14</p></td><td char="." align="char"><p>1.29</p></td><td char="." align="char"><p>0.195</p></td><td char="." align="char"><p>0.0650</p></td></tr><tr><td align="left"><p>Learner-content interaction</p></td><td char="." align="char"><p>11.8</p></td><td char="." align="char"><p>4.04</p></td><td char="." align="char"><p>12.0</p></td><td char="." align="char"><p>4.11</p></td><td char="." align="char"><p>0.783</p></td><td char="." align="char"><p>0.434</p></td><td char="." align="char"><p>0.0393</p></td></tr><tr><td align="left"><p>Internet self-efficacy</p></td><td char="." align="char"><p>30.1</p></td><td char="." align="char"><p>12.72</p></td><td char="." align="char"><p>32.8</p></td><td char="." align="char"><p>12.62</p></td><td char="." align="char"><p>4.311</p></td><td char="." align="char"><p> <.001</p></td><td char="." align="char"><p>0.2163</p></td></tr><tr><td align="left"><p>Self-regulated learning</p></td><td char="." align="char"><p>55.2</p></td><td char="." align="char"><p>10.42</p></td><td char="." align="char"><p>56.2</p></td><td char="." align="char"><p>9.67</p></td><td char="." align="char"><p>2.009</p></td><td char="." align="char"><p>0.045</p></td><td char="." align="char"><p>0.1008</p></td></tr><tr><td align="left"><p>Satisfaction</p></td><td char="." align="char"><p>13.5</p></td><td char="." align="char"><p>5.43</p></td><td char="." align="char"><p>13.8</p></td><td char="." align="char"><p>5.42</p></td><td char="." align="char"><p>1.018</p></td><td char="." align="char"><p>0.307</p></td><td char="." align="char"><p>0.0511</p></td></tr></tbody></table> </ephtml> </p> <p>The ANOVA results showed that student age significantly influences learner-learner interaction, learner-instructor interaction, learner-content interaction, self-regulated learning and level of satisfaction. Post-hoc results showed that all three types of interactions, self-regulated learning and satisfaction, improved with the students' age, as seen in Table 8. The results align with earlier studies, wherein Narimani et al. ([<reflink idref="bib90" id="ref147">90</reflink>]) reported that satisfaction with e-learning increases with age. This could be due to the fact that older students believe online learning is more appropriate due to its flexibility and ability to allow them more time for work and family (Narimani et al., [<reflink idref="bib90" id="ref148">90</reflink>]). Internet self-efficacy results indicated no statistical difference when compared across different age groups. Hence, internet self-efficacy does not vary with age among higher education students.</p> <p>Table 8 Descriptive statistics and F test (age of the students)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="3" /><th align="left" colspan="3"><p>Age</p></th><th align="left" rowspan="3"><p>F-test</p></th><th align="left" rowspan="3"><p>Post hoc analysis</p></th></tr><tr><th align="left" colspan="3"><p>Mean (SD)</p></th></tr><tr><th align="left"><p>15–20 years (1)</p></th><th align="left"><p>20–25 years (2)</p></th><th align="left"><p>More than 25 years (3)</p></th></tr></thead><tbody><tr><td align="left"><p>Learner-learner interaction</p></td><td char="(" align="char"><p>25.5 (6.43)</p></td><td char="(" align="char"><p>25.7 (6.29)</p></td><td char="(" align="char"><p>28.0 (4.46)</p></td><td char="." align="char"><p>4.44*</p></td><td align="left"><p>3 > 1, 3 > 2</p></td></tr><tr><td align="left"><p>Learner-instructor interaction</p></td><td char="(" align="char"><p>20.2 (4.28)</p></td><td char="(" align="char"><p>20.8 (4.34)</p></td><td char="(" align="char"><p>22.4 (3.49)</p></td><td char="." align="char"><p>8.20*</p></td><td align="left"><p>3 > 1, 3 > 2, 2 > 1</p></td></tr><tr><td align="left"><p>Learner-CONTENT INTERACTION</p></td><td char="(" align="char"><p>11.6 (4.21)</p></td><td char="(" align="char"><p>12.1 (3.95)</p></td><td char="(" align="char"><p>14.3 (3.67)</p></td><td char="." align="char"><p>9.03*</p></td><td align="left"><p>3 > 1, 3 > 2, 2 > 1</p></td></tr><tr><td align="left"><p>Internet self-efficacy</p></td><td char="(" align="char"><p>30.9 (12.82)</p></td><td char="(" align="char"><p>31.8 (12.64)</p></td><td char="(" align="char"><p>32.9 (3.35)</p></td><td char="." align="char"><p>1.17</p></td><td align="left" /></tr><tr><td align="left"><p>Self-regulated learning</p></td><td char="(" align="char"><p>55.0 (10.61)</p></td><td char="(" align="char"><p>56.1 (9.61)</p></td><td char="(" align="char"><p>58.7 (9.72)</p></td><td char="." align="char"><p>4.03*</p></td><td align="left"><p>2 > 1</p></td></tr><tr><td align="left"><p>Satisfaction</p></td><td char="(" align="char"><p>13.2 (5.54)</p></td><td char="(" align="char"><p>13.9 (5.30)</p></td><td char="(" align="char"><p>17.0 (5.15)</p></td><td char="." align="char"><p>10.10*</p></td><td align="left"><p>3 > 1, 3 > 2, 2 > 1</p></td></tr></tbody></table> </ephtml> </p> <p>Hours spent per week on online education did not significantly influence interactions and satisfaction; however, internet self-efficacy and self-regulated learning have shown better results when the hours spent per week increase Table 9.</p> <p>Table 9 Descriptive statistics and F test (hours spent per week)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="3" /><th align="left" colspan="5"><p>Hours spent for online education per week</p></th><th align="left" rowspan="3"><p>F-test</p></th><th align="left" rowspan="3"><p>Post hoc analysis</p></th></tr><tr><th align="left" colspan="5"><p>Mean (SD)</p></th></tr><tr><th align="left"><p>Less than 6 h (1)</p></th><th align="left"><p>6–10 h (2)</p></th><th align="left" colspan="2"><p>11–15 h (3)</p></th><th align="left"><p>More than 16 h (4)</p></th></tr></thead><tbody><tr><td align="left"><p>Learner-Learner Interaction</p></td><td char="(" align="char"><p>25.2 (6.54)</p></td><td char="(" align="char" colspan="2"><p>25.9 (6.31)</p></td><td char="(" align="char"><p>25.9 (5.91)</p></td><td char="(" align="char"><p>25.6 (6.44)</p></td><td align="left"><p>0.943</p></td><td align="left" /></tr><tr><td align="left"><p>Learner-Instructor Interaction</p></td><td char="(" align="char"><p>19.9(4.48)</p></td><td char="(" align="char" colspan="2"><p>20.7 (4.43)</p></td><td char="(" align="char"><p>20.6 (4.23)</p></td><td char="(" align="char"><p>20.9 (4.10)</p></td><td align="left"><p>3.921</p></td><td align="left" /></tr><tr><td align="left"><p>Learner-Content Interaction</p></td><td char="(" align="char"><p>11.5 (4.18)</p></td><td char="(" align="char" colspan="2"><p>12.0 (4.25)</p></td><td char="(" align="char"><p>11.8 (3.96)</p></td><td char="(" align="char"><p>12.3 (3.88)</p></td><td align="left"><p>2.459</p></td><td align="left" /></tr><tr><td align="left"><p>Internet Self-efficacy</p></td><td char="(" align="char"><p>28.2 (13.13)</p></td><td char="(" align="char" colspan="2"><p>30.3 (12.57)</p></td><td char="(" align="char"><p>32.9 (12.4)</p></td><td char="(" align="char"><p>34.1 (12.06)</p></td><td align="left"><p>17.78*</p></td><td align="left"><p>4 > 1,4 > 2,3 > 1,3 > 2</p></td></tr><tr><td align="left"><p>Self-Regulated Learning</p></td><td char="(" align="char"><p>52.9 (10.45)</p></td><td char="(" align="char" colspan="2"><p>55.7 (10.19)</p></td><td char="(" align="char"><p>56.3 (9.58)</p></td><td char="(" align="char"><p>57.5 (9.49)</p></td><td align="left"><p>14.92*</p></td><td align="left"><p>4 > 1, 4 > 2, 3 > 1, 2 > 1</p></td></tr><tr><td align="left"><p>Satisfaction</p></td><td char="(" align="char"><p>13.3 (5.52)</p></td><td char="(" align="char" colspan="2"><p>13.9 (5.60)</p></td><td char="(" align="char"><p>13.5 (4.49)</p></td><td char="(" align="char"><p>13.9 (5.47)</p></td><td align="left"><p>1.086</p></td><td align="left" /></tr></tbody></table> </ephtml> </p> <hd id="AN0177624875-26">SEM results</hd> <p>The PLS-SEM has been used to test the relationship among endogenous and exogenous variables using SmartPLS 3 software. The proposed model has been tested for reliability and validity issues with the help of different measures like Cronbach's Alpha, Composite Reliability and Heterotrait-Monotrait Ratio before testing the structural model. Hypotheses testing was done with bootstrapping and predictive relevance of the model.</p> <hd id="AN0177624875-27">Measurement model</hd> <p>Before proceeding with further analysis, the internal reliability of the measurement model was established. The model has both reflective (causes indicators) and formative constructs (caused by indicators); therefore, the measurement model was tested differently for both types of constructs. The results for reliability and validity for reflective constructs (learner-learner interaction, learner-instructor interaction, learner-content interaction, internet self-efficacy and satisfaction) are presented in Table 3, showing the constructs' reliability and validity tests. Almost all construct item loadings exceeded the 0.708 threshold value (Hair et al., [<reflink idref="bib49" id="ref149">49</reflink>]); however, some of the construct items whose value was less than 0.70 were retained if the reliability and validity were satisfactory. All Cronbach's alpha values and composite reliability values are more than 0.70, thereby confirming that the measurement model for reflective constructs holds a high degree of construct validity. The magnitudes of average variance extracted (AVE) for all the reflective constructs are above 0.50, which confirms convergent validity. The Fornell-Larcker criterion (Table 10) indicates that the AVE square root is more than the corresponding latent variable correlations (Hadi & Abdullah, [<reflink idref="bib48" id="ref150">48</reflink>]).</p> <p>Table 10 Fornell-Larcker criterion</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>ISE</p></th><th align="left"><p>LCI</p></th><th align="left"><p>LII</p></th><th align="left"><p>LLI</p></th><th align="left"><p>S</p></th></tr></thead><tbody><tr><td align="left"><p>Internet self-efficacy (ISE)</p></td><td char="." align="char"><p><bold>0.897</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Learner-content interaction (LCI)</p></td><td char="." align="char"><p>0.313</p></td><td char="." align="char"><p><bold>0.878</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Learner-instructor interaction (LII)</p></td><td char="." align="char"><p>0.285</p></td><td char="." align="char"><p>0.592</p></td><td char="." align="char"><p><bold>0.765</bold></p></td><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Learner-learner interaction (LLI)</p></td><td char="." align="char"><p>0.270</p></td><td char="." align="char"><p>0.626</p></td><td char="." align="char"><p>0.714</p></td><td char="." align="char"><p><bold>0.761</bold></p></td><td char="." align="char" /></tr><tr><td align="left"><p>Satisfaction (S)</p></td><td char="." align="char"><p>0.254</p></td><td char="." align="char"><p>0.769</p></td><td char="." align="char"><p>0.527</p></td><td char="." align="char"><p>0.551</p></td><td char="." align="char"><p><bold>0.892</bold></p></td></tr><tr><td align="left"><p>Self-Regulated Learning (SRL)</p></td><td char="." align="char"><p>0.278</p></td><td char="." align="char"><p>0.420</p></td><td char="." align="char"><p>0.470</p></td><td char="." align="char"><p>0.432</p></td><td char="." align="char"><p>0.386</p></td></tr></tbody></table> </ephtml> </p> <p>The square root of AVE is indicated by bold values, while the other values are squared inner construct correlation</p> <p>The assessment of formative constructs deploys different sets of metrics. According to the theory and claims, items have been asked in terms of causes, demonstrating that self-regulated learning has a formative construct, which means observed indicators are causing the latent construct. The value of AVE (< 0.50) further confirmed this. The formative construct was examined for collinearity issues, and all of the indicators' VIF values were found to be less than 3. As a result, we can conclude that it is collinearity-free (Chuah et al., [<reflink idref="bib29" id="ref151">29</reflink>]) because collinearity can affect weight estimation and its statistical significance (Ramayah et al., [<reflink idref="bib101" id="ref152">101</reflink>]). Furthermore, complete bootstrapping using 5000 subsamples (as recommended by Hair et al., [<reflink idref="bib50" id="ref153">50</reflink>]) was used to analyze the relevance and significance of each indicator. These subsamples are created based on randomly drawn observations from the original data set. All of the outer weights (see Table 4) except for SLR_4, SLR_5, SLR_9, SLR_10 and SLR_12, were found to be significant. After examining the loadings of the indicators, it was found that all indicators have loads greater than 0.50; therefore, all were retained due to their importance to the construct.</p> <hd id="AN0177624875-28">Structural model assessment</hd> <p>Bootstrapping has been used to analyze the structural relationships between the constructs with 5000 samples, and the results indicate that all the relationships have been significant except for H4. The value of R square focuses on the in-sample predictive power of the model. The structural model is shown in Fig. 2, and the results in Table 11 explain 60.3% variance in satisfaction, which shows that the model has substantial sample predictive power (Hair et al., [<reflink idref="bib52" id="ref154">52</reflink>]).</p> <p>Graph: Fig. 2 Structural model with t-value and R2 value</p> <p>Table 11 Assessment of structural model</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Sr. No</p></th><th align="left"><p>Hypothesized path</p></th><th align="left"><p>β-value</p></th><th align="left"><p>T-statistics</p></th><th align="left"><p>p-value</p></th><th align="left"><p>95% confidence interval</p></th><th align="left"><p>VIF</p></th><th align="left"><p>Effect size (f<sup>2</sup>)</p></th><th align="left"><p>p-value (f<sup>2</sup>)</p></th><th align="left"><p>R<sup>2</sup></p></th><th align="left"><p>Q<sup>2</sup></p></th></tr></thead><tbody><tr><td align="left"><p>1.</p></td><td char="." align="char"><p>H1 = Learner-instructor interaction > satisfaction</p></td><td char="." align="char"><p>0.059</p></td><td char="." align="char"><p>2.241</p></td><td align="left"><p>0.028</p></td><td align="left"><p>[0.006, 0.111]</p></td><td char="." align="char"><p>2.203</p></td><td char="." align="char"><p>0.004</p></td><td align="left"><p>0.301</p></td><td char="." align="char"><p>0.603*</p></td><td char="." align="char"><p>0.476</p></td></tr><tr><td align="left"><p>2.</p></td><td char="." align="char"><p>H2 = Learner-learner interaction > satisfaction</p></td><td char="." align="char"><p>0.069</p></td><td char="." align="char"><p>2.660</p></td><td align="left"><p>0.008</p></td><td align="left"><p>[0.018, 0.121]</p></td><td char="." align="char"><p>2.363</p></td><td char="." align="char"><p>0.005</p></td><td align="left"><p>0.211</p></td><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>3.</p></td><td char="." align="char"><p>H3 = Learner-content interaction > satisfaction</p></td><td char="." align="char"><p>0.672</p></td><td char="." align="char"><p>32.708</p></td><td align="left"><p>0</p></td><td align="left"><p>[0.63, 0.721]</p></td><td char="." align="char"><p>1.849</p></td><td char="." align="char"><p>0.615</p></td><td align="left"><p>0</p></td><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>4.</p></td><td char="." align="char"><p>H4 = Internet self-efficacy > satisfaction</p></td><td char="." align="char"><p>− 0.005</p></td><td char="." align="char"><p>0.307</p></td><td align="left"><p>0.758</p></td><td align="left"><p>[− 0.038, 0.029]</p></td><td char="." align="char"><p>1.150</p></td><td char="." align="char"><p>0.00</p></td><td align="left"><p>0.949</p></td><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>5.</p></td><td char="." align="char"><p>H5 = Self-regulated learning > satisfaction</p></td><td char="." align="char"><p>0.047</p></td><td char="." align="char"><p>2.370</p></td><td align="left"><p>0.018</p></td><td align="left"><p>[0.01, 0.088]</p></td><td char="." align="char"><p>1.374</p></td><td char="." align="char"><p>0.04</p></td><td align="left"><p>0.274</p></td><td char="." align="char" /><td char="." align="char" /></tr></tbody></table> </ephtml> </p> <p>*Significant at 0.05 level of significance</p> <p>The model's predictive accuracy is assessed by calculating Q<sups>2</sups>, and this matrix is based on the blindfolding procedure (Hair et al., 2019a) The blindfolding procedure with seven omitted distance has been used to calculate the model's predictive power. The Q<sups>2</sups> value of the path model for satisfaction is 0.476, which is near 0.50, depicting almost medium predictive relevance (Hair et al., 2019a).</p> <p>We examined the impact of each predictor variable on student satisfaction with ERT through their path coefficient and effect size (Table 11). Learner-content interaction, learner-learner interaction, learner-instructor interaction and self-regulating learning have a significant, positive effect on satisfaction with ERT (ß = 0.672, ß = 0.069, ß = 0.059 and ß = 0.47, respectively). The impact of internet self-efficacy was insignificant. Furthermore, considering Cohen's (1988) limits, we observed that learner-content interaction has a large impact on satisfaction with ERT, while the remaining constructs have little or very little impact.</p> <p>R2 results showed that the model had significant in-sample predictive power, but they did not demonstrate anything about the out-sample predictive power of the model (Shmueli, [<reflink idref="bib109" id="ref155">109</reflink>]). In smartPLS software, the PLSpredict procedure is used to make out-sample predictions by generating holdout sample-based predictions (Hair et al., [<reflink idref="bib52" id="ref156">52</reflink>]). PLSpredict performs k-fold cross-validation. A fold is a subgroup of the total sample, where k is the number of subgroups. It means the complete data set is randomly divided into k equally sized data subsets (Hair et al., [<reflink idref="bib52" id="ref157">52</reflink>]). As recommended by Shmueli et al. ([<reflink idref="bib110" id="ref158">110</reflink>]), PLSpredict with tenfold (90% training sample and 10% testing sample), and 10 repetitions have been run to calculate the predictive relevance of the model. PLSpredict procedure is based on statistics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), which are used to quantify the amount of prediction errors (Shmueli et al., [<reflink idref="bib110" id="ref159">110</reflink>]). According to the interpretation procedure, initially predicted errors were examined for symmetric distribution, but in this case, the prediction errors followed a non-symmetric distribution. When the predicted errors follow a symmetric distribution, the RMSE values are compared to the naive benchmark (Hair et al., [<reflink idref="bib52" id="ref160">52</reflink>]), and in the case of a non-symmetric distribution, as in the present case, the MAE values are used for the prediction taken into account (Shmueli et al., [<reflink idref="bib110" id="ref161">110</reflink>]). The recommended naïve benchmark uses a linear regression (LM) model to generate predictions for the manifest variables by performing a linear regression of each dependent construct indicator on the exogenous latent variable indicators in the PLS path model (Danks & Ray, [<reflink idref="bib32" id="ref162">32</reflink>]). As can be seen in Table 12, all MAE values in the PLS-SEM model are lower than the linear regression model (LM) values, and all the indicators yield Q<sups>2</sups><subs>predict</subs> values higher than zero, indicating that the model has good predictive power.</p> <p>Table 12 Results of PLSpredict</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>PLS-MAE</p></th><th align="left"><p>LM-MAE</p></th><th align="left"><p>PLS-MAE-</p><p>LM-MAE</p></th><th align="left"><p>Q<sup>2</sup>_predict</p></th></tr></thead><tbody><tr><td align="left"><p>S_4</p></td><td char="." align="char"><p>0.656</p></td><td align="left"><p>0.672</p></td><td align="left"><p>− 0.016</p></td><td align="left"><p>0.469</p></td></tr><tr><td align="left"><p>S_1</p></td><td char="." align="char"><p>0.636</p></td><td align="left"><p>0.649</p></td><td align="left"><p>− 0.013</p></td><td align="left"><p>0.518</p></td></tr><tr><td align="left"><p>S_2</p></td><td char="." align="char"><p>0.615</p></td><td align="left"><p>0.622</p></td><td align="left"><p>− 0.007</p></td><td align="left"><p>0.526</p></td></tr><tr><td align="left"><p>S_5</p></td><td char="." align="char"><p>0.761</p></td><td align="left"><p>0.78</p></td><td align="left"><p>− 0.019</p></td><td align="left"><p>0.395</p></td></tr><tr><td align="left"><p>S_3</p></td><td char="." align="char"><p>0.662</p></td><td align="left"><p>0.669</p></td><td align="left"><p>− 0.007</p></td><td align="left"><p>0.47</p></td></tr><tr><td align="left"><p>SLR_2</p></td><td char="." align="char"><p>1.012</p></td><td align="left"><p>1.016</p></td><td align="left"><p>− 0.004</p></td><td align="left"><p>0.158</p></td></tr><tr><td align="left"><p>SLR_5</p></td><td char="." align="char"><p>1.012</p></td><td align="left"><p>1.028</p></td><td align="left"><p>− 0.016</p></td><td align="left"><p>0.095</p></td></tr><tr><td align="left"><p>SLR_3</p></td><td char="." align="char"><p>1.069</p></td><td align="left"><p>1.089</p></td><td align="left"><p>− 0.02</p></td><td align="left"><p>0.124</p></td></tr><tr><td align="left"><p>SLR_9</p></td><td char="." align="char"><p>0.999</p></td><td align="left"><p>1.021</p></td><td align="left"><p>− 0.022</p></td><td align="left"><p>0.079</p></td></tr><tr><td align="left"><p>SLR_4</p></td><td char="." align="char"><p>1.085</p></td><td align="left"><p>1.114</p></td><td align="left"><p>− 0.029</p></td><td align="left"><p>0.094</p></td></tr><tr><td align="left"><p>SLR_10</p></td><td char="." align="char"><p>0.98</p></td><td align="left"><p>1</p></td><td align="left"><p>− 0.02</p></td><td align="left"><p>0.096</p></td></tr><tr><td align="left"><p>SLR_12</p></td><td char="." align="char"><p>1.064</p></td><td align="left"><p>1.07</p></td><td align="left"><p>− 0.006</p></td><td align="left"><p>0.115</p></td></tr><tr><td align="left"><p>SLR_11</p></td><td char="." align="char"><p>1.033</p></td><td align="left"><p>1.037</p></td><td align="left"><p>− 0.004</p></td><td align="left"><p>0.154</p></td></tr><tr><td align="left"><p>SLR_6</p></td><td char="." align="char"><p>1.029</p></td><td align="left"><p>1.04</p></td><td align="left"><p>− 0.011</p></td><td align="left"><p>0.149</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>PLS-MAE</emph> partial least square-based mean absolute error, <emph>LM-MAE</emph> linear regression model-based mean absolute error</p> <p>Mediation analysis unfolds the mechanism that underlies an observed relationship between variables (MacKinnon et al., [<reflink idref="bib84" id="ref163">84</reflink>]). It has recently gained popularity due to its ability to provide deep insight into multiple perspectives, often at odds and implicit (Agler & De Boeck, [<reflink idref="bib2" id="ref164">2</reflink>]). Therefore, considering the context of the study and the inconsistent results of review studies, mediating effect of self-regulated learning has been studied. Mediation results presented in Table 13 show that learner-learner interaction (B = 0.069, p < 0.05) and learner-instructor interaction (B = 0.059, p < 0.05) positively predict students' satisfaction with ERT as the values of path coefficients for direct effect are positive and significant. This means that learner interaction with the instructor and other learners is crucial in achieving student satisfaction. Analysis of indirect effect analysis shows that self-regulated learning significantly mediates the relationship between learner-learner interaction and satisfaction (B = 0.008, p < 0.05) and learner-instructor interaction (B = 0.014, p < 0.05). Hence, in Path 1 & Path 2, both direct and indirect effects are significant; therefore, both paths show partial mediation. However, in the case of partial mediation, it is recommended that variance accounted for (VAF) should be calculated for the mediating construct (self-regulated learning) (Hair et al., [<reflink idref="bib49" id="ref165">49</reflink>]). Since VAF values for Path 1 and Path 2 are less than 0.20 (Table 13); thus, it is equal to no mediation. In other words, we do not have enough evidence to claim the mediating role of self-regulated learning in Path 1 and Path 2. However, if we look at Path 3, mediation analysis was performed to assess the mediating role of self-regulated learning in the linkage between internet self-efficacy and satisfaction with ERT. The results (see Table 13) revealed that the direct effect of internet self-efficacy on satisfaction is insignificant (B = − 0.005, p > 0.05), and the indirect effect of internet self-efficacy on satisfaction through self-regulated learning is significant (B = 0.007, p < 0.05), indicating full mediation. Hence, it can be said that self-regulated learning fully mediates the relationship between internet self-efficacy and satisfaction.</p> <p>Table 13 Mediation result</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Path</p></th><th align="left"><p>Relationships</p></th><th align="left"><p>Direct effect</p></th><th align="left"><p>Indirect effect</p></th><th align="left"><p>VAF</p></th><th align="left"><p>Result</p></th></tr></thead><tbody><tr><td align="left"><p>H6 (Path 1)</p></td><td align="left"><p>Learner- learner interaction > self-regulated learning > satisfaction</p></td><td char="." align="char"><p>0.069*</p></td><td char="." align="char"><p>0.008*</p></td><td char="." align="char"><p>0.10</p></td><td align="left"><p>No mediation</p></td></tr><tr><td align="left"><p>H7 (Path 2)</p></td><td align="left"><p>Learner-instructor interaction > self-regulated learning > satisfaction</p></td><td char="." align="char"><p>0.059*</p></td><td char="." align="char"><p>0.014*</p></td><td char="." align="char"><p>0.18</p></td><td align="left"><p>No mediation</p></td></tr><tr><td align="left"><p>H8 (Path 3)</p></td><td align="left"><p>Internet self-efficacy > self-regulated learning > satisfaction</p></td><td char="." align="char"><p>− 0.005</p></td><td char="." align="char"><p>0.007*</p></td><td char="." align="char" /><td align="left"><p>Full mediation</p></td></tr></tbody></table> </ephtml> </p> <p>*Significant at 0.05 level of significance</p> <hd id="AN0177624875-29">Artificial neural network (ANN) analysis</hd> <p>We used a dual-stage SEM-ANN technique to predict the antecedents of student satisfaction with ERT. A two-stage multi-analytical SEM-ANN technique has been chosen for several reasons. Firstly, ANN is a robust technique against noise and can be applied for non-normal data distribution. It can also accommodate a non-compensatory model (Leong et al., [<reflink idref="bib75" id="ref166">75</reflink>]) and even capture both linear as well as non-linear relationships. SEM is more suitable for discovering linear relationships in a compensating model, wherein a drop in one component is assumed to be balanced by an increase in other components in the mathematical model. However, this may oversimplify complex human decision-making processes that do not always follow a linear equation (Chong, [<reflink idref="bib27" id="ref167">27</reflink>]). ANN provides additional verification of SEM results and enables in-depth analysis by providing the relative importance of predictor variables (Sternad Zabukovšek et al., [<reflink idref="bib112" id="ref168">112</reflink>]). However, if used alone, the artificial neural network has its own limitations. One issue with neural networks is their "black box" approach, making them unsuitable for testing hypotheses and evaluating causal relationships (Chong, [<reflink idref="bib27" id="ref169">27</reflink>]). It is usually challenging for academics to grasp how neural networks arrive at their results when analyzing a model because of the black-box approach (Stern, [<reflink idref="bib111" id="ref170">111</reflink>]). Therefore, both ANN and SEM were used together to compensate for each other's limitations.</p> <p>This study first employed PLS-SEM to analyse the overall research model and hypotheses. Then, based on the PLS framework, significant variables were used as input to the neural network. This was done to overcome the model overfitting issue, a major weakness of neural networks when used for new data sets (Chong, [<reflink idref="bib27" id="ref171">27</reflink>]). We applied a multilayer perceptron neural network training algorithm with ten hidden nodes because it was complex enough to map the datasets without introducing additional errors into the neural network model. A neural network has three layers: an input layer, a hidden layer and an output layer. The input layer included the five independent significant factors from the SEM (learner-instructor interaction, learner-learner interaction, learner-content interaction, internet self-efficacy and self-regulated learning), while the output layer included one output variable (satisfaction). The hidden layer takes input from one layer and passes the output to another layer. In the present study, the hidden layer uses the Hyperbolic Tangent activation function (function takes any value as input and output value in the range of − 1 to 1) because it typically performs better than the logistic sigmoid. In a neural network, an activation function defines how the weighted sum of the input is transformed into an output from a node or nodes in a network layer (Brownlee, [<reflink idref="bib20" id="ref172">20</reflink>]). The output layer uses identity or linear activation function because our model is regression-based (Brownlee, [<reflink idref="bib20" id="ref173">20</reflink>]).</p> <p>This study used tenfold cross-validation, a typical procedure that used 90% of the data to train the neural network and the remaining 10% to assess the prediction accuracy of the trained network. tenfold cross-validation would repeat the fitting procedure ten times to estimate out-of-sample error and make results free from any kind of sample bias. The validations' root mean square error (RMSE) values are measured to calculate the ANN model's predictive accuracy (Liébana-Cabanillas et al., [<reflink idref="bib77" id="ref174">77</reflink>]). According to Table 14, the training model's average cross-validated RMSE was 0.454, whereas the testing model was 0.427. Smaller and closer values of RMSE for training and testing data sets indicate higher prediction accuracy of the ANN model (Leong et al., [<reflink idref="bib75" id="ref175">75</reflink>]). RMSE values for the training and testing models indicate that the network model is reliable and accurately represents the numerical relationships between predictors and outputs.</p> <p>Table 14 RMSE values</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Network</p></th><th align="left"><p>Sample (training)</p></th><th align="left"><p>Sample (testing)</p></th><th align="left"><p>SSE (training)</p></th><th align="left"><p>SSE (testing)</p></th><th align="left"><p>RMSE (training)</p></th><th align="left"><p>RMSE (testing)</p></th><th align="left"><p>RMSE (training)-RMSE (testing)</p></th><th align="left"><p>Total sample size</p></th></tr></thead><tbody><tr><td align="left"><p>1</p></td><td align="left"><p>1410</p></td><td align="left"><p>180</p></td><td char="." align="char"><p>296.391</p></td><td char="." align="char"><p>28.852</p></td><td char="." align="char"><p>0.458</p></td><td char="." align="char"><p>0.400</p></td><td char="." align="char"><p>0.058</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>2</p></td><td align="left"><p>1437</p></td><td align="left"><p>153</p></td><td char="." align="char"><p>278.922</p></td><td char="." align="char"><p>38.927</p></td><td char="." align="char"><p>0.441</p></td><td char="." align="char"><p>0.504</p></td><td char="." align="char"><p>− 0.064</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>3</p></td><td align="left"><p>1423</p></td><td align="left"><p>167</p></td><td char="." align="char"><p>303.461</p></td><td char="." align="char"><p>29.369</p></td><td char="." align="char"><p>0.462</p></td><td char="." align="char"><p>0.419</p></td><td char="." align="char"><p>0.042</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>4</p></td><td align="left"><p>1434</p></td><td align="left"><p>156</p></td><td char="." align="char"><p>298.602</p></td><td char="." align="char"><p>24.234</p></td><td char="." align="char"><p>0.456</p></td><td char="." align="char"><p>0.394</p></td><td char="." align="char"><p>0.062</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>5</p></td><td align="left"><p>1423</p></td><td align="left"><p>167</p></td><td char="." align="char"><p>285.923</p></td><td char="." align="char"><p>35.515</p></td><td char="." align="char"><p>0.448</p></td><td char="." align="char"><p>0.461</p></td><td char="." align="char"><p>− 0.013</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>6</p></td><td align="left"><p>1420</p></td><td align="left"><p>170</p></td><td char="." align="char"><p>286.681</p></td><td char="." align="char"><p>28.758</p></td><td char="." align="char"><p>0.449</p></td><td char="." align="char"><p>0.411</p></td><td char="." align="char"><p>0.038</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>7</p></td><td align="left"><p>1427</p></td><td align="left"><p>163</p></td><td char="." align="char"><p>304.65</p></td><td char="." align="char"><p>30.033</p></td><td char="." align="char"><p>0.462</p></td><td char="." align="char"><p>0.429</p></td><td char="." align="char"><p>0.033</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>8</p></td><td align="left"><p>1439</p></td><td align="left"><p>151</p></td><td char="." align="char"><p>299.93</p></td><td char="." align="char"><p>21.37</p></td><td char="." align="char"><p>0.457</p></td><td char="." align="char"><p>0.376</p></td><td char="." align="char"><p>0.080</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>9</p></td><td align="left"><p>1419</p></td><td align="left"><p>171</p></td><td char="." align="char"><p>302.59</p></td><td char="." align="char"><p>32.143</p></td><td char="." align="char"><p>0.462</p></td><td char="." align="char"><p>0.434</p></td><td char="." align="char"><p>0.028</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left"><p>10</p></td><td align="left"><p>1439</p></td><td align="left"><p>151</p></td><td char="." align="char"><p>287.304</p></td><td char="." align="char"><p>29.034</p></td><td char="." align="char"><p>0.447</p></td><td char="." align="char"><p>0.438</p></td><td char="." align="char"><p>0.008</p></td><td char="." align="char"><p>1590</p></td></tr><tr><td align="left" colspan="3"><p>Mean</p></td><td char="." align="char"><p>294.445</p></td><td char="." align="char"><p>29.824</p></td><td char="." align="char"><p>0.454</p></td><td char="." align="char"><p>0.427</p></td><td char="." align="char"><p>0.027</p></td><td char="." align="char" /></tr><tr><td align="left" colspan="3"><p>Standard deviation</p></td><td char="." align="char"><p>8.9</p></td><td char="." align="char"><p>5.01</p></td><td char="." align="char"><p>0.007</p></td><td char="." align="char"><p>0.037</p></td><td char="." align="char"><p>0.013</p></td><td char="." align="char" /></tr></tbody></table> </ephtml> </p> <p> <emph>SSE</emph> sum square of errors, <emph>RMSE</emph> root mean square of errors</p> <p>Sensitive analysis was used to determine the strength of each input neuron and assess the role of each predictor in the development of satisfaction with ERT. We calculated each input variable's average relative importance and normalized importance to perform a sensitivity analysis. The average importance was calculated using the importance results of ten networks for each variable. The normalized importance of each input variable was obtained by dividing the relative importance of each input neuron by the maximum importance in percentage form (Leong et al., [<reflink idref="bib76" id="ref176">76</reflink>]). As shown in Table 15, based on the order of importance, learner-content interaction (most significant), learner-instructor interaction, learner-learner interaction, self-regulated learning, and internet self-efficacy are important predictors in the ten networks. The results of normalized importance to ANN confirmed PLS-SEM results (Table 11) regarding learner-content interaction, self-regulated learning, and internet self-efficacy. Both analyses found learner-content interaction (SEM (ß = 0.672) and ANN (average importance = 0.62)) as the most influential factor and internet self-efficacy as the least important for student satisfaction with ERT. However, little variation was observed in the case of learner-learner interaction (second most important factor according to PLS-SEM results) and learner-trainer interaction (second most important factor according to ANN results). The difference in results between the ANN and SEM analyses can be explained by the non-compensatory, non-linear and higher prediction accuracy of neural network models Fig. 3.</p> <p>Table 15 Sensitivity Analysis</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Neural Network (NN)</p></th><th align="left"><p>NN (I)</p></th><th align="left"><p>NN (II)</p></th><th align="left"><p>NN (III)</p></th><th align="left"><p>NN (IV)</p></th><th align="left"><p>NN (V)</p></th><th align="left"><p>NN (VI)</p></th><th align="left"><p>NN (VII)</p></th><th align="left"><p>NN (VIII)</p></th><th align="left"><p>NN (IX)</p></th><th align="left"><p>NN (X)</p></th><th align="left"><p>Average Importance</p></th><th align="left"><p>Normalized Importance (%)</p></th></tr></thead><tbody><tr><td align="left"><p>LCI</p></td><td char="." align="char"><p>0.703</p></td><td char="." align="char"><p>0.678</p></td><td char="." align="char"><p>0.437</p></td><td char="." align="char"><p>0.698</p></td><td char="." align="char"><p>0.651</p></td><td char="." align="char"><p>0.647</p></td><td char="." align="char"><p>0.634</p></td><td char="." align="char"><p>0.659</p></td><td char="." align="char"><p>0.581</p></td><td char="." align="char"><p>0.535</p></td><td char="." align="char"><p>0.6223</p></td><td char="." align="char"><p>100.00</p></td></tr><tr><td align="left"><p>LII</p></td><td char="." align="char"><p>0.177</p></td><td char="." align="char"><p>0.138</p></td><td char="." align="char"><p>0.152</p></td><td char="." align="char"><p>0.058</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>0.104</p></td><td char="." align="char"><p>0.121</p></td><td char="." align="char"><p>0.091</p></td><td char="." align="char"><p>0.266</p></td><td char="." align="char"><p>0.234</p></td><td char="." align="char"><p>0.1481</p></td><td char="." align="char"><p>23.80</p></td></tr><tr><td align="left"><p>LLI</p></td><td char="." align="char"><p>0.1</p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p>0.196</p></td><td char="." align="char"><p>0.081</p></td><td char="." align="char"><p>0.124</p></td><td char="." align="char"><p>0.152</p></td><td char="." align="char"><p>0.159</p></td><td char="." align="char"><p>0.166</p></td><td char="." align="char"><p>0.056</p></td><td char="." align="char"><p>0.119</p></td><td char="." align="char"><p>0.1183</p></td><td char="." align="char"><p>19.01</p></td></tr><tr><td align="left"><p>SLR</p></td><td char="." align="char"><p>0.015</p></td><td char="." align="char"><p>0.146</p></td><td char="." align="char"><p>0.164</p></td><td char="." align="char"><p>0.102</p></td><td char="." align="char"><p>0.053</p></td><td char="." align="char"><p>0.074</p></td><td char="." align="char"><p>0.038</p></td><td char="." align="char"><p>0.026</p></td><td char="." align="char"><p>0.075</p></td><td char="." align="char"><p>0.097</p></td><td char="." align="char"><p>0.079</p></td><td char="." align="char"><p>12.69</p></td></tr><tr><td align="left"><p>ISE</p></td><td char="." align="char"><p>0.006</p></td><td char="." align="char"><p>0.008</p></td><td char="." align="char"><p>0.051</p></td><td char="." align="char"><p>0.061</p></td><td char="." align="char"><p>0.031</p></td><td char="." align="char"><p>0.023</p></td><td char="." align="char"><p>0.048</p></td><td char="." align="char"><p>0.058</p></td><td char="." align="char"><p>0.023</p></td><td char="." align="char"><p>0.015</p></td><td char="." align="char"><p>0.0324</p></td><td char="." align="char"><p>5.21</p></td></tr></tbody></table> </ephtml> </p> <p>Graph: Fig. 3 Artificial neural network.hidden layer activation function-hyperbolic tangent, output layer activation function-Identity, ISL internet self-efficacy, LCI learner content interaction, LII learner instructor interaction, LLI learner learner interaction, SRL self regulated learning</p> <hd id="AN0177624875-30">Discussion</hd> <p>This study aimed to find out key factors predicting students' satisfaction with ERT. Based on theoretical evidence, a model was constituted by integrating learner-learner interaction, learner-content interaction, learner-instructor interaction, self-regulated learning, internet self-efficacy and student satisfaction. This study used a two-stage SEM-ANN approach to analyse the propositional model. In addition, t-test and ANOVA were used to gain better insight into the influence of demographics on the variables under study.</p> <p>Student satisfaction is an important learning trait as it impacts academic achievement and engagement in online classrooms and can prove helpful in preventing school dropouts, especially in the current pandemic. The study shows that gender substantially impacted learner-learner interaction, learner-instructor interaction, self-regulated learning, and internet self-efficacy. Female students have shown better self-regulated learning, learner-learner interaction, and learner-instructor interaction, while males have shown better internet self-efficacy. Better self-regulated learning among females may be attributed to the fact that females have better emotional self-regulation (Haron et al., [<reflink idref="bib54" id="ref177">54</reflink>]) and mood management (Liu et al., [<reflink idref="bib81" id="ref178">81</reflink>]). Furthermore, female differences in interaction may be explained by the fact that they attach more value to the pastoral aspect of tutoring and value the convenience of being able to chat with other students without having to meet them in person or use the phone (Price, [<reflink idref="bib98" id="ref179">98</reflink>]) whereas, men use the internet more for leisure activities (Weiser, [<reflink idref="bib120" id="ref180">120</reflink>]). Results regarding internet self-efficacy are in line with previous findings (Chang et al., [<reflink idref="bib23" id="ref181">23</reflink>]; Peng et al., [<reflink idref="bib94" id="ref182">94</reflink>]; Wu et al., [<reflink idref="bib125" id="ref183">125</reflink>]), and a possible explanation could be that internet usage by boys is exploration oriented and they use the internet more frequently for leisure time (Weiser, [<reflink idref="bib120" id="ref184">120</reflink>]). Contrary to previous findings conducted before this pandemic, wherein female students had shown higher satisfaction with e-learning than male students (Yekefallah et al., [<reflink idref="bib128" id="ref185">128</reflink>]), both males and females have shown similar satisfaction levels with emergency remote teaching. The goal of identifying gender differences in an online learning environment is to ensure that both male and female differences are taken into account in course design.</p> <p>It has been observed that the students' locality does not influence interactions or satisfaction levels, but urban students have shown significantly better internet self-efficacy and self-regulated learning. This may be because urban students get better facilities and more opportunities to use online resources. Study results also confirm that the age of the respondents influences their interactions, self-regulated learning, and satisfaction level. The results align with earlier studies, wherein (Narimani et al., [<reflink idref="bib90" id="ref186">90</reflink>]) reported that satisfaction with e-learning increases with age. Internet self-efficacy remained unaffected by the age of respondents. Further, the study also analyzed the impact of hours spent online on all the variables under study. It has been observed that internet self-efficacy and self-regulated learning increased with hours spent, and rest remained unaffected. The results demonstrated that internet self-efficacy and self-regulated learning are optimum when students spend at least 16 h per week of online education.</p> <p>The relationship between the constructs in the hypothesized model was assessed using PLS-SEM analysis. In the first set of three hypotheses (H1, H2, and H3), it was anticipated that better learner-instructor, learner-learner interaction, and learner-content interaction enhance the satisfaction level of the learner with emergency remote teaching. These findings support the Community of Inquiry (COI) framework (one of the most extensively used in online teaching) created to investigate potential opportunities for interaction between students and instructors (Castellanos-Reyes, [<reflink idref="bib22" id="ref187">22</reflink>]). Like the COI framework, study results highlight the importance of teacher presence in the form of learner-instructor interaction and social presence in the form of learner-learner interaction as essential elements in determining a successful learning experience. The present study results are in tune with earlier findings that improving student satisfaction requires facilitating communication and engagement between the instructor and students and amongst students during e-learning (Aldhahi et al., [<reflink idref="bib5" id="ref188">5</reflink>]). Hence, the study results confirm the importance of all interactions for improving student satisfaction and agree with previous studies' results (Kuo et al., [<reflink idref="bib71" id="ref189">71</reflink>]; Kuo, [<reflink idref="bib69" id="ref190">69</reflink>]). These findings are particularly significant in light of the growing demand for online education and confirm the importance of learner-learner interaction, learner-content interaction and learner-instructor interaction in determining student satisfaction even during emergency remote teaching.</p> <p>Further, it was predicted in H4 that internet self-efficacy did not directly influence students' satisfaction levels, which supports previous results (Robles, [<reflink idref="bib104" id="ref191">104</reflink>]), but is quite contrary to the findings of Chu & Chu, [<reflink idref="bib28" id="ref192">28</reflink>] and Kuo et al., [<reflink idref="bib71" id="ref193">71</reflink>]. The research on the effect of internet self-efficacy on student satisfaction is limited and inconclusive, hence requiring further exploration. However, hypothesis H5 predicted the influence of self-regulated learning on satisfaction. Previous studies have reported mixed results in this context (Kuo et al., [<reflink idref="bib72" id="ref194">72</reflink>]; Kuo, [<reflink idref="bib69" id="ref195">69</reflink>]; Kuo & Kuo, [<reflink idref="bib70" id="ref196">70</reflink>]; Robles, [<reflink idref="bib104" id="ref197">104</reflink>]). Recent research in the area of the COI framework proposed learning presence as an additional factor that reflects students' self-regulated learning(Wertz, [<reflink idref="bib122" id="ref198">122</reflink>]). Similarly, the present study results also confirm the importance of self-regulated learning as a crucial factor in determining student satisfaction. Finally, H6 and H7 assumed the mediating role of self-regulated learning between the relationship of learner-learner interaction and learner-instructor interaction with satisfaction with ERT. In this regard, this study's findings suggested that self-regulated learning did not mediate these relationships. However, study findings related to hypothesis H8 observed full mediation by self-regulated learning in the relationship between internet self-efficacy and satisfaction levels of students with emergency remote teaching. This finding adds to what we know from previous studies. It means internet self-efficacy can positively influence the satisfaction level only in the presence of self-regulated learning. This finding is significant because it could explain why the results on the influence of internet self-efficacy on satisfaction in online education have been inconclusive. The authors have presented a model that unites learner-learner interaction, learner-instructor interaction, learner-content interaction, self-regulated learning, and internet self-efficacy with student satisfaction. This model proved that these five factors could explain 60.3% of the variability in satisfaction.</p> <p>The ANN analysis verifies the SEM results by confirming the model's predictive accuracy and providing insight into the relative importance of predictor variables, which can benefit educational technology research. ANN results complement the SEM results as PLS-SEM determines the significant predictors of ERT satisfaction, while the robustness of the ANN approach was used to confirm the results of PLS-SEM in terms of the model's predictive power. Similar results were produced in SEM and ANN models keeping learner-content interaction in the first position, self-regulated learning in the fourth, and internet self-efficacy in the last position. Slight variations were observed in the importance of learner-learner interaction and learner-instructor interaction. SEM model places learner-learner interaction in second place, while ANN places learner-instructor interaction at second place. Such results open doors for future researchers to study the phenomenon further.</p> <p>The ANN results confirm that out of these five factors, learner-content interaction (62.2%) is the most influential factor in determining student satisfaction with ERT, followed by learner-instructor interaction (14.8%), learner-learner interaction (11.83%), self-regulated learning (7.9%) and internet self-efficacy (3.2%). These findings align with results reported by other publications in the literature, wherein learner-content interaction is the most significant predictor of student satisfaction in tech-mediated instruction (Chejlyk, [<reflink idref="bib24" id="ref199">24</reflink>]; Keeler, [<reflink idref="bib65" id="ref200">65</reflink>]; Kuo et al., [<reflink idref="bib71" id="ref201">71</reflink>]). Hence, content quality, organization, document structure, and ease of access to content may impact their satisfaction (Kuo et al., [<reflink idref="bib71" id="ref202">71</reflink>]). This means that edu-tech designers need to design the content very carefully, as it is one of the most important factors in determining student satisfaction. Learner-content interaction is the most influential factor in emergency remote teaching like in distance education and asynchronous settings. Mediation analysis also confirms the importance of internet self-efficacy and self-regulated learning in emergency remote teaching. It adds to the new findings of self-regulated learning's mediating effect in the relationship between internet self-efficacy and student satisfaction. This paper presents valuable feedback to institutions and teachers on the extent to which different factors contribute to students' satisfaction levels. The relative importance of these predictor variables can prove beneficial in planning better online education programs.</p> <hd id="AN0177624875-31">Limitations</hd> <p>However, the following limitations persist in the present study and require additional research:</p> <p></p> <ulist> <item> The focus of this work is solely on student satisfaction with online learning based on student self-perception variables. Indeed, the views of teachers and parents have always been important. As a result, future research may examine satisfaction with online education from a different perspective.</item> <p></p> <item> Authors included only higher education students, and data collection was done through remote contacts of authors and their colleagues due to COVID-19 restrictions.</item> <p></p> <item> Furthermore, because the questionnaires were completed online, there was a possibility of not paying enough attention to completing the questionnaire.</item> <p></p> <item> This research examines the factors that predict students' satisfaction with synchronous online education during the COVID-19 pandemic.</item> </ulist> <hd id="AN0177624875-32">Conclusion</hd> <p>This research adds to the corpus of knowledge in several ways. This study examines the various factors that can predict student satisfaction with synchronous online instruction amid the global COVID-19 pandemic. The study reported that learner-content interaction, learner-instructor interaction, learner-learner interaction, self-regulated learning, and internet self-efficacy are important predictors of student satisfaction during ERT like distance education and asynchronous online education. The results of the SEM were further verified using ANN analysis for robustness. Given that learner-content interaction is the most potent predictor of student satisfaction, this study's practical implications are that instructional designers (instructional content designers), edtech designers (instructional interface and instructional interaction designers), and tech designers and developers (back-end software designers and programmers) should pay close attention to comprehensive design and structure. These findings are very important from the EdTech perspective. They suggest that Ed-tech designers need to pay additional attention to the design of the instructional interface and the instructional interaction. Clark and Mayer ([<reflink idref="bib30" id="ref203">30</reflink>]) also suggested that too much emphasis on technology may neglect the learner, and understanding how humans learn is essential for any instructional process to be successful. The learning process should be humanised to the best possible extent (Dhawan, [<reflink idref="bib34" id="ref204">34</reflink>]), as merely focusing on technology will not give desired results until the interaction of technology with the learner is not managed properly (Bhatt, [<reflink idref="bib16" id="ref205">16</reflink>]). Because the effectiveness of active learning is highly dependent on student motivation and engagement, it is extremely important that students recognize the benefits of engaging with course materials during active learning from the outset (Deslauriers et al., [<reflink idref="bib33" id="ref206">33</reflink>]).</p> <p>The study further explored the significant effect of self-regulated learning on student satisfaction. In this context, it is recommended that teachers should focus on improving pupils' self-regulation skills to improve satisfaction. Our study further demonstrates that self-regulated learning is critical since internet self-efficacy on satisfaction is fully mediated through self-regulated learning. This can also help in understanding the inconsistent results in the literature on the relationship between internet self-efficacy and satisfaction. Students should learn internet self-efficacy skills in order to succeed in online education (Wang et al., [<reflink idref="bib119" id="ref207">119</reflink>]), as perceived self-efficacy acts as an antecedent for online education acceptance(Lee & Mendlinger, [<reflink idref="bib73" id="ref208">73</reflink>]) and ultimately influences student satisfaction (Chang et al., [<reflink idref="bib23" id="ref209">23</reflink>]). Given the findings, it may be helpful for institutions to provide appropriate internet skills training to improve students' internet self-efficacy before implementing online courses. Since this study is cross-sectional, the study's outcomes may be limited to a definite point in time. Therefore, a study based on a longitudinal approach may be undertaken to explore the temporal effect.</p> <p>Further enhancement of the proposed model may enable us to find other factors influencing satisfaction. Despite its limitations, this study makes significant contributions and has crucial implications for teachers, educational management, school administrators, EdTech vendors and EdTech designers, as it proved the significance of learner-content interaction as the most important predictor of student satisfaction during emergency remote teaching. It is recommended that the content should be built in such a way that it complies with evidence-based instructional usability guidelines to improve the learner-user experience. The present study emphasized the importance of self-regulated learning during emergency remote teaching like in-class education (Paris & Paris, [<reflink idref="bib93" id="ref210">93</reflink>]). The findings also add to previous research by elucidating the role of self-regulated learning as a mediator in the relationship between internet self-efficacy and satisfaction. This means that internet self-efficacy can only lead to student satisfaction if self-regulated learning skills are present. This explains why few researchers have reported internet self-efficacy as a poor predictor of student satisfaction. We were able to provide a very accurate assessment of the relative (linear and nonlinear) effects of each construct by building neural networks into the PLS framework. Future research may use this technique as a powerful, refined tool for evaluating complicated structural models.</p> <hd id="AN0177624875-33">Acknowledgements</hd> <p>All study participants are greatly appreciated and thanked for their collaboration with the research team, which enabled this study to be performed.</p> <hd id="AN0177624875-34">Declarations</hd> <p></p> <hd id="AN0177624875-35">Conflict of interest</hd> <p>The authors declare that they have no conflict of interest.</p> <hd id="AN0177624875-36">Ethical approval</hd> <p>This article does not report on any studies with animals performed by any of the authors. However, ethical approval from the participants was taken at the time of filling out the questionnaire as a Google form.The research was carried out in conformity with the ethical principles outlined in the 1964 Declaration of Helsinki and its subsequent revisions or equivalent ethical norms.</p> <hd id="AN0177624875-37">Informed consent</hd> <p>Informed consent has been obtained at the time of filling out the questionnaire as a Google form.</p> <hd id="AN0177624875-38">Supplementary Information</hd> <p>Below is the link to the electronic supplementary material.</p> <p>Graph: Supplementary file1 (DOCX 18 KB)</p> <hd id="AN0177624875-39">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0177624875-40"> <title> References </title> <blist> <bibl id="bib1" idref="ref28" type="bt">1</bibl> <bibtext> Adekannbi JO, Ipadeola O. 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Her research interests are A two-staged SEM- artificial neural network approach for understanding and predicting the determinants of students' satisfaction with online education Networking, Data Science and Machine Learning.</p> <p>Anurag Sangwan Dr. Anju is currently working as an Assistant professor in the Department of Computer Science and Engineering, Guru Jambheshwar University of Science & Technology (GJUS&T), Hisar, India. She has received the M.Sc (Pure Mathematics) and M.Tech (IT) degrees from the Banathali University, Rajasthan, India in 2007 and 2009, respectively and Ph.D from Guru Jambheshwar University of Science & Technology, Hisar, India. She has total 14 publications in different journals and international conferences to her credits. Her area of interest is wireless communication and sensor networks.</p> <p>Anju Sangwan Er. Anurag Sangwan has done B.Tech. (Biotechnology) in 2006 from Kurukshetra University, Kurukshetra, India and M.Tech. (Nanotechnology) in 2009 from the National Institute of Technology, Kurukshetra, India. Presently, he is working as an Assistant Professor at UGC- Human Resource Development Centre, Guru Jambheshwar University of Science & Technology Hisar, Haryana since 2014 and pursuing a Ph.D. in Nano Science & Technology. He has more than 9 years of teaching experience. His research areas of interest are Teachers Education and Materials Science.</p> <p>Poonam Punia Dr. Poonam Punia is an Assistant Professor at the Department of Education, BPSMV, Khanpur Kalan, Sonepat, India. She received her PhD (Education) from BPS Mahila Vishawavidhalya, Khanpur Kalan, Sonepat, India, Master of Education and Masters of Science in Zoology (Gold Medal) from MDU, Rohtak, India in 2009 and 2005, respectively. She has more than 10 years of research and postgraduate teaching experience. She has published more than 25 research papers in journals of National and International repute. She has delivered more than 30 invited talks in seminars/ conferences/ orientation programs/ refresher courses. 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  Data: <searchLink fieldCode="AR" term="%22Anupma+Sangwan%22">Anupma Sangwan</searchLink><br /><searchLink fieldCode="AR" term="%22Anurag+Sangwan%22">Anurag Sangwan</searchLink><br /><searchLink fieldCode="AR" term="%22Anju+Sangwan%22">Anju Sangwan</searchLink><br /><searchLink fieldCode="AR" term="%22Poonam+Punia%22">Poonam Punia</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3560-2859">0000-0002-3560-2859</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Educational+Technology+Research+and+Development%22"><i>Educational Technology Research and Development</i></searchLink>. 2024 72(2):1249-1286.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 38
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Distance+Education%22">Distance Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Satisfaction%22">Student Satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Internet%22">Internet</searchLink><br /><searchLink fieldCode="DE" term="%22Interaction%22">Interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Universities%22">Universities</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Management%22">Self Management</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22India%22">India</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11423-023-10335-9
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1042-1629<br />1556-6501
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study seeks to address knowledge gaps regarding the role of self-regulated learning as a mediator in the relationship between interactions, internet self-efficacy, and student satisfaction. We conducted a survey of 1590 students from north Indian universities about their level of satisfaction, self-regulated learning, internet self-efficacy, and different interactions (learner-learner interaction, learner-content interaction, and learner-instructor interaction) during emergency remote teaching. By employing a two-stage SEM-ANN approach, this study contributes to methodological advancements and provides a comprehensive analysis of complex relationships. According to the findings, the identified factors are significant predictors of students' satisfaction with online education in synchronous settings. Our research also shows that self-regulated learning fully mediates the effect of internet self-efficacy on student satisfaction during emergency remote teaching. This suggests that internet self-efficacy alone may not guarantee student satisfaction unless accompanied by self-regulated learning skills.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1424604
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1424604
RecordInfo BibRecord:
  BibEntity:
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      – Type: doi
        Value: 10.1007/s11423-023-10335-9
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 38
        StartPage: 1249
    Subjects:
      – SubjectFull: Distance Education
        Type: general
      – SubjectFull: Student Satisfaction
        Type: general
      – SubjectFull: Self Efficacy
        Type: general
      – SubjectFull: Internet
        Type: general
      – SubjectFull: Interaction
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Universities
        Type: general
      – SubjectFull: Learning Strategies
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      – SubjectFull: Self Management
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      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: India
        Type: general
    Titles:
      – TitleFull: A Two-Staged SEM: Artificial Neural Network Approach for Understanding and Predicting the Factors of Students' Satisfaction with Emergency Remote Teaching
        Type: main
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              Y: 2024
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            – Type: issn-print
              Value: 1042-1629
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              Value: 1556-6501
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              Value: 72
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            – TitleFull: Educational Technology Research and Development
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