Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability

Abstract

E-learning is expected to become a common teaching and learning approach in educational institutions in the near future; thus, the success of e-learning initiatives must be ensured in order to make this a sustainable mode of learning. In order to improve students’ learning performance through the use of e-learning in Saudi Arabia’s higher education, it was the objective of this paper to examine the relationships between social cognitive theory and learning input factors and the reflective thinking and inquiry learning style as well as the indirect effects of student problem-solving and critical thinking skills. As a result, this study thoroughly assessed the social cognitive theory that is currently in use, along with learning input components and situational factors that should be carefully taken into account while introducing an online education system into Saudi Arabia’s top universities as a way of ensuring learning sustainability. As a result, 294 university students completed a questionnaire that served as the initial dataset for the research study, and the proposed conceptual model was comprehensively assessed using SEM. The research results demonstrated that the inquiry style of learning and reflective thinking have always had a significant impact on the social involvement, human engagement, social power, social identity, and social support. Similar findings were obtained regarding the impact of problem-solving and critical thinking skills on the inquiry-based learning approach and reflective thinking. Thus, students’ ability to learn in Saudi Arabia’s higher education is greatly influenced by their ability to solve problems and think critically. Therefore, it is almost certain that this research study will aid university policy makers in their decision on whether to fully deploy an online learning system as a way of ensuring learning sustainability at educational institutions throughout Saudi Arabia.

1. Introduction

Many institutions throughout the world have adopted e-learning systems due to the quick growth and development of e-learning as a way of sustaining education as well as the numerous advantages it offers. According to Bervell and Umar [1], e-learning has been widely implemented at higher academic levels over the past ten years. Many universities in Africa use learning management systems (LMS) of different types to supplement the standard face-to-face setting [2]. Between 2011 and 2016, the predicted growth rate for LMS adoption in Africa was estimated to be 15% yearly [3]. According to a recent prediction by Palvia et al. [4], e-learning will become widely used by 2025. Saudi Arabia places a strong priority on education. Saudi Arabia has attempted to enhance the educational process by incorporating computers into the curriculum for more than 25 years [5]. King Fahad University of Petroleum and Minerals was the first institution of higher learning in Saudi Arabia to be linked to the internet in 1993 [6]. By 2008, Saudi Arabia had created a national strategy for integrating IT into higher education [6]. As a result, the COVID-19 pandemic has not presented challenges for higher education while employing e-learning [7]. Universities in Saudi Arabia conducted most of the studies on teachers’ attitudes toward e-learning before or during the COVID-19 pandemic. Faculty members at King Abdulaziz University and Qassim University, according to Alkhalaf et al. [8], showed favorable attitudes toward e-learning. Hoq [9] looked into professors’ attitudes toward e-learning during the COVID-19 outbreak at the Jubail Industrial College’s Information and Management Engineering Department and found that they had favorable attitudes. Additionally, Almaghaslah and Alsayari [10] found that the College of Pharmacy at King Khalid University had a majority of faculty members who were supportive of e-learning. Prior to COVID-19, schools did not effectively use e-learning, despite several attempts. For instance, the Department of Education created the Future Gate platform in 2016 to gradually activate e-learning through 2020 [11].

However, this platform was not yet used successfully in many school systems by March 2020, when Saudi Arabia announced the shutdown of all institutions. At the time, “it was embracing about 3700 middle and high schools, left out more than 50% of middle and high schools, in addition to preschools and early childhood schools” [12]. As a result, the Education Ministry made courses asynchronously accessible through iEN channels [10]. The Madrasati Platform was then introduced by the Education ministry at the start of the 2020–2021 academic year. To learn how to utilize this platform, teachers were required to attend mandatory training sessions. E-learning is currently being used successfully in Saudi Arabia’s public schools because to the use of this platform [13].

Students are expected to achieve significant progress in their learning activities by using the f o system [9]. During the COVID-19 epidemic, using e-learning as the sole method of education and learning had both advantages and disadvantages, which have been covered in several publications. One of the obvious benefits of e-learning, for instance, is its flexibility regarding time and location [8].

E-learning improves IT skills, increasing society’s employability and industry competitiveness [14]. To sustainably raise the standard and level of pleasure with learning in society, e-learning and web-based collaborative learning technologies are essential [15]. Thus, through the implementation of sustainable education and the global expansion of open course resources and open courseware [16], Saudi Arabia is achieving sustainable development. Learning is an active process; knowledge is constructed actively rather than passively; knowledge is invented rather than discovered; knowledge is personal, socially cognitive, and socially constructed; learning is concerned with connecting with the outside world; and through robust learning, individual learners can solve challenging global problems [17]. As a result, the learning created via education and training combined with technological innovation will lead to the creation of sustainable employment, social empowerment, and social cognition, ultimately resulting in sustainable education.

Additionally, in [9], the authors said that e-learning enables teachers to distribute identical resources to every student, providing every student with an equal opportunity to learn. According to Niculescu-Aron et al. [18], the main benefit of e-learning is the removal of formal barriers, which is accomplished by removing physical barriers, providing flexibility, and fostering new kinds of relationships between professors and students. To learn from trustworthy internet information, a reader’s ability to assess the reliability of a source is crucial [19]. Therefore, it is crucial to understand whether students can access the appropriate information about their course of study during online learning to give them the quality and satisfaction they want.

1.1. Education for Sustainable Development and E-Learning

According to the sustainable development of a society, education is the cornerstone for developing a more sustainable society of individuals and incorporating sustainable development into the educational system at all levels. With the use of information and digital technology, education can now take place in an electronic environment as well as be a socio-cultural interaction between a teacher and a student.

Therefore, education sustainability enables students to acquire the knowledge, competencies, skills, and values required to engage in social interactions and learning [7]. Previous research [7,20] investigated the factors influencing the sustainability feasibility of smaller-scale e-learning initiatives in Saudi Arabia and New Zealand’s tertiary sectors. The results also offered a thorough review of recent developments in university-level e-learning sustainability challenges. Thus, E-Learning and Curriculum Sustainability [21], which contains principles, resources, and some admirable educational approaches, offers a thorough analysis of these problems. Thus, the implementation of e-learning in higher education appears to be sustainable. The methods and strategies utilized in e-learning and information and communications technology (ICT) are typically less involved than those utilized in conventional education [22]. Additionally, it was found that applying online learning promotes sustainability by efficiently reducing content demand and energy consumption [23]. Since there were few early attempts to adapt and employ e-learning, it is now difficult to ignore the shift in higher education toward e-learning to assure sustainability [21].

Intergenerational schooling for sustainability has been advanced by this technology-assisted e-learning paradigm, which has established a wider environment for learning at any time and from any location [24]. Currently, it is widely understood that higher education and sustainability are two concepts that are intertwined. In addition to its two traditional responsibilities of research and teaching, higher education has a responsibility and a crucial role to play in redefining education for the sake of sustainability. As a result, this study adds to the continuing discussion in the information society about the sustainability of online learning tools, and this is consistent with [25].

The goal of education for sustainable development, according to UNESCO [26], is to provide individuals with the information and skills necessary to solve economic, social, and environmental issues. It inspires students to consider issues including excessive consumerism, poverty, the promotion of solidarity and collaboration, and the realization that existing economic development trends are unsustainable, necessitating the need for initiatives that address these issues [27]. Thus, universities must act as a catalyst for educating students about sustainability and the adjustments it entails. Therefore, fostering attitudes that encourage reflection and critical thinking as well as incorporating sustainability-related concepts into the subject matter are top priorities in education [28]. The primary abilities for sustainable development are holistic understanding communication, collaborative skills, critical thinking, reflection, creativity, innovation, and entrepreneurship [29]. To develop these competencies, sustainability must be considered a continuous process. Students’ understanding of their roles in sustainable development and collaboration across disciplines for knowledge sharing should be encouraged by teachers and online [30]. In a study conducted by Azeteiro et al. at a university in Portugal [31], the authors discovered that e-learning in sustainability education can be very important for sustainable development.

1.2. Problem Background

All universities have switched to online learning since the COVID-19 epidemic in Saudi Arabia began to spread. However, there have been numerous and varied difficulties, which could negatively affect how both teachers and students perceive the online education and learning delivery method as a way of ensuring learning sustainability. These difficulties include maintaining a high level of involvement and interest [32] as well as internet outages and teachers’ lack of knowledge or experience with the use of various e-tools [9,20]. The traditional Saudi Arabian approach to education and training, in which teachers played a central role and had complete control over students [33], which unintentionally limited opportunities for active internet student participation [34], is another factor that frequently affects Saudi students’ engagement in online classes. It is true that e-learning technologies have become crucial in this pandemic. Similar conclusions were reached by Alhabeeb and Rowley [35], who found that academic staff members, through their expertise with educational technology, computer system users, and technological infrastructure, play a vital role in promoting effective e-learning at Saudi Arabian universities. Due to government mandates, universities in Saudi Arabia are utilizing online learning (e.g., geographic spread and female education).

Furthermore, it was discovered in [7] that Saudi universities are shifting from traditional face-to-face education toward sustainable e-learning. The Ministry of Higher Education faces significant obstacles in this area, including ethical and moral issues. To keep up with the trend internationally, the KSA Higher Education Ministry has been paying closer attention to e-learning sustainability [2]. To tackle the challenges facing upcoming cohorts, Saudi Arabia is expanding its educational goals and contributing significantly to global educational changes online [36,37]. As a result, higher education in Saudi Arabia is shifting away from traditional face-to-face instruction and toward more sustainable e-learning.

Therefore, there was a significant change toward online learning in many different countries around the world. The Saudi government is enacting strict regulations to stop the spread of COVID-19. Following the discovery of the first known COVID-19 case in the country, it ordered all schools and higher education institutions to close in March 2020 [38]. To ensure a safe learning environment, the Saudi Arabian Ministry of Education (MOE) ordered online classes. As a result, all HEIs, including medical institutions, switched to online education [39]. In accordance with the MOE’s directive, HEIs started employing digital tools, including Microsoft Teams and Zoom, to engage their students through online education [7,40]. To improve and maintain the quality of higher education, however, such a quick transition from traditional to online courses created the need to disclose the students’ feedback on the latter [40]. As a result, only a few studies [7,38,40,41,42,43] have examined how Saudi Arabian students feel about online learning. Upon examining the literature, that research showed how students in the English language and health sciences programs felt about online learning. Their equipment, however, was unable to gauge the pupils’ connection and participation. According to several of these studies, students who learn online encounter difficulties such as missing out on face-to-face interactions with classmates and teachers [38,42]. According to Sun and Chen [44], both teachers and students should play a significant role in fostering communication and teamwork to create a vibrant online learning community. Online programs such as Zoom, Collab, and Microsoft Teams provide features to promote active learning through student engagement and participation [40].

Furthermore, [7,36] advocated disclosing how technology use affects students’ academic achievements. In the KSA, e-learning is still in its infancy, and few studies have been conducted on the variables that affect academic performance in e-learning. There has been a lot written about the introduction of e-learning in Saudi universities [8,13], but there is a dearth of pertinent research on academic achievement. The problem of academic achievement can be serious and requires suitable tactics and instruments to address, but studies [45,46] also suggest that Saudi institutions are shifting away from traditional face-to-face learning toward e-learning.

However, no studies have looked at how social cognitive theory and learning input characteristics might be combined to influence how well students learn in the context of educational sustainability, which holds that social engagement and interaction have an impact on the inquiry learning style, critical thinking skills, and reflective thinking, which in turn have an impact on how well students learn through online learning. One of the key contributions of this research is the investigation of the integration of social cognitive theory with learning input elements and the use of e-learning as a sustainable strategy to impact student learning performance and educational sustainability. By bridging the gap between the acceptance of social cognitive theory and the learning input factors of using e-learning sustainably, the results may help managers and academics better understand how the use of e-learning systems affects problem-solving skills and critical thinking skills in student learning performance as well as educational sustainability in higher education. As a result, this paper suggests a new model to examine how social cognitive theory and learning input factors affect reflective thinking and inquiry learning styles as well as the indirect effects of students’ critical thinking and problem-solving skills, which in turn enhance students’ learning performance as a means of ensuring educational sustainability.

2. Research Hypotheses and Theoretical Framework

According to social cognition, people actively participate in their lives rather than being passively subjected to changes in their brains brought on by environmental circumstances. People use their sensory, motor, and cognitive systems as tools to carry out the tasks and accomplish the goals that give their lives direction and significance [47]. Social cognitive theory supports the emergent interactive agency hypothesis [48]. People are neither autonomous performers nor mechanical carriers of animating environmental stimuli. Mental events are brain activity, as opposed to immaterial entities that exist outside of neural systems. Perceived social effectiveness and social support have an influence on human adaptability and change in both positive and negative ways. A support network is not a self-forming force waiting in the wings to protect stressed-out individuals from pressures. Instead, individuals must go out and discover or construct supportive relationships for themselves, then be able to sustain such interactions. The circumstances that people build for themselves are more supportive when they are viewed as having high social efficacy [47]. However, materialism does not necessarily entail reductionism. In non-dualistic mysticism, thought processes emerge as brain activity that is not ontologically traceable [49].

According to the social cognitive perspective, social construction [50] and active social engagement [51] both have roles in how we see the environment. In order to make e-learning sustainable, social cognition emphasizes learning as a process rather than a result [52]. By integrating technology into training, education, and sustainable e-learning, this study makes a significant contribution to Saudi Arabia, the Middle East, and the rest of the globe. Since it has connected learning and knowledge building on a global scale, technological integration in higher education has been considered to have a good social impact on cognitive outcomes [53]. Students are committed lifelong learners who are capable of handling challenges in the real world as professionals [52]. The ability of students to apply their knowledge in the outside world, which they created via innovation and beneficial e-learning, will make them societal assets [54]. Social and cognitive outcomes from innovation in education result in the professional growth of both students and teachers [55]. Students build their understanding of specific social connectedness, cultural development, and real-world circumstances through active learning within the social cognitive framework [56]. As a result of this strategy, a school encourages the growth of scientific knowledge and proficiency using online learning resources as a way of ensuring learning sustainability. According to a recent study [7], inquiry-based education is among the most popular and effective teaching strategies utilized in online learning environments as a way of ensuring learning sustainability. The importance of reflection in e-learning has been researched as a key strategy for student growth since it is an important educational theme [36]. Problem solving calls for the use of various sources of data to come up with the best solution, and e-learning provides these sources thanks to its supportive learning environments. As a result, students promote the utilization of a relaxed setting with both digital and analogue resources [9]. Students’ learned problem-solving skills aid in achieving academic objectives and a higher class average. Additionally, these skills not only assist students in their college careers but also enable them to deal with and overcome challenges later in life. Furthermore, e-learning fosters productive learning environments for students, which is where critical thinking skills are best developed [2]. The use of e-learning to enhance students’ academic achievement in higher education was therefore investigated in this study by integrating social cognition theory with learner input elements. Human contact, social support, social identity, support networks, the inquiry learning style, critical reflection, problem-solving skills, critical thinking skills, and learning performance are the components developed in the study model (see Figure 1).

2.1. Social Engagement (SEN)

Participation in the formal (such as in a club or association) and informal (such as with a group of friends) communal activities of social groupings is referred to as social engagement [57]. For young adults, participating in social activities and belonging to a society are crucial problems because their sense of community may impact their interpersonal well-being, self-efficacy, and socialization [58]. Young adults can create social networks and obtain social support by participating in social activities [59].

Investigating social media usage in daily life can help lay the groundwork for a deeper investigation of the importance of social networking for engagement [60]. People now have the chance to engage in social events in far-off areas thanks to digital media, especially social media [61]. Researchers have looked at how digital media might integrate offline and online areas for social interaction [62]. Social media use has been shown to influence people’s communication practices by offering accessible and engaging aspects of a communication arena where many people from different origins are connected [63]. According to these earlier studies, it is feasible that since social media networks are built on interpersonal connections, people may be more interested in finding out about social gatherings that their friends post and may be inspired to socialize through the social media, which then, in turn, may encourage them to participate in social activities. The following hypotheses were suggested based on the discussion above:

H1. 

SEN is positively correlated with ILS.

H2. 

SEN is positively correlated with RT.

2.2. Social Interaction (SIT)

When instructors employ tactics to promote interpersonal support and social inclusion, this contact between learners and lecturers is referred to as “social interaction” [64]. Learner–learner, student, and learner–instructor social interactions are the three categories that [65] described. Whether teachers are present, learner-to-learner exchanges happen in a virtual environment [66]. Students’ perceptions of their academic achievement and engagement increase when they have access to information through a variety of platforms, including social media and online courses [67]. The term “learner-instructor interaction” refers to the exchange of information, the provision of appropriate assistance, the clarification of student misunderstandings, and the escalation of student excitement [68]. These three kinds of social contact play a significant role in gauging student satisfaction. The pleasure in the learning process rises when various forms of interaction are implemented in the educational environment [69]. By incorporating extracurricular activities within an academic program, many forms of contact can be formed.

The frequency, caliber, and promptness of pupil contact are the most important determinants of student satisfaction, even though student–student engagement is crucial for online students’ satisfaction [70]. Findings from a study of 120 student nurses obtaining online degrees found that knowing the teacher, choosing the evaluation techniques, and receiving prompt feedback from the teacher all influenced student satisfaction. These findings are congruent with those of [71].

When asked how well they knew their instructors, the respondents who offered the best answers said they actively participated in online discussions more often. These results demonstrate how crucial it is to foster student–teacher contact to foster active learning. The study [72] also found a statistically significant relationship between teacher comments on completed projects and learning goals, as determined by pupil satisfaction and total grades. This study involved 186 online graduates. These findings emphasize the value of student–instructor interaction in improving student performance and emphasize the significance of pleasure in online learning. The following hypotheses were suggested based on the discussion above:

H3. 

SIT is positively correlated with ILS.

H4. 

SIT is positively correlated with RT.

2.3. Social Influence (SIF)

Social influence is the process by which other people’s presence or activity affects a person’s beliefs, attitudes, or behavior. Compliance, obedience, conformity, and minority influence are the four components of social influence. The theory of reasoned action (TRA), created by Fishbein and Ajzen [73], serves as a framework for analyzing how social norms of identification, compliance, and conformity may affect behavior.

They referred to these social influences as “subjective norms” and demonstrated how they could be used to forecast conduct when combined with a person’s personal views. Subjective norms are the pressures pupils feel to accept the system from professors, other students, or significant figures in the learning environment. The perceived construct shows that students’ use and ownership of the online educational system are affected by others [74].

Based on earlier studies on the functions of teachers and peers in promoting good virtual learning, a study [75] discovered that subjective norms influence the frequency with which students use ICT in education. It has been demonstrated that a person’s decision to enroll in an online school is influenced by subjective standards established by superiors (such as parents or employers) [76].

The involvement of the teacher and social influence amongst students have an impact on course participation, student motivation, academic accomplishment, and viewpoints on virtual learning platforms, according to [77]. Peers play a significant role in influencing technology uptake and usage behaviors for e-learning, according to several studies. A study [78] looked at how peer pressure impacts academic performance and attitudes toward online learning. Shin’s research did not find evidence of a connection between peer influence and performance. Recent studies, however, show that students who strongly identify with their school mates are happier and much more likely to persist with online learning [79]. The following hypotheses were suggested based on the discussion above:

H5. 

SIF is positively correlated with ILS.

H6. 

SIF is positively correlated with RT.

2.4. Social Identity (SID)

Self-categorization theory and identity theory are both parts of the social identity [80,81]. A person’s self-concept regarding their membership in a social group might be characterized as their social identity [81]. People self-identify as belonging to a variety of social classes or groupings [82,83]. To organize and situate themselves in their social environments, individuals employ categories [84], a relative strategy that results in the identification of an in-group and out-groups [85]. A study [86] experimentally examined the connection between students’ social identities and their online learning performance and discovered that social identities are influenced by online learning performance.

In order to improve online learning results and pleasure, their research also emphasized the necessity of bolstering students’ social identities. Because social identification increases in-group homogeneity, social ties within a group as well as individual students’ commitment to learning, academic success, and satisfaction with their curriculum and organization all improve [87]. Students are more likely to be satisfied with their coursework and school if they achieve their educational goals [88]. Learning is an identification experience that influences a person and what they are capable of doing; hence, learning and societal identity are strongly intertwined [89]. When students initially enroll in college, they have an academic self-concept, or a belief in their own academic abilities. Students with high grade point averages have an academic self-concept that is linked to extraordinary goal achievement [90]. The social identity characteristics of postgraduate students who have a lengthy history of employment, such as junior or middle managers, are similar [91]. A person’s demeanor and interactions with students and teachers may be greatly influenced by their opinion of themselves as “proven” managers. The following hypotheses were suggested based on the discussion above:

H7. 

SID is positively correlated with ILS.

H8. 

SID is positively correlated with RT.

2.5. Social Support (SSU)

Social support is a three-part concept with numerous components. It is described as an action in which people engage in human interaction and undergo, perceive, and communicate emotion concern, useful assistance, or knowledge [92]. It was defined as “contact with others that gives pupils insight and great learning experiences” in a study [93]. Social support is “knowledge, evaluation, and psychological support which comes from a number of sources, including teachers, parents, friends, and coworkers that promotes student satisfaction,” according to Demaray et al. [94]. One of the most important and crucial aspects of intermediate study is social support. It is a crucial element that is frequently used in socio-educative research [95]. According to Bean’s research [96], close friends and coworkers help students integrate into society at their schools. According to a study [97], social support enhances intragroup and intergroup connections. Social support from peers or relatives is positively correlated with student satisfaction [98].

Students that have active social lives exhibit higher levels of contentment, according to [99]. Their quality of life increases when they are included in the campus social scene. According to a study [100], getting pupils involved in a variety of social activities promotes learning and helps them develop a happy outlook. The ability to speak for oneself, preserve autonomy, and develop relationships, all of which have a significant impact on a person’s life and learning, may be more challenging when there is a lack of social support. Both student–student and student–instructor interactions can be used to provide social support for online learners [101]. Institutions must therefore play a crucial part in helping students develop their personal interactions and practice social inclusion as they learn and develop. The following hypotheses were suggested based on the discussion above:

H9. 

SSU is positively correlated with ILS.

H10. 

SSY is positively correlated with RT.

2.6. Reflective Thinking (TR)

The students’ inquiry-based learning strategy makes use of a student-centered chemistry learning technique that focuses on the rate of a reaction [102]. The inquiry style of learning is a type of learning that encourages students to learn by doing and for which the teacher only provides support and facilitation [103]. The questions and concepts that are part of this learning approach might inspire pupils to come up with new ways to express what they have learned. What to believe and what steps to take are the main topics of critical thinking, which involves rational and reflective thinking [104].

Critical thinking also aids in issue solving and knowledge advancement. This learning technique ensures self-sufficiency, engagement, and the ability to solve problems based on knowledge and awareness [105] by having students actively investigate their information. Importantly, an inquiry-based learning strategy was created to teach students how to think critically and hone their critical thinking skills [106]. Additionally, by gathering facts and knowledge from diverse sources, the inquiry learning method develops critical thinking skills. Based on this knowledge, Müller et al. [107] examined the relationship between the inquiry learning style and critical thinking among physics students in an online learning environment and discovered a positive relationship between these factors. Based on prior research, a study of this association has not yet been conducted [49]. The following hypotheses were suggested based on the discussion above:

H11. 

RT is positively correlated with ILS.

H12. 

RT is positively correlated with PSS.

H13. 

RT is positively correlated with CTS.

2.7. Inquiry Learning Style (ILS)

The ability to gather and use information discovered online is a talent that is acquired through the problem-solving concept [108]. Students encounter several issues in e-learning inquiry-based learning that can be resolved with the help of online resources [109]. Because they gather information from a variety of sources to choose the best and most accurate answer to a problem, kids who learn via inquiry can build problem-solving skills [110]. Additionally, several researchers have discovered that students’ learning preferences influence their problem-solving skills. However, when trying to self-regulate their learning, pupils employ poor techniques and refrain from asking for assistance [111]. For instance, students who solve a problem without considering its aim may have trouble analyzing the sources of information and information outcomes [112]. In addition, Farahian et al. [113] investigated how inquiry learners solve an issue through their problem-solving skills, and the findings revealed that inquiry learners identify every potential answer to a problem before making the optimal selection. Investigating the impact of inquiry-based learning on problem-solving skills was recommended in [53,114], despite the paucity of research in this area. The following hypotheses were suggested based on the discussion above:

H14. 

ILS is positively correlated with PSS.

H15. 

ILS is positively correlated with CTS.

2.8. Problem-Solving Skills (PSS)

The introspective thinking style that individuals have chosen enables them to view the big picture and comprehend all the repercussions. Students perform in-depth study based on this, which ultimately aids in problem solving [115]. A recent study [116] looked at the relationship between reflective thinking and problem solving in the context of completing technology and science courses online. The study’s findings demonstrated that reflective thinking is a prerequisite for developing problem-solving skills when taking science and technology courses. Similar research was conducted by Toker and Akbay [117] on the effects of the introspective thinking style on students’ problem-solving skills and attitudes. The results indicated that reflective thinking skills modify and improve students’ problem-solving skills and attitudes as they engage in information gathering, which improves their problem-solving skills. In addition, Tanujaya et al. [118] studied the impacts of learning styles on college students and discovered that students have an impact on their ability to solve problems. Further research on the connection between reflective thinking and problem-solving skills in an online learning environment was recommended by Lu et al. [114]. The following hypotheses were suggested based on the discussion above:

H16. 

PSS is positively correlated with CTS.

H17. 

PSS is positively correlated with LP.

2.9. Critical Thinking Skills (CTS)

Critical and reflective thinking are both necessary for cooperative learning [119]. Thinking critically is rational and introspective thinking that is concerned with what to think and what to do. Critical thinking also aids in issue solving and knowledge advancement. English education includes critical thinking as a key component, including questioning, problem solving, and analysis [115]. One must develop sociability and contemplation because of the nature of rational reflection [117]. According to MacLeod et al. [116], reflective thinking necessitates critical thinking, and for that reason reflective thinking is linked to critical thinking skills. A reflective thinker also possesses strong critical thinking skills. According to studies, students’ efforts to develop their metacognitive learning style may have an impact on their critical thinking skills [120]. There have not been many studies that look into how reflective thinking affects critical thinking skills, especially in the context of the learning environment [114]. The following hypothesis was suggested based on the discussion above:

H18. 

CTS is positively correlated with LP.

2.10. Learning Performance (LP)

By predicting students’ achievement on future exams, lowering the likelihood that students will fail the course, and ensuring the accuracy of e-learning as a way of ensuring learning sustainability, the research of learning system performance gives teachers a foundation on which to modify their teaching techniques for classmates who may have problems. The results of numerous empirical studies examining the connection between e-learning behavior and learning ability have shown that students’ e-learning behavior has a significant influence on learning outcomes. As a result, learning system performance based on the process data has drawn a lot of interest recently. Teachers can change their teaching tactics early on and begin during their students’ learning processes by employing monitoring and early warning systems [121] using the measurement, gathering, and evaluation of learning process data to accomplish learning performance prediction [122].

In addition, data collection and utilization are more convenient for e-learning behavior, which has a significant impact on e-learning success [123]. Because of this, researchers have studied e-learning behavior in depth [124] and built various learning performance predictors depending on e-learning behavior [125]. Since predicting learning is the main benefit of machine learning techniques, they are frequently used to train models for predicting learning performance in a straightforward way [126]. Behavioral performance indicators and tendency indicators are typical e-learning performance predictors [127].

Learning behavior is a significant component determining academic achievement and a major indicator for forecasting learning performance, according to the learning input theory, which describes the connection between training, learning, and behavior performance [128,129]. Observing learning activities at a more granular level can help to improve the understanding of learning conditions and foster constructive learning, according to numerous studies that have found a strong association between pupil internet activities and academic performance [130]. Learning performance prediction has recently benefited from the application of numerous machine learning classification techniques.

3. Research Methodology

Education institutions have shown throughout the isolation time caused by the COVID-19 epidemic that information and communication technology in the classroom ensure the sustainability of education. Additionally, synchronous online communication between teachers and students is performed using video conferencing platforms, such as Zoom and Google, and chat during remote classes and the real-time e-learning process. Information and communication technology are thus used in education to support long-term educational sustainability. Consequently, this paper is devoted to a novel model by examining the interactions between social cognition theories and learning input elements in the reflective thinking and inquiry learning styles as well as the unexpected effects of students’ critical thinking and problem-solving skills.

By testing hypotheses, this study developed responses to its research questions using a quantitative survey approach. A questionnaire based on social cognition theory and learning input theory that was distributed via Google Forms was used to gather empirical data. The questionnaire was divided into three sections: The first asked about the demographics of the lecturers. The second asked about 45 statements from the learning input theory and was rated on a five-point scale with “strongly disagree” and “strongly agree” as the endpoints. It was decided to record usage information and experience in the third area. The questionnaire was completed by the respondents in a maximum of 20 min. To assess e-learning adoption as a way of ensuring learning sustainability in HEI settings, the items in the questionnaire were operationalized using the social cognitive theory and the learning input theory scale [47,48,55,56]. Many of the questions were closed-ended, but a handful were open-ended ones asking for comments on any topics that were not sufficiently covered.

To increase the questionnaire’s content validity, a pre-test was carried out. To assess the accuracy and relevance of the language used in the scale items, two university lecturers took part in this pre-test. After the changes, the questionnaire had a pilot test with two professors who were chosen at random to determine the validity and consistency of the scale items. The questionnaire met the criteria for content reliability and validity (Cronbach’s alpha > 0.7), indicating that it could be used to gather actual data. Five thousand registered students at King Faisal University were counted as the total population. Therefore, King Faisal University in Saudi Arabia was included in the sample, which was chosen using a straightforward random approach. The data were gathered in July 2022.

A mailer list was used to disseminate 300 online questionnaires for the primary study, with an expectation of a 90% response rate. However, 294 completed surveys were returned, indicating a 98% response rate, while six replies were excluded owing to missing data. Additionally, six surveys from the final study were eliminated because the respondents indicated they were unaware of or had no expertise with e-learning. The respondents were asked to rate their own experience with and present use of electronic learning. These two standards were used to choose the final sample. The other 294 records were reviewed for outliers. No univariate outliers were found; however, a small number of multivariate outliers were found and kept since all replies were provided on a five-point Likert scale. To make statistical inferences, 294 data points were taken into account.

Measurement Development

Primary data were gathered from those who were anticipated to exhibit excessive e-learning usage [131,132] and who widely embraced e-learning [133], using both manual and online questionnaire methods due to geographic restrictions, for faster dispersion, and to minimize the issue of invalid or incomplete information [134]. Nine concepts were assessed using a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly disagree) in order to study the adoption of e-learning.

These structures included social engagement, for which five items were modified from [57,62]; social interaction, for which five items were modified from [66,68]; social influence, for which five items were modified from [73]; and social identity, for which five items were modified from [80,81], five items were modified from [92,94], five items were modified from [92,94], five items were modified from [102], and five items were modified from [102].

4. Results and Data Analysis

To comprehend the characteristics of the responders, a demographic study was conducted. After that, the sample’s descriptive statistics were obtained. The presented hypotheses were tested using structural equation modeling (SEM). The SEM approach was appropriate for this study since it sought to validate the suitability of social cognitive theory and learning input theory for comprehending e-learning adoption as a way of ensuring learning sustainability. For data analysis, IBM SPSS version 26 and AMOS version 23 were employed.

4.1. Demographic Analysis

Table 1. Demographic characteristics of use of e-learning.

Demographic	Description	N	%	Cumulative Percent
Gender	Male	118	40.1	40.1
	Female	176	59.9	100.0
Age	18–21	66	22.4	22.4
	22–25	162	55.1	77.6
	26–29	35	11.9	89.5
	30–33	20	6.8	96.3
	>34	11	3.7	100.0
Level of study	Undergraduate	216	73.5	73.5
	Postgraduate	78	26.5	100.0
Specialization	Social Science	98	33.3	33.3
	Engineering	139	47.3	80.6
	Science and Technology	57	19.4	100.0
Length of use e-learning	1 year	7	2.4	2.4
	1–2 years	4	1.4	3.7
	2–3 years	35	11.9	15.6
	more than 4 years	248	84.4	100.0

lists the participants’ racial and ethnic characteristics. There were 300 total responses, 294 of which were assessed to be reliable. According to Table 1, 59.9% of the respondents were female, making up the bulk of the sample. Table 1 shows that the responders were mostly between the ages of 22 and 25. The sample adequately represented each student. The population was fairly represented by the sample.

4.2. Descriptive Statistics

To give the sample a useful representation, descriptive statistics (such as means, standard deviations, and ranges) were produced for each construct. According to the median values in

Table 2. Descriptive Statistics.

	Indicators	N	Minimum	Maximum	Mean	SD	Cronbach’s Alpha (α)
SEN	4	294	1.00	5.00	3.70	0.90	0.904
SIT	4	294	1.00	5.00	3.92	0.67	0.804
SIF	4	294	1.00	5.00	3.67	0.77	0.814
SID	4	294	1.00	5.00	3.35	0.88	0.872
SSU	4	294	1.00	5.00	3.79	0.78	0.912
ILS	5	294	1.00	5.00	3.71	0.79	0.915
RT	5	294	1.00	5.00	3.68	0.74	0.886
PSS	5	294	1.00	5.00	3.84	0.77	0.925
CTS	5	294	1.00	5.00	3.89	0.70	0.873
LP	5	294	1.00	5.00	3.96	0.74	0.897

, the participants responded positively to many social-interaction-related measures (means above 3.5 on a 1–5 Likert scale). However, a few constructs’ standard deviations (such as SEN, SIF, SID, SSU, ILS, RT, LP, PSS, CTS, and LP) exceeded 1.00, showing that respondents’ perceptions regarding the idea being measured varied.

4.3. Instrument Reliability and Validity

First, an item analysis was conducted to see whether the items on the instrument fit together and to evaluate the measure’s usefulness. To ensure uniform measurement among instrument items, the dependability of the measurement was then investigated. Lastly, to ensure that the item measured what it was intended to assess, a validity test was conducted [134]. The consistency of a respondent’s responses to each item in a measure was tested using Cronbach’s coefficient alpha (α). A > 0.70 suggested acceptable converging and consistency in the scale items, as recommended by [123].

The results in Table 2 show that all constructs had Cronbach’s alpha (α) values over 0.8, indicating the reliability of the measurement scales. The high Cronbach’s alpha (α) results indicated that the instrument was trustworthy for measuring the hypothesized phenomenon. These results matched the values from the social cognitive theory and the learning input theory of Fit Indices in

Table 3. Results of measurement model.

Fit Indices	Cut-Off	Measurement Model
CMIN/DF	≤3.000	1.67
GFI	≥0.90	0.945
TLI	≥0.90	0.929
IFI	≥0.90	0.946
RMSEA	≤0.08	0.060

. Second, the convergent validity and divergent validity of the scales were evaluated. The average variance extracted (AVE) was determined to determine the scale’s convergent validity. To obtain convergent validity, the AVE should, according to [135], be more than 0.50. The accuracy of the measuring scale increases with an increasing AVE. The results in

Table 4. Discriminant validity.

	SEN	SSU	SIF	SIT	SID	RT	ILS	PSS	CTS	LP
SEN	0.808
SSU	0.065	0.606
SIF	0.126	0.196	0.586
SIT	0.078	0.199	0.301	0.445
SID	0.174	0.282	0.428	0.244	0.765
RT	0.202	0.249	0.334	0.237	0.382	0.540
ILS	0.157	0.266	0.374	0.269	0.449	0.449	0.615
PSS	0.105	0.280	0.335	0.253	0.365	0.335	0.372	0.593
CTS	0.089	0.256	0.298	0.259	0.294	0.303	0.342	0.349	0.490
LP	0.077	0.262	0.203	0.215	0.212	0.263	0.245	0.378	0.275	0.539

show that all structures had AVE values of more than 0.5. The values of composite reliability (CR) surpassed the threshold of 0.5 (see

Table 5. Construct validity.

Latent Constructs	Items	FL	Latent Constructs	Items	FL
Social engagement	SEN1	0.801	Inquiry learning style	ILS1	0.813
	SEN2	0.882		ILS2	0.832
	SEN3	0.828		ILS3	0.816
	SEN4	0.841		ILS4	0.845
				ILS5	0.831
Social interaction	SIT1	0.722	Reflective thinking	RT1	0.769
	SIT2	0.732		RT2	0.766
	SIT3	0.704		RT3	0.817
	SIT4	0.685		RT4	0.751
				RT5	0.825
Social influence	SIF1	0.710	Problem-solving skills	PSS1	0.695
	SIF2	0.751		PSS2	0.891
	SIF3	0.747		PSS3	0.883
	SIF4	0.693		PSS4	0.874
				PSS5	0.882
Social identity	SID1	0.828	Critical thinking skills	CTS1	0.739
	SID2	0.761		CTS2	0.738
	SID3	0.891		CTS3	0.784
	SID4	0.716		CTS4	0.796
				CTS5	0.753
Social support	SSU1	0.896	Learning performance	LP1	0.702
	SSU2	0.879		LP2	0.720
	SSU3	0.832		LP3	0.838
	SSU4	0.786		LP4	0.837
				LP5	0.881

). There was no obvious overlap between the employed measures.

Additionally, the AVE for each construct showed a value higher than the bare minimum of 0.50 [134]. Based on the criterion in [135], the discriminant validity was calculated. The square root of AVE attained by a certain construct needed to be compared to other constructs’ inter-scale correlations. According to Hair et al. [134], discriminant validity is attained if the square root of AVE is greater than the highest value of construct correlation. The results of this investigation demonstrated that all constructs satisfied the discriminant validity requirement.

4.4. Confirmatory Factor Analysis

To verify the measurement instrument created using the AMOS software, a CFA was used. The degree to which the real data (observations) fit the hypothesized theory was suggested by the CFA [134]. The social cognition theory, learning input theory conceptions, and their reflecting measures were all included in the measuring model. To determine whether further modification of the measurement estimate was required, convergent validity indices were produced. In order to ensure that all measurements were within the suggested values, the chi-square (CMIN = 1503.368, df = 900), relative tests to assess (x2/df = 1.67, RMSEA = 0.060, SRMR = 0.049), and progressive fit (CFI = 0.945, TLI = 0.929, IFI = 946) (see Table 3) were obtained [134]. As a result, the measurement model was approved for use in the CFA and structural path analysis. Table 5 presents the results of the CFA, and Figure 2 shows a graphic representation of the measurement model.

As shown in Table 4, all undeleted items had standardized regression weights that were economically significant with values less than 0.001 and were over the cut-off value of 0.50 [134]. When the AMOS 23 output file’s correlation table was examined, it was discovered that all inter-correlation estimations were below the cut-off value of 0.85 [136] (see Table 4). The loadings, CA, and AVE displayed for each latent variable were higher than those of the inter-correlation estimations with other related constructs, as seen in Table 5 and Figure 2 [135].

4.5. Hypothesis Testing and Model Validation

A graphical image of structural (causal) links between constructs is a structural model. The independent variables (five) and the dependent variables (two) were connected using single-headed arrows to create the structural model in AMOS. The suggested structural model’s overall model fit was evaluated for its goodness of fit prior to calculating the pls structural coefficients [134]. The variable estimate of each postulated dependence link was therefore examined for statistical significance as the next step.

Table 6. Hypothesis testing results of structural model.

H	Hypothesis	Estimate	S.E.	C.R.	P
1	SEN ----------> ILS	0.020	0.030	−0.667	0.505
2	SEN ----------> RT	0.136	0.035	3.912	0.000
3	SIT ----------> ILS	0.107	0.050	2.140	0.032
4	SIT ----------> RT	0.145	0.058	2.504	0.012
5	SIF ----------> ILS	0.103	0.051	2.030	0.042
6	SIF ----------> RT	0.250	0.058	4.318	0.000
7	SID ----------> ILS	0.200	0.042	4.797	0.000
8	SID ----------> RT	0.221	0.047	4.669	0.000
9	SSU ----------> ILS	0.045	0.038	1.181	0.238
10	SSU ----------> RT	0.165	0.044	3.727	0.000
11	RT ----------> ILS	0.567	0.050	11.407	0.000
12	RT ----------> PSS	0.300	0.075	4.001	0.000
13	RT ----------> CTS	0.143	0.064	2.241	0.025
14	ILS ----------> PSS	0.385	0.070	5.473	0.000
15	ILS ----------> CTS	0.233	0.061	3.806	0.000
16	PSS ----------> CTS	0.361	0.048	7.433	0.000
17	PSS ----------> LP	0.529	0.053	9.907	0.000
18	CTS ---------->PL	0.186	0.059	3.159	0.002

and Figure 3 and Figure 4 provide examples of the suggested structural model. The sixteen assumptions between the eighteen important constructs were accepted, and only alternative hypotheses were rejected, as shown in Figure 3 and Figure 4 and Table 6. According to the first hypothesis (H1), there was no correlation between social interaction and the inquiry learning style when using e-learning (t-value = −0.667, β = −0.020). The findings showed no significant or positive link; hence, hypothesis H1 was not supported. A similar association between socializing and reflective thinking when using e-learning was suggested by the second hypothesis (H2) (β = 0.136, t-value = 3.912). The data demonstrated a substantial and favorable association, supporting hypothesis H2.

Additionally, the fourth and fifth hypotheses (H3 and H4) tested the impacts of social contact on the inquiry-based learning fashion (H3: t-value = 2.140) as well as critical reflection (H4: t-value = 2.504), with highly significant indirect effects (β = 0.107 and β = 0.145), exposing that the three parameters had a favorable direct relationship. Similarly, the SEM outputs indicated that social influence (H5 and H6) had effects on the inquiry-based learning style (H5: t-value = 2.030) and critical reflection (H6: t-value = 4.318), with highly significant path coefficients (β = 0.103 and β = 0.250), supporting hypotheses five and six (H5 and H6). Additionally, the seventh and eighth proposed hypotheses (H7 and H8) tested the influence of social identity on the individual unit (IU) of the inquiry learning style and critical reflection.

The SEM outcomes showed positive and significant correlations between the four latent dimensions (H7: t-value = 4.797 and H8: t-value = 4.669), with a high path coefficients (β = 0.200 and β = 0.221), supporting hypotheses seven and eight (H7 and H8). However, according to the ninth hypothesis (H9), there was no correlation between social support and the inquiry learning style when using e-learning (H9: β = 0.045, t-value = 1.181). Hypothesis H9 was not supported by the results because they did not show a significant and positive association. An association between social support and reflective thinking when using e-learning was suggested by the tenth assumption (H10: β = 0.165, t-value = 3.727).

The results demonstrated a significant and positive link; hence, assumption H10 was supported. With the hugely important path coefficients of H11 (β = 0.567), H12 (β = 0.300), and H13 (β = 0.143), the SEM outputs indicated the impact of reflection (H11, H12, and H13) on the inquiry-based learning style, problem-solving skills, and critical thinking skills (H11: t-value = 11.407; H12: t-value = 4.001; and H13: t-value = 2.241). According to the fourteenth hypothesis (H14; β = 0.385, t-value = 5.473), there is a connection between the inquiry learning style and problem-solving skills while using e-learning. The results showed a substantial and positive connection, supporting hypothesis H14.

Furthermore, according to the fifteenth hypothesis (H15: β = 0.233, t-value = 3.806), there is a connection between the inquiry-based learning style and the critical thinking skills while using e-learning. The data showed a significant and positive link; hence, hypothesis H15 cannot be accepted. The sixteenth and seventeenth proposed hypotheses tested the effects of problem-solving skills on critical thinking skills and learning performance. The SEM results showed positive and significant correlations between learning performance and the three latent dimensions (H16: t-value = 7.433) and between learning performance and learning performance (H17: t-value = 9.907), with high path coefficients (H16: β = 0.361 and H17: β = 0.529), supporting the sixteenth and seventeenth proposed hypotheses (H16 and H17).

Finally, the eighteenth hypothesis (H18) stated that there is a connection between learning performance in e-learning and critical thinking skills (β = 0.186, t-value = 3.159). The hypothesis was confirmed because the data showed a positive and substantial association.

5. Discussion and Implementations

This research developed a new model centered on the role of social cognitive theory with learning input factors that affect reflective thinking and inquiry learning styles, as well as the indirect effects of students’ critical thinking and problem-solving skills, which in turn enhance students’ learning performance as a means of ensuring educational sustainability. This study improved our understanding of how to use e-learning as a source of educational sustainability. As a result, the research model pinpoints social cognitive variables with learning input variables as having the greatest influence on reflective thinking, inquiry learning styles, students’ critical thinking, and problem-solving skills, all of which improve students’ learning performance when using e-learning as a sustainability strategy for education. By examining the social psychological approach in the context of two moderator variables, the inquiry learning style and reflective thinking, this study attempted to ascertain the impacts of problem-solving skills and critical thinking skills on learning performance. This study primarily examined the structure equation model (SEM) in relation to the moderator factors, the inquiry-based learning style, and the critical reflection of online learning as a way of ensuring learning sustainability.

These factors included social engagement, social contact, social influence, social identification, and social support. The findings make it clear that the inquiry learning style and the critical reflection of online learning utilized in Saudi Arabia have substantial direct associations with social engagement, social contact, social power, social identification, and social support. This supports the claim that the inquiry learning approach and critical reflection of online learning directly contribute to the effects of problem-solving and thinking skills, which in turn have an impact on learning outcomes in Saudi Arabia. This finding is in line with earlier studies in the area.

This suggests that students must decide whether they will achieve their study requirements or be valuable in their studies by employing an inquiry learning style and reflective thinking before choosing to use an e-learning system. Students will not accept that e-learning systems are better for learning performance unless they realize how much better they are than formal instruction without e-learning [9,20,79,137].

Engagement, social contact, social power, social identification, and social support are highlighted in the article by the idea of social cognitive theory [57,66,73,81,94]. According to the social cognitive perspective, engagement and influence are integrated with active social contact with others to mediate the world [47]. Therefore, this study investigating how students acquire knowledge and create it in relation to social cognitive theory circumstances seems to have a strong theoretical foundation [48]. The results of this study also revealed that social cognitive learning, which fosters an inquiry-based style of learning, reflective thinking, problem-solving skills, and critical thinking skills, has an impact on students’ academic performances in Saudi Arabian higher education. The gaps between the inquiry-based learning style, critical reflection, dilemma skills, or critical thinking skills can be closed by implementing the social cognitive educational and learning strategy [102,108,116]. Additionally, as learners, students express their ideas and engage with the external world [116], building a stable society [119] and enhancing social cognition and learning performance in Saudi Arabia.

Based on these results, the study concluded that raising the mean levels of social support, social power, social identity, or social engagement would likewise raise the average levels of inquiry learning and reflective thinking. Additionally, if the standardization of reflective thinking and the inquiry learning style were raised, the average standard of problem-solving skills and critical thinking skills would follow suit. Additionally, this study model was able to test a variety of effects of critical thinking and problem-solving skills on learning performance and employ online learning as a complement to the integrative social cognition theory with learning input factors. Additionally, a significant number of those asked about their use of online learning (84.4%) reported doing so for more than four years; thus, e-learning is sustainable.

This result appears to be in line with other studies whose findings were compiled in the review of the literature, which discovered that interaction and involvement are two of the key elements that positively affect the use of e-learning [9,36,37]. Similar to this, the three other factors—social impact, social identification, and social support—were discovered to have positive impacts on the inquiry learning style, critical reflection, problem-solving skills, and critical thinking skills, four factors that have positive impacts on how well students learn [7,121,124]. An earlier study found that the participation, influence, and interactions of students had substantial impacts on their effectiveness in the online learning environment as a way of ensuring learning sustainability [7,138].

Therefore, the current study found that social interplay, impact, engagement, identity, and assistance are potential factors that affect students’ learning performance when using e-learning, as are their investigation learning styles, introspective thinking, issue skills, and critical thinking skills. Additionally, using the right tools, online learning platforms enable both teachers and students to easily exchange their perspectives [37]. The users of those platforms can utilize them to find evidence that already exists, find solutions to their problems, and expose responses based on “student-to-faculty” and “student-to-student” interactions [139].

Furthermore, connection between students is essential for their academic success and enjoyment in online learning since it enables them to contribute their ideas to group projects [140]. According to a recent study, interactions between students and teachers in online learning foster engagement and have an impact on how well they learn [141]. The typical example in Saudi higher education promotes online learning, so it is critical to understand how students see using technology for online learning. As a result, this study also found that it is crucial for online learning to evaluate how well students are using technology as a way of ensuring learning sustainability. Additionally, students’ intentions to study online are influenced by technological infrastructure, broadband speeds, and access, which have impacts on perceived enjoyment (satisfaction) [142]. According to Leal Filho et al. [143], academics should adopt collaborative strategies and value the multicultural vision of sustainability, particularly in the context of education degrees, because their graduates will serve as teachers of future generations of citizens and may also serve as catalysts for socio-environmental change and transformation [144], which will help to create societies that are more equitable, sustainable, and balanced. Moreover, according to Morland-Painter et al., “systemic institutional integration must be closely connected with integrating sustainability into the curriculum” [145].

However, policy and decision makers at universities are frequently not sufficiently willing to take steps toward a sustainable future [146]. Despite several sustainable development initiatives and an increase in universities’ involvement in sustainable development, most higher education institutions remain conventional and rely on Newtonian and Cartesian reductionist and mechanistic paradigms, according to Lozano et al. [147]. Therefore, this study also discovered that “student-to-student” and “student-to-faculty” social support is an important and relevant component that affects students’ opinions of the usage of online learning for educational sustainability in Saudi Arabian higher education.

Higher education institutions have been forced to quickly innovate, rethink, and pivot as a result of the COVID-19 pandemic. Prior to 2020, the global higher education community started to intensely concentrate its efforts on developing sustainable institutions and included the Sustainable Development Goals (SDGs) of the United Nations [148]. For HEIs, participation in reaching SDG 4 targets is a necessity, and more intentional and coordinated efforts are required to succeed [149]. According to [150], during COVID-19 households were required to purchase ICT equipment, and universities provided assistance to students who were in need. Students struggled to follow courses online, spent more time studying each day, were not ready for the move, and performed worse academically. As a result, in order to close the digital divide and encourage sustainable practices, higher education institutions must provide an inclusive, equitable, and high-quality education. Therefore, educational institutions must ensure that sufficient efforts are made to contribute to the SDGs by providing quality education. To this end, e-learning and its integration in the classroom should be a way to improve the education system and sustainability.

Additionally, it was discovered that students’ learning performance was significantly impacted by their inquiry-based learning style, introspective thinking, problem-solving skills, and critical thinking skills. Figure 4 and Table 6 show some of the study’s findings in relation to social contact, social power, social identification, support networks, the inquiry learning style, critical reflection, issue skills, critical thinking skills, and academic success. Therefore, this study illustrates students’ readiness for sustainable online learning. Additionally, this research led to the creation of a validated tool that combines social cognition with learner input elements to examine the use of e-learning to enhance student performance in Saudi Arabia’s higher education system. The future research areas are as follows, in order:

• Regarding the independent factors, it was discovered that the social engagement, interpersonal contact, social power, social identity, and social support hypotheses directly impacted the reflective thinking and inquiry learning styles.
• Regarding the mediators’ assumptions, it was discovered that reflective thinking and the inquiry style of learning directly influenced both problem-solving and critical thinking skills.
• Regarding the mediators’ hypothesis, it was discovered that problem-solving skills directly influenced critical thinking skills.
• Regarding the dependent variables, it was discovered that students’ learning ability in Saudi Arabia’s higher education was directly impacted by their ability to solve problems and think critically.

Limitations and Recommendations

This study solely included students studying educational science at one public university. To generalize the findings, additional research that includes all the students enrolled in the various degrees provided by Saudi universities is necessary. The survey used in this study could also include a general question about how satisfied students are with online education as a sustainable educational process. It is possible to quantify how five social cognition theory variables affect how satisfied students are overall. Future research can be conducted with an equal sample of male and female students to identify the effects of sexual identity on students’ perceptions of digital learning, especially during or following the pandemic, as this study concentrated on identifying the variables that were appropriate to assess the students’ understanding of online learning as a way of achieving educational sustainability. Furthermore, the Saudi Arabian setting can be used to measure aspects influencing students’ experiences with online courses during the pandemic as a way of ensuring educational sustainability.

6. Conclusions

Using social cognition theory and learning input elements, this study built a new model and demonstrated its suitability for assessing students’ perceptions of using online learning to enhance learning performance as a way of ensuring educational sustainability. Learning performance is a dependent factor, and the factors of social engagement, human engagement, social power, social identification, and social support are independent factors. As mediator factors, the inquiry-based learning style, critical reflection, issue skills, and skills for critical thinking are also included. These factors give policy makers the ability to gauge how students feel about online learning as a way of ensuring educational sustainability, which helps them create effective measures to improve its quality and effectiveness and raise students’ preparation for future learning. By concentrating on how students use e-learning as a way of ensuring learning sustainability, the social cognitive theory and learning input elements were found to be closely associated. To clarify the state of online learning at universities and provide a helpful direction for future research, the current study adds to the growing body of literature.