In terms of skills, higher education has recently moved its emphasis from knowledge to lifelong learning (Greenhow et al., 2020). Cooperation skills are highly valued by employers, hence they are included in this list (Raza et al., 2020). The most broad definition of active collaborative learning was offered by Bui et al. (2021), who defined it as a scenario in which two or more individuals study or seek to learn something new together. Given the broad definition of the term “social media,” the bulk of research looked at specific social media platforms like MySpace, Facebook, and Twitter as educational successes, which seems sensible. The objective and functions of social media sites are to make it easier for users to exchange emails, add friends, construct personal profiles, join groups, develop apps, and locate other users (Stockdale and Coyne, 2020). Web 2.0, as opposed to its predecessor, web 1.0, which was more static and less dynamic, allows for increased user engagement, collaboration, and customization (Tajvidi and Karami, 2021). They include active collaborative learning via Facebook, blogs, and YouTube, among other things, as noted in Shahbaznezhad et al. (2021).

Individuals work together to attain their shared learning goals through cooperative learning, which takes occur in groups (Khan and Mansoor, 2020). Positive interdependence, individual accountability, promotive interaction, interpersonal and small group abilities, and group processing are the five core aspects of collaborative learning (Lee and Yang, 2020). Positive interdependence, which “exists when group members think that they are linked in such a way that no one can achieve unless everyone succeeds,” is the most important aspect. “Everyone fails if one person fails” (Khan and Mansoor, 2020). Group processing is the last component, which entails group members analyzing their work in accordance to their objectives and maintaining positive working relationships (Khan and Mansoor, 2020). To guarantee that all five key aspects are in place, teachers must dedicate substantial planning time to cooperative learning (Junco et al., 2013; Al-Rahmi and Zeki, 2017; Al-Rahmi et al., 2019a). Cooperative learning groups, according to Khan and Mansoor (2020), are generally 2–4 students in size, with smaller groups being recommended. There is no ideal group size for cooperative learning. The number of students in a group is decided by a variety of factors, including the length of time the group will be working together, the ages and previous group work experience of the students, as well as the resources and equipment available (Chatterjee and Correia, 2020).

The theory of student engagement arose from a proportionate educational approach to increase student involvement. Today, engagement is defined as the amount of energy a student devotes to educational activities that are scientifically linked to better college outcomes (Bond et al., 2020). Academic interactions with professors, participation in class activities, and interaction with classmates in the quest are all forms of engagement (Meintjes and van Wyk, 2020). Because this type of online tool has been proved to boost student academic success and experience by fostering cognitive engagement and social connection, the social media network was chosen (Aljuboori et al., 2020).

Perceived usefulness is the most crucial measure of IT use among users, according to the TAM model (Davis, 1989). On the other hand (Acharya and Ganesan, 2019), discovered that the utility of information systems (IS) had a negative influence on attitudes about their utilization. According to previous study, PU has the largest influence on attitude (Martinho et al., 2018). Furthermore, PU had a significant impact on future intentions to utilize social media (Abdullah et al., 2016; Scherer et al., 2019). According to Al-Rahmi et al. (2018a) perceived utility (PU) in the context of social media is defined as the degree to which users believe that social media may assist them achieve teaching and learning objectives.

In the context of social media, Al-Rahmi et al. (2018a) defined perceived ease of use (PEU) as the degree to which consumers believe that utilizing social media is straightforward. According to past research, PEU has a significant influence on perceived usefulness (Abdullah et al., 2016; Binyamin et al., 2019). In addition, past research has found that PEU is a significant predictor of attitudes toward social media use (Fokides, 2017; Al-Rahmi et al., 2021a). PEU influences perceived usefulness, behavioral attitude, intention, and actual usage, according to TAM (Davis, 1989). The regression results, according to Davis (1989), indicate that, from a causative standpoint, PEU may come before perceived usefulness, rather than being a parallel and direct driver of utilization (Venkatesh and Davis, 2000).

Perceived joy has an impact on the rising popularity of social media as a motivator (Rauniar et al., 2014; Al-Rahmi and Zeki, 2017). “The degree to which the act of utilizing technology is designed to be pleasurable in and of itself, independent of any potential performance concerns,” according to the definition (Davis et al., 1992). Users on social media are more interested in a service if they can experience it in a more immersive environment (Rauniar et al., 2014). People who love using a web system have a positive perspective of their interactions with it, according to Moon and Kim (2001), and are thus more inclined to utilize it to increase their collaborative learning (Sa’nch et al., 2014). However, when compared to other external factors, perceived enjoyment as an external factor in the context of student learning has not been frequently detected in previous TAM investigations.

Triandis (1971) defines attitude as a representation of sentiments and understanding about a topic or situation, as well as an individual’s positive or negative behavior. Attitude includes three components, according to Mantle-Bromley (1995), which are concerned with people’s preferences, knowledge of the attitudinal object, and reactions and intents toward the object, respectively. Previous study on the acceptability of social media has discovered that attitude is a key factor in determining whether or not to utilize it (e.g., Al-Rahmi et al., 2018b, 2021b). Attitude is discovered to be a significant influencer on behavioral intention (Al-Rahmi et al., 2018b; Rasheed et al., 2020). As a result, the researchers observed that students’ opinions had an impact on their desire to use online learning technologies. Furthermore, according to a study done by Sujeet and Jyoti (2013), attitude and PEU may influence enhanced behavioral intention among Omani students. Rupak et al. (2014) discovered that TAM has a substantial positive association between perceived usefulness and PEU, and that both categories had a favorable influence on behavioral intention in their research of social media usage behavior.

Many earlier research on technology adoption have highlighted attitude as an important determinant of online earning and social media learning (Tosuntaş et al., 2015). Users that have a good mindset are more likely to use social media, according to Cheung and Vogel (2013). Given the rapid rise of online learning, Wang and Wang (2009) claim that the desire to utilize it differs greatly from actual use. To have a better knowledge of how different social media platforms are used. Al-Rahmi et al. (2020a) argue that there is a chance that the intention and behavior are conflicting, since the intention to embrace social media might be impacted by unanticipated occurrences. As a result, Dlalisa and van Niekerk (2015) performed an assessment of users’ intentions and discovered that there is no substantial link between their intention and their actual usage of learning management systems and social media platforms. TAM is an extension of the TRA established by Davis (1986), and it gives a theoretical foundation for analyzing how external variables affect interior beliefs, attitudes, and actual usage, which determines their pleasure and use of technology. It also serves as a framework for explaining user behavior when it comes to adopting new technology and investigating the factors that impact attitudes toward such technology use (Davis, 1989). The purpose of this study is to analyze students’ and instructors’ satisfaction with the use of social media for active collaborative learning and participation, as well as its impact on academic advancement in higher education institutions, using a TAM-based questionnaire.

Active participation in collaborative learning is encouraged by social media. To say that social scientists’ attention is drawn to social media by the increased interest in active collaborative learning is an understatement (Abdillah et al., 2020). Clapp et al. (2021) used a social bookmarking application to increase active collaborative learning by using the method and virtual framework built for the benefit of student motivation and collaborative group learning among peers, teachers, and owners in another study (SSCL). Knowledge and experience transfer are seen as complements to precise knowledge development that is relevant to real-world circumstances (Sohaei et al., 2020). The primary advantages of using social media to aid learning and teaching will not be fully appreciated until more research is conducted into how social media’s social characteristics can be used to attract low- and disengaged students to interact in educationally purposeful ways with their high-engaged peers and teachers, ensuring that it contributes to the prosperity of many students (Al-Rahmi et al., 2015). However, data reveal a beneficial association between social media platforms and how they may be utilized to increase learning in a few rare circumstances (Bond et al., 2020).

Despite the fact that Likert scales are widely used in IS research and have been fully evaluated in marketing and social science, they were utilized to gauge responses in this study (Krejcie and Morgan, 1970; Garland, 1991; Shih, 2004). All of the factors in this study are rated on a five-point Likert scale from 1 to 5: The five alternatives are (1) Strongly disagree, (2) Disagree, (3) Undecided, (4) Agree, and (5) Strongly Agree. When choosing and creating a measuring scale, several elements that affect the scale’s reliability, validity, and usability must be taken into account. With the support of a pilot study with students, the questionnaire was fine-tuned and tested ahead of time to examine how students and researchers in higher education felt about using social media for active collaborative learning and engagement, as well as how it influenced their academic performance during the COVID-19 Pandemic. A 30-item questionnaire was prepared after minor layout adjustments, and the online survey was given near the end of the semester in September 2021. In the questionnaire adapted from previous studies, four items extracted from Lee and Yang (2020) were utilized to investigate the usage of social media for collaborative learning. Three questions obtained from the study were used to assess students’ use of social media (Shahbaznezhad et al., 2021). To measure PEU, perceived enjoyment, and perceived usefulness, a subset of four questions from Davis et al. (1992) were employed. Students’ attitudes toward using social media were assessed using three questions adapted from Davis (1986), four items from Davis et al. (1992), and four items from Davis et al. (1992). Students’ academic success was assessed using four items from Davis et al. (1992) and Al-Rahmi et al. (2020b).

In higher education, sampling is a statistical strategy for selecting a subset of individual observations from a population with the goal of influencing student and researcher academic performance during the COVID-19 Pandemic through the use of social media for active involvement and collaborative learning. Random and non-random sampling are the two types of sampling methods. Because a random sample strategy assures that the research target group has an equal chance of being picked, it was adopted in this study.

A range of statistical approaches, such as the split-half methodology, internal consistency, intra-observer, and test–retest, can be used to investigate reliability (see Tables 1, 2). Individual item and scale reliability were investigated in this work, with scale reliability comprised of three types of measures: internal reliability (IR), composite reliability (CR), and average variance extracted (AVE). IR, are examples of scale reliability. Because internal consistency dependability is a widely used approach, it was adopted in this investigation. IR is critical in this study because it involves many item scales (Bryman and Cramer, 2004; Hair et al., 2012). The item is deemed dependable if the squared multiple correlations in (R²) of each item in the measurement model exceed 0.5. Individual item dependability is established when the standardized loading is equal to or greater than 0.50. Furthermore, the AVE displays the overall amount of variation explained by the hidden construct in the indicators (Hair et al., 2012).

According to Hair et al. (2012), a range of goodness of fit metrics may be used to evaluate the overall model measure, which are split into three categories: absolute fit measures, incremental fit measures, and parsimonious fit measures. The measurement model is first examined for its validity (unidimensionality, reliability, and validity) using the two-step technique. Second, the structural model will perform further estimations between variables to see whether it matches the observed data (Hair et al., 2012). The adjusted goodness-of-fit index (AGFI), Tucker- Lewis index (TLI), incremental fit index (IFI), and comparative fit index are examples of incremental fit measures (CFI) shown in Figure 2 and Table 3.

According to Hair et al. (2012), a direct impact is a relationship between two constructions that follows a single path. In other words, it is the direct relationship between variables and their impact on one another. The Critical Ratio (CR) and p-value recommendation values for the present research, which contains sixteen direct impacts, must be estimated to guarantee that all routes in the model are supported. To arrive at a suggested value, the CR parameter estimate divided by an estimate of its standard error should be more than 1.96 (see Figures 3, 4, and Table 4).

This study updates the TAM Model to better understand students’ views regarding social media and their plans to use it to improve their academic performance during the COVID-19 Pandemic. The findings suggest that utilitarian orientations to a social media site’s perceived utility, enjoyment, and ease of use are important determinants of collaborative learning and student engagement, which is a driver of students’ academic performance during the COVID-19 Pandemic. Using the TAM model, we found a link between students’ attitudes about social media and their behavior intention to use in the context of social media usage for education during the COVID-19 Pandemic. We studied and included additional crucial elements, such as using social media for collaborative learning and students’ participation, to make the TAM model more meaningful in the context of understanding the acceptance and usage of social media to affect students’ academic performance during the COVID-19 Pandemic. A structural equation modeling (SEM) approach and a 30-question online questionnaire were used to analyze these characteristics. Students’ perceptions of social media’s usefulness, enjoyment, and ease of use are positively influenced by collaborative learning and engagement, according to the findings; three variables influenced students’ attitudes toward social media and intentions to use it to improve their academic performance during the COVID-19 pandemic. In addition, the following four categories of strategic recommendations were discussed: using social media as a platform for education learning during the COVID-19 pandemic; using social media as a platform for collaboration and student engagement; social media as a useful, enjoyable, and easy-to-use platform; students’ attitudes toward social media and intentions to use it to improve their academic performance during the COVID-19 Pandemic. When these approaches are paired with a set of standards for using social media in higher education, students may be able to perform better in class. According to the current study, future research could incorporate other and additional variables to analyze the impact of various aspects on students’ academic performance during the COVID-19 Pandemic through the use of social media for collaborative learning and student engagement. Future research should look into additional studies and variables that influence the usage of social media for collaborative learning and participation in order to improve students’ academic performance during the COVID-19 Pandemic (e.g., environmental and cultural).