Purpose – Social media has shown a substantial influence on the daily lives of students, mainly due to the overuse of smartphones. Students use social media both for academic and non-academic purposes. Due to an increase in the usage of social media, academicians are now confronting pedagogical issues, and the question arises as to whether the use of social media affects students’ performance or not. Considering this, this study aims to examine the role of social media usage on students’ academic performance in the light of cognitive load theory.
Design/methodology/approach – Using a quantitative research approach, 220 valid responses were received through an e-survey administered to university students. The proposed claims were tested through structural equation modeling using AMOS version 24.
Findings – Findings revealed that social media usage for non-academic purposes harmed students’ academic performance. Additionally, social media usage for academic purposes and social media multitasking did not affect students’ academic performance. Most importantly, social media self-control failure moderates the relationship between ‘‘social media usage for non-academic purposes’’ and students’ academic performance.
Practical implications – The findings of the study can be used by the academic policymakers of institutions and regulatory bodies.
Originality/value – The study suggests that teachers not only rely on using social media as a learning tool but also concentrate on improving student self-control over the use of social media through various traditional and non-traditional activities, such as online readings, group discussions, roleplays and classroom presentations.
Keywords Social media, Self-control failure, Teaching/learning strategies, Mobile learning,
Pedagogical issues, Social media multitasking, Academic performance
Paper type Research paper
The use of social media is like an addiction and its use is inevitable. Junco (2014) defined social media as the “applications, services, and systems that allow users to create, remix, and share content.” Social networks bring people together by removing territorial barriers and by providing knowledge from around the globe. An average person spends about three
hours a day on social networks (Hameed and Irfan, 2021; Celestine and Nonyelum, 2018). In modern times, the use of internet-enabled mobile phones is very common among students (Giunchiglia, 2018). The students can use the internet for both academic and non-academic activities. The rapid increase in internet usage has shown a critical impact on the academic journey of students (Owusu-Acheaw and Larson, 2015). This social media buzz influences their personal and social lives (Jacobsen and Forste, 2011). Furthermore, the literature has shown substantial evidence of a strong connection between social media use and students’ academic performance (Giunchiglia, 2018; Lepp et al., 2015; Samaha and Hawi, 2016).
Another important element in the use of social media is the “addiction” that defines a person’s eagerness to use social media. This is due to easy access to social media applications and websites at any place with the help of mobile devices (Van den Eijnden et al., 2018). The use of social media has become a norm and students cannot resist using it through their smartphones (Du et al., 2018). Social media constitutes the websites that are useful for collaborative interaction, information sharing and community building. Facebook, WhatsApp, Twitter and Instagram are the most popular social media sites amongst the students.
Owusu-Acheaw and Larson (2015) proclaim that the increased use of social networking websites has become a social norm and way of life for people from all over the world. Teenagers and young adults have especially embraced these sites as a way to connect with their peers, share information, reinvent their personalities and showcase their social lives (Panek, 2014). Social media enables the students to interact with their classmates and teachers virtually from any place (Owusu-Acheaw and Larson, 2015).
Social media users find it very difficult to control their use of social media (Junco et al., 2011). Youngsters tend to show more interest and involvement with technology-oriented products including social media (Fuchs, 2018). The usage of social media gives them instant access to entertainment content. They get to know what is happening in the lives of other people. They can also easily communicate with their friends and family (Fuchs, 2015). Apart from the user interface the entire content is being uploaded/shared by the users. Such kinds of features keep the students engaged in social media, resultantly they spend several hours in social media usage (Vardeman-Winter and Place, 2015). Recent literature reports that the
use of social media further increased during COVID-19 for academic and non-academic purposes (Ahmad and Murad, 2020). The question arises as to whether the use of social media affects student academic performance or not (van den Eijnden et al., 2018).
The fast growth of social media networks into educational systems, as well as the provision of educational services via these platforms, has pushed researchers and educators to investigate how these technologies have changed the education system (Saini and Abraham, 2019). While there are some reservations about the use of social media applications in education, it is believed that they can help students improve their cognitive and writing skills as well as their training capacities (Al-Qaysi et al., 2020). By providing online lectures and allowing students to communicate with their lecturers and classmates to
gain and share information efficiently, social media has changed the educational environment (Al-Qaysi et al., 2021). This paper aims to find out the effects of using social media in teaching-learning concerning cognitive load theory. Moreover, it examines the role of social media multitasking (SMM) and social media self-control failure (SMSCF) in students’ academic performance.
Literature review and theoretical framework
2.1 Theory grounding the study
Cognitive load theory suggests that every learning material causes cognitive load on the working memory. Cognitive load pertains to the number of factors demanding attention and the cumulative mental effort required from working memory. In this theory, the learning process is explained by the role of working memory (Sweller, 1988). Following are the four pillars on which this theory is based:
- capacity of working memory is limited;
- essential unlimited capacity of long term memory;
- working memory is actively involved in the processing and coding information into the long term memory thus completes the learning process; and
- working memory overload leads to futile learning.
Total cognitive load has three components; intrinsic load, extraneous load and germane load (DeJong, 2010; Mayer and Moreno, 2003). The intrinsic load cannot be manipulated, as it is a property of learning material. However, extraneous and germane load are the functions of instructional design, thus, can be manipulated. Extraneous load is undesirable and does not contribute to learning, it is caused by ineffective instructions, unnecessary and excessive activities (Edwards et al., 2015).
From a cognitive load theory perspective, multitasking demands a correspondingly huge working memory capacity that can easily become unaffordable for the memory system. This is extraneous processing and can result in ineffective learning. Materials capable of causing extraneous processing include unnecessary texts, graphics, sounds and other similar cognitive activities. They constitute the extraneous load, causing unnecessary processing, transfer losses and poor storage. All aforementioned affects defeats the ultimate goal of instruction (Edwards et al., 2015).
The components of cognitive load are related to each other and collectively comprise the total capacity. The total cognitive capacity remains the same, hence, using it on one activity reduces the space for other undertakings. Social media has images, texts, graphs and videos which all take the extraneous load. The usage of social media causes more utilization of extraneous load and less space is available for intrinsic and germane load. Similarly, SMM increases cognitive usage to a greater extent because more than one activity is being undertaken at a single point in time. Hence, the cognitive resources are wasted by doing excessive social media usage and SMM. The current study analyzed the propositions proposed in Section 2 of the paper based on the cognitive load theory.
Social media usage for academic purposes
Social media is transforming the means of communication in society due to its ease of use, speed and usability (Junco et al., 2011). The communications range from the discussions concerning the environment, technology and solutions to various issues (Van den Eijnden et al., 2018). As per the research of Owusu-Acheaw and Larson (2015), social media sites and academic performance have a direct relationship. They recommended that students who have internet available on their mobile phones should use it for academic purposes. Students should use this facility for reading novels and improving their knowledge. There is a need to introduce innovative ways of developing reading materials and novels to improve the knowledge of students (Muraven et al., 1998).
Boahene et al. (2019) in their quantitative study on tertiary level students found that social media usage for academic purposes (SMUAP) is positively related to their academic performance. Moreover, the effect of academic self-efficacy as a mediator has additionally improved the academic performance of learners. It was also analyzed by Al Ahmad and Obeidallah (2019) in their research on Jordanian University students that learning objectives enhanced and results increased by 10.49% by the use of Facebook and YouTube in teaching methodology. Additionally, a study on Pakistani university students found a positive relationship between social media usage and academic performance (Ahmed et al., 2020). Alamri (2019) found in his research with Saudi University students that their perceptions about social media use for academic activities were positive. Participants reported that social media saved their time, allowed self-learning and promoted collaboration with peers and teachers. These all factors contributed to their learning processes.
Chak and Leung (2004) stated that social networking has become a typical international trend that has stretched virtually across each corner of the globe. Students can make content, share, bookmark and interact at an exceptional rate on social media. OwusuAcheaw and Larson (2015) conducted a study on the relationship between the usefulness of time spent on social media and its impact on the academic performance of students.
They found out that the majority of students have mobile phones, they have access to the internet and they know about many social media sites. Furthermore, they also said that students visit these social media sites regularly. Their study revealed that there is a strong positive relationship between social media usage and academic performance, thus we
hypothesize the following:
H1. SMUAP has a positive impact on students’ academic performance.
Social media usage for non-academic purposes
The world has changed dramatically over the past 10 years because of the major developments in the field of social media (Martin and Yeung, 2006). Young students share their thoughts, emotions, private information, images and videos at a startling pace on social media (Khan et al., 2021). The use of social media has taken themselves away from face-to-face socialization and people feel comfortable connecting through technologies. Showcasing their lives on social media has become the norm (Celestine and Nonyelum, 2018).
Social media use has a negative relationship with academic performance and active social media use among students can lead to lower academic grades (Hofmann et al., 2012). The use of social media for non-academic purposes such as being too involved in social media can have a major effect on academic performance (Alamri, 2019; Boahene et al., 2019; Lau, 2017). Similarly, a study conducted on Australian post-secondary students found that using social networking sites has little impact on high-achievers while it jeopardizes low-achievers (Wakefield and Frawley, 2020). Panek (2014) found that heavy use of social media channels harms students’ educational performance. Therefore, there has been a strong correlation between social media use and academic performance (Jacobsen and Forste, 2011). Researchers such as Choney (2010) and Zahid et al. (2016) worked on the social media life of students and concluded that it has a negative effect on their academic performance (Asur and Huberman, 2010). Hence, it has been hypothesized as follows:
H2. Social media usage for non-academic purposes (SMUNAP) has a negative impact on students’ academic performance.
Social media multitasking and academic performance
Multitasking is the involvement in more than one activity at a particular time. SMM means performing multiple tasks at a given time wherein at least a single task is performed through social media (Lau, 2017). People use Facebook, WhatsApp, Twitter and LinkedIn, etc. for sharing life events in the form of pictures and videos with their friends, family, colleagues and others (Martin and Yeung, 2006). This SMM may take place on one or multiple devices (Kononova and Chiang, 2015). A study reported that 85% of undergraduates do multitasking and this number is still growing (Lau, 2017).
Studies investigating the effect of SMM on academic performance report that it has a negative impact on three dimensions of students, namely academic performance, behaviors and attitudes, and perceived academic learning (Van Der Schuur et al., 2015). Lau (2017) asserted that academic performance is significantly negatively predicted by SMM. University students who were indulged in SMM during class lectures stated that their learning was hindered (Demirbilek and Talan, 2018).
The negative effect of SMM can be related to time displacement and limited information processing capacity premise. Time displacement suggests that the students spend more time on social media than on their academic activities. This is due to the appealing nature of the media. Their focus is distracted and also affects their performance adversely. Few even prioritize media usage over their routine activities of student’s life for example attending classes (Walsh et al., 2013). The idea of limited information processing capacity is linked with the cognitive load theory as discussed in Section 2.1. Theory suggests that multiple activities at the same time consequence in cognitive bottlenecks due to limited cognitive capabilities. This results in distraction in the thinking and learning process (Sweller, 1988). The following hypothesis is proposed in the specific context of SMM:
H3. SMM has a negative impact on academic performance.
Social media self-control failure as a moderator
Most of the time, social media users go through the dilemma of either using social media or doing their work. Study to get good marks in exams, make phone calls to friends or do dishes clean-up and perform such duties which will help in accomplishing goals by leaving short term pleasure of social media is social media self-control (Hofmann et al., 2012).
Numerous studies have revealed the benefits of social media self-control behaviors. Most of the time, social media users fail to control their desire to use mobile phones and related applications. The study of Lee et al. (2017) revealed that the desire to use social media is more than doing routine activities.
Alamri (2019) interestingly found that there is no significant relationship between the amount of time spent on social media and academic performance. This may depend on the purpose of use. Whelan et al. (2020) conducted a study in Finland, Ireland and the USA and found that social media overload damages self-regulation, which in turn is essential for high
academic performance. Du et al. (2018) said that social media is easily accessible for everyone at every place through portable devices and Wireless Fidelity (Wi-Fi) connections.
Now in this situation for social media users, it is very difficult to stop themselves from using internet facilities. Conflict arises between their goals and the desire to use social media. Lee et al. (2017) said that self-control for social media users is very difficult and results in failure in general.
The significance of self-control of social media has been discussed in many studies. Users of social media commonly fail in their adaptable media behaviors (Muraven et al., 1998). For example, in a single day a person experiences various situations where he/she has to choose between his/her desire and work (Waris et al., 2021), but most of the time a person fails to choose his work against the desire to use social media (Lee et al., 2017). The desire to use the media has a conflict with so many other goals. A study by Hofmann et al. (2012) concluded that media use has a conflict with the study goals and using time efficiently. Thus we hypothesize that (Figure 1):
H4. SMSCF moderates the negative relationship between SMUNAP and students’ academic performance.
Sampling and procedure
This study has been carried out to examine the role of social media in influencing students’ academic performance. The data has been collected with the help of a close-ended questionnaire based on Likert scale ranging from 1 to 5 (strongly disagree to strongly agree or almost never to very often). The unit of analysis was university students. A self administered anonymous questionnaire was circulated online through Google forms for the purpose of data collection. Standardized, reliable and valid scales were adopted from existing studies to formulate the survey instrument. Initially, 40 undergraduate students
were selected for pilot testing following the recommendation of Peterson and Merunka (2014). The questionnaire was further distributed to 500 students and a reminder was given to the non-responding students after one month. Data collection was completed in three months from June to August 2020. We received 276 responses with a response rate of
55.2% out of which 220 were useable after data cleaning. We then compared the characteristics of respondents who returned completed surveys and non-respondents who failed to return a completed survey. The results revealed that there are no significant differences in the mean test of both groups, hence we preceded further with the data analysis by using IBM SPSS AMOS version 24.
As per the analysis of the profile of the respondents, full-time students were 52.7%, whereas part-time students were 47.3%. In total, the male respondents were 53.6% and 46.4% females. Respondents included in the age bracket of fewer than 18 years were only 1.8%. The majority of the respondents lived in the 18–24 (55.9%) age bracket with 41.4% of
respondents included in the 25–34 age bracket. The degree program classification tells us that 64.1% of respondents were found to be enrolled in business administration-related degree programs. The rest of the respondents were found to be enrolled in information technology, media, education and other social science-related degree programs.
The instrument of data collection was divided into two components, one comprised of demographic information of the respondents and the other about measurement scales for constructs.
Social media usage for academic purpose. The seven-item unidimensional scale of social media learning developed and refined by Mills et al. (2013) was used to assess student perceptions of the application of SMUAP. Sample items include: “I feel a sense of community learning becomes interactive.”
Social media usage for non-academic purpose. Twelve items media usage subscale of the media and technology usage and attitudes developed by Rosen et al. (2013) was used to evaluate SMUNAP (SMUNAP). Sample items include: “I Watch TV shows, movies, etc. on TV.”
Social media multitasking. SMM was measured using a three items scale from Ozer’s (2014) study. Sample items include “I multitask with my social media account while studying.”
Social media self-control failure. Three items scale developed by Du et al. (2018) has been used for the measurement of SMSCF. Sample items include: “How often do you give in to a desire to use social media even though your social media use at that particular moment conflicts with other goals (for example: doing things for school/study/work or other tasks)?”
Academic performance. Academic performance was evaluated on a single-item scale, in which the participants were required to provide their cumulative grade point averages (CGPAs) in an open response format (Paul et al., 2012).
The results section is discussed in two parts. The first part highlights the validation of the model through the reliability and validity measures of scales and model fitness. Whereas, the second part represents the testing of hypotheses through SEM.
Confirmatory factor analysis was performed to determine the validity of the hypothesized model. According to Kline (2011), the model fitness criteria comprises multiple fit indices, out of which four fit indices are commonly reported. The first fit index is relative chi-square, which is the ratio of chi-square and the degrees of freedom. The relative chi-square value is 1.533, which is less than the threshold of three. The comparative fit indices e.g. TLI and the CFI are 0.912 and 0.924, respectively, that are greater than 0.90, suggesting an acceptable level of model fit. In addition, the RMSEA value is 0.049, which suggests that the model is a good fit since it fulfills the criteria of being less than 0.07. In summary, the chi-square, TLI,
CFI and RMSEA indices paint a picture that the hypothesized model fulfills the criteria of goodness of fit as shown in Figure 2.
The reliability of the data has been tested and determined through Cronbach’s alpha. The questionnaire was comprised of 25 questions and a reliability test was performed through SPSS 24 software. According to Nunnally (1979) and Hair et al. (2013), if the value of Cronbach’s alpha (a) is more than 0.7, the scales can be considered consistent and reliable. The reliability statistics for watching TV, media sharing, internet searching and video gaming as dimensions of SMUNAP have Cronbach’s alpha coefficient values greater than the minimum criteria of 0.7 as shown in Table 1.
Additionally, the reliability statistics for the construct SMUAP have a Cronbach’s alpha coefficient value of 0.833 for a seven-item scale, which reflects the good reliability statistics. Moreover, the reliability statistics of SMM has a Cronbach’s alpha coefficient value of 0.755 on a three-item scale, which is considered acceptable. Lastly, the reliability statistics for the
construct SMSCF have a Cronbach’s alpha coefficient value of 0.689 for a 3-item scale, which reflects the reliability statistics closer to the minimum criteria of 0.7. Moreover, the composite reliability (CR)and average variance extracted (AVE) of each variable, as shown above, reflect a good measurement model.
The discriminant validity of the model was assessed to see if the scales corresponded in such a way that their conceptualization as distinct constructs was justified. The AVE values for the constructs are greater than 0.5, suggesting that the model holds convergent validity.
Additionally, the MSV values of all constructs are less than the respective AVE values, indicating that the model contains discriminatory validity. To sum it up, it is worth arguing that the hypothesized model does not have a serious issue of model reliability and validity (Table 2).
One of the key sources for measurement errors in models is the issue of common method bias (Podsakoff, 2003). According to Bagozzi et al. (1991), one of the key sources of systematic measurement error is method variance, which may come from a variety of sources. Podsakoff (2003) reported several method bias sources that cause variances and proposed some statistical techniques to remove the bias from the model. In this analysis,
Harman’s single-factor test was used to solve the problem of common method variance (CMV). All of the variables in the sample were loaded into exploratory factor analysis and the unrotated factor solution was examined to see how many factors were required to
account for the variance in the variables. The exploratory factor analysis extraction was limited to one factor, which accounted for only 19.59% of the total variance, suggesting that one common factor does not account for the majority of the covariance among the indicators. So, it is determined that there is no serious issue of CMV in the hypothesized model.
The structural equation modeling (SEM) technique was applied to test the hypotheses. Table 3 represents the coefficients, standard errors (S.E.) and t-values. Each estimated path is tested and it is found that all the paths are statistically significant except the paths from SMUAP and SMM to academic performance. The first hypothesis claims that SMUAP is significantly related to a student’s academic performance. According to the results, the path between SMUAP and academic performance is insignificant (p> 0.05). Therefore, this hypothesis is not accepted and it is concluded that SMUAP is not related to a student’s academic performance. The second hypothesis reveals that SMUNAP affects students’ academic performance. According to the results, the path between SMUNAP and academic performance is found significant (p < 0.05). Moreover, the beta value for the paths between SMUNAP and CGPA is 0.7488. This implies that this hypothesis is accepted, and it is concluded that the more you use social media for non-academic purposes, the lower will be your academic performance. The third hypothesis claims that SMM is related to students’ academic performance. According to the results, the path between SMM and academic performance is insignificant (p> 0.05). Therefore, the hypothesis, SMM affects the student’s academic performance, has been rejected. The fourth hypothesis claims that SMSCF moderates the relationship between SMUNAP and students’ academic performance. The results of coefficient of determination (R2 ) indicates that 4.47% change in CGPA is due to SMUNAP. The SMSCF shows a statistically insignificant (p> 0.05) relationship with CGPA. Moreover, the interaction between SMUNAP and SMSCF is
statistically significant (p < 0.05) with a beta value of 0.69. This implies that “social media usage for non-academic purposes” will have a lesser impact on the “academic performance” of those who have low levels of SMSCF and vice versa. Figure 3 shows a graphical representation of the coefficients of interaction effects generated through SPSS. 5. Discussions, implications, future research and conclusion 5.1 Discussions The hypothesis that SMUAP has a positive impact on students’ academic performance (H1) has been rejected, which is in alignment with the findings of Lau (2017) and Abbas et al. (2019). However, Junco et al. (2011) have found that the use of Twitter and Facebook can enhance academic performance. Also, Al Ahmad and Obeidallah (2019) argue that learning objectives were enhanced and results improved by the use of Facebook and YouTube in teaching methodology, whereas Ahmed et al. (2020) found that students’ usage of smartphones positively impacted academic performance. There is an insignificant relationship between social media usage and academic performance because students mostly use social media in their leisure time and for fun purposes. Students mostly go for traditional and recommended means (Hameed et al., 2021) i.e. textbooks, teachers or self-notes derived from lecture/books and other course materials, etc. rather than using social media for such purposes.
However, it needs to be studied as to why there is no significant relationship between the two. Was the quality of interactions with social media not effective? Are traditional methods of studying by using notes and textbooks the more effective mediums? Perhaps students are not using their smartphones adequately or for meaningful academic purposes. Consequently, it may be because when intending to use social media for academic purposes, students get diverted and start indulging in SMM. In the light of cognitive load theory (Sweller, 1988), this eventually conversely affects their learning. However, the findings for SMM reveal something different as discussed in the consequent paragraphs.
It was also found that “social media usage for non-academic purposes” harmed students’ academic performance H2.This implies that the more you use social media for nonacademic purposes, the lower will be your academic performance. It was also reported by Lau (2017) that the use of video gaming negatively affected students’ academic performance. Also, usage of different social media sites revealed negative effects on academic performance in the previous studies (Asur and Huberman, 2010; Choney, 2010; Zahid et al., 2016). This is logical if the effect of the moderating variable is understood,
because if SMSCF is high, then students will indulge more in SMUNAP and, hence, their academic performance will be negatively impacted.
Results also demonstrate that “social media multitasking” did not affect students’ academic performance H3 . This is in contrast with Lau’s (2017) finding that SMM hindered student learning. Similar findings were also reported by Demirbilek and Talan (2018), in which SMM was an obstacle to university students’ learning. The findings of the current study are contrary to the theory of cognitive load as well as the understanding that multitasking is said to be negatively affecting learning. However, it may be because students are not multitasking on many mediums at one time or perhaps, they are able to regulate their multitasking behavior.
There is an insignificant relationship between SMM and students’ academic performance. The reason is that students do their studies and academic tasks in their schedule (Hameed and Haq, 2021). As for SMM is concerned, most students do these social media multitasking activities in their leisure and free time. So there is no connection between SMM
activity and academic performance of the students.
Empirical results also show that SMSCF moderates the relationship between “social media usage for non-academic purposes” and students’ academic performance H4. This implies that “social media usage for non-academic purposes” will have a lesser impact on the “academic performance” of those who have low levels of SMSCF and vice versa. This is in line with the findings of Muraven et al. (1998), Hofmann et al. (2012) and Lee et al. (2017). It is the students’ propensity to put off the effort required for achieving strategic goals because short-term, instant enjoyment increases the chance of SMSCF (Du et al., 2019). The respondents in this study who could focus on the long-term goal moderated their use of social media for non-academic use and hence, their academic performance as measured by their CGPA was not negatively impacted as presented in Figure 3.
Theoretical and practical implications
The main contribution of this study is the development of the moderating role of SMSCF between SMUNAP (SMUNAP) and academic performance (CGPA), which was not discussed before in the cognitive load theory context. The results of the study show that teachers and parents should discourage students from using social media for nonacademic purposes because SMUNAP hurts the student’s academic performance. On the other hand, students should have a strong purpose and career goal before using social media, because if students are goal-conscious i.e. concerned about their studies and career, then they use social media in a more controlled manner, so this will have a less negative impact on their academic performance.
The findings of the study can be used by government officials especially the education department including the Higher Education Commission. They can make certain plans on the additional usage of social media accounts, for example, a limit can be imposed on the usage within a particular day. Students should not be allowed to use social media forums more than the prescribed limit. This will be a forced measure to control the additional usage of social media and hence their academic performance will not be hampered. The study further suggests that SMSCF is also a cause of poor academic performance. The students can be engaged in more learning-oriented activities than social media. National television channels and media houses under a centrally controlled authority can develop a series of academic-oriented entertainment programs. These programs will be a source of leisure for the students as well as they will be able to learn from them.
The trends of teaching are changing especially after COVID-19 the education and teaching shifted to online mode in various parts of the world. The faculty members can engage their students through social media platforms to influence their behavior. Faculty members can develop short videos of the lectures with animated characters to guide students better. These mentioned initiatives can be facilitated by the management of the university/ institution. Universities/institutions have the resources to develop campaigns at a mass level to replace the additional use of social media with curriculum-related activities to enhance learning.
As a result, decision-makers may use the findings of this study to help them create collaborative learning communities by using a social media-based educational framework to enhance the learning process. We may have to deal with other crises in the future other than COVID-19 that pose a threat to educational institutions’ stakeholders (Al-Emran, 2020).
These initiatives will help the students in identifying and getting benefits from the related resources for getting knowledge. While watching and using these resources the extraneous load capacity will be used. Thus the space would be less for intrinsic and germane load however the consumption of space through the extraneous load will also contribute towards
the learning process.
Limitations and future research
This study shows significant contributions in cognitive load theory as well as social media use and students’ academic performance. But still, some limitations exist. First, this study used a quantitative method for data analysis, but qualitative as well as mixed method is also a very effective tool to understand a more in-depth individual experience of social media usage and their impact on academic performance. Second, here we used SMSCF as a moderator between SMUNAP and academic performance. Other important variables could be used as a moderator, i.e. teacher regulation on social media use, student personality types (introvert vs extrovert), etc. Similarly, in this COVID-19 scenario, the COVID-19 pandemic could also be used as a moderator between social media usage and academic performance. Third, this study used social media in general. In the future, we can specify a particular type of social media, i.e. WhatsApp or Facebook or Zoom app, Google classroom, Google meet, etc. Fourth, in the future, we could also add antecedents of social media usage, for example, social media could be used as a mediator between teacher-specific tasks or other related variables and academic performance. Finally, similar studies could be conducted by including other age groups besides university students or by examining other cities in Pakistan in particular and other cities in the world in general.
The current study is aimed at examining the role of social media in influencing students’ academic performance. Findings revealed that SMUAP and social media marketing did not affect students’ academic performance. Additionally, “social media usage for nonacademic purposes” had a negative effect on students’ academic performance. This implies that the more they use social media for non-academic purposes, the lower will be their academic performance. This suggests that the assumptions of cognitive load are fully applicable in the context of SMUNAP and their academic performance. Lastly, SMSCF moderates the relationship between “social media usage for non-academic purposes” and students’ academic performance. This implies that “social media usage for non-academic purposes” will have a lesser impact on the “academic performance” of those who have low levels of SMSCF. This result further highlights the importance of cognitive load theory in the presence of SMSCF.