Next Article in Journal
Development of Design Considerations as a Sustainability Approach for Military Protective Structures: A Case Study of Artillery Fighting Position in South Korea
Next Article in Special Issue
Knowledge Society Failure? Barriers in the Use of ICTs and Further Teacher Education in the Czech Republic
Previous Article in Journal
Immigrant Entrepreneurship in Sweden: The Liability of Newness
Previous Article in Special Issue
Integrating Design Thinking into a Packaging Design Course to Improve Students’ Creative Self-Efficacy and Flow Experience
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 12
Issue 16
10.3390/su12166471
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education

by
Mahdi M. Alamri
1,*,
Mohammed Amin Almaiah
2 and
Waleed Mugahed Al-Rahmi
3
1
Educational Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
2
Computer and Information Science, King Faisal University, Al-Ahsa 31982, Saudi Arabia
3
Faculty of Social Sciences and Humanities, School of Education, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(16), 6471; https://doi.org/10.3390/su12166471
Submission received: 2 June 2020 / Revised: 4 August 2020 / Accepted: 7 August 2020 / Published: 11 August 2020
(This article belongs to the Special Issue Technology and Innovation Management in Education)
Browse Figures
Versions Notes

Abstract

:
Nowadays, social media applications (SMAs) which are quite popular among students have a significant influence on education sustainability. However, there is a lack of research that explores elements of the constructivist learning approach with the technology acceptance model (TAM) in higher education. Therefore, this research aimed to minimize the literature gap by examining the SMA factors used for active collaborative learning (ACL) and engagement (EN) to affect the students’ academic performance in measuring education sustainability, as well as examining their satisfaction from its use. This study employed constructivism theory and TAM as the investigation model, and applied a quantitative method and analysis through surveying 192 university students at King Faisal University. Using structural equation modeling (SEM), the responses were sorted into nine factors and analyzed to explain students’ academic performance in measuring education sustainability, as well as their satisfaction. The results were analyzed with structural equation modelling; it was shown that all the hypotheses were supported and positively related to sustainability for education, confirming significant relationships between the use of SMAs and the rest of the variables considered in our model (interactivity with peers (IN-P), interactivity with lecturers (IN-L), ACL, EN, perceived ease of use (PEOU), perceived usefulness (PU), SMA use, student satisfaction (SS), and students’ academic performance (SAP).

1. Introduction

The use of social media applications (SMAs) in learning design in higher education may offer diverse educational advantages. For the technology acceptance model (TAM), it facilitates a significant relationship between student satisfaction (SS) and students’ academic performance (SAP) [1]. Additionally, the perceived ease of use (PEOU) and perceived usefulness (PU) of SMAs help learners to become more understanding, active, and engage with peers and lecturers [2]. PEOU and PU are statistically significant predictors of satisfaction and acceptance [3,4]. However, SMAs provides challenges in students’ academic transition from academy- to university-level educational experiences, which might hinder the SAP on measuring education sustainability [5]. Furthermore, learners who were interactive in the groups stated that they found help to solve problems based on learning [6]. Furthermore, using SMAs could increase learning achievement in active collaborative learning (ACL) environments [7]. Therefore, students have to track and analyze the collaboration patterns that occur during ACL. ACL and motivating cognitive skills, reflection and metacognition, are fundamental to SMAs for learning [8]. The current research looks at the issues of education sustainability by using SMAs, thus, some studies have demonstrated that a higher level of learning was achieved as a result of using SMAs for student assignments [9]. Similar to many other countries, Saudi Arabia has been hit by the SMA phenomenon [10,11,12]. However, there is a lack of research on SMA use in Saudi Arabian higher education. Therefore, the current study attempted to minimize the literature gap by examining the use of SMAs for ACL and engagement (EN) to enhance the SAP on measuring education sustainability. The Web 2.0 family are an important part of our daily life activities, with SMAs connecting millions of people, allowing resource sharing, information sharing, collaboration and communication [6]. The model of this study was developed on the basis of the theory of constructivism [13] and the TAM model [14]. This research is also a new step in the research carried out thus far under the two frameworks of constructivism and TAM, whose first results show that ACL is influential in EN and students’ academic performance on measuring education sustainability [10,11]. Previous studies have reported negative attitudes towards SMAs from students who believe that most SMAs do not assist them in achieving SAP [15] and are burdensome [16]. Alenazy et al. [17] reported skeptical attitudes of students toward using SMAs to aid them on measuring education sustainability. Others, such as [18], argued that students had a positive attitude toward learning activities combined with SMAs, even though they still preferred direct contact with peers and lecturers. Therefore, additional investigation is needed in the area of attitude antecedents towards SMA use for ACL and on measuring education sustainability [19]. Both psychological and emotional problems such as fear, discomfort, anger, insecurity, and sadness were reported as results of cyberstalking and cyberbullying via SMAs [20,21,22]. SMAs use effect SAP and ACL on measuring education sustainability however, some users’ get risk of being affected by cyberstalking and cyberbullying [20,23]. Therefore, the present research attempts to address this gap in the literature by conducting an investigation on the usage of SMAs for the purposes of sustainability in education that influences both academic performance and student satisfaction. Additionally, these research gaps are related to the fact that earlier models have focused either on interactive elements or perceptual aspects but not both in developing a model [4,8,10]. There is a lack of models regarding student satisfaction and academic performance including the utilization of SMAs in Saudi Arabian higher education [11,21]. Hence, this research aimed to minimize the literature gap by examining the SMA factors used for active collaborative learning (ACL) and engagement (EN) to affect the students’ academic performance on measuring education sustainability, as well as their satisfaction from its use.

2. Research Model and Hypotheses Development

To facilitate this research, we developed a model illustrated later in Figure 1, which shows the impact of SMA use on interaction, ACL and EN at the King Faisal University. Figure 1 shows the relationship between interactivity with peers (IN-P) and EN, interactivity with lecturers (IN-L) and EN, ACL and EN, PEOU, PU of SMAs and integration of SMAs for EN among students. Based on previous studies related to the constructivist theory and the TAM model [14,24], this research developed 14 hypotheses of how SMAs can affect SAP in Saudi Arabian higher education. Moreover, frameworks that illustrate the adoption of SMAs are based on a temporary element and its impact on sustainability issues are not available for higher education. Accordingly, this research attempts to combine crucial features from the constructivist learning approach and TAM with sustainability for education. See Figure 1.

2.1. Factors of IN-P and IN-L

SMAs for learning allow the promotion of discussion among students and lecturers about content and tasks [25]. It has been observed that students in online environments spend more time using SMAs to complete their learning processes [26]. These interactions in educational contexts may promote EN in learning communities both for collaboration and communication [27]. Previous studies [28] showed that interaction with group members and peers has a significant relationship with ACL and EN, which could support the idea that IN-P and IN-L affect SAP. Therefore, this research suggested the following hypotheses:
Hypothesis 1 (H1).
A significant relationship between IN-P and EN.
Hypothesis 2 (H2).
A significant relationship between IN-L and EN.

2.2. ACL

ACL is a situation in which two or more students learn together (e.g., face-to-face or computer-mediated, synchronous or asynchronous) [29]. The effect of SMA use on IN-P facilitates communication and knowledge creation, and thus facilitates ACL [30]. In this regard, SMAs enable the creation of spaces for the construction of knowledge as long as it extends learning out of the classroom [26]. Teachers are then responsible for creating a learning design that enhances ACL through SMAs [31]. First, the selection of SMAs is important, as it is known that different SMAs have unique characteristics [32]. Second, the design of elements such as the task or assessment, which can act as facilitators or as barriers for collaboration, should also be considered [33]. Furthermore, online ACL in massive open online courses (MOOCs) has been observed to be positively influenced by the extended confirmation model (ECM), which shows SS, and PU [34]. In previous research stages, the hypothesis about the significant relationship between ACL and EN was supported [4]. Therefore, this research suggested the following hypothesis:
Hypothesis 3 (H3).
A significant relationship between ACL and EN.

2.3. PEOU

TAM, the adaptation of Fishbein and Ajzen’s [35] theory of reasoned action by [14], has had a great impact in research measuring the acceptance of technology and SMAs for learning. In TAM, PEOU and PU are major predictors of intention and behavioral usage [36]. PEOU refers to the degree to which an individual believes that the use of a particular technology does not require much effort [14], and in most TAM models, it is considered as linked to ease of use [14]. Early research observed that SMAs that can enhance student learning processes are easy to use [37]. As TAM is a combination of PEOU and PU and other personal and contextual factors, there exists a wide range of TAM variations that combine in diverse ways all these elements along with SAP [36]. In this regard, our model relates fundamental TAM elements such as PEOU and PU along with students’ EN, satisfaction and students’ academic performance. Therefore, this research suggested the following hypotheses:
Hypothesis 4 (H4).
A significant relationship between PEOU and EN.
Hypothesis 6 (H6).
A significant relationship between PEOU and SMA use.
Hypothesis 8 (H8).
A significant relationship between PEOU and PU.

2.4. PU

PU refers to the degree to which a person perceives that the use of a particular technology may influence her or his performance [14]. If a person finds an SMA service useful, he or she will start thinking about it in a positive way [38]. Hsu, Hwang, Chuang, and Chang [39] showed that students using open networks perceived their usefulness and had high intentions to use the online resources. PU was observed as the largest influencer to adopting mobile technology [40,41,42]. Furthermore, in educational contexts, usefulness was observed by [43] as one of the most relevant factors for teachers to adopt SMAs in their lessons. Therefore, this research suggested the following hypotheses:
Hypothesis 5 (H5).
A significant relationship between PU and EN.
Hypothesis 7 (H7).
A significant relationship between PU and SMA use.

2.5. Students’ EN

Students’ EN is described as the level of emotional involvement and motivation to collaborate during learning [44] as well as the time and work students invest in learning tasks [45]. Furthermore, the personalization SMAs enable and the learning design itself may engage students in knowledge construction, which eventually involves higher levels of perceived learning [46]. When students are engaged in the learning tasks, their performance and results improve [12,44,47,48]. Therefore, this research suggested the following hypotheses:
Hypothesis 9 (H9).
A significant relationship between EN and SMA use.
Hypothesis 10 (H10).
A significant relationship between EN and SS.
Hypothesis 11 (H11).
A significant relationship between EN and SAP.

2.6. SMA Use

The use of SMAs contributes to the improvement of students’ academic performance on measuring education sustainability, which is positively related to SS [8,15,49]. Therefore, this research explored the connection between these different elements in light of Saudi Arabian higher education. SMAs are helpful in enhancing SAP [43], as learners have increased the popularity of SMA usage among students and lecturers. In literature, SMAs are argued to provide opportunities in learning enhancement through assistance in social learning, encouraging interaction between students and instructors, which enhances ACL and EN [2,43]. Therefore, this research suggested the following hypotheses:
Hypothesis 12 (H12).
A significant relationship between SMA use and SS.
Hypothesis 13 (H13).
A significant relationship between SMA use and SAP.

2.7. SS

Satisfaction has been described as the degree to which students’ expectations about teaching and teachers are met [44]. It has also been described as the degree to which a person is pleased with previous usage of technology [50,51]. Research has stated that there is satisfaction when learners feel they have achieved learning and they meet their own expected outcomes [51]. SMAs can provide students with opportunities for enjoyment and reduce feelings of isolation, thus providing them with more opportunities for interaction for learning [44]. Thus, this research focuses on the connection between SS and SAP. Therefore, this research suggested the following hypothesis:
Hypothesis 14 (H14).
A significant relationship exists between SS and SAP.

2.8. SAP

Students’ academic performance on measuring education sustainability is about the achievement of educational aims in terms of the acquisition of knowledge and the development of skills [44]. There is little research on SMAs and students’ academic performance [8]. Thus, the current research aimed to explore the relationship among constructivism theory and TAM model to measuring students’ academic performance on education sustainability. Therefore, this research considered IN-P and IN-L, ACL, PEOU and PU to be independent variables, and EN and SMAs use to be mediator variables, and the dependent variables were SS and SAP, see Figure 1.

3. Research Methodology

For the purpose of the study, we distributed 255 questionnaires, of which 227 were retrieved from the respondents; after the manual analysis of the questionnaires, 12 of 227 questionnaires were respondents incomplete—“students did not finish the survey”—and had to be dropped, making the remaining number 215. Of the remaining 215 questionnaire copies, 11 had missing data—“missing values in the survey”—when entered into SPSS and 12 contained outliers—“the data an abnormal distance from other values in a random sample”—making the number of remaining useable questionnaires 192. Such exclusions were recommended by [52], who related that outliers could lead to inaccurate statistical results and have to be eliminated. For the purpose of the study, we developed a conceptual model using the constructivism theory and TAM model to monitor SS and SAP in adopting the model for education sustainability. The questionnaire was sent to two experts to evaluate the validity content; the experts were selected based on their expertise and research interests in the adoption of studies, such as expertise on validity content was recommended by [52]. This study investigated the opinions of students on the use of SMAs for ACL and EN to measure SAP in Saudi Arabian higher education, and adoption of the model for education sustainability. The questionnaire used in the present study consisted of both open- and closed-ended questions; 39 questions were designed to collect background information (see Appendix A).
The questionnaire was distributed manually, and the respondents were asked to fill it in anonymously to obtain their feedback on SMA use for ACL and EN, and their view of its influence on SS and ASP on measuring education sustainability. The King Faisal University granted written consent for the data collection, and students could withdraw from the questionnaire at any time without consequences. The collected data were analyzed with structural equation modelling via IBM SPSS Statistics version 23, and Amos version 23. A total of 192 completed questionnaires were obtained from students, of whom 78 (40.6%) were male and 114 (59.4%) were female. From the respondents, 35 (18.2%) were in the age range of 18–19, 82 (42.7%) were in the age range of 20–21, 50 (26.0%) were in the age range of 22–23, and 25 (13.0%) were over 24 years of age. With regard to the educational background of the undergraduate students, 41 (21.4%) were in level one, 30 (15.6%) in level two, 41 (21.4%) in level three, whereas 80 (41.7%) were in a level four program. The majority of the respondents (94.3%) used SMAs for ACL and EN to affect education sustainability, and the remaining (5.7%) had no interaction with SMAs (see Table 1).

Data Collection and Measurement Model

The questionnaire in this research was adopted from previous researchers for measuring a model. An interaction factor was measured using three items recommended by [2,30,53]. The four items used to measure ACL were adapted from [8,54]. Four items were constructed to assess EN, and these were based on recommendations made by [33,55,56]. In addition, PEOU and PU were measured using six items from [14]. Moreover, four items adapted from [33,57] were used to measure the students’ use of SMAs. Four items to investigate SS were constructed from the work of [8,19,58]. Finally, five items of SAP were evaluated using items based on the suggestions of [8,59,60].

4. Results and Analysis

4.1. Measurement and Model Analysis

Kline [61] and Hair et al. [52] suggested the model estimation to be predicted through the maximum likelihood estimation procedures by using the goodness-of-fit guidelines such as normed chi-square, chi-square/degree of freedom, incremental fit index, Tucker–Lewis coefficient, comparative fit index, the parsimonious goodness of fit index, the root-mean-square residual and the root mean square error of approximation, as proposed by [52,62]. Thus, in this research, the measurement model was examined through unidimensionality, reliability, convergent validity and discriminant validity. Table 2 contains the summary of the goodness-of-fit indices used to evaluate the measurement model of SMAs adoption for education sustainability. Table 3 contains the constructs, items and crematory factor analysis results (see the questionnaire in Appendix A), and Table 4 displays discriminant validity.
Discriminant validity in this research evaluated SMA adoption for education sustainability by three criteria: correlation index among variables is less than 0.80 [52], the value of average variance extracted (AVE) of each construct is equal to or greater than 0.5, average variance extracted (AVE) of each construct is higher than the inter-construct correlations associated with that factor [63]. Moreover, the constructs, items and confirmatory factor analysis results factor loading of 0.5 or greater is acceptable, Cronbach’s alpha ≥0.70, and composite reliability ≥0.70 [52].

4.2. Structural Equation Model Analysis

The influence of interactive factors on SAP and of TAM model factors on SMA use for ACL and EN were examined by employing a path modelling analysis. The results are illustrated and discussed in conjunction with the hypothesis testing results. In the next step of the structural equation model, the authors ran CFA to test the structural model. Thus, Figure 2 shows the structural model (T-values), and Figure 3 shows the valid model and the suitability to test the proposed hypotheses. Table 5 shows the structural model; from the table, it can be clearly seen that the model’s key statistics are very good, indicating a valid model and the suitability to test the proposed hypotheses. The results of this research confirm that SMA use positively affects SS and SAP on adoption model in education sustainability, and they show that all hypotheses were supported. Moreover, the results provide support for the structural model and hypotheses regarding the directional linkage between the model’s variables. The parameters of the unstandardized coefficients and standard errors of the structural model are shown in Table 5.

4.3. Results of Hypothesis Testing

The results of this research, shown in Table 5 and Figure 2 and Figure 3, confirm that IN-P positively and significantly related with EN (β = 0.888, t = 0.147, p < 0.001). Thus, Hypothesis 1 is supported, indicating the impact of SMAs use on students’ interaction and EN for education sustainability. Moreover, IN-L positively and significantly related with EN (β = 0.137, t = 0.156, p < 0.001). Hence, hypothesis 2 is supported, indicating the impact of SMA use on students’ IN-L and EN. Next, the results confirmed that ACL positively and significantly related with EN (β = 0.335, t = 0.511, p < 0.001). Consequently, hypothesis 3 is supported, indicating the impact of SMA use on ACL and EN on education sustainability. Moving on to the fourth hypothesis, the results show that PEOU positively and significantly related with EN (β = 0.199, t = 0.276, p < 0.001). Therefore, hypothesis 4 is supported, indicating the ease of SMA use for EN among students. Similarly, the results show that PU positively and significantly related with EN (β = 0.185, t = 0.246, p < 0.001). Thus, hypothesis 5 is supported. The sixth hypothesis proposed that PEOU positively and significantly related with SMA use (β = 0.272, t = 0.362, p < 0.001). Thus, hypothesis 6 is supported, indicating the ease of SMA use for interaction, ACL, and EN among students. Next, hypothesis 7 confirmed that PU positively and significantly related with SMA use (β = 0.193, t = 0.248, p < 0.001). Hence, hypothesis 7 is supported, indicating that SMA use is useful for interaction, ACL, and EN among students’ adoption for education sustainability. The results further show that PEOU positively and significantly related with PU (β = 0.806, t = 0.840, p < 0.001). Therefore, hypothesis 8 is supported. Moving on to the mediator factors of the model, the results show that EN positively and significantly related with SMAs use (β = 0.184, t = 0.176, p < 0.001). Thus, hypothesis 9 is supported, indicating the effect of SMA use on EN among students. Moreover, the results show that EN positively and significantly related with SS (β = 0.477, t = 0.481, p < 0.001). Therefore, Hypothesis 10 is supported, indicating the impact of SMA use on interaction, ACL, and EN among students. Furthermore, the result of this research confirmed that EN positively and significantly related with SAP (β = 0.419, t = 0.366, p < 0.001). Hence, Hypothesis 11 is supported, indicating that the impact of SMAs use for interaction, ACL, and that EN affects SAP positively an adoption for education sustainability. The second factor is the relationship between SMA use and SS and SAP for education sustainability. The results show that SMA use positively and significantly related with SS (β = 0.290, t = 0.304, p < 0.001). Thus, Hypothesis 12 is supported, indicating that the impact of SMA use on interaction, ACL, and EN affects SS positively. Additionally, the next hypothesis confirmed that SMA use positively and significantly related with SAP (β = 0.268, t = 0.243, p < 0.001). Therefore, Hypothesis 13 is supported, indicating that the impact of SMA use for interaction, ACL, and EN affect SAP positively. Finally, Hypothesis 14 proposed that SS positively and significantly related with SAP (β = 0.361, t = 0.313, p < 0.001). Consequently, Hypothesis 14 is supported, indicating that the impact of SS with SMAs use for interaction, ACL, and EN in turn affects SAP positively adoption for education sustainability.

5. Discussion and Implementation

In this research, SMA use adoption for education sustainability in higher education learning activities was confirmed to have a positive effect on SS and SAP, which represents a supporting reason to enhance the educational use of SMAs in Saudi Arabian higher education. These results are aligned with previous reported that SMA adoption for education sustainability positively influences students [4,10,11,27,33,64]. The findings also provide two significant contributions to the constructivism theory and TAM model in the context of education sustainability [10,64]. Therefore, they suggest enhancing SMA use adoption for education sustainability in higher education, SMAs facilitate interaction with peers and lecturers, engagement, and collaboration that enhances student education sustainability. In addition, managers should provide students with support in using SMAs for education sustainability. Furthermore, all hypotheses were accepted, which contradicts what some past studies have reported regarding the negative impact on SAP related to the usage of SMAs [45]. However, previous researchers provided evidence of a positive impact on SAP, noting that the majority of students reported positive perceptions in their courses, including increased ACL, EN and exchange of information compared to face-to-face courses [2,10,21,24,27,64]. The contributions of this research lie in several areas of theoretical, implementation, and empirical analysis. It is worth mentioning that theories are located within and generated from within practice, which in turn acts as grounds for the development of new theories and new practices understood in the context of Saudi Arabia’s adoption for education sustainability. It is noted that this may be the first time that constructivism theory has been used in Saudi Arabian higher education, in particular to explore the impact of SMAs on EN to affect SAP adoption for education sustainability. The research has revealed that constructivism theory was an effective theory to be used in conjunction with TAM for the effects of SMA use on students’ EN on SAP in Saudi Arabian higher education.

5.1. Limitations of the Research

Regardless of its contribution to the research field, the limitations of the research should also be acknowledged. One of them is the sample, which includes only students at a specific higher education level and from a specific Saudi Arabian university; the results could be different in other contexts, even in the same country.

5.2. Conclusions

In general, the proposed extension to constructivism theory can also be valid to all cultures, and the research showed that TAM is moveable and can be utilized to examine the use of SMAs for EN in diverse cultures, such as Saudi Arabia in this case. No research so far had been conducted in Saudi Arabian higher education using SMAs for EN to affect SAP through constructivism theory. Thus, the use of constructivism theory in this research could be considered as a major contribution and strongly suggests the variables to use SMAs for ACL and EN among students’ adoption for education sustainability, as well as the TAM model in this research could be considered as a major contribution and strongly suggests the variables to use SMAs for PEOU and PU among students’ adoption for education sustainability. Another consideration from the research is that it is based on the students’ perceptions, which is not always the same thing as real implications in action. The significance that students give to the use of SMAs and their positive assessment related to its possible educational use; future work should study planning guidelines for teachers on ACL with the use of SMAs in different fields. If elements such as IN-P, IN-L and ACL have a positive impact on students’ EN as well as on SS and SAP, as supported by the confirmation of our hypotheses, these are actions that should be boosted in learning activities planned in courses. That can be done, for example, by including activities that involve peer feedback and teacher feedback, and group work. In addition, SMAs that focus on adoption for education sustainability, which means simple, familiar and easy to handle applications, should be carefully selected for learning scenarios in higher education. Future studies in this area must also take into account the teachers and other higher education stakeholders regarding adopting the use of SMAs for education sustainability. Finally, comparing and exploring views from and with other countries could also enrich the results obtained in this research and generate a broader view of how this topic is being dealt with in higher education.

Author Contributions

Conceptualization, W.M.A.-R. and M.M.A.; methodology, W.M.A.-R., and M.A.A.; software, W.M.A.-R. and M.M.A.; formal analysis, W.M.A.-R., M.M.A. and M.A.A. resources, W.M.A.-R., and M.M.A.; writing—original draft preparation, W.M.A.-R., M.M.A. and M.A.A.; writing—review and editing, W.M.A.-R., and M.M.A. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

The authors acknowledge the Deanship of Scientific Research at King Faisal University for their financial support under grant number 1811027.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Questionnaire.
Table A1. Questionnaire.
FactorsItemsQuestions
Interactivity with PeersIN-P1SMAs facilitate interaction with peers.
IN-P2SMAs give me the opportunity to discuss with peers.
IN-P3SMAs allow the exchange of information with peers.
Interactivity with LecturersIN-L1SMAs facilitate interaction with lecturers.
IN-L2SMAs give me the opportunity to discuss with lecturers.
IN-L3SMAs allow the exchange of information with lecturers.
Active Collaborative Learning ACL1By using SMAs I felt that I actively collaborated in my experience.
ACL 2By using SMAs I felt that I have co-created my own experience.
ACL3By using SMAs I felt that I had free reign to co-create my own experience.
ACL4By using SMAs I am satisfied with active collaborative in my research.
Engagement EN1By using SMAs I engage in interactions with my peers.
EN2By using SMAs I engage in interactions with my lecturers.
EN3By using SMAs I learned how to work with others effectively.
EN4By using SMAs I am satisfied with the EN in my studies.
Perceived Ease of Use PEOU1I feel that using SMAs will be easy in my studies.
PEOU2I feel that using SMAs will be easy to incorporate in my studies.
PEOU3I feel that using SMAs makes it easy to reach peers.
PEOU4I feel that using SMAs makes it easy to reach lecturers.
PEOU5Using SMAs is clear and understandable.
PEOU6SMAs do not require a lot of my mental effort.
Perceived UsefulnessPU1I believe that using SMAs is useful for learning.
PU2I feel that using SMAs will help me to learn more.
PU3I believe that using SMAs enhances my effectiveness.
PU4SMAs enable me to accomplish tasks more quickly.
PU5SMAs enhance my learning performance.
PU6SMAs enhance effectiveness in my studies.
Social Media UseSMU1I use SMAs for interaction with my peers.
SMU2I use SMAs for interaction with my lecturers.
SMU3I use SMAs for active collaborative learning.
SMU4I use SMAs for engagement.
Students’ SatisfactionSS1I enjoy the experience of using SMAs with peers.
SS2I enjoy the experience of using SMAs with lecturers.
SS3I am satisfied with using SMAs for learning.
SS4I am satisfied with using SMAs to improve my studies.
Students’ Academic PerformanceSAP1Has improved my comprehension of the concepts studied.
SAP2Has led to a better learning experience in this module.
SAP3SMAs have allowed me to better understand my studies.
SAP4SMAs are helpful in my studies and make it easy to learn.
SAP5SMAs improve my academic performance.

References

  1. Al-Maatouk, Q.; Othman, M.S.; Aldraiweesh, A.; Alturki, U.; Al-Rahmi, W.M.; Aljeraiwi, A.A. Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia. IEEE Access 2020, 8, 78427–78440. [Google Scholar] [CrossRef]
  2. Alalwan, N.; Al-Rahmi, W.M.; Alfarraj, O.; Alzahrani, A.; Yahaya, N.; Al-Rahmi, A.M. Integrated Three Theories to Develop a Model of Factors Affecting Students’ Academic Performance in Higher Education. IEEE Access 2019, 7, 98725–98742. [Google Scholar] [CrossRef]
  3. Almaiah, M.A.; Jalil, M.A.; Man, M. Extending the TAM to examine the effects of quality features on mobile learning acceptance. J. Comput. Educ. 2016, 3, 453–485. [Google Scholar] [CrossRef]
  4. Al-Rahmi, W.M.; Yahaya, N.; Alamri, M.M.; Alyoussef, I.Y.; Al-Rahmi, A.M.; Kamin, Y.B. Integrating innovation diffusion theory with technology acceptance model: Supporting students’ attitude towards using a massive open online courses (MOOCs) systems. Interact. Learn. Environ. 2019, 27, 1–13. [Google Scholar] [CrossRef]
  5. Dahlstrom, E.; Grunwald, P.; Boor, D.; Vockley, M. Ecar national study of students and information technology in higher education. Study Overv. 2011, 1, 1–35. [Google Scholar]
  6. Ktoridou, D.; Eteokleous, N. Social Networking Sites: Creating Special Interest Groups in Higher Education. In Proceedings of the ICICTE 2012—International Conference on ICT in Education, Rhodes island, Greece, 5–7 July 2012; pp. 363–375. [Google Scholar]
  7. Su, A.; Yang, S.; Hwang, W.; Zhang, J. A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments. Comput. Educ. 2010, 55, 752–766. [Google Scholar] [CrossRef]
  8. Al-Rahmi, W.M.; Yahaya, N.; Aldraiweesh, A.A.; Alturki, U.; Alamri, M.M.; Saud, M.S.B.; Kamin, Y.B.; Alhamed, O.A. Big data adoption and knowledge management sharing: An empirical investigation on their adoption and sustainability as a purpose of education. IEEE Access 2019, 7, 47245–47258. [Google Scholar] [CrossRef]
  9. Ertmer, P.; Newby, J.; Liu, W.; Tomory, A.; Yu, J.H.; Lee, Y.M. Students’ confidence and perceived value for participating in cross-cultural wiki-based collaborations. Educ. Technol. Res. Dev. 2011, 59, 213–228. [Google Scholar] [CrossRef]
  10. Alamri, M.M. Undergraduate Students’ Perceptions toward SMAs Usage and Academic Performance: A Study from Saudi Arabia. Int. J. Emerg. Technol. Learn. (IJET) 2019, 14, 61–79. [Google Scholar] [CrossRef] [Green Version]
  11. Alyoussef, I.Y.; Alamri, M.M.; Al-Rahmi, W.M. Social Media Use (SMU) for Teaching and Learning in Saudi Arabia. Int. J. Recent Technol. Eng. (IJRTE) 2019, 8, 942–946. [Google Scholar]
  12. Moafa, F.A.; Ahmad, K.; Al-Rahmi, W.M.; Alias, N.; Obaid, M.A.M. Factors for minimizing cyber harassment among university students: Case study in kingdom of Saudi Arabia (KSA). J. Theor. Appl. Inf. Technol. 2018, 96, 1606–1618. [Google Scholar]
  13. Vygotsky, L. Mind in Society: Development of Higher Psychological Processes; Harvard University Press: Cambridge, MA, USA, 1978. [Google Scholar]
  14. Davis, F.D. Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  15. Al-Maatouk, Q.; Othman, M.S.; Alsayed, A.O.; Al-Rahmi, A.M.; Abuhassna, H.; Al-Rahmi, W.M. Applying Communication Theory to Structure and Evaluate the Social Media Platforms in Academia. Int. J. Adv. Trends Comput. Sci. Eng. 2020, 9, 1505–1517. [Google Scholar] [CrossRef]
  16. Meishar-Tal, H.; Kurtz, G.; Pieterse, E. Facebook groups as LMS: A case study. Int. Rev. Res. Open Distance Learn. 2012, 13, 33–48. [Google Scholar] [CrossRef] [Green Version]
  17. Alenazy, W.M.; Al-Rahmi, W.M.; Khan, M.S. Validation of TAM model on social media use for collaborative learning to enhance collaborative authoring. IEEE Access 2019, 7, 71550–71562. [Google Scholar] [CrossRef]
  18. Morreale, S.; Staley, C.; Stavrositu, C.; Krakowiak, M. First-year college students’ attitudes toward communication technologies and their perceptions of communication competence in the 21st century. Commun. Educ. 2015, 64, 107–131. [Google Scholar] [CrossRef]
  19. Liao, Y.W.; Huang, Y.M.; Chen, H.C.; Huang, S.H. Exploring the antecedents of collaborative learning performance over social networking sites in a ubiquitous learning context. Comput. Hum. Behav. 2015, 43, 313–323. [Google Scholar] [CrossRef]
  20. Al-Rahmi, W.M.; Yahaya, N.; Alturki, U.; Alrobai, A.; Aldraiweesh, A.A.; Omar Alsayed, A.; Kamin, Y.B. Social media–based collaborative learning: The effect on learning success with the moderating role of cyberstalking and cyberbullying. Interact. Learn. Environ. 2020, 28, 1–14. [Google Scholar] [CrossRef]
  21. Al-Rahmi, W.M.; Yahaya, N.; Alamri, M.M.; Aljarboa, N.A.; Kamin, Y.B.; Saud, M.S.B. How cyber stalking and cyber bullying affect students’ open learning. IEEE Access 2019, 7, 20199–20210. [Google Scholar] [CrossRef]
  22. Fenaughty, N.; Harré, N. Factors associated with distressing electronic harassment and cyberbullying. Comput. Hum. Behav. 2013, 29, 803–811. [Google Scholar] [CrossRef]
  23. Gasser, U.; Maclay, M.C.; Palfrey, J. Working towards a deeper understanding of digital safety for children and young people in developing nations. Harv. Public Law Work. Pap. 2010, 1, 10–36. [Google Scholar]
  24. Al-Rahmi, W.M.; Yahaya, N.; Alamri, M.M.; Aljarboa, N.A.; Kamin, Y.B.; Moafa, F.A. A model of factors affecting cyber bullying behaviors among University students. IEEE Access 2018, 7, 2978–2985. [Google Scholar] [CrossRef]
  25. Patera, M.; Draper, S.; Naef, M. Exploring magic cottage: A virtual reality environment for stimulating children’s imaginative writing. Interact. Learn. Environ. 2008, 16, 245–263. [Google Scholar] [CrossRef] [Green Version]
  26. Bryer, T.A.; Chen, B. The Use of Social Media and Networks in Teaching Public Administration. Cutting-Edge Social Media Approaches to Business Education: Teaching with LinkedIn, Facebook, Twitter, Second Life, and Blogs (HC); Information Age Publishing: Charlotte, NC, USA, 2010. [Google Scholar]
  27. Ainin, S.; Naqshbandi, M.M.; Mogavvemi, S.; Jaafar, N.I. Facebook usage, socialization and academic performance. Comput. Educ. 2015, 83, 64–73. [Google Scholar] [CrossRef]
  28. Al-Rahmi, W.M.; Aldraiweesh, A.; Yahaya, N.; Kamin, Y.B. Massive open online courses (MOOCS): Systematic literature review in Malaysian higher education. Int. J. Eng. Technol. 2018, 7, 2197–2202. [Google Scholar] [CrossRef] [Green Version]
  29. Dillenbourg, P. What Do You Mean by Collaborative Leraning? In Collaborative-Learning: Cognitive and Computational Approaches; Dillenbourg, P., Ed.; Elsevier: Amsterdam, The Netherlands, 1999. [Google Scholar]
  30. Liu, Y. Developing a scale to measure the interactivity of websites. J. Advert. Res. 2003, 43, 207–216. [Google Scholar] [CrossRef]
  31. Fewkes, A.M.; McCabe, M. Facebook: Learning tool or distraction? J. Digit. Learn. Teach. Educ. 2012, 28, 92–98. [Google Scholar] [CrossRef]
  32. Cheng, E.W.; Chu, S.K.; Ma, C.S. Tertiary students’ intention to e-collaborate for group projects: Exploring the missing link from an extended theory of planned behaviour model. Br. J. Educ. Technol. 2016, 47, 958–969. [Google Scholar] [CrossRef]
  33. Al-Rahmi, W.M.; Alias, N.; Othman, M.S.; Ahmed, I.A.; Zeki, A.M.; Saged, A.A. Social media use, collaborative learning and students’ academic performance: A systematic literature review of theoretical models. J. Theor. Appl. Inf. Technol. 2017, 95, 5399–5414. [Google Scholar]
  34. Junjie, Z. Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective. Australas. J. Educ. Technol. 2017, 33, 123–135. [Google Scholar]
  35. Fishbein, M.; Ajzen, I. Predicting and understanding consumer behavior: Attitude-behavior correspondence. Underst. Attitudes Predict. Soc. Behav. 1980, 1, 148–172. [Google Scholar]
  36. Jan, A.U.; Contreras, V. Technology acceptance model for the use of information technology in universities. Comput. Hum. Behav. 2011, 27, 845–851. [Google Scholar] [CrossRef]
  37. Zeithaml, V.A. Service quality, profitability, and the economic worth of customers: What we know and what we need to learn. J. Acad. Mark. Sci. 2000, 28, 67–85. [Google Scholar] [CrossRef] [Green Version]
  38. Lin, K.Y.; Lu, H.P. Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Comput. Hum. Behav. 2011, 27, 1152–1161. [Google Scholar] [CrossRef]
  39. Hsu, C.K.; Hwang, G.J.; Chuang, C.W.; Chang, C.K. Effects on learners’ performance of using selected and open network resources in a problem-based learning activity. Br. J. Educ. Technol. 2012, 43, 606–623. [Google Scholar] [CrossRef]
  40. Almaiah, M.A.; Alamri, M.M.; Al-Rahmi, W.M. Analysis the Effect of Different Factors on the Development of Mobile Learning Applications at different stages of usage. IEEE Access 2019, 8, 16139–16154. [Google Scholar] [CrossRef]
  41. Park, S.Y.; Nam, M.W.; Cha, S.B. University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. Br. J. Educ. Technol. 2012, 43, 592–605. [Google Scholar] [CrossRef]
  42. Almaiah, M.A.; Alamri, M.M.; Al-Rahmi, W. Applying the UTAUT model to explain the students’ acceptance of Mobile learning system in higher education. IEEE Access 2019, 7, 174673–174686. [Google Scholar] [CrossRef]
  43. Ajjan, H.; Hartshorne, R. Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. Internet High. Educ. 2008, 11, 71–80. [Google Scholar] [CrossRef]
  44. Rueda, L.; Benítez, J.; Braojos, J. From traditional education technologies to student satisfaction in Management education: A theory of the role of social media applications. Inf. Manag. 2017, 54, 1059–1071. [Google Scholar] [CrossRef]
  45. Junco, R.; Elavsky, C.M.; Heiberger, G. Putting Twitter to the test: Assessing outcomes for student collaboration, engagement and success. Br. J. Educ. Technol. 2013, 44, 273–287. [Google Scholar] [CrossRef]
  46. Camus, M.; Hurt, N.E.; Larson, L.R.; Prevost, L. Facebook as an online teaching tool: Effects on student participation, learning, and overall course performance. Coll. Teach. 2016, 64, 84–94. [Google Scholar] [CrossRef]
  47. Moafa, F.A.; Ahmad, K.; Al-Rahmi, W.M.; Yahaya, N.; Kamin, Y.B.; Alamri, M.M. Develop a model to measure the ethical effects of students through social media use. IEEE Access 2018, 6, 56685–56699. [Google Scholar] [CrossRef]
  48. Pineda-Báez, C.; Bermúdez-Aponte, J.-J.; Rubiano-Bello, Á.; Pava-García, N.; Suárez-García, R.; Cruz-Becerra, F. Student engagement and academic performance in the Colombian University context. RELIEVE 2014, 20, 1–19. [Google Scholar]
  49. Cao, Y.; Ajjan, H.; Hong, P. Using social media applications for educational outcomes in college teaching: A structural equation analysis. Br. J. Educ. Technol. 2013, 44, 581–593. [Google Scholar] [CrossRef]
  50. Almaiah, M.A.; Alismaiel, O.A. Examination of factors influencing the use of mobile learning system: An empirical study. Educ. Inf. Technol. 2019, 24, 885–909. [Google Scholar] [CrossRef]
  51. Lee, D.Y.; Lehto, M.R. User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Comput. Educ. 2013, 61, 193–208. [Google Scholar] [CrossRef]
  52. Hair, J.F.; Sarstedt, M.; Ringle, C.M.; Mena, J.A. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 2012, 40, 414–433. [Google Scholar] [CrossRef]
  53. McMillan, J.; Hwang, S. Measures of perceived interactivity: An exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. J. Advert. 2002, 31, 29–42. [Google Scholar] [CrossRef]
  54. So, J.; Brush, T. Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Comput. Educ. 2008, 51, 318–336. [Google Scholar] [CrossRef]
  55. Gallini, S.M.; Moely, B.E. Service-learning and engagement, academic challenge and retention. Mich. J. Community Serv. Learn. 2003, 5, 14. [Google Scholar]
  56. Medlin, B.; Green, K.W., Jr. Enhancing performance through goal setting, engagement, and optimism. Ind. Manag. Data Syst. 2009, 109, 943–956. [Google Scholar] [CrossRef]
  57. Kim, H.; Kim, T.; Shin, S.W. Modeling roles of subjective norms and eTrust in customers’ acceptance of airline B2C eCommerce websites. Tour. Manag. 2009, 30, 266–277. [Google Scholar] [CrossRef]
  58. Moore, J. A synthesis of Sloan-C effective practices: December 2009. J. Asynchronous Learn. Netw. 2009, 13, 84–94. [Google Scholar]
  59. MacGeorge, E.L.; Homan, S.R.; Dunning, J.B., Jr.; Elmore, D.; Bodie, G.D.; Evans, E. The influence of learning characteristics on evaluation of audience response technology. J. Comput. High. Educ. 2008, 19, 25–46. [Google Scholar] [CrossRef]
  60. Banks. Reflections on the Use of ARS with Small Groups. In Audience Response Systems in Higher Education; Banks, D.A., Ed.; Information Science: Hershey, PA, USA, 2006. [Google Scholar]
  61. Kline, R.B. Principles and Practice of Structural Equation Modeling, 3rd ed.; The Guilford Press: New York, NY, USA, 2011. [Google Scholar]
  62. Byrne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 2nd ed.; Routledge: New York, NY, USA, 2010. [Google Scholar]
  63. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1998, 18, 39–50. [Google Scholar] [CrossRef]
  64. Sarwar, B.; Zulfiqar, S.; Aziz, S.; Ejaz Chandia, K. Usage of social media tools for collaborative learning: The effect on learning success with the moderating role of cyberbullying. J. Educ. Comput. Res. 2019, 57, 246–279. [Google Scholar] [CrossRef]
Sustainability 12 06471 g001 550
Figure 1. Research model.
Figure 1. Research model.
Sustainability 12 06471 g001
Sustainability 12 06471 g002 550
Figure 2. Results of the structural model (T-values).
Figure 2. Results of the structural model (T-values).
Sustainability 12 06471 g002
Sustainability 12 06471 g003 550
Figure 3. Results of the structural model (hypothesis testing).
Figure 3. Results of the structural model (hypothesis testing).
Sustainability 12 06471 g003
Table
Table 1. Demographic profile of respondents.
Table 1. Demographic profile of respondents.
CategoriesFrequency%Cumulative %
GenderMale7840.640.6
Female11459.4100
Age18–193518.218.2
20–218242.760.9
22–23502687
Above 242513100
DegreeLevel 14121.421.4
Level 23015.637
Level 34121.458.3
Level 48041.7100
SMAs UseActual Use of SMAs 18194.394.3
No use115.794.8
Table
Table 2. Summary of goodness-of-fit indices for the measurement model.
Table 2. Summary of goodness-of-fit indices for the measurement model.
Type of MeasureAcceptable Level of FitValues
Chi-square (χ2)≤3.5 to 02753.98
Normed chi-square (χ2)More than1.0 and less than 5.02.143
Root-mean-square Close to 0 (perfect fit)0.034
Incremental fit index≤0.900.914
Tucker–Lewis co-efficient≤0.900.908
Comparative fit index≤0.900.914
Root mean square error Below 0.10 a very good fit0.040
Table
Table 3. Constructs, items and crematory factor analysis results.
Table 3. Constructs, items and crematory factor analysis results.
Constructs and ItemsFactor LoadingComposite ReliabilityAverage Variance ExtractedCronbach’s Alpha
IN-PSMAs facilitate interaction with peers.
Gives me the opportunity to discuss with peers.
Allows the exchange of information with peers.		0.784
0.795
0.825		0.8640.6010.896
IN-LSMAs facilitate IN-L.
Gives me the opportunity to discuss with lecturers.
Allows the exchange of information with lecturers.		0.705
0.840
0.798		0.9220.6410.752
ACLI felt that I actively collaborated in my experience.
I felt that I have co-created my own experience.
I felt that I had free reign to co-create my own experience.
I am satisfied with active collaboration in my research.		0.755
0.796
0.753
0.891		0.8390.5620.868
ENI engage in interactions with my peers.
I engage in interactions with my lecturers.
I learned how to work with others effectively.
I am satisfied with the EN in my study.		0.835
0.784
0.720
0.850		0.8610.6040.838
PEOUI feel that using SMAs will be easy in my studies.
I feel that using SMAs will be easy to incorporate in my studies.
I feel that using SMAs makes it easy to reach peers.
I feel that using SMAs makes it easy to reach lecturers.
Using SMAs is clear and understandable.
SMAs does not require a lot of my mental effort.		0.762
0.704
0.862
0.795
0.861
0.838		0.8740.6300.870
PUI believe that using SMAs is useful for learning.
I feel that using SMAs will help me to learn more.
I believe that using SMAs enhances my effectiveness.
SMAs enable me to accomplish tasks more quickly.
SMAs enhance my learning performance.
SMAs enhance effectiveness in my study.		0.773
0.758
0.622
0.870
0.786
0.855		0.9280.6070.878
SMUI use SMAs for interaction with my peers.
I use SMAs for interaction with my lecturers.
I use SMAs for ACL.
I use SMAs for EN.		0.713
0.706
0.766
0.819		0.8740.6310.791
SSI enjoy the experience of SMA use with peers.
I enjoy the experience of SMA use with lecturers.
I am satisfied with using SMAs for learning.
I am satisfied with using SMAs to improve my study.		0.779
0.727
0.641
0.826		0.9170.6770.814
SAPHas improved my comprehension of the concepts studied.
Has led to a better learning experience in this module.
SMAs have allowed me to better understand my studies.
SMAs is helpful in my studies and makes it easy to learn.
SMAs improve my academic performance.		0.859
0.774
0.794
0.831
0.803		0.9290.6430.857
Table
Table 4. Discriminant validity
Table 4. Discriminant validity
IN-PIN-LACLENPEOUPUSMUSSSAP
IN-P0.874
IN-L0.7990.891
ACL0.7060.6570.876
EN0.7450.7060.6650.792
PEOU0.7270.7110.6550.6350.820
PU0.6260.7110.6660.6170.7970.799
SMU0.7560.7000.6700.6340.7540.6700.837
SS0.7770.7300.6470.7090.7930.6800.6980.829
SAP0.7700.7290.6940.6860.7150.6100.6640.7140.846
Table
Table 5. Regression weights.
Table 5. Regression weights.
HIRDEstimateSECRtpResult
H1IN-P➝EN0.0880.0422.0980.1470.036Supported
H2IN-L➝EN0.1370.0592.3270.1560.020Supported
H3ACL➝EN0.3350.0714.7420.5110.000Supported
H4PEOU➝EN0.1990.0762.6120.2760.009Supported
H5PU➝EN0.1850.0742.4940.2460.013Supported
H6PEOU➝SMU0.2720.0813.3730.3620.000Supported
H7PU➝SMU0.1930.0812.3940.2480.017Supported
H8PEOU➝PU0.8060.03821.390.8400.000Supported
H9EN➝SMU0.1840.0732.5110.1760.012Supported
H10EN➝SS0.4770.0529.1080.4810.000Supported
H11EN➝SAP0.4190.0636.6840.3660.000Supported
H12SMU➝SS0.2900.0644.5160.3040.000Supported
H13SMU➝SAP0.2680.0673.9740.2430.000Supported
H14SS➝SAP0.3610.0724.9990.3130.000Supported
Notes: I: independent; R: relationship; D: dependent; CR: critical ratio; t: t-value; p: p-value; SE: standard error. H: hypothesis.

Share and Cite

MDPI and ACS Style

Alamri, M.M.; Almaiah, M.A.; Al-Rahmi, W.M. Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education. Sustainability 2020, 12, 6471. https://doi.org/10.3390/su12166471

AMA Style

Alamri MM, Almaiah MA, Al-Rahmi WM. Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education. Sustainability. 2020; 12(16):6471. https://doi.org/10.3390/su12166471

Chicago/Turabian Style

Alamri, Mahdi M., Mohammed Amin Almaiah, and Waleed Mugahed Al-Rahmi. 2020. "Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education" Sustainability 12, no. 16: 6471. https://doi.org/10.3390/su12166471

APA Style

Alamri, M. M., Almaiah, M. A., & Al-Rahmi, W. M. (2020). Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education. Sustainability, 12(16), 6471. https://doi.org/10.3390/su12166471

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop