Abstract
Social networking has modernized digital education through the provision of novel functionalities, such as reacting, commenting, motivation or group formation. In the light of the new developments, this paper presents SNAKE (Social Networking for Advancing Knowledge in E-learning environment), which is an e-learning software incorporating social characteristics for the tutoring of computer programming. However, investigating the impact of e-learning software holding social characteristics is yet a quite under-researched area. To this end, an extensive exploration of SNAKE has been conducted which examined different factors affecting social networking-based learning. The population of this study included 200 undergraduate students of computer science. To analyze the disposable data, the structural equation modeling was utilized. Upon analysis and structural model validities, the experimentation led to an extended Technology Acceptance Model (TAM) utilized for estimating the impact of the various variables. In more detail, the research model consisted of the TAM core constructs and three external variables. Concluding, the study confirmed that the model adequately explained causal relationships between variables and presented direct and indirect significant impacts of them on SNAKE which can promote learners’ better academic performance and knowledge acquisition.




Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Alalwan, N., Al-Rahmi, W. M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A. M. (2019). Integrated three theories to develop a model of factors affecting students’ academic performance in higher education. IEEE Access, 7, 98725–98742.
Al-Busaidi, K. A., & Al-Shihi, H. (2010). Instructors' acceptance of learning management systems: A theoretical framework. Communications of the IBIMA, 2010, 1–10.
Al-Maatouk, Q., Othman, M. S., Aldraiweesh, A., Alturki, U., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2020). Task-technology fit and technology acceptance model application to structure and evaluate the adoption of social Media in Academia. IEEE Access, 8, 78427–78440.
Al-Rahmi, W. M., & Zeki, A. M. (2017). A model of using social media for collaborative learning to enhance learners’ performance on learning. Journal of King Saud University - Computer and Information Sciences, 29(4), 526–535.
Ayyash, M. M. (2017). Proposing a model for social media networks adoption in education, 2017 IEEE International Conference on Engineering and Technology (ICET), Antalya, pp. 1-5.
Babu, K. M., Gopalakarishnan, G., Girish, S., Suryanarayan, S. S. (2017). Implementation and measurement of technology enabled social learning in engineering education, 2017 5th IEEE International Conference on MOOCs, Innovation and Technology in Education (MITE), Bangalore, pp. 31-36.
Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analysing computer user satisfaction. Management Science, 29, 530–545.
Bamansoor, S., Kayode, B., Alhazmi, A. K., Ahmad Saany, S. I. (2018). The adoption of social learning Systems in Higher Education: Extended TAM," 2018 IEEE International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, pp. 1-7.
Bates, A. T. (2005). Technology, E-learning and distance education. London: Routledge.
Becheru, A.E., Popescu, E. (2017). Design of a conceptual knowledge extraction framework for a social learning environment based on Social Network Analysis methods, 2017 18th IEEE International Carpathian Control Conference (ICCC), Sinaia, pp. 177–182.
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS quarterly, 319–340.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19, 9–30.
Durak, G. (2017). Using social learning networks (SLNs) in higher education: Edmodo through the lenses of academics. The International Review of Research in Open and Distance Learning, 18(1), 84–109.
Ellahi, A. (2017). Fear of using technology: Investigating impact of using social networking sites in business education, 2017 IEEE 15th Student Conference on Research and Development (SCOReD), Putrajaya, pp. 234-237.
Fishbein M., Ajzen I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research, 1975.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gamo, J. (2019). Assessing a virtual Laboratory in Optics as a complement to on-site teaching. IEEE Transactions on Education, 62(2), 119–126.
Hair Jr., J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, G. V. (2014). Partial least squares structural equation modeling (PLS-SEM) an emerging tool in business research. European Business Review, 26(2), 106–121.
Hair, J. F., Hult, T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling. Thousand Oakes: Sage.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.
Krouska, A., Troussas, C., & Virvou, M. (2019). SN-learning: An exploratory study beyond e-learning and evaluation of its applications using EV-SNL framework. Journal of Computer Assisted Learning, 35(2), 168–177.
Laserna MS, Miguel MC (2018). Social media as a teaching innovation tool for the promotion of interest and motivation in higher education, 2018 International Symposium on Computers in Education (SIIE), Jerez, pp. 1-5.
Lytras, M. D., Visvizi, A., Daniela, L., Sarirete, A., & Ordonez De Pablos, P. (2018). Social networks research for sustainable smart education. Sustainability, 10, 2974.
Mäkiö, E., Mäkiö, J., Colombo, A. W., Harrison, R., Ahmad, B., Azmat, F. (2020). Work in Progress: Task-centric holistic teaching approach to teaching programming with Java, 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, pp. 1487–1492.
Marzano, R. J., Pickering, D. J., & Pollock, J. E. (2001). Classroom instruction that works: Research-based strategies for increasing student achievement (1st ed.pp. 1–192). London: Pearson.
Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational usage of Facebook. Computers in Education, 55(2), 444–453.
Moghavvemi, S., Paramanathan, T., Md Rahin, N., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour & Information Technology, 36(10), 1081–1100.
Moreno-Marcos, P. M., Alario-Hoyos, C., Muñoz-Merino, P. J., & Kloos, C. D. (2019). Prediction in MOOCs: A review and future research directions. IEEE Transactions on Learning Technologies, 12(3), 384–401.
Olivares, D., Ferreira Leite de Mello, R., Adesope, O., Rolim, V., Gaševic, D., Hundhausen, C. (2019). Using social network analysis to measure the effect of learning analytics in computing education, 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceió, Brazil, pp. 145–149.
Park, S. Y. (2009). An analysis of the technology acceptance model in Understanding University Students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150–162.
Park, S.-Y., Cha, S.-B., Lim, K., & Jung, S.-H. (2014). The relationship between university student learning outcomes and participation in social network services, social acceptance and attitude towards school life. BJET, 45(1), 97–111.
Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Computers in Human Behavior, 28(6), 2117–2127.
Rasheed Hinnawi, M. M. (2018). The role of social Media in Lifelong Informal Learning among Members of society, 2018 JCCO Joint International Conference on ICT in Education and Training, IEEE International Conference on Geocomputing, Tunisia, pp. 1–13.
Rejeesh E., Anupama, M. (2017). Social media and data mining enabled pre-counseling session: A system to perk up effectiveness of counseling in distance education, 2017 IEEE International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, pp. 153-156.
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS quarterly. Management Information Systems Quarterly, 36(1), 3–8.
Rogers Everett, M. (1995). Diffusion of innovations (Vol. 1995). New York: Free Press.
Roy, K., Singh, S., Ratra, S. (2018). Social-Network-Sites (SNS) & Its impact on Students' academic learning, 2018 IEEE Tenth International Conference on Technology for Education (T4E), Chennai, pp. 174-177.
Sanmamed, M. G., Muñoz Carril, P. C., & Dans Álvarez de Sotomayor, I. (2017). Factors which motivate the use of social networks by students. Psicothema, 29(2), 204–210.
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 561–570.
Troussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers & Education, 144, 103698.
Ursavaş, Ö. F., & Reisoglu, I. (2017). The effects of cognitive style on Edmodo users’ behaviour: A structural equation modeling-based multi-group analysis. The International Journal of Information and Learning Technology, 34(1), 31–50.
Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance intentions to use Gamification for training in higher education: Integrating the technology acceptance model, social motivation, and task technology fit. IEEE Access, 8, 21473–21484.
Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425–478.
Weng, F., Yang, R.-J., Ho, H.-J., & Su, H.-M. (2018). A TAM-based study of the attitude towards use intention of multimedia among school teachers. Applied System Innovation, 1, 36.
Wu, J.-Y., Hsiao, Y.-C., & Nian, M. W. (2020). Using supervised machine learning on large-scale online forums to classify course-related Facebook messages in predicting learning achievement within the personal learning environment. Interactive Learning Environments, 28(1), 65–80.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Troussas, C., Krouska, A. & Sgouropoulou, C. Impact of social networking for advancing learners’ knowledge in E-learning environments. Educ Inf Technol 26, 4285–4305 (2021). https://doi.org/10.1007/s10639-021-10483-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-021-10483-6