Abstract
The adoption of e-learning technology by the academic community, has been a long source of research from multiple disciplines including education, psychology and computer science. As more and more academic institutions have opted to use online technology for their course delivery and pedagogical activities, there has been a surge of interest in evaluating the acceptance of the academic community to adopt and accept the use of e-learning management systems. This is due to the increasing concerns that despite the wide use and deployment of e-learning technologies, the intended impact on education is not achieved. We review the conducted studies on the use of objective procedures for evaluating e-learning systems in tandem with subjective data analysis. The evaluation process consists of understanding further the factors related to the acceptance and adoption of online educational systems by instructors and students in order to devise strategies for improving the teaching and research quality.
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References
Ajzen, I., & Fishbein, M. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
Albert, W., & Tullis, T. ( 2013). Measuring the user experience: Collecting, analyzing, and presenting usability metrics. Newnes.
Asarbakhsh, M., & Sandars, J. (2013). E-learning: The essential usability perspective. Clin. Teach., 10(1), 47–50.
Atterer, R., & Schmidt, A. (2007). Tracking the interaction of users with AJAX applications for usability testing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1347–1350). ACM.
Barlow, K., & Lane, J. (2007). Like technology from an advanced alien culture: Google apps for education at ASU. In Proceedings of the 35th Annual ACM SIGUCCS Fall Conference (pp. 8–10). ACM.
Bringula, R. P. (2013). Influence of faculty-and web portal design-related factors on web portal usability: A hierarchical regression analysis. Computers & Education, 68, 187–198.
Brooke, J. (1996). SUS—A quick and dirty usability scale. In Usability evaluation in industry (Vol. 189, no. 194, pp. 4–7).
Burton-Jones, A., & Grange, C. (2012). From use to effective use: A representation theory perspective. Information Systems Research, 24(3), 632–658.
Cassino, R., & Tucci, M. (2011). Developing usable web interfaces with the aid of automatic verification of their formal specification. Journal of Visual Languages & Computing, 22(2), 140–149.
Cassino, R., Tucci, M., Vitiello, G., & Francese, R. (2015). Empirical validation of an automatic usability evaluation method. Journal of Visual Languages & Computing, 28, 1–22.
Chua, B. B., & Dyson, L. E. (2004). Applying the ISO 9126 model to the evaluation of an e-learning system. In Proceedings of ASCILITE (pp. 5–8).
D’Ambra, J., Wilson, C. S., & Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of e-books by academics. Journal of the Association for Information Science and Technology, 64(1), 48–64.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
de Santana, V. F., & Baranauskas, M. C. C. (2015). Welfit: A remote evaluation tool for identifying web usage patterns through client-side logging. International Journal of Human-Computer Studies, 76, 40–49.
de Vasconcelos, L. G., & Baldochi Jr., L. A. (2012). Towards an automatic evaluation of web applications. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (pp. 709–716). ACM.
Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Gefen, D., et al. (2012). On the use of neurophysiological tools in is research: Developing a research agenda for neurois. MIS Quarterly, 36(3), 679–702.
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9–21.
Eckhardt, A., Maier, C., & Buettner, R. (2012). The influence of pressure to perform and experience on changing perceptions and user performance: A multi-method experimental analysis. In ICIS 2012 Proceedings.
Escobar-Rodriguez, T., & Monge-Lozano, P. (2012). The acceptance of moodle technology by business administration students. Computers & Education, 58(4), 1085–1093.
Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice. Taylor & Francis.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 213–236.
Harrati, N., Bouchrika, I., & Mahfouf, Z. (2016). Investigating the uptake of educational systems by academics using the technology to performance chain model. Library Hi Tech, 35(4).
Harrati, N., Bouchrika, I., Tari, A., & Ladjailia, A. (2015). Automating the evaluation of usability remotely for web applications via a model-based approach. In 2015 First International Conference on New Technologies of Information and Communication (NTIC) (pp. 1–6). IEEE.
Harrati, N., Bouchrika, I., Tari, A., & Ladjailia, A. (2016). Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis. Computers in Human Behavior, 61, 463–471.
Henriksson, A., Yi, Y., Frost, B., & Middleton, M. (2007). Evaluation instrument for e-government websites. Electronic Government, an International Journal, 4(2), 204–226.
Hornbæk, K. (2006). Current practice in measuring usability: Challenges to usability studies and research. International Journal of Human-Computer Studies, 64(2), 79–102.
Hrtoňová, N., Kohout, J., Rohlíková, L., & Zounek, J. (2015). Factors influencing acceptance of e-learning by teachers in the Czech Republic. Computers in Human Behavior, 51, 873–879.
Hsu, C.-L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65–74.
Ismailova, R. (2017). Web site accessibility, usability and security: A survey of government web sites in Kyrgyz Republic. Universal Access in the Information Society, 16(1), 257–264.
Ivory, M. Y., & Hearst, M. A. (2001). The state of the art in automating usability evaluation of user interfaces. ACM Computing Surveys, 33(4), 470–516.
Joo, S., & Choi, N. (2015). Factors affecting undergraduates selection of online library resources in academic tasks: Usefulness, ease-of-use, resource quality, and individual differences. Library Hi Tech, 33(2), 272–291.
Laurillard, D., Oliver, M., Wasson, B., & Hoppe, U. (2009). Implementing technology-enhanced learning. In Technology-enhanced learning (pp. 289–306). Springer.
Liapis, A., Katsanos, C., Sotiropoulos, D., Xenos, M., & Karousos, N. (2015). Recognizing emotions in human computer interaction: Studying stress using skin conductance. In Human-Computer Interaction–INTERACT 2015 (pp. 255–262). Springer.
Liaw, S.-S., Huang, H.-M., & Chen, G.-D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49(4), 1066–1080.
Marco, F. A., Penichet, V. M. R., & Gallud, J. A. (2013). Collaborative e-learning through drag and share in synchronous shared workspaces. Journal of UCS, 19(7), 894–911.
Mayhew, D. J. (1999). The usability engineering lifecycle. In CHI’99 Extended Abstracts on Human Factors in Computing Systems (pp. 147–148). ACM.
McGill, T. J., & Hobbs, V. (2008). How students and instructors using a virtual learning environment perceive the fit between technology and task. Journal of Computer Assisted Learning, 24(3), 191–202.
McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52(2), 496–508.
McGill, T. J., Klobas, J. E., & Renzi, S. (2014). Critical success factors for the continuation of e-learning initiatives. The Internet and Higher Education, 22, 24–36.
McGill, T., Klobas, J., & Renzi, S. (2011). Lms use and instructor performance: The role of task-technology fit. International Journal on E-Learning, 10(1), 43–62.
Mott, J. (2010). Envisioning the post-LMS era: The open learning network. Educause Quarterly, 33(1), 1–9.
Navimipour, N. J., & Zareie, B. (2015). A model for assessing the impact of e-learning systems on employees satisfaction. Computers in Human Behavior, 53, 475–485.
Orfanou, K., Tselios, N., & Katsanos, C. (2015). Perceived usability evaluation of learning management systems: Empirical evaluation of the system usability scale. The International Review of Research in Open and Distributed Learning, 16(2).
Paganelli, L., & Paternò, F. (2002). Intelligent analysis of user interactions with web applications. In International Conference on Intelligent User Interfaces (pp. 111–118).
Paternò, F., Santoro, C., & Spano, L. D. (2012). Improving support for visual task modelling. In Human-centered software engineering (pp. 299–306). Springer.
Pentland, B. T. (1989). Use and productivity in personal computing: An empirical test. In Proceedings of the Tenth International Conference on Information Systems, MA, Boston (pp. 211–222).
Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614–622.
Phillips, L. A., Calantone, R., & Lee, M.-T. (1994). International technology adoption: Behavior structure, demand certainty and culture. Journal of Business & Industrial Marketing, 9(2), 16–28.
Phillips, R., McNaught, C., & Kennedy, G. (2012). Evaluating e-learning: Guiding research and practice. Routledge.
Quade, M., Lehmann, G., Engelbrecht, K.-P., Roscher, D., & Albayrak, S. (2013). Automated usability evaluation of model-based adaptive user interfaces for users with special and specific needs by simulating user interaction. In User modeling and adaptation for daily routines (pp. 219–247). Springer.
Raven, A., Leeds, E. M., & Park, C. (2010). Digital video presentation and student performance: A task technology fit perspective. International Journal of Information and Communication Technology Education, 6(1), 17.
Renaut, C., Batier, C., Flory, L., & Heyde, M. (2006). Improving web site usability for a better e-learning experience. In Current developments in technology-assisted education (pp. 891–895). Badajoz, Spain: FORMATEX.
Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.
Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
Rogers Everett, M. (1995). Diffusion of innovations 12. New York.
Sauro, J., & Lewis, J. R. (2009). Correlations among prototypical usability metrics: Evidence for the construct of usability. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1609–1618). ACM.
Simões, A. P., & de Moraes, A. (2012). The ergonomic evaluation of a virtual learning environment usability. Work-Journal of Prevention Assessment and Rehabilitation, 41, 1140.
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625–649.
Šumak, B., HeričKo, M., & PušNik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85–92.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
Tiedtke, T., Märtin, C., & Gerth, N. (2002). AWUSA—A tool for automated website usability analysis. In Workshop on Interactive Systems. Design, Specification, and Verification. Rostock, Germany, June (pp. 12–14).
Tsai, P. C.-F., Yen, Y.-F., Huang, L.-C., & Huang, C. (2007). A study on motivating employees learning commitment in the post-downsizing era: Job satisfaction perspective. Journal of World Business, 42(2), 157–169.
Vaquero, L. M., Rodero-Merino, L., Caceres, J., & Lindner, M. (2008). A break in the clouds: Towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1), 50–55.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 115–139.
Welsh, E. T., Wanberg, C. R., Brown, K. G., & Simmering, M. J. (2003). E-learning: Emerging uses, empirical results and future directions. International Journal of Training and Development, 7(4), 245–258.
Yi, Y. J., You, S., & Bae, B. J. (2016). The influence of smartphones on academic performance: The development of the technology-to-performance chain model. Library Hi Tech, 34(3), 480–499.
Zhang, D., & Nunamaker, J. F. (2003). Powering e-learning in the new millennium: An overview of e-learning and enabling technology. Information Systems Frontiers, 5(2), 207–218.
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Bouchrika, I., Harrati, N., Mahfouf, Z., Gasmallah, N. (2018). Evaluating the Acceptance of e-Learning Systems via Subjective and Objective Data Analysis. In: Caballé, S., Conesa, J. (eds) Software Data Engineering for Network eLearning Environments. Lecture Notes on Data Engineering and Communications Technologies, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-68318-8_10
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