Abstract
AbstractE-learning students have a tendency to get demotivated and easily dropout from online courses. Refining the learners’ involvement and reducing dropout rates in these e-learning based scenarios is the main drive of this study. This study also shares the results obtained and crafts a comparison with new and emerging commercial solutions. In a primary phase, the goal was to center the study and research made (background, state of the art, survey and interviews to uncover motives and behavior patterns). In an additional stage, the development, trails and validation of an operating prototype of an Intelligent Alerting System to grant and evaluate concepts, gather statistical data on its efficiency, explore and detect if course accomplishment rates did actually improve. The results measured the effectiveness of learning (accomplishment and dropout rates) before and after the application of the proposed solution. Finally, some related work is considered, as well as emerging commercial solutions are compared with the proposed solution.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Education
Reference31 articles.
1. Adnan, M., & Anwar, K. (2020). Online learning amid the COVID-19 pandemic: Students’ perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51. https://doi.org/10.33902/JPSP.2020261309
2. Alameri, J., Masadeh, R., Hamadallah, E., Ismail, H. B., & Fakhouri, H. N. (2020). Students’ perceptions of e-learning platforms (Moodle, Microsoft Teams and Zoom platforms) in The University of Jordan Education and its Relation to self-study and Academic Achievement During COVID-19 pandemic. The University of Jordan.
3. Anni Silvola, A., Näykki, P., Kaveri, A., & Muukkonen, H. (2021). Expectations for supporting student engagement with learning analytics: An academic path perspective. Computers & Education, 168, 104192. https://doi.org/10.1016/j.compedu.2021.104192
4. AspirEDU Educational Analytics. (2017). Dropout detective—Identify, prioritize and support your at-risk students. Available at: http://aspiredu.com/wp-content/uploads/2017/03/Dropout-Detective-Higher-Ed-Overview.pdf. Accessed 15 May 2021.
5. Atif, A., Richards, D., Liu, D., & Bilgin, A. (2020). Perceived benefits and barriers of a prototype early alert system to detect engagement and support ‘at-risk’ students: The teacher perspective. Computers & Education, 56, 103954. https://doi.org/10.1016/j.compedu.2020.103954
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