On the introduction of intelligent alerting systems to reduce e-learning dropout: a case study

Author:

Luis Ricardo M. Meira FerrãoORCID,Llamas-Nistal MartinORCID,Iglesias Manuel J. FernándezORCID

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

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3