Abstract
AbstractIn this study, an intervention engine based on learning analytics was designed and developed. The intervention engine is named the Intelligent Intervention System (In2S). Within the scope of this research; In2S system and its components have been introduced, and the system is evaluated based on learners’ views. In2S includes three types of intervention that are instructional, supportive, and motivational intervention. The instructional intervention was structured based on assessment tasks. The supportive and motivational interventions were structured based on the learning experiences of the learners. Signal lights (red, yellow, and green) are presented to the learners for each assessment task as an instructional intervention. Supportive intervention is presented to the learners via the dashboard. In the context of motivational intervention, elements of gamification as a leader board, badges, and notifications have been used. In order to obtain the learner’s views about the In2S, semi-structured interviews were conducted with the learners who had a previous learning experience with the system. The learning environment was evaluated based on their views. Learners had a nine-week learning experience in the e-learning environment. Then, eight students who used the system most actively and eight students who used the system most passively were selected for focus group interviews.. According to the findings, it was seen that the learners who use the intervention engine indicated that the system is useful and want to use it in the context of other courses.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Education
Reference46 articles.
1. Acar, T. (2006). Sato uyarı indeksleri ile madde ve başarı analizleri. Retrieved April 09, 2018, from http://www.parantezegitim.net/hakkimizda/Sato-TulinACAR.pdf
2. AECT. (2008). Definition. In A. Januszewski & M. Molenda (Eds.), Educational technology: A definition with commentary (pp. 1–14). New York: Lawrence Erlbaum Associates.
3. Ali, L., Hatala, M., Gasavic, D., & Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58, 470–489.
4. Argyris, C. (1970). Intervention theory and method: A behavioral science view. Addison Wesley: Reading, MA.
5. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267–270). ACM.
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