Abstract
Applications of adaptive e-learning, recommender systems and learning analytics are typically presented individually, however, their combination poses several challenging requirements ranging from organizational to technical issues. This article presents a technical study from a holistic application of a variety of e-learning assistance technologies, including recommender systems, chatbots, adaptivity, and learning analytics. At its core we operationalize interoperability standards such as the Experience API (xAPI) and Learning Tools Interoperability (LTI), and control the data flow via a standard-encapsulating middleware approach. We report on the challenges regarding organization, methodology, content, didactics, and technology. A systematic evaluation with the target group discusses the users’ expectations with the measured interactions.
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Altun, D. et al. (2022). Lessons Learned from Creating, Implementing and Evaluating Assisted E-Learning Incorporating Adaptivity, Recommendations and Learning Analytics. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2022. Lecture Notes in Computer Science, vol 13332. Springer, Cham. https://doi.org/10.1007/978-3-031-05887-5_18
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