Supporting Trust and Engagement in Personalized Learning

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Abstract

This paper discusses the benefits and challenges associated with supporting learner trust and engagement in personalized learning. Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Quality of personalization depends on information collected from the learner and stored in a personal profile. The authors elaborate on research and design solutions to support privacy, trust and engagement in the context of the Learning and Performance Support (LPSS) program at the National Research Council of Canada. The paper builds on earlier work by the authors related to learning personalization, trust and privacy, and on the results of user surveys and usability studies of the LPSS system related to trust, privacy and user engagement.

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APA

Kondratova, I., Molyneaux, H., & Fournier, H. (2018). Supporting Trust and Engagement in Personalized Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10925 LNCS, pp. 44–59). Springer Verlag. https://doi.org/10.1007/978-3-319-91152-6_4

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