Abstract
Learning, an active cognitive activity, differs from one learner to another, suggesting the need for personalized learning. The development of personalized recommender systems typically involves a learner model component, which is used to capture and store the personal information, preferences and other characteristics of the learner. While reading, learners engage in number of metacognitive activities e.g. text marking/highlights. These metacognitive interactions could serve as useful information for the learner model, to achieve personalization. The recommender system developed is integrated with nStudy, an online learning platform that provides a number of annotation tools (e.g. highlighting, tags) that support metacognitive activities. A user study was conducted to evaluate the effectiveness of using the highlights (a metacognitive activity) a learner makes while reading, as a preference elicitation method for the learner model. The findings show that the learner generated metacognitive activities while reading serve as an appropriate input mechanism to guide personalized learning recommendations.
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Odilinye, L., Popowich, F. (2020). Personalized Recommender System Using Learners’ Metacognitive Reading Activities. In: Vittorini, P., Di Mascio, T., Tarantino, L., Temperini, M., Gennari, R., De la Prieta, F. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference. MIS4TEL 2020. Advances in Intelligent Systems and Computing, vol 1241. Springer, Cham. https://doi.org/10.1007/978-3-030-52538-5_20
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