Abstract
This paper describes Pique, a web-based recommendation system that applies word embedding and a sequence generator to present students with a sequence of scientific paper recommendations personalized to their background and interest. The use of natural language processing (NLP) on learning materials enables educational environments to present students with papers with content that is responsive to their knowledge history and interests. Instructors tend to focus on presentation of learning materials based on overall learning goals in a course rather than personalizing the presentation for each student. The ultimate goal of Pique is to provide learners with content that will encourage their curiosity to learn more by presenting sequences of papers with increasingly more novel content. We piloted Pique with students in a course and report on their responses to the recommended sequences. The next steps are to improve the identification of relevant keywords to represent content and the algorithm for the sequence generator.
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The research reported in this article is funded by NSF IIS1618810 CompCog: RI: Small: Pique: A cognitive model of curiosity for personalizing sequences of learning resources.
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Mohseni, M., Maher, M.L., Grace, K., Najjar, N., Abbas, F., Eltayeby, O. (2019). Pique: Recommending a Personalized Sequence of Research Papers to Engage Student Curiosity. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_38
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