Abstract
The information overload in the past two decades has enabled question-answering (QA) systems to accumulate large amounts of textual fragments that reflect human knowledge. Therefore, such systems have become not just a source for information retrieval, but also a means towards a unique learning experience. Recently developed recommendation techniques for search engine queries try to leverage the order in which users navigate through them. Although a similar approach might improve the learning experience with QA systems, questions would still be considered as abstract objects, without any content or meaning. In this paper, a new learning-oriented technique is defined that exploits not only the user’s history log, but also two important question attributes that reflect its content and purpose: the topic and the learning objective. In order to do this, a domain-specific topic-taxonomy and Bloom’s learning framework is employed, whereas for modeling the order in which questions are selected, variable length Markov chains (VLMC) are used. Results show that the learning-oriented recommender can provide more useful, meaningful recommendations for a better learning experience than other predictive models.
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Kosorus, H., Küng, J. (2014). Learning-Oriented Question Recommendation Using Bloom’s Learning Taxonomy and Variable Length Hidden Markov Models. In: Hameurlain, A., Küng, J., Wagner, R., Dang, T., Thoai, N. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XVI. Lecture Notes in Computer Science(), vol 8960. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45947-8_3
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