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Harnessing background knowledge for e-learning recommendation.

Mbipom, Blessing; Craw, Susan; Massie, Stewart

Authors

Blessing Mbipom

Susan Craw

Stewart Massie



Contributors

Max Bramer
Editor

Miltos Petridis
Editor

Abstract

The growing availability of good quality, learning-focused content on the Web makes it an excellent source of resources for e-learning systems. However, learners can find it hard to retrieve material well-aligned with their learning goals because of the difficulty in assembling effective keyword searches due to both an inherent lack of domain knowledge, and the unfamiliar vocabulary often employed by domain experts. We take a step towards bridging this semantic gap by introducing a novel method that automatically creates custom background knowledge in the form of a set of rich concepts related to the selected learning domain. Further, we develop a hybrid approach that allows the background knowledge to influence retrieval in the recommendation of new learning materials by leveraging the vocabulary associated with our discovered concepts in the representation process. We evaluate the effectiveness of our approach on a dataset of Machine Learning and Data Mining papers and show it to outperform the benchmark methods. This paper has won the Donald Michie Memorial Award for Best Technical Paper at AI-2016.

Start Date Dec 13, 2016
Publication Date Dec 13, 2016
Publisher Springer (part of Springer Nature)
Pages 3-17
ISBN 9783319471747
Institution Citation MBIPOM, B., CRAW, S. and MASSIE, S. 2016. Harnessing background knowledge for e-learning recommendation. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (SGAI 2016), 13-15 December 2016, Cambridge, UK. Cham: Springer [online], pages 3-17. Available from: https://dx.doi.org/10.1007/978-3-319-47175-4_1
DOI https://doi.org/10.1007/978-3-319-47175-4_1
Keywords Data mining; Elearning systems; Background knowledge; Learning resources; Knowledge acquisition

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