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Improving e-learning recommendation by using background knowledge.

Mbipom, Blessing; Craw, Susan; Massie, Stewart

Authors

Blessing Mbipom



Abstract

There is currently a large amount of e-Learning resources available to learners on the Web. However, learners often have difficulty finding and retrieving relevant materials to support their learning goals because they lack the domain knowledge to craft effective queries that convey what they wish to learn. In addition, the unfamiliar vocabulary often used by domain experts makes it difficult to map a learner's query to a relevant learning material. We address these challenges by introducing an innovative method that automatically builds background knowledge for a learning domain. In creating our method, we exploit a structured collection of teaching materials as a guide for identifying the important domain concepts. We enrich the identified concepts with discovered text from an encyclopedia, thereby increasing the richness of our acquired knowledge. We employ the developed background knowledge for influencing the representation and retrieval of learning resources to improve e-Learning recommendation. The effectiveness of our method is evaluated using a collection of Machine Learning and Data Mining papers. Our method outperforms the benchmark, demonstrating the advantage of using background knowledge for improving the representation and recommendation of e-Learning materials.

Citation

MBIPOM, B., CRAW, S. and MASSIE, S. 2018. Improving e-learning recommendation by using background knowledge. Expert systems [online], Early View. Available from: https://doi.org/10.1111/exsy.12265

Journal Article Type Article
Acceptance Date Dec 8, 2017
Online Publication Date Jan 26, 2018
Deposit Date Jan 5, 2018
Publicly Available Date Jan 27, 2019
Journal Expert systems
Print ISSN 0266-4720
Electronic ISSN 1468-0394
Publisher Wiley Open Access
Peer Reviewed Peer Reviewed
Article Number e12265
DOI https://doi.org/10.1111/exsy.12265
Keywords eLearning; Knowledge; Learning materials
Public URL http://hdl.handle.net/10059/2640

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