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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. 2021. Improving e-learning recommendation by using background knowledge. Expert systems [online], 38(7): artificial intelligence/EDMA 2017, article e12265. Available from: https://doi.org/10.1111/exsy.12265

Journal Article Type Article
Acceptance Date Dec 8, 2017
Online Publication Date Jan 26, 2018
Publication Date Nov 30, 2021
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
Volume 38
Issue 7
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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Copyright Statement
This is the peer reviewed version of the following article: MBIPOM, B., CRAW, S. and MASSIE, S. 2021. Improving e-learning recommendation by using background knowledge. Expert systems [online], 38(7): artificial intelligence/EDMA 2017, article e12265, which has been published in final form at https://doi.org/10.1111/exsy.12265. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.





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