Harnessing Background Knowledge for E-Learning Recommendation

Abstract

Section 6 of the "Repository policy for OpenAIR @ RGU" (available from http://www.rgu.ac.uk/staff-and-currentstudents/library/library-policies/repository-policies) provides guidance on the criteria under which RGU will consider withdrawing material from OpenAIR. If you believe that this item is subject to any of these criteria, or for any other reason should not be held on OpenAIR, then please contact openair-help@rgu.ac.uk with the details of the item and the nature of your complaint. 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.

Improving e‐learning recommendation by using background knowledge

“…The identified topics are then enriched with discovered text from Wikipedia in order to enhance our representation. In addition, we refine the methods developed in previous work (Mbipom et al, 2016) so that we can generate a richer set of relevant topics that provide a good coverage of the learning domain. Consequently, our approach is employed to influence the representation and retrieval of relevant learning resources.…”

“…Finally, we explore a larger concept vocabulary which provides a better coverage of the domain. We refine our method presented in (Mbipom et al, 2016) to generate a richer and focused set of domain concepts. The results from our evaluation show the improvement in e-Learning recommendation when the richer concept vocabulary is used for representing learning resources.…”

An E-Learning Recommender That Helps Learners Find the Right Materials

“…We use the background knowledge that we developed in earlier work (Mbipom, Craw, and Massie 2016), to underpin e-Learning recommendation for a broad learning topic such as Machine Learning and Data Mining. In this work, the background knowledge is employed to support the refinement of queries.…”