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

Authors

  • Blessing Mbipom Robert Gordon University
  • Stewart Massie Robert Gordon University
  • Susan Craw Robert Gordon University

Keywords:

e-Learning, Recommender Systems, Background Knowledge, Query Refinement

Abstract

Learning materials are increasingly available on the Web making them an excellent source of information for building e-Learning recommendation systems. However, learners often have difficulty finding the right materials to support their learning goals because they lack sufficient domain knowledge to craft effective queries that convey what they wish to learn. The unfamiliar vocabulary often used by domain experts creates a semantic gap between learners and experts, and also makes it difficult to map a learner's query to relevant learning materials. We build an e-Learning recommender system that uses background knowledge extracted from a collection of teaching materials and encyclopedia sources to support the refinement of learners' queries. Our approach allows us to bridge the gap between learners and teaching experts. We evaluate our method using a collection of realistic learner queries and a dataset of Machine Learning and Data Mining documents. Evaluation results show our method to outperform benchmark approaches and demonstrates its effectiveness in assisting learners to find the right materials.

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Published

2018-04-27

How to Cite

Mbipom, B., Massie, S., & Craw, S. (2018). An E-Learning Recommender That Helps Learners Find the Right Materials. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11389