Knowledge driven approaches to e-learning recommendation.
Prof Susan Craw email@example.com
Dr Stewart Massie firstname.lastname@example.org
Learners often have difficulty finding and retrieving relevant learning materials to support their learning goals because of two main challenges. The vocabulary learners use to describe their goals is different from that used by domain experts in teaching materials. This challenge causes a semantic gap. Learners lack sufficient knowledge about the domain they are trying to learn about, so are unable to assemble effective keywords that identify what they wish to learn. This problem presents an intent gap. The work presented in this thesis focuses on addressing the semantic and intent gaps that learners face during an e-Learning recommendation task. The semantic gap is addressed by introducing a method that automatically creates background knowledge in the form of a set of rich learning-focused concepts related to the selected learning domain. The knowledge of teaching experts contained in e-Books is used as a guide to identify important domain concepts. The concepts represent important topics that learners should be interested in. An approach is developed which leverages the concept vocabulary for representing learning materials and this influences retrieval during the recommendation of new learning materials. The effectiveness of our approach is evaluated on a dataset of Machine Learning and Data Mining papers, and our approach outperforms benchmark methods. The results confirm that incorporating background knowledge into the representation of learning materials provides a shared vocabulary for experts and learners, and this enables the recommendation of relevant materials. We address the intent gap by developing an approach which leverages the background knowledge to identify important learning concepts that are employed for refining learners' queries. This approach enables us to automatically identify concepts that are similar to queries, and take advantage of distinctive concept terms for refining learners' queries. Using the refined query allows the search to focus on documents that contain topics which are relevant to the learner. An e-Learning recommender system is developed to evaluate the success of our approach using a collection of learner queries and a dataset of Machine Learning and Data Mining learning materials. Users with different levels of expertise are employed for the evaluation. Results from experts, competent users and beginners all showed that using our method produced documents that were consistently more relevant to learners than when the standard method was used. The results show the benefits in using our knowledge driven approaches to help learners find relevant learning materials.
MBIPOM, B. 2018. Knowledge driven approaches to e-learning recommendation. Robert Gordon University, PhD thesis.
|Publication Date||May 1, 2018|
|Deposit Date||Sep 6, 2018|
|Publicly Available Date||Sep 6, 2018|
|Keywords||eLearning recommendation; Semantic gap; Intent gap; Query refinement; Background knowledge|
MBIPOM 2018 Knowledge driven approaches
Publisher Licence URL
Copyright: the author and Robert Gordon University
You might also like
Wifi-based human activity recognition using Raspberry Pi.
Representing temporal dependencies in smart home activity recognition for health monitoring.
Representing temporal dependencies in human activity recognition.
Fall prediction using behavioural modelling from sensor data in smart homes.
Case based reasoning as a model for cognitive artificial intelligence.