Leveraging user profile attributes for improving pedagogical accuracy of learning pathways

TL;DR

This paper proposes a machine learning-based approach to incorporate user profile attributes into personalized learning pathway recommendations, enhancing pedagogical accuracy and resource tagging in web-based education.

Contribution

It introduces a systematic method to utilize learner profile attributes for improving educational content recommendations and resource metadata enrichment.

Findings

Learner profile attributes show significant similarity patterns.

The system effectively identifies learner subsets indicating resource preferences.

Enhanced tagging of learning resources improves recommendation relevance.

Abstract

In recent years, with the enormous explosion of web based learning resources, personalization has become a critical factor for the success of services that wish to leverage the power of Web 2.0. However, the relevance, significance and impact of tailored content delivery in the learning domain is still questionable. Apart from considering only interaction based features like ratings and inferring learner preferences from them, if these services were to incorporate innate user profile attributes which affect learning activities, the quality of recommendations produced could be vastly improved. Recognizing the crucial role of effective guidance in informal educational settings, we provide a principled way of utilizing multiple sources of information from the user profile itself for the recommendation task. We explore factors that affect the choice of learning resources and explain in what way are they helpful to improve the pedagogical accuracy of learning objects recommended. Through a systematical application of machine learning techniques, we further provide a technological solution to convert these indirectly mapped learner specific attributes into a direct mapping with the learning resources. This mapping has a distinct advantage of tagging learning resources to make their metadata more informative. The results of our empirical study depict the similarity of nominal learning attributes with respect to each other. We further succeed in capturing the learner subset, whose preferences are most likely to be an indication of learning resource usage. Our novel system filters learner profile attributes to discover a tag that links them with learning resources.