Extracting Topics from Open Educational Resources

TL;DR

This paper presents a text mining approach using LDA to extract and improve topic metadata for OERs, enhancing search and personalization in educational resources.

Contribution

The work introduces a novel application of LDA for topic extraction from OERs, achieving high accuracy and aiding better metadata quality.

Findings

Achieved 79% F1-score in topic extraction

Collected and analyzed 123 data science lectures

Validated results with expert evaluation

Abstract

In recent years, Open Educational Resources (OERs) were earmarked as critical when mitigating the increasing need for education globally. Obviously, OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 123 lectures from Coursera and Khan Academy in the area of data science related skills, 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to these skills, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 79% of F1-score.