Extracting Topics from Open Educational Resources
Mohammadreza Molavi, Mohammadreza Tavakoli, and G\'abor Kismih\'ok

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)…
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