Mapping Moodle Resources to Course Topics Using Text Similarity Methods and Expert Evaluation

In this research, the alignment and mapping between Modular Object-Oriented Dynamic Learning Environment (Moodle) learning resources and course topics are described using text similarity methods. The goal of this work is to improve the accuracy of automated alignment between Moodle course learning resources and course topics by analyzing text similarity method performance and examining factors that affect how closely they match expert evaluation. During this research, an expert first mapped the e-course learning resources to course topics, after which multiple text similarity techniques were applied to match resource titles and descriptions to those topics. The findings show that the Large Language Model (LLM)-based solution achieves the lowest mean absolute error (MAE), the lowest mean squared error (MSE), and the strongest agreement with expert evaluation. Traditional keyword-based methods, such as Jaccard similarity and Term Frequency–Inverse Document Frequency (TF-IDF), demonstrate moderate performance, while the Sentence Bidirectional Encoder Representations from Transformers (SBERT)-based model shows the weakest alignment with expert evaluation.