Designing a Hybrid Learning Resource Recommender Model using Learning Object Rating Algorithm for Adaptive Learning
DOI:
https://doi.org/10.32890/jict2025.24.4.3Keywords:
Adaptive learning, collaborative filtering model, content-based filtering model, hybrid recommender model, learning object rating algorithmAbstract
E-learning has become a key component of modern education that provides access to digital learning resources. However, the overwhelming volume of content can make it difficult for learners to find materials suited to their needs. This has led to a growing demand for adaptive learning, which personalises content based on learner characteristics. To support this, e-learning platforms adopt recommender systems through machine learning techniques. While effective, these systems often depend heavily on historical data, such as user ratings and interactions, to generate meaningful recommendations. This dependency introduces a significant challenge known as data sparsity, where insufficient interaction data limits the model’s ability to provide accurate recommendations. This study addresses this challenge by proposing a hybrid learning resource recommender model that combines collaborative and content-based filtering and introduces the Learning Object Rating Algorithm (LORA). This hybrid approach reduces reliance on user-generated ratings by allowing LORA to generate initial ratings based on the learners’ profiles and resource characteristics, thus filling gaps in interaction history. The model was evaluated through experiments assessing its prediction accuracy and relevance of recommendations by using Mean Absolute Error (MAE), Precision, and Recall. Additionally, the performance of the proposed hybrid model was compared with existing hybrid models through a comparative analysis. Results revealed that the proposed model outperformed previous hybrid recommender models, generating better prediction accuracy and recommendation relevance. The integration of a hybrid approach and LORA enabled the model to generate ratings based on learning styles and resource characteristics, mitigating the data sparsity issue and reducing dependence on user-generated ratings.
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