Hybrid Explainable Educational Recommender Using Self-attention and Knowledge-Based Systems for E-Learning in MOOC Platforms

Shareef, Mehbooba P; Jimson, Linda Rose; Jose, Babita R.

doi:10.1007/978-981-19-4453-6_4

Cited by 1 publication

(1 citation statement)

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“…In the specific domain of MOOCs recommender systems using neural networks (NN), multiple research directions have been pursued. These include optimizing the accuracy of recommendation [15,16,24,25,41], ensuring fairness [4,5,11,16,18], and augmenting explainability [26,28]. While NN-based approaches have set benchmarks in predictive accuracy, this efficacy frequently comes at the cost of model interpretability, raising concerns about the trade-off between performance and transparency.…”

Section: Introductionmentioning

confidence: 99%

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Frej,

Shah,

Knezevic

et al. 2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations, explainability remains a significant challenge which is especially problematic for certain domains with significant impact such as education and online learning.Recently, a novel class of recommender systems that uses reinforcement learning and graph reasoning over KGs has been proposed to generate explainable recommendations in the form of paths over a KG. Despite their accuracy and interpretability on e-commerce datasets, these approaches have scarcely been applied to the educational domain and their use in practice has not been studied. In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions of our new explainable recommendations. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets: COCO and Xuetang.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Neural networks.

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