Learner-centered Ontology for Explainable Educational Recommendation

Ontologies form the core of knowledge graphs, which act as faithful, semantic-rich sources for training models in delivering explainable recommendations. These models learn to extract logical paths between learners and resources to be recommended within the knowledge graph, according to behavior- and content-based patterns. Extracted paths are then used not only to provide recommendations, but also to generate accompanying textual explanations. Despite the potential of this approach, current ontologies derived from the traditional learner-resource interaction data fall short in terms of richness from an educational perspective. Conversely, general-purpose ontologies, while comprehensive in educational aspects, are overly complex for recommendation tasks. Unfortunately, a suboptimal ontology might prevent to articulate reasoning paths, and thus explanations, relevant for learners within the knowledge graph. To counter this limitation, in this paper, we propose LOXER, a novel ontology designed to unlock learner-centered logical paths for explainable educational recommendation. Our design integrates insights from diverse sources, including feedback from a local co-design group of learners, observations from specialized traditional large-scale educational recommendation datasets, and connections with well-known vocabularies of other existing ontologies. To validate our ontology, we conducted an evaluation of the explanation types it enables, involving university and lifelong learners and assessing explanation properties like effectiveness, decision-making speed, motivation, satisfaction, and confidence. Results show our ontology’s ability to foster diverse considerations during the learners’ decision-making process and to establish a semantic structure for knowledge graphs for explainable recommendation.