Development and Evaluation of Adaptive Learning Support System Based on Ontology of Multiple Programming Languages

Bibliographic Data

ID22041831
AuthorsLalita Na Nongkhai (Kochi University of Technology, corresponding author), Jingyun Wang (0000-0002-3578-471X, Durham University), Takahiko Mendori (Kochi University of Technology, corresponding author)
Year2025
Volume15
Issue6
Pages724
Publication date2025-06-09
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci15060724
OpenAlexW4411186908
LanguageEN
References cited31

This paper introduces an ontology-based approach within an adaptive learning support system for computer programming. This system is designed to deliver personalized programming exercises that are tailored to individual learners’ skill levels. This proposed framework utilizes an ontology, named CONTINUOUS, which encompasses common concepts across multiple programming languages. The system leverages this ontology not only to visualize programming concepts but also to provide hints during practice programming exercises and recommend subsequent programming concepts. The adaptive mechanism is driven by the Elo Rating System, applied in an educational context to dynamically estimate the most appropriate exercise difficulty for each learner. An experimental study compared two instructional modes, adaptive and random, based on six features derived from 1186 code submissions across all the experimental groups. The results indicate significant differences in four of six analyzed features between these two modes. Notably, the adaptive mode demonstrates a significant difference over the random mode in two features: the submission of correct answers and the number of pass concepts. Therefore, these results underscore that this adaptive learning support system may support learners in practicing programming exercises

Natural language processing · Ontology · Programming language · Software engineering · Computer Science · Educational Technology and Assessment · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Artificial Intelligence

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Open DOIOpen Access