Ontology-Based Automatic Generation of Learning Materials for Python Programming

Learning materials in programming education are essential for effective instruction. This study introduces an ontology-based approach for automatically generating learning materials for Python programming. The method harnesses ontologies to capture domain knowledge and semantic relationships, enabling the creation of personalized, adaptive content. The ontology serves as a knowledge base to identify key concepts and resources and map them to learning objectives aligned with user preferences. The study outlines the design of a dual-module ontology: a general and a specific domain-specific concepts module. This design supports enhanced, tailored learning experiences, enhancing Python education by meeting individual needs and learning styles. The approach also increases the quality and uniformity of generated content, which can be reused for educational reasons. The system ensures alignment with reference materials by using BERT embeddings for a semantic similarity measurement, achieving a quality accuracy of 98.5%. It can be applied to improve Python education by providing personalized recommendations, hints, and problem-solution generation. Future developments could further support the functionality to strengthen teaching and learning outcomes in programming education, and it could expand to automated problem generation.

@article{Alshboul2025,
  title     = {Ontology-Based Automatic Generation of Learning Materials for Python Programming},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jawad Alshboul and Erika Baksa-Varga},
  doi       = {10.14569/IJACSA.2025.0160508},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160508}
}