A MODEL FOR IMPROVING SOFTWARE SUPPORT FOR TEACHING THE COURSE “THEORETICAL FOUNDATIONS OF COMPUTER SCIENCE” TO STUDENTS BASED ON INTERNATIONAL EXPERIENCE
Keywords:
digital education, theoretical foundations of computer science, software, international experience, JFLAP, Web-CAT, Intelligent Tutoring Systems, logical errors, algorithmic errors, semantic analysis, adaptive learning, pedagogical feedback, diagnostics.Abstract
This article examines the scientific and methodological foundations for improving
the software used to teach the course “Theoretical Foundations of Computer Science” in
higher education institutions based on international experience. The relevance of the
study stems from the fact that automated assessment tools in contemporary digital
learning environments are often limited to evaluating students’ final results and do not
sufficiently identify the underlying causes of errors in their logical and algorithmic
thinking. JFLAP, Web-CAT, and Intelligent Tutoring Systems were selected as examples of
international practice, and their functional, didactic, and diagnostic capabilities were
comparatively analyzed. Based on this analysis, a conceptual model of diagnostic-adaptive
software tailored to the “Theoretical Foundations of Computer Science” course was
developed. The model consists of a task database, student model, knowledge base, error
classifier, semantic analysis module, adaptive feedback generator, and teacher analytics
component. The proposed model provides mechanisms for analyzing student responses
at syntactic, structural, and semantic levels, classifying errors according to their causes,
and delivering individualized pedagogical feedback. The article also substantiates the
scientific novelty of the model, the pedagogical conditions for its implementation, and the
criteria for its experimental validation.
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