Content area
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.
Details
Semantics;
Rating Scales;
Influence of Technology;
Distance Education;
Experimental Groups;
Addition;
Learning Processes;
Computers;
Learning Theories;
Learning Experience;
Individualized Instruction;
Cognitive Style;
Instructional Materials;
Cooperative Learning;
Time;
Syntax;
Programming Languages;
Fundamental Concepts;
Problem Solving;
Integrated Activities;
Educational Facilities Improvement;
Classroom Environment;
Educational Strategies
1 Graduate School of Engineering, Kochi University of Technology, Kochi 782-8502, Japan
2 Department of Computer Science, Durham University, Durham DH1 3LE, UK; [email protected]