UNIVERSITY STUDENTS' PERCEPTION OF PERSONALIZED ADAPTIVE LEARNING IN THE PROCESS OF STUDYING COMPUTER SCIENCE
DOI:
https://doi.org/10.48371/PEDS.2026.82.3.009Keywords:
personalized adaptive learning, computer science teaching, computer technology, adaptive learning, self-regulated learning, student perception, learning engagement, learning effectivenessAbstract
Personalized adaptive learning has become an important direction in contemporary computer science education because it enables instructional processes to be adjusted to students’ individual learning needs, prior knowledge, and learning pace. This study investigates how university students perceive the implementation of personalized adaptive learning in computer science courses and examines its influence on learning engagement, problem-solving abilities, and academic experiences. Particular attention is given to the factors that facilitate or hinder students’ acceptance of adaptive educational technologies.
The research employed a mixed-methods design. Quantitative data were collected from 384 university students using an author-developed questionnaire based on a five-point Likert scale, while qualitative insights were obtained through semi-structured interviews with 20 participants selected from the survey sample. Quantitative findings were processed using descriptive and inferential statistical techniques, whereas qualitative responses were examined through thematic analysis to identify recurring perceptions and experiences.
The findings indicate that students recognize the educational value of personalized adaptive learning, particularly for improving problem-solving skills, analytical thinking, and engagement in practical learning activities. Nevertheless, many participants expressed uncertainty regarding their readiness for self-regulated learning and their ability to assume greater responsibility for their own educational progress. The study also demonstrates that the educational benefits of adaptive learning remain constrained by the limited integration of adaptive technologies into instructional practices, course design, and assessment procedures.
The results suggest that the successful implementation of personalized adaptive learning in computer science education requires not only appropriate technological infrastructure but also pedagogically grounded instructional design, continuous teacher support, and assessment strategies that better reflect students’ individual learning trajectories.
