Enhancing Algorithmic Design through Mobile-Supported Constructivist Learning: A Problem-Based Approach

This study examines a mobile-supported constructivist learning environment (CLE) grounded in problem-based learning (PBL) to foster computational thinking (CT) and algorithm design skills among first-year engineering students. A quasi-experimental pretest and posttest design (n = 62) was implemented using a custom web and mobile platform that enabled ubiquitous access, immediate automated feedback, and iterative practice on structured algorithmic challenges aligned with CT dimensions related to knowing, doing, and being. This instructional design builds upon established CLE and PBL frameworks reported in prior studies, and is consistent with recent evidence on the role of mobile and intelligent technologies in supporting CT development in higher education contexts. Paired sample analyses showed statistically significant improvements across CT components, with t values ranging between 8.22 and 22.92 and Cohen’s d values exceeding 1.0, indicating very large effect sizes. These findings demonstrate a substantial increase in student proficiency from the baseline. Qualitative evidence derived from rubric-based reflections highlighted increased autonomy, collaboration, and learner motivation facilitated by mobile access and interactive problem solving. The contribution of this work lies in three aspects: a hybrid CLE and PBL instructional model operationalized through mobile-supported practice, a competency framework integrating knowing, doing, and being dimensions of learning, and empirical evidence of learning gains in algorithmic problem solving. The findings support mobile and interactive technologies as effective catalysts for flexible and learner-centered instruction in programming education.