Behavior Based Learning Analytics in Pair Programming: A Conceptual Approach to Enhance Programming Competency
DOI:
https://doi.org/10.53797/jthkks.v6i2.4.2025Keywords:
Learning behavior, learning analytics, pair programming, engagement, self-regulated learning, programming competencyAbstract
Programming remains a significant challenge for students in higher education, particularly within technical and vocational institutions where foundational computing skills are often limited. Despite the adoption of various instructional strategies, high failure rates and low programming competency persist. Pair programming, a collaborative learning approach, has shown potential to enhance students’ coding skills, engagement, and confidence. However, traditional pairing methods—often based on random assignment or technical ability—fail to account for individual learning behaviors that critically influence collaborative outcomes. This concept paper proposes a behavior-based pairing framework that integrates learning analytics to enhance programming competency through more strategic pair formation. Drawing on data extracted from Learning Management Systems (LMS) such as login frequency, activity completion, forum participation, and assignment submissions, the framework identifies two key learning behaviors: engagement and self-regulated learning (SRL). Clustering techniques are employed to group students according to these behavioral attributes, and heterogeneous pairing is applied to match partners with contrasting learning profiles. This approach aims to promote complementary collaboration, foster peer support, and enhance problem-solving effectiveness in programming tasks. The proposed framework aligns with the Malaysian Education Blueprint 2015–2025 (Higher Education), the National TVET Policy, and global initiatives linked to Education 4.0 and Industry 4.0. It also addresses the national agenda on graduate employability and supports data-informed teaching practices. By integrating behavioral insights with learning analytics, this concept introduces a personalized and evidence-based approach to programming education. It provides a foundation for future empirical validation and offers practical implications for improving curriculum design, instructional strategies, and student outcomes in computing education.
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