Abstract
Background and Context. Metacognitive skills are important for all students learning to program and interest in applying pedagogical approaches in early programming courses that focus on metacognitive aspects is growing. However, most studies of such approaches are not rigorously based in theory, and when they are, almost always utilize foundational education and psychology theories from as far back as the 1970s. More recent theory is less tested, and not all relevant metacognitive theories have been explored in the computing education research literature.
Objectives. We present the first use in a programming education context of a newer metacognitive theory that explicitly examines the differences between self-regulation, co-regulation, and socially shared regulation. Our research questions are: 1) How do students express their learning strategies, both when working alone and when working in groups, and how do these align with existing models of self-regulation and co-regulation? and 2) To what extent do written expressions of self-regulation, co-regulation, and socially shared regulation relate to student performance?
Methods. Grounded in the above mentioned theory, we collected qualitative self-reflection and quantitative course performance data from nearly 1,000 students in an introductory programming course. We use these data to explore students’ self-regulation habits when studying alone and their co-regulation habits when studying in groups.
Findings. Our findings indicate that higher self-regulation correlates with higher performance, but higher co-regulation had the opposite effect. We explore these differences through a qualitative analysis of the self-reflection statements and identify co-regulation strategies to build upon existing models of self-regulation.
Implications. We identify emergent themes in our data that align with those in recent literature in self-regulated learning in computing education and present the first set of co-regulation themes in computing education. This work is at the frontier of self- and co-regulation in introductory programming and identifies several factors that can be used to advance future work and, most importantly, improve student outcomes.
