TY - GEN
T1 - Getting By With Help From My Friends
T2 - 18th Annual ACM International Computing Education Research Conference, ICER 2022
AU - Prather, James
AU - Margulieux, Lauren
AU - Whalley, Jacqueline
AU - Denny, Paul
AU - Reeves, Brent N.
AU - Becker, Brett A.
AU - Singh, Paramvir
AU - Powell, Garrett
AU - Bosch, Nigel
N1 - This work is funded in part by the National Science Foundation under grant #1941642.
PY - 2022/8/3
Y1 - 2022/8/3
N2 - 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.
AB - 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.
KW - CS1
KW - co-regulation
KW - group work
KW - groups
KW - introductory programming
KW - metacognition
KW - programming education
KW - self-regulation
KW - socially shared regulation
KW - study habits
KW - studying
UR - http://www.scopus.com/inward/record.url?scp=85137099727&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137099727&partnerID=8YFLogxK
U2 - 10.1145/3501385.3543970
DO - 10.1145/3501385.3543970
M3 - Conference contribution
AN - SCOPUS:85137099727
T3 - ICER 2022 - Proceedings of the 2022 ACM Conference on International Computing Education Research
SP - 164
EP - 176
BT - ICER 2022 - Proceedings of the 2022 ACM Conference on International Computing Education Research
PB - Association for Computing Machinery
Y2 - 7 August 2022 through 11 August 2022
ER -