Self-Regulated Learning Skills Research in Computer Science: The State of the Field

Academic success requires not only taking in content, but also understanding how to learn best. Self Regulated Learning (SRL) is process by which humans regulate their thinking, emotions, and behavior. It broadly describes the process of knowing (or learning) how to learn. Education research has found Self-Regulated Learning to be a key predictor of academic success along with other constructs like motivation and self-efficacy. It may be particularly critical in learning to program at the post-secondary level. Studies have shown that students benefit greatly from targeted instruction in these skills. Teaching students how to better self-regulate is both important and valuable for Computer Science students. The solution here may seem straightforward: educators should give instruction on self-regulation skills. However, there are a number of skills that encompass a student's proficiency with self-regulate; including time management, problem decomposition, and reflection. Self regulation also tends to be a highly cognitive and internal process making it difficult to observe directly, let alone measure. Which skills should be prioritized for targeted instruction? How could we empirically measure those skills? What limitations should we keep in mind when making such decisions? Within this dissertation, I will seek to address these questions. In order to get an idea of what skills the Computing Education Research community should be prioritizing, my co-authors and I conducted two studies. First, a Delphi Process study that expanded the field by gaining an understanding of what SRL skills CS post-secondary educators value most. This gave a more firm view of what skills were most important for CS students. Second, a systematic literature review to examine what skills had been studied within the Computing Education Research community. Ultimately, I created a finalized list of 12 SRL skills that appear to be particularly important to CS education. This list also includes behaviors an outside observer could use as indicators of the presence or absence of SRL. After creating this list, I then considered how best to measure these each of these 12 skills. One form of measurement comes from using data traces collected from educational software. These allow researchers to make strong inferences about a student's internal state empirically. They also allow for measurement of students at greater scale and through automated means, making them advantageous for large classes. For my third publication, I then set about identifying a set of data traces for these skills taking a theory-first approach. I also make the case that CS is well situated to make great gains in trace-based approaches as they make use of a whole ecosystem of data sources. This is important as it is currently common for studies to utilize just one.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Domino, Molly Rebecca

Chair dc:contributor.committeechair

• Shaffer, Clifford A.

Committee members dc:contributor.committeemember

• Jones, Brett D.
• Hooshangi, Sara
• Edwards, Stephen H.
• Edmison, Kenneth Robert
• Jamieson, Alan

• Self-Regulation
• Metacognition
• Learning Analytics
• Programming Education
• Study Habits
• Delphi Process
• Systematic Literature Review

Statement dc:rights

• Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-sa/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41217

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/120983