Metacognition and self-regulation are important skills for successful learning and have been discussed and researched extensively in the general education literature for several decades. More recently, there has been growing interest in understanding how metacognitive and self-regulatory skills contribute to student success in the context of computing education. This article presents a thorough systematic review of metacognition and self-regulation work in the context of computer programming and an in-depth discussion of the theories that have been leveraged in some way. We also discuss several prominent metacognitive and self-regulation theories from the literature outside of computing education—for example, from psychology and education—that have yet to be applied in the context of programming education. In our investigation, we built a comprehensive corpus of papers on metacognition and self-regulation in programming education, and then employed backward snowballing to provide a deeper examination of foundational theories from outside computing education, some of which have been explored in programming education, and others that have yet to be but hold much promise. In addition, we make new observations about the way these theories are used by the computing education community, and present recommendations on how metacognition and self-regulation can help inform programming education in the future. In particular, we discuss exemplars of studies that have used existing theories to support their design and discussion of results as well as studies that have proposed their own metacognitive theories in the context of programming education. Readers will also find the article a useful resource for helping students in programming courses develop effective strategies for metacognition and self-regulation.

Recent empirical results suggest that the instructional material used to teach computing may actually overload students' cognitive abilities. Better designed materials may enhance learning by reducing unnecessary load. Subgoal labels have been shown to be effective at reducing the cognitive load during problem solving in both mathematics and science. Until now, subgoal labels have been given to students to learn passively. We report on a study to determine if giving learners subgoal labels is more or less effective than asking learners to generate subgoal labels within an introductory CS programming task. The answers are mixed and depend on other features of the instructional materials. We found that student performance gains did not replicate as expected in the introductory CS task for those who were given subgoal labels. Computer science may require different kinds of problem-solving or may generate different cognitive demands than mathematics or science.

Mental models are mental representations of how an action changes a problem state. Creating a mental model early in the learning process is a strong predictor of success in computer science classes. One major problem in computer science education, however, is that novices have difficulty creating mental models perhaps because of the cognitive overload caused by traditional teaching methods. The present study employed subgoal-labeled instructional materials to promote the creation of mental models when teaching novices to program in Android App Inventor. Utilizing this and other well-established educational tools, such as scaffolding, to reduce cognitive load in computer science education improved the performance of participants on novel tasks when learning to develop mobile applications.

Background: Programming a computer is an increasingly valuable skill, but dropout and failure rates in introductory programming courses are regularly as high as 50%. Like many fields, programming requires students to learn complex problem-solving procedures from instructors who tend to have tacit knowledge about low-level procedures that they have automatized. The subgoal learning framework has been used in programming and other fields to breakdown procedural problem solving into smaller pieces that novices can grasp more easily, but it has only been used in short-term interventions. In this study, the subgoal learning framework was implemented throughout a semester-long introductory programming course to explore its longitudinal effects. Of 265 students in multiple sections of the course, half received subgoal-oriented instruction while the other half received typical instruction. Results: Learning subgoals consistently improved performance on quizzes, which were formative and given within a week of learning a new procedure, but not on exams, which were summative. While exam performance was not statistically better, the subgoal group had lower variance in exam scores and fewer students dropped or failed the course than in the control group. To better understand the learning process, we examined students’ responses to open-ended questions that asked them to explain the problem-solving process. Furthermore, we explored characteristics of learners to determine how subgoal learning affected students at risk of dropout or failure. Conclusions: Students in an introductory programming course performed better on initial assessments when they received instructions that used our intervention, subgoal labels. Though the students did not perform better than the control group on exams on average, they were less likely to get failing grades or to drop the course. Overall, subgoal labels seemed especially effective for students who might otherwise struggle to pass or complete the course.

We report on a study that used subgoal labels to teach students how to write while loops with a Parsons problem learning assessment. Subgoal labels were used to aid learning of programming while not overloading students' cognitive abilities. We wanted to compare giving learners subgoal labels versus asking learners to generate subgoal labels. As an assessment for learning we asked students to solve a Parsons problem -- to place code segments in the correct order. We found that students who were given subgoal labels performed statistically better than the groups that did not receive subgoal labels or were asked to generate subgoal labels. We conclude that a low cognitive load assessment, Parsons problems, can be more sensitive to student learning gains than traditional code generation problems.

We explored how undergraduate introductory programming students naturalistically used generative AI to solve programming problems. We focused on the relationship between their use of AI to their self-regulation strategies, self-efficacy, and fear of failure in programming. In this repeated-measures, mixed-methods research, we examined students' patterns of using generative AI with qualitative student reflections and their self-regulation, self-efficacy, and fear of failure with quantitative instruments at multiple times throughout the semester. We also explored the relationships among these variables to learner characteristics, perceived usefulness of AI, and performance. Overall, our results suggest that student factors affect their baseline use of AI. In particular, students with higher self-efficacy, lower fear of failure, or higher prior grades tended to use AI less or later in the problem-solving process and rated it as less useful than others. Interestingly, we found no relationship between students' self-regulation strategies and their use of AI. Students who used AI less or later in problem-solving also had higher grades in the course, but this is most likely due to prior characteristics as our data do not suggest that this is a causal relationship.