In Situ Identification of Student Self-Regulated Learning Struggles in Programming Assignments

Arakawa, Kai; Hao, Qiang; Greer, Tyler; Ding, Li; Hundhausen, Christopher D.; Peterson, Abigayle

doi:10.1145/3408877.3432357

Cited by 17 publications

(4 citation statements)

References 21 publications

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“…As such, we defined a student as struggling if, by the time the assignment is due, they failed at least one test case -thus, only the students whose code passed every test case were labeled as not struggling. This is supported by Arakawa et al which found a substantial overlap between our definition of struggling and students that were confirmed to be struggling via testimony [42]. On average, 20.9% of all students were labeled as struggling across all assignments.…”

Section: Data Collection and Processingsupporting

confidence: 78%

Early Identification of Student Struggles at the Topic Level Using Context-Agnostic Features

Arakawa

Hao

Deneke

et al. 2022

Proceedings of the 53rd ACM Technical Symposium on Computer Science Education

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The identification of student struggles has drawn increasing interests from computing education and learning analytics communities in recent years, considering the high failure rate and fast enrollment growth of computer science courses. Prior studies on this topic employed a multitude of data sources and methodologies with varying degrees of success. Nearly all studies attempted to predict low overall course performance to identify struggling students, risking oversimplifying student learning and struggles. Additionally, many studies utilize data sources that are limited to their original contexts or local student demographics, making it difficult to replicate or put the findings into practice. To address these gaps, we studied the feasibility of identifying student struggles at the topic level using features that are agnostic to courses and contexts. Our results show that it is possible to identify student struggles at a more fine-grained level within days. Our findings contribute new insights into automatic identification of student struggles at the topic level on a large scale, which can be used to guide meaningful interventions on student learning.

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Section: Data Collection and Processingsupporting

confidence: 78%

Early Identification of Student Struggles at the Topic Level Using Context-Agnostic Features

Arakawa

Hao

Deneke

et al. 2022

Proceedings of the 53rd ACM Technical Symposium on Computer Science Education

Self Cite

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“…In particular, ACM SIGCSE grants łin-cooperationž status to non-SIGCSE events that are sponsored by other non-proit organizations. In 2018, conferences such as Koli Calling and WiPSCE were listed as being łin-cooperationž, however this designation was removed in 2019 2 . The original search, executed by Prather et al using the SIGCSE-sponsored ilter, returned several articles published in these venues prior to 2018, however our new search failed to return articles from these venues when the same ilter was applied.…”

Section: 12mentioning

confidence: 99%

“…(1) What CS-speciic SRL strategies do inal-year students articulate as using in programming assignments? (2) What are the diferences between the SRL strategies as identiied by inal-year students and those identiied by irst-year students? The SRL strategies the authors identiied are discussed extensively and, while the details are left to their prior paper, the axial coding framework that was used within this study was the one developed previously in [32].…”

Section: Use Of Theorymentioning

confidence: 99%

Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of Use

Loksa

Margulieux

Becker

et al. 2022

ACM Trans. Comput. Educ.

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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 paper 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.

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“…Then, they should test their code to proactively test edge cases and seek out errors, and when these errors inevitably arise, they must embark on a systematic process of debugging and testing their own assertions. Given this process, it is no wonder that many students who are new to programming struggle to grasp the concepts [4].…”

Section: Introductionmentioning

confidence: 99%

Motivating Students to Learn How to Write Code Using a Gamified Programming Tutor

Grey

Gordon

2023

Education Sciences

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Engagement and retention are widely acknowledged problems in computer science and more general higher education. The need to develop programming skills is increasingly ubiquitous, but especially so in computer science where it is one of the core competencies. Learning to write code is a particularly challenging skill to master, which can make retention and success even more difficult. We attempt to address student engagement within an introductory programming module by attempting to motivate students using a gamified interactive programming tutor application that provides immediate feedback on the student’s work. In this paper, we describe the design of the gamified programming tutor application, along with a related topology to characterize student engagement. We discuss the design of the software, the gamified elements, and the structured question design. We evaluate the engagement with the gamified programming tutor of two cohorts of students in the first year of a computer science programme, with over two hundred students taking part. We attempt to frame this engagement in terms of frequency, duration, and intensity of interactions, and compare these engagement metrics with module performance. Additionally, we present quantitative and qualitative data from a survey of students about their experience using the programming tutor application to demonstrate the efficacy of this approach.

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In Situ Identification of Student Self-Regulated Learning Struggles in Programming Assignments

Cited by 17 publications

References 21 publications

Early Identification of Student Struggles at the Topic Level Using Context-Agnostic Features

Early Identification of Student Struggles at the Topic Level Using Context-Agnostic Features

Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of Use

Motivating Students to Learn How to Write Code Using a Gamified Programming Tutor

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