Real-time Metacognition Feedback for Introductory Programming Using Machine Learning

Beck, Phyllis J.; Mohammadi-Aragh, Mahnas Jean; Archibald, Christopher; Jones, Bryan A.; Barton, Amy K.

doi:10.1109/fie.2018.8658973

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Vieira et al [34] worked on promoting in-code comments to self-explain the code written by students. Beck et al [35] studied the structure and proportion of student comments and code.…”

Section: Analysis Of Student Code Commentsmentioning

confidence: 99%

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

et al. 2023

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Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity.

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“…Vieira et al [34] worked on promoting in-code comments to self-explain the code written by students. Beck et al [35] studied the structure and proportion of student comments and code.…”

Section: Analysis Of Student Code Commentsmentioning

confidence: 99%

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

et al. 2023

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“…Abdul-Rahman and du Boulay (2014) suggested that scaffolding for metacognition, such as question prompts and modeling strategies, could help students reflect upon their understanding. Studies have developed metacognitive support such as questioning (Chetty & van der Westhuizen, 2014), providing real-time feedback to students about their metacognition (Beck et al, 2018), collaborative discussion (Bernard & Bachu, 2015) and self-assessment (Rum & Ismail, 2017) for learning programming. Chetty and van der Westhuizen designed metacognition scaffolding to support first-year university students in making their programming plans.…”

Section: Introductionmentioning

confidence: 99%

Application of Metacognitive Planning Scaffolding for the Cultivation of Computational Thinking

Zhou

Chai

et al. 2023

Journal of Educational Computing Research

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Computational thinking is a way of thinking that helps people “think like a computer scientist” to solve practical problems. However, practicing computational thinking through programming is dependent on the problem solvers’ metacognition. This study investigated students’ metacognitive planning and problem-solving performance in programming through two quantitative studies. First, we analyzed the performance of metacognitive planning and of problem solving through the programming of 21 freshmen, and found that the metacognitive planning performance related to “problem description” and “program comprehension” was significantly correlated with problem-solving performance. Second, semi-scaffolding and full-scaffolding were designed based on the first study. Another 89 freshmen were randomly divided into three groups and were asked to write their programming plan with no-scaffolding, semi-scaffolding, or with full-scaffolding. ANCOVA revealed that the problem-solving performance of the no-scaffolding group was significantly weaker than that of the other two groups, but there was no significant difference between the semi-scaffolding and the full-scaffolding groups. The study indicated that semi-scaffolding had a similar effect to full-scaffolding on problem-solving performance. The study suggests that teachers should emphasize supporting students’ “problem description” and “program comprehension” using semi-scaffolding. This scaffolding technique is sufficient and efficient for training students’ computational thinking through problem solving in programming.

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“…Then, we examine the visual pattern created by the placement of comments within the source code to determine a visual organization strategy. As part of previous research efforts [10] a set of features, which is summarized in Table 3, was developed to help support Visual Organization classification. Each visual organization strategy has a set of distinct properties that is illustrated with these features and support a given classification.…”

Section: Case Study: Three Programming Assignment Submissionsmentioning

confidence: 99%

A Case Study of Writing to Learn to Program: Codebook Implementation and Analysis

Mohammadi-Aragh¹,

Beck²,

Barton³

et al.

2019 ASEE Annual Conference &Amp; Exposition Proceedings

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Dr. Mohammadi-Aragh investigates the use of digital systems to measure and support engineering education, specifically through learning analytics and the pedagogical uses of digital systems. She also investigates fundamental questions critical to improving undergraduate engineering degree pathways.. She earned her Ph.D. in Engineering Education from Virginia Tech. In 2013, Dr. Mohammadi-Aragh was honored as a promising new engineering education researcher when she was selected as an ASEE Educational Research and Methods Division Apprentice Faculty.

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Real-time Metacognition Feedback for Introductory Programming Using Machine Learning

Cited by 6 publications

References 5 publications

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Application of Metacognitive Planning Scaffolding for the Cultivation of Computational Thinking

A Case Study of Writing to Learn to Program: Codebook Implementation and Analysis

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