Misconception-Based Peer Feedback: A Pedagogical Technique for Reducing Misconceptions

Kennedy, Cazembe; Lawson, Aubrey; Feaster, Yvon; Kraemer, Eileen

doi:10.1145/3341525.3387392

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…Given that code walkthroughs can be mentally demanding, being presented with an explanation of one's own code may reduce the cognitive demands associated with debugging McCauley et al (2008) and evidence from other educational domains suggests that being presented with explanations produced by others can improve learning Williams et al (2016). Within computing education, techniques like pair programming Hanks et al (2011) and misconception-based peer feedback Kennedy et al (2020) provide some opportunities for walking through code with others, but are not always feasible and may not be suitable for individual student assessments. Therefore, the automatic generation of code explanations, particularly for supporting student debugging, is an attractive idea and one which is made feasible with the introduction of tools like OpenAI Codex.…”

Section: Code Explanations and Their Assessmentmentioning

confidence: 99%

Automatic Generation of Programming Exercises and Code Explanations with Large Language Models

Sarsa,

Denny,

Hellas

et al. 2022

Preprint

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This article explores the natural language generation capabilities of large language models with application to the production of two types of learning resources common in programming courses. Using OpenAI Codex as the large language model, we create programming exercises (including sample solutions and test cases) and code explanations, assessing these qualitatively and quantitatively. Our results suggest that the majority of the automatically generated content is both novel and sensible, and in some cases ready to use as is. When creating exercises we find that it is remarkably easy to influence both the programming concepts and the contextual themes they contain, simply by supplying keywords as input to the model. Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students. We further discuss the implications of OpenAI Codex and similar tools for introductory programming education and highlight future research streams that have the potential to improve the quality of the educational experience for both teachers and students alike.

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Section: Code Explanations and Their Assessmentmentioning

confidence: 99%

Automatic Generation of Programming Exercises and Code Explanations with Large Language Models

Sarsa,

Denny,

Hellas

et al. 2022

Preprint

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“…The ability to detect such bug patterns in student code makes it possible to provide learners and teachers with feedback. Kennedy et al [11] demonstrated-using peer feedback and discussions of coding assignments-that misconceptions can be cleared with feedback in principle. In this paper, we evaluate the effects of feedback that is generated automatically by static analysis.…”

Section: Feedback On Codementioning

confidence: 99%

Effects of Hints on Debugging Scratch Programs: An Empirical Study with Primary School Teachers in Training

Greifenstein

Obermueller

Wasmeier

et al. 2021

The 16th Workshop in Primary and Secondary Computing Education

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Bugs in learners' programs are often the result of fundamental misconceptions. Teachers frequently face the challenge of first having to understand such bugs, and then suggest ways to fix them. In order to enable teachers to do so effectively and efficiently, it is desirable to support them in recognising and fixing bugs. Misconceptions often lead to recurring patterns of similar bugs, enabling automated tools to provide this support in terms of hints on occurrences of common bug patterns. In this paper, we investigate to what extent the hints improve the effectiveness and efficiency of teachers in debugging learners' programs using a cohort of 163 primary school teachers in training, tasked to correct buggy Scratch programs, with and without hints on bug patterns. Our experiment suggests that automatically generated hints can reduce the effort of finding and fixing bugs from 8.66 to 5.24 minutes, while increasing the effectiveness by 34% more correct solutions. While this improvement is convincing, arguably teachers in training might first need to learn debugging "the hard way" to not miss the opportunity to learn by relying on tools. We therefore investigate whether the use of hints during training affects their ability to recognise and fix bugs without hints. Our experiment provides no significant evidence that either learning to debug with hints or learning to debug "the hard way" leads to better learning effects. Overall, this suggests that bug patterns might be a useful concept to include in the curriculum for teachers in training, while tool-support to recognise these patterns is desirable for teachers in practice.

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“…In software engineering, there have been a number of studies concerning misconceptions [4][5][6][7]; different methods have been developed for detecting misconceptions [8]. Some studies were conducted to fix misconceptions during classroom work [9]. Usually, methods for detecting and fixing misconceptions are based on domain knowledge and typical errors in students' mental models.…”

Section: Introductionmentioning

confidence: 99%

Explain Trace: Misconceptions of Control-Flow Statements

Sychev,

Denisov

2023

Computers

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Control-flow statements often cause misunderstandings among novice computer science students. To better address these problems, teachers need to know the misconceptions that are typical at this stage. In this paper, we present the results of studying students’ misconceptions about control-flow statements. We compiled 181 questions, each containing an algorithm written in pseudocode and the execution trace of that algorithm. Some of the traces were correct; others contained highlighted errors. The students were asked to explain in their own words why the selected line of the trace was correct or erroneous. We collected and processed 10,799 answers from 67 CS1 students. Among the 24 misconceptions we found, 6 coincided with misconceptions from other studies, and 7 were narrower cases of known misconceptions. We did not find previous research regarding 11 of the misconceptions we identified.

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Misconception-Based Peer Feedback: A Pedagogical Technique for Reducing Misconceptions

Cited by 8 publications

References 30 publications

Automatic Generation of Programming Exercises and Code Explanations with Large Language Models

Automatic Generation of Programming Exercises and Code Explanations with Large Language Models

Effects of Hints on Debugging Scratch Programs: An Empirical Study with Primary School Teachers in Training

Explain Trace: Misconceptions of Control-Flow Statements

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