On the differences between correct student solutions

Luxton-Reilly, Andrew; Denny, Paul; Kirk, Diana; Tempero, Ewan; Yu, Se-young

doi:10.1145/2462476.2462505

Cited by 27 publications

(10 citation statements)

References 9 publications

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“…While others have already shown the great diversity in student solutions (e.g. [12]), our study also shows this diversity in teacher solutions. Remarkable is the difference in final states for program 2a and 2b that solve the same problem.…”

Section: X: Otherssupporting

confidence: 51%

“…3.1.1 Programs. The first exercise was taken from another study, including the most popular student solution (see Program 1) [12]. Its description is: 'Implement the sumValues method, which adds up all numbers from the array parameter, or only the positive numbers if the positivesOnly boolean parameter is set to true.…”

Section: Methodsmentioning

confidence: 99%

“…They found instances in both code of novices and more experienced students. Luxton-Reilly et al [12] investigated differences between correct solutions to programming exercises, identifying variation in structure, syntax and presentation. The authors found that even for simple exercises there are numerous variations in structure, and in some instances the teacher's solution was not the most popular one among students.…”

Section: Code Quality In Educationmentioning

confidence: 99%

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How Teachers Would Help Students to Improve Their Code

Keuning

Heeren

Jeuring

2019

Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

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Code quality has been receiving less attention than program correctness in both the practice of and research into programming education. Writing poor quality code might be a sign of carelessness, or not fully understanding programming concepts and language constructs. Teachers play an important role in addressing quality issues, and encouraging students to write better code as early as possible.In this paper we explore to what extent teachers address code quality in their teaching, which code quality issues they observe and how they would help novices to improve their code. We presented student code of low quality to 30 experienced teachers and asked them which hints they would give and how the student should improve the code step by step. We compare these hints to the output of professional code quality tools.Although most teachers gave similar hints on reducing the algorithmic complexity and removing clutter, they gave varying subsets of hints on other topics. We found a large variety in how they would solve issues in code. We noticed that professional code quality tools do not point out the algorithmic complexity topics that teachers mention. Finally, we give some general guidelines on how to approach code improvement.

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Section: X: Otherssupporting

confidence: 51%

Section: Methodsmentioning

confidence: 99%

Section: Code Quality In Educationmentioning

confidence: 99%

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How Teachers Would Help Students to Improve Their Code

Keuning

Heeren

Jeuring

2019

Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

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“…A recent example is the research presented in [12]. The authors investigated differences in the correct solutions of students.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Source Code with Item Response Theory

Berges

Hubwieser

2015

Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education

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The analysis of source code produced by novice programmers could provide interesting insights into their learning progress, particularly in introductory programming courses. Yet, as the programming ability of a person is assumed to be quite complex, it is not likely that it would be observable directly in its total. Instead, we regard those abilities as latent psychometric constructs and apply the methodology of item response theory (IRT) to assess their manifestations. In preparatory work, we had identified a list of items that represent the central concepts of object-oriented programming. In this paper we propose a methodology that allows the evaluation of coding abilities by analyzing the application of those concepts. We demonstrate this methodology by exemplarily analyzing source code that was produced during programming projects. The results provide interesting information about the difficulty of the concepts' application and the distribution of the respective coding abilities among the students.

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“…Luxton-Reilly et al [2] label types of variations as structural, syntactic, or presentation-related. The structural similarity is captured by the control flow graph of the student solutions.…”

Section: Related Workmentioning

confidence: 99%

Feature engineering for clustering student solutions

Glassman¹,

Singh²,

Miller³

2014

Proceedings of the First ACM Conference on Learning @ Scale Conference

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Open-ended homework problems such as coding assignments give students a broad range of freedom for the design of solutions. We aim to use the diversity in correct solutions to enhance student learning by automatically suggesting alternate solutions. Our approach is to perform a two-level hierarchical clustering of student solutions to first partition them based on the choice of algorithm and then partition solutions implementing the same algorithm based on low-level implementation details. Our initial investigations in domains of introductory programming and computer architecture demonstrate that we need two different classes of features to perform effective clustering at the two levels, namely abstract features and concrete features.

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On the differences between correct student solutions

Cited by 27 publications

References 9 publications

How Teachers Would Help Students to Improve Their Code

How Teachers Would Help Students to Improve Their Code

Evaluation of Source Code with Item Response Theory

Feature engineering for clustering student solutions

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