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Using Machine Learning to Identify Patterns in Learner-Submitted code for the Purpose of Assessment 1

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15th Mexican Conference on Pattern Recognition (MCPR)

作者： Tarcsay, Botond Perez-Tellez, Fernando Vasic, Jelena Technol Univ Dublin Sch Enterprise Comp & Digital Transformat Dublin Ireland

ISBN: (数字)9783031337833

ISBN: (纸本)9783031337826;9783031337833

programming has become an important skill in today's world and is taught widely both in traditional and online settings. Instructors need to grade increasing amounts of student work. Unit testing can contribute to the automation of the grading process but it cannot assess the structure or partial correctness of code, which is needed for finely differentiated grading. This paper builds on previous research that investigated machine learning models for determining the correctness of programs from token-based features of source code and found that some such models can be successful in classifying source code with respect to whether it passes unit tests. This paper makes two further contributions. First, these results are scrutinized under conditions of varying similarity between code instances used for model training and testing, for a better understanding of how well the models generalize. It was found that the models do not generalize outside of groups of code instances performing very similar tasks (corresponding to similar coding assignments). Second, selected binary classification models are used as a base for multi-class prediction with two different methods. Both of these exhibit prediction success well above the random baseline, with potential to contribute to automated assessment with multi-valued measures of quality (grading schemes), in contrast to the binary pass/fail measure associated with unit testing.

关键词： Applied Machine Learning for code Assessment student programming code grading Automated grading

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Use of Machine Learning Methods in the Assessment of programming Assignments 25th

Use of Machine Learning Methods in the Assessment of Program...

引用

25th Annual International Conference on Text, Speech and Dialogue Conference (TSD)

作者： Tarcsay, Botond Vasic, Jelena Perez-Tellez, Fernando Technol Univ Dublin Dublin Ireland

ISBN: (纸本)9783031162701;9783031162695

programming has become an important skill in today's world and is taught widely both in traditional and online settings. Educators need to grade increasing numbers of student submissions. Unit testing can contribute to the automation of the grading process;however, it cannot assess the structure, or partial correctness, which are needed for finely differentiated grading. This paper builds on previous research that investigated several machine learning models for determining the correctness of source code. It was found that some such models can be successful, provided that the code samples used for fitting and prediction fulfil the same sets of requirements (corresponding to coding assignments). The hypothesis investigated in this paper is that code samples can be grouped by similarity of the requirements that they fulfil and that models built with samples of code from such a group can be used for determining the quality of new samples that belong to the same group, even if they do not correspond to the same coding assignment, which would make for a much more useful predictive model in practice. The investigation involved ten different machine learning algorithms used on over four hundred thousand student code submissions and it confirmed the hypothesis.

关键词： Applied Machine Learning for code Assessment student programming code grading Automated grading

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