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An Evaluation of CODE2VEC Embeddings for Scratch

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Názov: An Evaluation of CODE2VEC Embeddings for Scratch
Jazyk: English
Autori: Fein, Benedikt, Graßl, Isabella, Beck, Florian, Fraser, Gordon
Zdroj: International Educational Data Mining Society. 2022.
Dostupnosť: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Počet strán: 8
Dátum vydania: 2022
Druh dokumentu: Speeches/Meeting Papers
Reports - Research
Descriptors: Artificial Intelligence, Learning Analytics, Programming, Programming Languages, Classification, Children, Gender Differences, Computer Science Education
Abstrakt: The recent trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question. A common approach to embedding source code lies in extracting syntactic information from a program's syntax tree and learning to merge these into continuous distributed vectors (e.g., CODE2VEC). CODE2VEC has been predominantly investigated in the context of professional programming languages, but learning analytics are particularly important in the context of educational programming languages such as SCRATCH. In this paper, we therefore instantiate the popular embedding approach CODE2VEC for SCRATCH programs, create three different classification tasks with corresponding datasets, and empirically evaluate CODE2VEC on them. Our experiments demonstrate that a transfer of CODE2VEC to the educational environment of SCRATCH is feasible. Our findings serve as a basis to apply code embeddings to further educational tasks such as automated detection of misconceptions of programming concepts in SCRATCH programs. [For the full proceedings, see ED623995.]
Abstractor: As Provided
Entry Date: 2022
Prístupové číslo: ED624093
Databáza: ERIC
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  Data: The recent trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question. A common approach to embedding source code lies in extracting syntactic information from a program's syntax tree and learning to merge these into continuous distributed vectors (e.g., CODE2VEC). CODE2VEC has been predominantly investigated in the context of professional programming languages, but learning analytics are particularly important in the context of educational programming languages such as SCRATCH. In this paper, we therefore instantiate the popular embedding approach CODE2VEC for SCRATCH programs, create three different classification tasks with corresponding datasets, and empirically evaluate CODE2VEC on them. Our experiments demonstrate that a transfer of CODE2VEC to the educational environment of SCRATCH is feasible. Our findings serve as a basis to apply code embeddings to further educational tasks such as automated detection of misconceptions of programming concepts in SCRATCH programs. [For the full proceedings, see ED623995.]
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