The paper deals with the automated grading of assignments made up of R commands, their output and comments written in natural language. Compared to other tools presented in the literature, our tool supports both students and teachers, uses static source code analysis for the code-snippets and a supervised classifier based on sentence embeddings for the open-ended answers, and provides a feedback to students and includes the instructor review. After more than one year of use, improvements in terms of the feedback provided to the students are discussed in the paper, that in turn should offer manifold benefits to both students and teachers. Finally, the paper proposes a study finalised to refine and test the effectiveness of the proposal.

Improved feedback in automated grading of data science assignments

Vittorini P.
2021

Abstract

The paper deals with the automated grading of assignments made up of R commands, their output and comments written in natural language. Compared to other tools presented in the literature, our tool supports both students and teachers, uses static source code analysis for the code-snippets and a supervised classifier based on sentence embeddings for the open-ended answers, and provides a feedback to students and includes the instructor review. After more than one year of use, improvements in terms of the feedback provided to the students are discussed in the paper, that in turn should offer manifold benefits to both students and teachers. Finally, the paper proposes a study finalised to refine and test the effectiveness of the proposal.
978-3-030-52286-5
978-3-030-52287-2
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/165200
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