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Toward Semi-Automatic Misconception Discovery Using Code Embeddings

Shi, Yang, Shah, Krupal, Wang, Wengran, Marwan, Samiha, Penmetsa, Poorvaja, Price, Thomas·2021·LAK21: 11th International Learning Analytics and Knowledge Conference
DOI 10.1145/3448139.3448205
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  • Deep learning methods for code analysis have shown feasibility for similar usage on KT tasks.

    “All these methods reported better performance than traditional data-driven models on their tasks, showing the feasibility of similar usage on KT tasks.”

    From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [43]

    Section 5.1 (page not provided) reports that the code2vec model outperformed all baselines when predicting students’ performance on a programming exercise. The paper does not test knowledge-tracing (KT) tasks, so feasibility for similar use on KT tasks remains an extrapolation rather than a finding of this source. (LLM, conf=0.86)

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Research works printed but not linked: 33

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Metadata

Authors
Shi, Y., Shah, K., Wang, W., Marwan, S., Penmetsa, P., & Price, T.
Affiliations
North Carolina State University, United States
Venue
LAK21: 11th International Learning Analytics and Knowledge Conference
Publisher
ACM
Published
2021-04-12
Pages
606-612
DOI
10.1145/3448139.3448205
Cited by
14
References
1
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
Amazon Web Services

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