Cross-Lingual Adversarial Domain Adaptation for Novice Programming
| Autor: | Ye Mao, Farzaneh Khoshnevisan, Thomas Price, Tiffany Barnes, Min Chi |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 36:7682-7690 |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v36i7.20735 |
| Popis: | Student modeling sits at the epicenter of adaptive learning technology. In contrast to the voluminous work on student modeling for well-defined domains such as algebra, there has been little research on student modeling in programming (SMP) due to data scarcity caused by the unbounded solution spaces of open-ended programming exercises. In this work, we focus on two essential SMP tasks: program classification and early prediction of student success and propose a Cross-Lingual Adversarial Domain Adaptation (CrossLing) framework that can leverage a large programming dataset to learn features that can improve SMP's build using a much smaller dataset in a different programming language. Our framework maintains one globally invariant latent representation across both datasets via an adversarial learning process, as well as allocating domain-specific models for each dataset to extract local latent representations that cannot and should not be united. By separating globally-shared representations from domain-specific representations, our framework outperforms existing state-of-the-art methods for both SMP tasks. |
| Databáze: | OpenAIRE |
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