@article{Mao_Khoshnevisan_Price_Barnes_Chi_2022, title={Cross-Lingual Adversarial Domain Adaptation for Novice Programming}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/20735}, DOI={10.1609/aaai.v36i7.20735}, abstractNote={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.}, number={7}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mao, Ye and Khoshnevisan, Farzaneh and Price, Thomas and Barnes, Tiffany and Chi, Min}, year={2022}, month={Jun.}, pages={7682-7690} }