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Ye Mao
Ye Mao
SAS Institute Inc. / North Carolina State University
Verified email at sas.com
Title
Cited by
Cited by
Year
Deep Learning vs. Bayesian Knowledge Tracing: Student Models for Interventions.
Y Mao, C Lin, M Chi
Journal of educational data mining 10 (2), 2018
672018
One minute is enough: Early prediction of student success and event-level difficulty during a novice programming task
Y Mao, R Zhi, F Khoshnevisan, TW Price, T Barnes, M Chi
In: Proceedings of the 12th International Conference on Educational Data …, 2019
502019
More with less: Exploring how to use deep learning effectively through semi-supervised learning for automatic bug detection in student code.
Y Shi, Y Mao, T Barnes, M Chi, TW Price
In Proceedings of the 14th International Conference on Educational Data …, 2021
192021
What time is it? student modeling needs to know
Y Mao, S Marwan, TW Price, T Barnes, M Chi
In proceedings of the 13th International Conference on Educational Data Mining, 2020
152020
Knowing" When" and" Where": Temporal-ASTNN for Student Learning Progression in Novice Programming Tasks.
Y Mao, Y Shi, S Marwan, TW Price, T Barnes, M Chi
International Educational Data Mining Society., 2021
122021
Cross-lingual adversarial domain adaptation for novice programming
Y Mao, F Khoshnevisan, T Price, T Barnes, M Chi
Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7682-7690, 2022
82022
Knowing both when and where: Temporal-astnn for early prediction of student success in novice programming tasks
Y Mao, Y Shi, S Marwan, TW Price, T Barnes, M Chi
In Proceedings of the 14th International Conference on Educational Data …, 2021
42021
Continuous student modeling for programming in the classroom: challenges, methods, and evaluation
Y Mao, S Marwan, P Shabrina, Y Shi, TW Price, M Chi, T Barnes
Handbook of Artificial Intelligence in Education, 287-308, 2023
2023
Temporal Skill Discovery for Modeling Student Learning Progression Across STEM Domains
Y Mao
North Carolina State University, 2021
2021
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