Yang Shi

Shi, Y., Chi, M., Barnes, T., & Price, T. W. (2022). Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks. ArXiv. https://doi.org/10.48550/arXiv.2206.03545

Castro, F., Suh, S., E, J. L., Naowaprateep, W., & Shi, Y. (2022, March). Developing Comic-based Learning Toolkits for Teaching Computing to Elementary School Learners. https://doi.org/10.1145/3545947.3576272

Skripchuk, J., Shi, Y., & Price, T. (2022). Identifying Common Errors in Open-Ended Machine Learning Projects. PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1, Vol. 1, pp. 216–222. https://doi.org/10.1145/3478431.3499397

Penmetsa, P., Shi, Y., & Price, T. (2021). Investigate Effectiveness of Code Features in Knowledge Tracing Task on Novice Programming Course. CEUR Workshop Proceedings, 3051. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85122896934&partnerID=MN8TOARS

Just a Few Expert Constraints Can Help: Humanizing Data-Driven Subgoal Detection for Novice Programming. (2021). Educational Data Mining 2021. Retrieved from https://eric.ed.gov/?id=ED615599

Knowing both when and where: Temporal-ASTNN for Early Prediction of Student Success in Novice Programming Tasks. (2021). Educational Data Mining 2021. Retrieved from https://eric.ed.gov/?id=ED615543

More With Less: Exploring How to Use Deep Learning Effectively through Semi-supervised Learning for Automatic Bug Detection in Student Code. (2021). Educational Data Mining 2021. Retrieved from https://eric.ed.gov/?id=ED615586

Shi, Y., Shah, K., Wang, W., Marwan, S., Penmetsa, P., & Price, T. W. (2021). Toward Semi-Automatic Misconception Discovery Using Code Embeddings. LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, pp. 606–612. https://doi.org/10.1145/3448139.3448205

Shi, Y., Shah, K., Wang, W., Marwan, S., Penmetsa, P., & Price, T. W. (2021). Toward semi-automatic misconception discovery using code embeddings. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85102809405&partnerID=MN8TOARS

Wang, W., Rao, Y., Shi, Y., Milliken, A., Martens, C., Barnes, T., & Price, T. W. (2020). Comparing feature engineering approaches to predict complex programming behaviors. CEUR Workshop Proceedings, 2734. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85096164837&partnerID=MN8TOARS

Chen, C., Huang, C.-Y., Hou, Y., Shi, Y., Dai, E., & Wang, J. (2020). Test_positive at W-nut 2020 shared task-3: Joint event multi-task learning for slot filling in noisy text. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85098406978&partnerID=MN8TOARS

Shi, Y., Li, F., Song, W. Z., Li, X.-Y., & Ye, J. (2019). Energy audition based cyber-physical attack detection system in IoT. Proceedings of the ACM Turing Celebration Conference - China. https://doi.org/10.1145/3321408.3321588

Li, F., Shi, Y., Shinde, A., Ye, J., & Song, W. (2019). Enhanced Cyber-Physical Security in Internet of Things Through Energy Auditing. IEEE Internet of Things Journal, 6(3), 5224–5231. https://doi.org/10.1109/JIOT.2019.2899492

Li, F., Shinde, A., Shi, Y., Ye, J., Li, X.-Y., & Song, W. (2019). System Statistics Learning-Based IoT Security: Feasibility and Suitability. IEEE Internet of Things Journal, 6(4), 6396–6403. https://doi.org/10.1109/jiot.2019.2897063

Shi, Y., Li, F., Liu, T., Beyette, F. R., & Song, W. (2018). Dynamic Time-frequency Feature Extraction for Brain Activity Recognition. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018-July, 3104–3107. https://doi.org/10.1109/embc.2018.8512914

PoTrojan: powerful neural-level trojan designs in deep learning models. (2018, February 8).