Publications

* denotes equal contribution.

Italic authors denote advised students.

Peer Reviewed Conference Papers

  1. Yang Shi, Min Chi, Tiffany Barnes and Thomas Price: Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks. The 15th International Conference on Educational Data Mining (EDM 2022), 2022. (Only student paper, Oral Presentation, Acceptance Rate 28.6%, 26/91 Full Papers) [paper|Slides]

  2. James Skripchuk, Yang Shi and Thomas Price: Identifying Common Errors in Open-ended Machine Learning Projects. The 53rd ACM Technical Symposium on Computing Science Education (SIGCSE 2022), 2022. [Paper|Artifact (Codebook)]

  3. Yang Shi*, Ye Mao*, Tiffany Barnes, Min Chi and Thomas Price: More With Less: Exploring How to Use Deep Learning Effectively through Semi-supervised Learning for Automatic Bug Detection in Student Code. The 14th International Conference on Educational Data Mining (EDM 2021), 2021. (Oral Presentation, Combined Acceptance Rate 27.2%, 44/162 Short Papers) [Paper|Slides]

  4. Ye Mao, Yang Shi, Samiha Marwan, Thomas Price, Tiffany Barnes and Min Chi: Knowing both when and where: Temporal-ASTNN for Early Prediction of Student Success in Novice Programming Tasks. The 14th International Conference on Educational Data Mining (EDM 2021), 2021. (Oral Presentation, Acceptance Rate 22%, 22/100 Full Papers) [Paper]

  5. Samiha Marwan, Yang Shi, Ian Menezes, Min Chi, Tiffany Barnes and Thomas Price: Just a Few Expert Constraints Can Help: Humanizing Data-Driven Subgoal Detection for Novice Programming. The 14th International Conference on Educational Data Mining (EDM 2021), 2021. (Oral Presentation, Acceptance Rate 22%, 22/100 Full Papers, Best Paper Award) [Paper|Presentation]

  6. Yang Shi, Krupal Shah, Wengran Wang, Samiha Marwan, Poorvaja Penmetsa, Thomas Price: Toward Semi-Automatic Misconception Discovery Using Code Embeddings. The 11th International Conference on Learning Analytics & Knowledge (LAK 21), 2021. (Oral Presentation, Acceptance Rate 29.3%, 29/99 Short Papers) (>=10 citations) [Paper|Slides]

  7. Yang Shi, Fangyu Li, Wenzhan Song, Xiang-Yang Li, Jin Ye: Energy Audition based Cyber-Physical Attack Detection System in IoT. ACM SigMobile China, 2019. (Oral Presentation, Only student paper) (>=10 citations) [paper]

  8. Yang Shi, Fangyu Li, Tianming Liu, Fred R. Beyette, WenZhan Song: Dynamic Time-frequency Feature Extraction for Brain Activity Recognition. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018. (Oral Presentation, only student paper) [paper|Slides]

Peer Reviewed Journal Papers

  1. Fangyu Li, Yang Shi, Aditya Shinde, Jin Ye, WenZhan Song: Enhanced Cyber-physical Security in Internet of Things through Energy Auditing. IEEE Internet of Things Journal (IOTJ), 2019. (>=50 citations) [paper]

  2. Fangyu Li, Aditya Shinde, Yang Shi, Jin Ye, Xiang-Yang Li, WenZhan Song: System Statistics Learning-Based IoT Security: Feasibility and Suitability. IEEE Internet of Things Journal (IOTJ), 2019. (>=50 citations) [paper]

Peer Reviewed Workshop Papers

  1. Poorvaja Penmetsa, Yang Shi, Thomas Price, “Investigate Effectiveness of Code Features in Knowledge Tracing Task on Novice Programming Course.” Work-In-Progress Track, CSEDM Workshop @ EDM’21 (Oral Presentation) [paper]

  2. Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang: TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text. 2020 The 6th Workshop on Noisy User-generated Text (W-NUT) at EMNLP 2020. [paper]

  3. Wengran Wang, Yudong Rao, Yang Shi, Alexandra Milliken, Chris Martens, Tiffany Barnes, Thomas W Price: Comparing Feature Engineering Approaches to Predict Complex Programming Behaviors: Comparing Feature Engineering Approaches to Predict Complex Programming Behaviors. CSEDM Workshop @ EDM’20. [paper]

Preprints

  1. Minhui Zou, Yang Shi, Chengliang Wang, Fangyu Li, WenZhan Song, Yu Wang: PoTrojan: powerful neural-level trojan designs in deep learning models. 2018. (>=50 citations) [paper]