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Mining Detailed Course Transaction Records for Semantic Information Permalink
EDM 2023
Similarity-based course recommendations from detailed enrollment data
Recommended citation: Y. Xu and Z. A. Pardos. Mining detailed course transaction records for semantic information. Proceedings of the 16th International Conference on Educational Data Mining, pages 388–395, Bengaluru, India, July 2023. International Educational Data Mining Society.
Extracting Course Similarity Signal using Subword Embeddings Permalink
LAK 2024
Recommended citation: Yinuo Xu and Zachary A. Pardos. 2024. Extracting Course Similarity Signal using Subword Embeddings. In The 14th Learning Analytics and Knowledge Conference (LAK ’24), March 18–22, 2024, Kyoto, Japan. ACM, New York, NY, USA, 7 pages.
Are You an Early Dropper or Late Shopper? Mining Enrollment Transaction Data to Study Procrastination in Higher Education Permalink
EDM 2024
Recommended citation: C. Borchers, Y. Xu, and Z. A. Pardos. Are you an early dropper or late shopper? mining enrollment transaction data to study procrastination in higher education. In B. Paaßen and C. D. Epp, editors, Proceedings of the 17th International Conference on Educational Data Mining, pages 426–433, Atlanta, Georgia, USA, July 2024. International Educational Data Mining Society.
Toward Computational Literature Reviews: Analyzing Academic Theories through Supervised and Unsupervised Text-Analysis Methods Permalink
SocArXiv 2024
Recommended citation: Haber, J. R., Haveman, H. A., & Xu, Y. (2024, September 12). Toward Computational Literature Reviews: Analyzing Academic Theories through Supervised and Unsupervised Text-Analysis Methods.
Causally Modeling the Linguistic and Social Factors that Predict Email Response Permalink
NAACL 2025
Recommended citation: Yinuo Xu*, Hong Chen*, Sushrita Rakshit*, Aparna Ananthasubramaniam*, Omkar Yadav*, Mingqian Zheng*, Michael Jiang*, Lechen Zhang*, Bowen Yi*, Kenan Alkiek*, Abraham Israeli*, Bangzhao Shu*, Hua Shen*, Jiaxin Pei*, Haotian Zhang*, Miriam Schirmer*, and David Jurgens. 2025. Causally Modeling the Linguistic and Social Factors that Predict Email Response. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 11842–11866, Albuquerque, New Mexico. Association for Computational Linguistics. *Equal contribution.
Workload Overload? Late Enrollment Leads to Course Dropout
JEDM 2025
Recommended citation: Borchers, C., Xu, Y., & Pardos, Z. A. (2025). Workload overload? Late enrollment leads to course dropout. Journal of Educational Data Mining, 17(1), 126–156.
Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives Permalink
ACL 2026 · Oral
Recommended citation: Xu, Y., Derricks, V., Earl, A., & Jurgens, D. (2026). Modeling annotator disagreement with demographic-aware experts and synthetic perspectives. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), oral.
Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP Permalink
EMNLP 2026
Recommended citation: Xu, Y., & Jurgens, D. (2026). Beyond consensus: Perspectivist modeling and evaluation of annotator disagreement in NLP. In Proceedings of EMNLP 2026.