Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction. - Ranran Haoran Zhang; Qianying Liu; Aysa Xuemo Fan; Heng Ji 001; Daojian Zeng; Fei Cheng 002; Daisuke Kawahara; Sadao Kurohashi (EMNLP (Findings), 2020)

Investigating Elements of Student Persistence in an Introductory Computer Science Course (Full Paper). - Juan D. Pinto; Yingbin Zhang; Luc Paquette; Aysa Xuemo Fan (EDM (Workshops), 2021)

ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining. - Haoran Zhang 004; Aysa Xuemo Fan; Rui Zhang 037 (EACL, 2023)

Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses. - Aysa Xuemo Fan; Haoran Zhang 004; Luc Paquette; Rui Zhang 037 (EMNLP (Findings), 2023)

Investigating the Relationship Between Programming Experience and Debugging Behaviors in an Introductory Computer Science Course. - Juan D. Pinto; Qianhui Liu; Luc Paquette; Yingbin Zhang; Aysa Xuemo Fan (ICQE, 2023)

Combining latent profile analysis and programming traces to understand novices' differences in debugging. - Yingbin Zhang; Luc Paquette; Juan D. Pinto; Qianhui Liu; Aysa Xuemo Fan (Educ. Inf. Technol., 2023)

Utilizing programming traces to explore and model the dimensions of novices' code-writing skill. - Yingbin Zhang; Luc Paquette; Juan D. Pinto; Aysa Xuemo Fan (Comput. Appl. Eng. Educ., 2023)

Evaluating the Quality of Code Comments Generated by Large Language Models for Novice Programmers. - Aysa Xuemo Fan; Arun Balajiee Lekshmi Narayanan; Mohammad Hassany; Jiaze Ke (CoRR, 2024)