Publications

Please check my Google Scholar page for a complete list of my publications.

Journal Articles

• Jiang, Y., Beigman Klebanov, B., Hao, J., Deane, P., & Livne, O. E. (2025). Unveiling patterns of interaction with automated feedback in Writing Mentor and their relationships with use goals and writing outcomes. Journal of Computer Assisted Learning, 41(2). e70014.

• Jiang, Y., Zhang, M., Hao, J., Deane, P., & Li, C. (2024). Using keystroke behavior patterns to detect nonauthentic texts in writing assessments: Evaluating the fairness of predictive models. Journal of Educational Measurement, 61(4), 571-594.

• Jiang, Y., Hao, J., Fauss, M., & Li, C. (2024). Detecting ChatGPT-generated essays in a large-scale writing assessment: Is there a bias against non-native English speakers? Computers and Education, 217, 105070.

• Andrews-Todd, J., Jiang, Y., Steinberg, J., Pugh, S., L., & D’Mello, S. K. (2023). Investigating collaborative problem solving skills and outcomes across computer-based tasks. Computers and Education, 207, 104928.

• Jiang, Y., Cayton-Hodges, G. A., Nabors Oláh, L., & Minchuk, I. (2023). Using sequence mining to study students’ calculator use, problem solving, and mathematics achievement in the National Assessment of Educational Progress (NAEP). Computers and Education, 193, 104680.

• Jiang, Y., Martín-Raugh, M., Yang, Z., Hao, J., Liu, L., & Kyllonen, P. C. (2023). Do you know your partner’s personality through virtual collaboration or negotiation? Investigating perceptions of personality and their impacts on performance. Computers in Human Behavior, 141, 107608.

• Jiang, Y. & Cayton-Hodges, G. A. (2023). Investigating problem solving on calculator items in a large-scale digitally-based assessment: A data mining approach. Journal for Research in Mathematics Education, 54(2), 118-140.

• Gong, T., Shuai, L., Jiang, Y., & Arslan, B. (2023). Using process features to investigate scientific problem-solving in large-scale assessments. Frontiers in Psychology, 14, 1131019.

• Jiang, Y., Brockway, D. & Moon, J. A., (2023). Incorporating an engineering context into science learning: The effects of task context and response structuring on science understanding and investigation behaviors in a simulation. Journal of Research in Science Teaching, 60(6), 1292-1328.

• Castellano, K., Mikeska, J., Moon, J., Holtzman, S., Gao, J., & Jiang, Y. (2022). Examining preservice elementary teachers’ answer changing behavior on a content knowledge for teaching science assessment. Journal of Science Education and Technology, 31, 528-541.

• Gong, T., Jiang, Y., Saldivia, L. E., & Agard, C. (2022). Using Sankey diagrams to visualize drag and drop action sequences in technology-enhanced items. Behavior Research Methods, 54, 117-132.

• Jiang, Y., Gong, T., Saldivia, L.E., Cayton-Hodges, G., Agard, C. (2021). Using process data to understand problem-solving strategies and processes for drag-and-drop items in a large-scale mathematics assessment. Large-Scale Assessments in Education, 9(2), 1-31.

• Arslan, B., Jiang, Y., Keehner, M., Gong, T., Katz, I. R., & Yan, F. (2020). The effect of drag-and-drop item features on test-taker performance and response strategies. Educational Measurement: Issues and Practice, 39(2), 96-106. 🏆 [Recognized as a top cited article in 2020-2021 by Wiley]

• Jiang, Y., Clarke-Midura, J., Keller, B., Baker, R. S., Paquette, L., & Ocumpaugh, J. (2018). Note-taking and science inquiry in an open-ended learning environment. Contemporary Educational Psychology, 55, 12–29.

Conference Papers in Stringently Refereed Proceedings

• Jiang, Y., Hao, J., Cui, W., Kerzabi, E., & Kyllonen, P. (2025). Uncovering transferable collaboration patterns across tasks using large language models. Proceedings of the 26th International Conference on Artificial Intelligence in Education (AIED 2025), pp. 320-335.

• Hou, X., Forsyth, C. M., Andrews-Todd, J., Rice, J., Cai, Z., Jiang, Y., Zapata-Rivera, D., & Graesser, A. C. (2025). An LLM-enhanced multi-agent architecture for conversation-based assessment. Proceedings of the 26th International Conference on Artificial Intelligence in Education (AIED 2025), pp. 119-134.

• Zhang, L., Zhai, X., Lin, J., Kleiman, J., Zapata-Rivera, D., Forsyth, C. M., Jiang, Y., Hu, X., & Graesser, A. C. (2025). Exploring communicative strategies for collaborative LLM agents in mathematical problem solving. Proceedings of the 26th International Conference on Artificial Intelligence in Education (AIED 2025), pp. 258-265.

• Jiang, Y., Graf, E. A., & Andrews-Todd, J. (2025). Using epistemic network analysis and sequential pattern mining to explore the impacts of human facilitation on collaborative mathematical problem solving. Proceedings of the 18th International Conference on Computer-Supported Collaborative Learning (CSCL 2025), pp 3-11. International Society of the Learning Sciences.

• Jiang, Y., Hao, J., Fauss, M., & Li, C. (2024). Towards fair detection of AI-generated essays in large-scale writing assessments. Proceedings of the 25th International Conference on Artificial Intelligence in Education (AIED 2024) (pp. 317-324). Springer.

• Forsyth, C., Zapata-Rivera, D., Graf, E., & Jiang, Y. (2024). Complex conversations: LLMs vs. knowledge engineered conversation-based assessment. Proceedings of the 17th International Conference on Educational Data Mining (EDM 2024), pp 868-871.

• Jiang, Y., Beigman Klebanov, B., Livne, O. E., & Hao, J. (2023). Analyzing users’ interaction with writing feedback and their effects on writing performance. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, O. C. Santos (Eds.), Proceedings of the 24th International Conference on Artificial Intelligence in Education (AIED 2023) (pp. 466-471). Springer.

• Zhang, J., Ober, T., Jiang, Y., Plass, J., & Homer, B. (2021). Predicting executive functions in a learning game: Accuracy and reaction time. Proceedings of the 14th International Conference on Educational Data Mining (EDM 2021), 688-693. 🏆 [Won Research and Scholarship Showcase Poster Presentation Award]

• Jiang, Y., Almeda, V., Kai, S., Baker, R.S., Ostrow, K., Inventado, P., & Scupelli, P. (2020). Single template vs. multiple templates: Examining the effects of problem structure on performance. Proceedings of the 14th International Conference of the Learning Sciences (ICLS 2020), 1015-1022.

• Gong, T., Shuai, L., Arslan, B., & Jiang, Y. (2020). Process based analysis on scientific inquiry tasks using large-scale national assessment dataset. Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020), pp. 417–423.

• Andres, A., Ocumpaugh, J., Baker, R.S., Slater, S., Paquette, L., Jiang, Y., Bosch, N., Munshi, A., Moore, A. & Biswas, G. (2019). Affect sequences and learning in Betty’s Brain. Proceedings of the 9th International Learning Analytics and Knowledge Conference (LAK 2019), 383-390.

• Jiang, Y., Bosch, N., Baker, R. S., Paquette, L., Ocumpaugh, J., Andres, J. M. A. L., Moore, A. L., Biswas, G. (2018). Expert feature-engineering vs. deep neural networks: Which is better for sensor-free affect detection? In Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018) (pp. 198–211). Berlin, Heidelberg: Springer. 🏆 [Won Best Student Paper Award] [Nominated for Best Paper Award]

• Jiang, Y., Paquette, L., Baker, R.S., Clarke-Midura, J. (2015). Comparing novice and experienced students in Virtual Performance Assessments. Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 136–143).

• Jiang, Y., Baker, R.S., Paquette, L., San Pedro, M.O., Heffernan, N.T. (2015). Learning, moment-by-moment, and over the long term. Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015) (pp. 654–657). Berlin, Heidelberg: Springer.

• Sao Pedro, M., Jiang, Y., Paquette, L., Baker, R.S., Gobert, J. (2014). Identifying transfer of inquiry skills across physical science simulations using educational data mining. Proceedings of the 11th International Conference of the Learning Sciences (ICLS 2014) (pp. 222–229).

• Jiang, Y. (2014). Design of invention-based simulations as preparation for future learning. Proceedings of Teachers College Educational Technology Conference (TCETC 2014), New York, NY.

Book chapters

• Jiang, Y., Clarke-Midura, J., Baker, R. S., Paquette, L., & Keller, B. (2018). How immersive virtual environments foster self-regulated learning. In R. Zheng (Ed.), Digital technologies and instructional design for personalized learning (pp. 28–54). Hershey, PA: IGI Global.

Workshop Papers

• Zapata-Rivera, D., Forsyth, C. M., Graf, A., & Jiang, Y. (2024). Designing and evaluating evidence-centered design based conversations for assessment with LLMs. Proceedings of EDM 2024 Workshop: Leveraging Large Language Models for Next Generation Educational Technologies.

Preprints

• Zhang, L., Lin, J., Sabatini, J., Zapata-Rivera, D., Forsyth, C., Jiang, Y., Hollander, J., Hu, X., & Graesser, A. C. (2025). Generative data imputation for sparse learner performance data using generative adversarial imputation networks. ArXiv.

• Zhang, L., Yeasin, M., Havugimana., F., Lin, J., Zapata-Rivera, D., Forsyth, C., Jiang, Y., Hu, X., & Graesser, A. C. (2025). Bridging global pretraining and similarity-based local fine-tuning in GAIN for robust imputation of sparse learner performance data. TechRxiv