Publications

Journal Article

[J03] Earle-Randell, T. V., Wiggins, J. B., Ma, Y., Celepkolu, M., Bounajim, D., Gao, Z., Ruiz, J., Boyer, K., Israel, M., Lynch, C. & Wiebe, E. (2024). The impact of near-peer virtual agents on computer science attitudes and collaborative dialogue. International Journal of Child-Computer Interaction, 100646.

[J02] Gao, Z., Erickson, B., Xu, Y., Lynch, C., Heckman, S., & Barnes, T. (2022). You Asked, Now What? Modeling Students' Help-Seeking and Coding Actions from Request to Resolution. Journal of Educational Data Mining, 14(3), 109-131.

[J01] Gitinabard, N., Gao, Z., Heckman, S., Barnes, T., & Lynch, C. F. (2023). Analysis of Student Pair Teamwork Using GitHub Activities. Journal of Educational Data Mining, 15(1), 32-62.

Conference Paper

[C05] Gao, Z., Silva de Oliveira, G., Babalola, D., Lynch, C. & Heckman, S.(2024) Who should I help next? Simulation of office hours queue scheduling strategy in a CS2 course. In the 17th International Conference on Educational Data Mining(EDM'24), 484-490.

[C04] Silva de Oliveira, G., Gao, Z., Heckm[]an, S., & Lynch, C. (2024). Exploring Novice Programmers' Testing Behavior: A First Step to Define Coding Struggle. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE' 24), pp. 1251-1257.

[C03] Gao, Z., Erickson, B., Xu, Y., Lynch, C., Heckman, S., & Barnes, T. (2022). Admitting you have a problem is the first step: Modeling when and why students seek help in programming assignments. In Proceedings of The 15th International Conference on Educational Data Mining (EDM ’22), 508-514

[C02] Gao, Z., Heckman, S., & Lynch, C. (2022). Who uses office hours? A comparison of in-person and virtual office hours utilization. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education-Volume 1(SIGCSE' 22), 300-306.

[C01] Gao, Z., Lynch, C., Heckman, S., & Barnes, T. (2021). Automatically Classifying Student Help Requests: A Multi-Year Analysis. In Proceedings of The 14th International Conference on Educational Data Mining (EDM ’ 21), 81-92

Workshop Paper

[W02] Gao, Z., Buddarapu, N., Babalola, D., Lynch, C., Deane, P., & Mitros, P.(2024). Automatic Essay Scoring for K-12 Writing Assignments with Open Source LLM. In Proceedings of the Educational Data Mining in Writing and Literacy Education (WLIEDM) Workshop. 13-17.

[W01] Gao, Z., Lynch, C., & Heckman, S. (2023). Too long to wait and not much to do: Modeling student behaviors while waiting for help in online office hours. In Proceedings of the 7th Educational Data Mining in Computer Science Education (CSEDM) Workshop.

Posters

[P03] Gao, Z., Islam, S., Scott, Lynch, C. & Heckman, S. (2025) Comparing Students’ and Teachers’ Assessments of Office Hours. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE’ 25), (Accepted)

[P02] Islam, S., Gao, Z., Bacher, J., Silva de Oliveira, G., Patwardhab, V., Lynch, C. & Heckman, S. (2025) EclipseMonitor: A Real-Time Student Programming Environment Data Collection Tool. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE’ 25), (Accepted)

[P01] Gao, Z., Gaweda, A., Lynch, C., Heckman, S., Babalola, D., Oliveira, G.(2024), Using Survival Analysis to Model Students' Patience in Online Office Hour Queues. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V.2(SIGCSE' 24),1646-1647

Doctoral Consortium

[DC] Gao, Z. & Lynch, C.(2024), Building Predictive Models for CS Students Help-Seeking Behaviors with Coding Log Data In the 17th International Conference on Educational Data Mining(EDM'24), 1003-1008.