Full Paper (strictly peer reviewed)
Prihar, E., Patikorn, T., Botelho, A., Sales, A., & Heffernan, N. (2021, June). Toward Personalizing Students' Education with Crowdsourced Tutoring. In Proceedings of the Eighth ACM Conference on Learning@ Scale (pp. 37-45).
Patikorn, T., & Heffernan, N. T. (2020, August). Effectiveness of crowd-sourcing on-demand assistance from teachers in online learning platforms. In Proceedings of the Seventh ACM Conference on Learning@ Scale (pp. 115-124). **Best Student Paper Award**
Patikorn, T., Deisadze, D., Grande, L., Yu, Z., & Heffernan, N. (2019, June). Generalizability of Methods for Imputing Mathematical Skills Needed to Solve Problems from Texts. In International Conference on Artificial Intelligence in Education (pp. 396-405). Springer, Cham.
Short Paper
Sales, A., Botelho, A. F., Patikorn, T., & Heffernan, N. T. (2018, July). Using Big Data to Sharpen Design-Based Inference in A/B Tests. In Proceedings of the Eleventh International Conference on Educational Data Mining 2018.
Sales, A., Patikorn, T., & Heffernan, N. T. (2018, January). Bayesian partial pooling to improve inference across a/b tests in edm. In Proceeding of the Educational Data Mining Conference.
Patikorn, T, Selent, D., Beck, J., Heffernan, N., & Zhou, J. (2017). Using a Single Model Trained Across Multiple Experiments to Improve the Detection of Treatment Effects. In Proceedings of the Tenth Conference of Educational Data Mining 2017.
Posters
Patikorn, T, Heffernan, N., & Zhou, J. (2017). An Offline Evaluation Method for Individual Treatment Rules and How to Find Heterogeneous Treatment Effects. Conference of Educational Data Mining 2017.
Yin, B., Patikorn, T., Botelho, A. F., & Heffernan, N. T. (2017, April). Observing personalizations in learning: Identifying heterogeneous treatment effects using causal trees. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale (pp. 299-302).
Yin, B., Botelho, A. F., Patikorn, T., Heffernan, N. T., & Zou, J. (2017, June). Causal Forest vs. Naïve Causal Forest in Detecting Personalization: An Empirical Study in ASSISTments. In Proceedings of the Tenth International Conference on Educational Data Mining, 388-389. ACM
Selent, D., Patikorn, T. & Heffernan, N. T. (2016) ASSISTments Dataset from Multiple Randomized Controlled Experiments. A "Work in progress" category presented at Learning at Scale 2016. At ACM Digital Library.Pp 181-184.
Other Works
I co-organized the Nation's Report Card Data Mining Competition in 2019, as well as a special issue of Journal of Education Data Mining on the research that resulted from the competition. The copyediting is currently in process.
I co-organized the ASSISTments Longitudinal Data Mining Competition in 2017, as well as a workshop and a special issue of Journal of Education Data Mining on the research that resulted from the competition.
Special issue of JEDM: Patikorn, T., Baker, R. S., & Heffernan, N. T. (2020). ASSISTments longitudinal data mining competition special issue: a preface. JEDM| Journal of Educational Data Mining, 12(2), i-xi.
Workshop: Patikorn, T., Heffernan, N. T., & Baker, R. S. (2018, July). ASSISTments Longitudinal Data Mining Competition 2017: A Preface. In Proceedings of the Workshop on Scientific Findings from the ASSISTments Longitudinal Data Competition, International Conference on Educational Data Mining.
The dataset: Patikorn, T., Selent, D., Heffernan, N. T., Yin, B., Botelho, A. (2016) ASSISTments Dataset for a Data Mining Competition to Improve Personalized Learning. Poster at MIT CodeCon 2016
Undergraduate-Level Projects
Kuang, J., Kuntanarumitkul, P., Patikorn, T., & Li, W. (2014). Interactive Map and Timeline Application for the Knights! Exhibition at Worcester Art Museum.
Chines, J., Iovanna, A., Patikorn, T., Venkatesh, A. (2015). Git Analytics Tool.