backup papers mar 26

Link to Full CV

Representative Research


Williams, J. J., Kim, J., Rafferty, A., Maldonado, S., Gajos, K., Lasecki, W., & Heffernan, N. (2016). AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning. Proceedings of the Third Annual ACM Conference on Learning at Scale. Nominee for Best Paper Award [top 4] 

Williams, J. J., Lombrozo, T., Hsu, A., Huber, B., & Kim, J. (2016). Revising Learner Misconceptions Without Feedback: Prompting for Reflection on Anomalous Facts. Proceedings of CHI (2016), 34th Annual ACM Conference on Human Factors in Computing Systems. Honorable Mention for Best Note [top 5%]     Slides Video of Talk

Williams, J. J., Rafferty, A. N., Ang, A., Tingley, D., Lasecki, W. S., & Kim, J. (2017). Connecting Instructors and Learning Scientists via Collaborative Dynamic Experimentation. In CHI'17 Extended Abstracts on Human Factors in Computing Systems. [PDF]

Williams, J. J., Rafferty, A., Maldonado, S., Ang, A., Tingley, D., & Kim, J. (working paper). Designing Tools for Dynamic Experimentation and Personalization. [PDF]

Williams, J. J., Lombrozo, T., & Rehder, B. (2013). The hazards of explanation: overgeneralization in the face of exceptions. Journal of Experimental Psychology: General, 142(4), 1006-1014. [PDF]

Bernecker, S. L., Williams, J. J., & Constantino, M. J. (2017, May). Enhancing mental health through scalable training for peer counselors. Extended abstract presented at the Computing and Mental Health symposium of the annual ACM CHI Conference on Human Factors in Computing Systems, Denver, CO. [PDF]


Late-Breaking and Working Papers

Williams, J. J., Rafferty, A. N., Ang, A., Tingley, D., Lasecki, W. S., & Kim, J. (2017). Connecting Instructors and Learning Scientists via Collaborative Dynamic Experimentation. In CHI'17 Extended Abstracts on Human Factors in Computing Systems. [PDF]

Williams, J. J., Rafferty, A., Maldonado, S., Ang, A., Tingley, D., & Kim, J. (working paper). Designing Tools for Dynamic Experimentation and Personalization. [PDF]

Bernecker, S. L., Williams, J. J., & Constantino, M. J. (2017, May). Enhancing mental health through scalable training for peer counselors. Extended abstract presented at the Computing and Mental Health symposium of the annual ACM CHI Conference on Human Factors in Computing Systems, Denver, CO. [PDF]

Archival Refereed Conference Proceedings

Williams, J. J., Kim, J., Rafferty, A., Maldonado, S., Gajos, K., Lasecki, W., & Heffernan, N. (2016). AXIS: Generating Explanations at Scale with Learnersourcing and Machine LearningProceedings of the Third Annual ACM Conference on Learning at ScaleNominee for Best Paper Award [top 4] 

Williams, J. J., Lombrozo, T., Hsu, A., Huber, B., & Kim, J. (2016). Revising Learner Misconceptions Without Feedback: Prompting for Reflection on Anomalous FactsProceedings of CHI (2016), 34th Annual ACM Conference on Human Factors in Computing SystemsHonorable Mention for Best Note [top 5%] 

Ostrow, K., Selent, D., Wang, Y., VanIngwen, E., Heffernan, N., & Williams, J. J. (2016). The Assessment of Learning Infrastructure (ALI): The Theory, Practice, and Scalability of Automated Assessment6th International Learning Analytics & Knowledge Conference.

Whitehill, J., Williams, J. J., Lopez, G., Coleman, C., & Reich, J. (2015). Beyond Prediction: First Steps Toward Automatic Intervention in MOOC Student Stopout. Paper presented at the 8th International Conference of Educational Data Mining, Madrid, Spain. [PDF]

Krause, M., Mogale, M., Pohl, H., & Williams, J. J. (2015). A Playful Game Changer: Fostering Student Retention in Online Education with Social Gamification. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale, 95-102. [PDF]

Pacer, M., Williams, J. J., Chen, X., Lombrozo, T., Griffiths, T. L. (2013). Evaluating computational models of explanation using human judgmentsTwenty Ninth Conference on Uncertainty in Artificial Intelligence. [PDF]

Griffiths, T. L., Lucas, C. G., Williams, J. J., Kalish, M. L. (2008). Modeling human function learning with Gaussian processes. Advances in Neural Information Processing Systems 21. [PDF]

Journal Articles

Walker, C. M., Lombrozo, T., Williams, J. J., Rafferty, A., & Gopnik, A. (2016). Explaining Constrains Causal Learning in Childhood. Child Development, 88(1), 229 - 246.

Heffernan, N., Ostrow, K., Kelly, K., Selent, D., Vanlnwegen, E., Xiong, X., & Williams, J. J. (2016). The Future of Adaptive Learning: Does the Crowd Hold the Key? International Journal of Artificial Intelligence in Education, 1 - 30. [PDF]

Miyamoto, Y. R., Coleman, C. A., Williams, J. J., Whitehill, J., Nesterko, S., & Reich, J. (2015)Beyond Time-on-Task: The Relationship Between Spaced Study and Certification in MOOCs. Journal of Learning Analytics, 2(2), 47 - 69. [PDF]

Gumport, N. B., Williams, J. J., & Harvey, A. G. (2015). Learning cognitive behavior therapy. Journal of Behavior Therapy and Experimental Psychiatry, 48, 164-169. [PDF]

Lucas, C. G., Griffiths, T. L., Williams, J. J., Kalish, M. L. (2015). A rational model of function learning. Psychonomic Bulletin & Review, 1-23. [PDF]

Harvey, A.G., Lee, J., Williams, J., Hollon, S. Walker, M.P., Thompson, M. & Smith, R. (2014). Improving Outcome of Psychosocial Treatments by Enhancing Memory and Learning. Perspectives in Psychological Science, 9, 161-179. [PDF]

Williams, J. J., & Griffiths, T. L. (2013). Why are people bad at detecting randomness? A statistical argument. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 1473-1490. [PDF]

Williams, J. J., Lombrozo, T., & Rehder, B. (2013). The hazards of explanation: overgeneralization in the face of exceptions. Journal of Experimental Psychology: General, 142(4), 1006-1014. [PDF]

Williams, J. J., & Lombrozo, T. (2013). Explanation and prior knowledge interact to guide learning. Cognitive Psychology, 66, 55–84. (PDF)

Williams, J. J., & Lombrozo, T. (2010). The role of explanation in discovery and generalization: evidence from category learning. Cognitive Science, 34, 776-806. [PDF]