Papers

Link to Full CV

Research Statement

Slides from Talk on Collaborative, Dynamic, Personalized Experimentation

Representative Research

Williams, J. J., Rafferty, A., Tingley, D., Ang, A., Lasecki, W. S., & Kim, J. (2018). Enhancing Online Problems Through Instructor-Centered Tools for Randomized Experiments. In CHI 2018, 36th Annual ACM Conference on Human Factors in Computing Systems. [PDF] [Talk Slides[Related Poster] [Video Figure] [Instructions to Use System] [Video of Talk] [Talk Transcription]

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]  [PDF] [Slides] 

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%] [PDF] [Slides][Video of Talk ]

Williams, J. J., Rafferty, A. N., Maldonado, S., Ang, A., Tingley, D., & Kim, J. (2017). MOOClets: A Framework for Dynamic Experimentation and Personalization. In Fourth (2017) ACM Conference on Learning @ Scale (pp. 287-290). [PDF] [Poster]

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]

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]

Archival Refereed Conference Proceedings

Rafferty, A., Ying, H., & Williams, J. J. (2018) Bandit assignment for educational experiments: Benefits to students versus statistical powerProceedings of the 19th International Conference on Artificial Intelligence in Education. [PDF]

Segal, A., David, Y. B., Williams, J. J., Gal, K., & Shalom, Y. (2018). Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content. Proceedings of the 19th International Conference on Artificial Intelligence in Education. [PDF] [Extended version on arXiv]

Williams, J. J., Rafferty, A., Tingley, D., Ang, A., Lasecki, W. S., & Kim, J. (2018). Enhancing Online Problems Through Instructor-Centered Tools for Randomized Experiments. In CHI 2018, 36th Annual ACM Conference on Human Factors in Computing Systems. [PDF[Related Poster]

Shin, H., Ko, E., Williams, J. J., & Kim, J. (2018). Understanding the Effect of In-Video Prompting on Learners and Instructors. In CHI 2018, 36th Annual ACM Conference on Human Factors in Computing Systems. [PDF] [Slides]

Foong, P. S., Zhao, S., Tan, F., & Williams, J. J. (2018). Harvesting Caregiving Knowledge: Design Considerations for Integrating Volunteer Input in Dementia Care. In CHI 2018, 36th Annual ACM Conference on Human Factors in Computing Systems. [PDF]

Macina, J., Srba, I., Williams, J. J., & Bielikova, M. (2017).  Educational Question Routing in Online Student Communities. Proceedings of the 10th ACM Conference on Recommender Systems. [PDF]

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] [PDF]

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%] [PDF]

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. [PDF]

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. [PDF]

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]


Workshop Papers

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]


Book Chapter

Williams, J. J., Kim, J., Glassman, E., Rafferty, A., & Lasecki, W. S. (2016). Making Static Lessons Adaptive through Crowdsourcing & Machine Learning. In R. Sottilare, A. Graesser, X. Hu, A. Olney, B. Nye, and A. Sinatra (Eds.). Design Recommendations for Intelligent Tutoring Systems: Volume 4 - Domain Modeling (pp. 127 - 137). Orlando, FL: U.S. Army Research Laboratory. [PDF]


White Papers

Ho, A. D., Chuang, I. R., Reich, J., Coleman, C. A., Whitehill, J., Northcutt, C.G., Williams, J. J., Hansen, J. D., Lopez, G., & Petersen, R. (March 30, 2015). HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014. Retrieved from: http://ssrn.com/abstract=2586847 


Using Online Environments to Simultaneously Conduct Basic Research on Learning & Improve Practical Outcomes (Symposia & Workshops)

Williams, J. J., Heffernan, N., Poquet, O. (2018). Design and Application of Collaborative, Dynamic, Personalized Experimentation. Workshop conducted at the 19th International Conference on Artificial Intelligence in Education. London, UK. [PDF]

Krause, M., Hall, M., Williams, J. J., Caton, S., & Pripc, J. (2016). Connecting Online Work and Online Education at Scale. In CHI'16 Extended Abstracts on Human Factors in Computing Systems. New York, NY: Association for Computing Machinery. 

Williams, J. J., Krause, M., Paritosh, P., Whitehill, J., Reich, J., Kim, J., Mitros, P., Heffernan, N., & Keegan, B. C. (2015). Connecting Collaborative & Crowd Work with Online EducationProceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing (pp. 313-318). [Extended Abstract[Website: tiny.cc/crowdworklearning]

Krause, M., Paritosh, P., & Williams, J. J. (2014). Crowdsourcing, Online Education, and Massive Open Online CoursesWorkshop conducted at the Second AAAI Conference on Human Computation and Crowdsourcing.

Williams, J.J., Goldstone, R.L., Rafferty, A., McClelland, J. M., & Mozer, M. (2014). Computational Models for Learning: From Basic Processes to Real World Education. Symposium conducted at the annual convention of the Association for Psychological Science. San Francisco, CA. [Abstract & Summary] [Youtube video of Symposium]. 

Williams, J.J., Teachman, B.A., Richland, L., Brady, S.T, & Aleahmad, T. (2014). Leveraging the Internet to do Laboratory Research in the Real World. Symposium conducted at the annual convention of the Association for Psychological Science. San Francisco, CA. [Abstract & Summary] [Youtube video of Symposium].

Williams, J.J., Kizilcec, R., Russel, D. R., & Klemmer, S. R. (2014). Learning Innovation at ScaleWorkshop at ACM CHI Conference on Human Factors in Computing Systems. Toronto, Canada. [PDF]

Williams, J. J., Linn, M., Edwards, A., Trumbore, A., Chae, H. S., Natriello, G., Saxberg, B., & Mitros, P. (2014). How online resources can facilitate interdisciplinary collaboration. Featured presentation & panel at the Special Interest Group on Computer and Internet Applications in Education, Annual Meeting of the American Educational Research Association. [Summary and More Information]

Williams, J. J., Renkl, A., Koedinger, K., Stamper, J. (2013). Online Education: A Unique Opportunity for Cognitive Scientists to Integrate Research and Practice. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society, 113-114. Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., Saxberg, B., Means, B., Mitros, P. (2013). Online Learning and Psychological Science: Opportunities to integrate research and practice. Symposium conducted at the annual convention of the Association for Psychological Science. [description]