Papers

Link to Full CV

Representative Research

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]

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]


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]


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

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 Education. Proceedings 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 Courses. Workshop 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 Scale. Workshop 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]


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 


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]


Workshop Papers

Williams, J. J., Li, N, Kim, J., Whitehill, J., Maldonado, S., Pechenizkiy, M., Chu, L., & Heffernan, N. (2015). Using the MOOClet Framework as a Problem Formulation to apply Machine Learning to automatically improve modular online educational resources through Experimentation and Personalization. Paper presented at the Human-Propelled Machine Learning Workshop at the Conference on Neural Information Processing Systems. [Overview Poster PDF]

Williams, J.J. (2013). Applying Cognitive Science to Online Learning. Paper presented at the Data Driven Education Workshop at Conference on Neural Information Processing Systems.

Williams, J.J. & Williams, B. A. (2013). Using Interventions to Improve Online Learning. Paper presented at the Data Driven Education Workshop at the Conference on Neural Information Processing Systems. [updated version]

Williams, J.J. (2013). Improving Learning in MOOCs by Applying Cognitive Science. Paper presented at the International Conference on Artificial Intelligence in Education, Memphis, TN. [PDF]

Williams, J.J., & Poldsam, H. (2013). Providing implicit formative feedback by combining self-generated and instructional explanations. Paper presented at the Formative Feedback in Interactive Learning Environments Workshop, at the International Conference on Artificial Intelligence in Education, Memphis, TN.


Refereed Conference Proceedings

Williams, J. J., Maldonado, S., Williams, B. A., Rutherford-Quach, S., & Heffernan, N. (2015). How can digital online educational resources be used to bridge experimental research and practical applications? Embedding In Vivo Experiments in “MOOClets”. Paper to be presented at the Spring 2015 Conference of the Society for Research on Educational Effectiveness, Washington, D. C.

Edwards, B. J., Williams, J. J., Gentner, D., & Lombrozo, T. (2014). Effects of comparison and explanation on analogical transfer. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., Walker, C., Maldonado, S. G., Lombrozo, T. (2013). Effects of Explaining Anomalies on the Generation and Evaluation of Hypotheses. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [PDF]

Edwards, B.J., Williams, J.J., & Lombrozo, T. (2013). Effects of explanation and comparison on category learning. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., Walker, C. M., & Lombrozo, T. (2012). Explaining increases belief revision in the face of (many) anomalies. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 1149-1154). Austin, TX: Cognitive Science Society. [PDF]

Walker, C. M., Williams, J. J., Lombrozo, T., & Gopnik, A. (2012). Explaining influences children's reliance on evidence and prior knowledge in causal induction. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 114-119). Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., Lombrozo, T., & Rehder, B. (2011). Explaining drives the discovery of real and illusory patterns. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 1352-1357). Austin, TX: Cognitive Science Society. [PDF]

Chi, M. T. H., DeJong, G., Legare, C., Lombrozo, T., Williams, J. J. (2011). Explanation-based mechanisms for learning: An interdisciplinary approach. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2756–2757). Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., & Lombrozo, T. (2010b). Explanation constrains learning, and prior knowledge constrains explanation. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2912-2917). Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., Lombrozo, T., & Rehder, B. (2010). Why does explaining help learning? Insight from an explanation impairment effect. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2906-2911). Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., & Lombrozo, T. (2009). Explaining promotes discovery: evidence from category learning. In N.A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1186-1191). Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., & Griffiths, T. L. (2008). Why are people bad at detecting randomness? Because it is hard. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), 
Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 64-70). Austin, TX: Cognitive Science Society. [PDF]

Williams, J. J., & Mandel, D. R. (2007). Do evaluation frames improve the quality of conditional probability judgment? In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 1653-1658), Mahwah, NJ: Erlbaum. [PDF]


Extended Abstracts

Williams, J. J., Ostrow, K., Xiong, X., Glassman, E., Kim, J., Maldonado, S. G., Reich, J., & Heffernan, N. (2015). Using and Designing Platforms for In Vivo Educational ExperimentsProceedings of the Second ACM Conference on Learning@Scale. [PDF]

Williams, J. J., Kim, J., Keegan, B. (2015). Supporting Collaborations between Instructors and Researchers using MOOCletsProceedings of the Second ACM Conference on Learning@Scale. [PDF]

Williams, J. J., Kovacs, G., Walker, C., Maldonado, S. G., & Lombrozo, T. (2014). Learning Online Via Prompts to ExplainIn Extended Abstracts of ACM CHI 2014. New York, NY: Association for Computing Machinery. [PDF]


Working Papers

Williams, J. J., Rafferty, A., Gajos, K. Z., Tingley, D. Lasecki, W. S., & Kim, J. (working paper). Connecting Instructors and Learning Scientists via Collaborative Dynamic Experimentation. [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., Li, N., Kim, J., Whitehill, J., Maldonado, S., Pechenizkiy, M., Chu, L., & Heffernan, N. (2014). MOOClets: A Framework for Improving Online Education through Experimental Comparison and Personalization of Modules (Working Paper No. 2523265). Retrieved from the Social Science Research Network: http://ssrn.com/abstract=2523265 [PDF from SSRN] [Google Doc]

Williams, J.J. (2014). Applying Cognitive Science to Online Learning (working paper). Retrieved from the Social Science Research Network: http://ssrn.com/abstract=2535549

Williams, J.J. (2014)Using Randomized Experiments as a Methodological and Conceptual Tool for improving the Design of Online Learning Environments (working paper). Retrieved from the Social Science Research Network: http://ssrn.com/abstract=2535556  [tiny.cc/experimentsonlinelearning]