Papers

2024

Bhattacharjee, A., Zeng, Y., Xu, S., Kulzhabayeva, D., Ma, M., Kornfield, R., Ahmed, S.I., Mariakakis, A., Czerwinski, M., Kuzminykh, A., Liut, M., and Williams, J.J. (2024). Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination. (Accepted) In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems [paper].

Reza, M., Laundry, N., Musabirov, I., Dushniku, P., Yu, Z.Y., Mittal, K., Grossman, T., Liut, M., Kuzminykh, A. and Williams, J.J., 2024. ABScribe: Rapid Exploration of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models. (Accepted) In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems [paper].

Bhattacharjee, A., Chen, P., Mandal, A., Hsu, A., O’Leary, K., Mariakakis, A., and Williams, J.J., 2024. Exploring User Perspectives on Brief Reflective Questioning Activities for Stress Management: Mixed-Methods Study. JMIR Formative Research. [paper]

Meyerhoff, J., Beltzer, M., Popowski, S., Karr, C.J., Nguyen, T., Williams, J.J., Krause, C.J., Kumar, H., Bhattacharjee, A., Mohr, D.C. and Kornfield, R., 2024. Small Steps over time: A longitudinal usability test of an automated interactive text messaging intervention to support self-management of depression and anxiety symptoms. Journal of Affective Disorders, 345, pp.122-130.

2023

Bhattacharjee, A., Williams, J.J., Meyerhoff, J., Kumar, H., Mariakakis, A. and Kornfield, R., 2023, April. Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-19).-- Best Paper Award [paper]

Reza, M., Zavaleta Bernuy, A., Liu, E., Li, T., Liang, Z., Barber, C.K. and Williams, J.J., 2023, April. Exam Eustress: Designing Brief Online Interventions for Helping Students Identify Positive Aspects of Stress. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-13). [slides] [video] [paper]

Bhattacharjee, A., Song, H., Wu, X., Tomlinson, J., Reza, M., Chowdhury, A.E., Deliu, N., Price, T., and Williams, J.J., 2023. Informing Users about Data Imputation: Exploring the Design Space for Dealing With Non-Responses. Proceedings of the 11th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2023). [paper]

Kornfield, R., Stamatis, C.A., Bhattacharjee, A., Pang, B., Nguyen, T., Williams, J.J., Kumar, H., Popowski, S., Beltzer, M., Karr, C.J., Reddy, M., Mohr, D.C., Meyerhoff, J., 2023. A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype. Internet Interventions Volume 34, December 2023, 100667


2022

[Working paper] Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization. https://arxiv.org/abs/2112.08507

Ye, R., Chen, P., Mao, Y., Wang-Lin, A., Shaikh, H., Zavaleta Bernuy, A., & Williams, J. J. (2022, September). Behavioral Consequences of Reminder Emails on Students’ Academic Performance: a Real-world Deployment. In The 23rd Annual Conference on Information Technology Education (SIGITE ’22)(pp. 16-22) [doi] -- Best Paper Award

Zavaleta Bernuy, A., Han, Z., Shaikh, H., Zheng, Q.Y., Lim, L.A., Rafferty, A., Petersen, A. and Williams, J.J., 2022, March. How can Email Interventions Increase Students’ Completion of Online Homework? A Case Study Using A/B Comparisons. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 107-118).[doi]

Bhattacharjee, A., Pang, J., Liu, A., Mariakakis, A. and Williams, J.J., 2022. Design Implications for One-Way Text Messaging Services that Support Psychological Wellbeing. ACM Transactions on Computer-Human Interaction. [PDF]

Bhattacharjee, A., Williams, J.J., Chou, K., Tomlinson, J., Meyerhoff, J., Mariakakis, A., Kornfield, R. (2022). “I Kind of Bounce off It”: Translating Mental Health Principles into Real Life Through Story-Based Text Messages. Proceedings of the ACM on Human-Computer Interaction (CSCW2), 1-31. [PDF]

Meyerhoff, J., Nguyen, T., Karr, C.J., Reddy, M., Williams, J.J., Bhattacharjee, A., Mohr, D.C. and Kornfield, R., 2022. System design of a text messaging program to support the mental health needs of non-treatment seeking young adults. Procedia Computer Science, 206, pp.68-80. [Full Text]

Kornfield, R., Meyerhoff, J., Levin, H., Bhattacharjee, A., Williams, J. J., Reddy, M., & Mohr, D. C. (2022). Meeting Users Where They Are: User-centered Design of an Automated Text Messaging Tool to Support the Mental Health of Young Adults. Proceedings of the 2022 ACM Conference on Human Factors in Computing Systems (CHI). [PDF] [Video of Talk]

Yanez, F.J., Zavaleta-Bernuy, A., Han, Z., Liut, M., Rafferty, A. and Williams, J.J., 2022. Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits. arXiv preprint arXiv:2208.05090. [doi]

2021

[Working paper] Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments. https://doi.org/10.48550/arXiv.2103.12198

[Working paper] Deliu, N., Villar, S., Williams, J. J. Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling. https://doi.org/10.48550/arXiv.2111.00137

Pathak, L.E., Aguilera, A., Williams, J.J., Lyles, C.R., Hernandez-Ramos, R., Miramontes, J., Cemballi, A.G., & Figueroa, C. (2021). Combining user centered design and crowdsourcing to develop messaging content for a physical activity smartphone application tailored to low-income patients. To appear in JMIR mHealth and uHealth. https://doi.org/10.2196/21177. [PDF]

Figueroa, F.A., Aguilera, A., Chakraborty, B., Modiri, A., Aggarwal, J., Deliu, N., Sarkar, U., Williams, J.J., & Lyles, C.R. (2021). Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. Journal of the American Medical Informatics Association, ocab001, 1-10. https://doi.org/10.1093/jamia/ocab001.

Cai, W., Grossman, J., Lin, Z. J., Sheng, H., Wei, J. T. Z., Williams, J. J., & Goel, S. (2021). Bandit algorithms to personalize educational chatbots. Machine Learning, 110, 2389-2418. doi

Reza, M., Kim, J., Bhattacharjee, A., Rafferty, A.N., & Williams, J.J. (2021). The MOOClet Framework: Unifying Experimentation, Dynamic Improvement, & Personalization in Online Courses. To appear in the Eighth Annual ACM Conference on Learning at Scale. [PDF] [Video of Talk]

Zavaleta Bernuy, A., Zheng, Q. Y., Shaikh, H., Petersen, A., & Williams, J. J. (2021). Investigating the Impact of Online Homework Reminders Using Randomized A/B Comparisons. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 921-927). [PDF] [Slides] 

Zavaleta Bernuy, A., Zheng, Q. Y., Shaikh, H., Nogas, J., Rafferty, Anna., Petersen, A., & Williams, J. J. (2021). Using Adaptive Experiments to Rapidly Help Students. Artificial Intelligence in Education. AIED 2021 (pp. 422-426). [PDF] [Slides] 

Solyst, J., Thakur, T., Dutta, M., Asano, Y., Petersen, A., & Williams, J. J. (2021). Procrastination and Gaming in an Online Homework System of an Inverted CS1. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 789-795). [PDF]

2020

Li, Z., Yee, L., Sauerberg, N., Sakson, I., Williams, J. J., & Rafferty, A. N. (2020). Getting too personal(ized): The importance of feature choice in online adaptive algorithms. Proceedings of the 13th International Conference on Educational Data Mining (pp. 159-170). [PDF]

Price, T.W., Williams, J.J., Solyst, J., Marwan, S. (2020) Engaging Students with Instructor Solutions in Online Programming Homework. In CHI 2020, 38th Annual ACM Conference on Human Factors in Computing Systems. [PDF]

Xia, M., Asano, Y., Williams, J. J., Qu, H., Ma, X. (2020) Using Information Visualization to Promote Students’ Reflection on “Gaming the system” in Online Learning. Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S ’20). [PDF]

Price, T. W., Marwan, S., Winters, M., & Williams, J. J. (2020). An Evaluation of Data-Driven Programming Hints in a Classroom Setting. In the International Conference on Artificial Intelligence in Education. [PDF]

Daskalova, N., Yoon, J., Wang, L., Beltran, G., Araujo, C., Nugent, N., McGeary, J., Williams, J., & Huang, J. (2020) SleepBandits: Guided Flexible Self-Experiments for Sleep. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. [PDF]

Asano, Y., Solyst, J., Williams, J. J. (2020). Characterizing and Influencing Students’ Tendency to Write Self-explanations in Online Homework. Proceedings of the 10th International Conference on Learning Analytics & Knowledge. [PDF]

Aguilera, A, Figueroa, C.A., Hernandez-Ramos, R., Sarkar, U., Cemballi, A., Gomez-Pathak, L., Miramontes, J., Yom-Tov, E., Chakraborty, B., Yan, X., Xu, J., Modiri, A., Aggarwal, J., Williams, J.J., & Lyles, C.R. (2020) mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study. BMJ Open. 2020 Aug 20. https://bmjopen.bmj.com/content/10/8/e034723. [PDF]

Bernecker, S.L., Williams, J.J., Caporale-Berkowitz, N.A., Wasil, A.R., & Constantino, M.J. (2020). Nonprofessional peer support to improve mental health: randomized trial of a scalable web-based peer counseling course. Journal of Medical Internet Research, 22(9). https://doi.org/10.2196/17164. [PDF]

Kizilcec, R. F., Reich, J., Yeomans, M., Dann, C., Brunskill, E., Lopez, D., Turkay, S., Williams, J., & Tingley, D. (2020). Scaling Up Behavioral Science Interventions in Online Education. Proceedings of the National Academy of Sciences (PNAS). [PDF]

Khrosravi, H., Kitto, K., Williams, J.J. (2020) RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities. To appear in Journal of Learning Analytics. [PDF]

Fischer, C., Pardos, Z. A., Baker, R. S. Williams, J.J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020) Mining Big Data in Education: Affordances and Challenges. To appear in Review of Research in Education. [PDF]

2019

Rafferty, A., Ying, H., & Williams, J. J. (2019). Statistical consequences of using multi-armed bandits to conduct adaptive educational experiments. JEDM | Journal of Educational Data Mining, 11(1), 47-79. [PDF]

Shaikh, H., Modiri, A., Williams, J. J., & Rafferty, A. N. (2019) Balancing Student Success and Inferring Personalized Effects in Dynamic Experiments. Proceedings of the 12th International Conference on Educational Data Mining. [PDF] [Poster]

Wanigasekara, N., Liang, Y., Goh, S. T., Liu, Y., Williams, J. J., & Rosenblum, D. S. (2019). Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking. Proceedings of the 28th International Joint Conference on Artificial Intelligence. [PDF]

Marwan, S., Williams, J. J., & Price, T. (2019). An Evaluation of the Impact of Automated Programming Hints on Performance and Learning. Proceedings of the 15th International Computing Education Research Conference. [PDF]

Marwan, S., N. Lytle, J. J. Williams and T. W. Price (2019). The Impact of Adding Textual Explanations to Next-step Hints in a Novice Programming Environment.  Proceedings of the Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE). [PDF] [Slides]

Zhang, L., Craig, M., Kazakevich, M., & Williams, J. J. (2019). Experience Report: Mini Guest Lectures in a CS1 Course via Video Conferencing. Proceedings of the 1st ACM Global Computing Education Conference. [PDF]

Edwards, B.J., Williams, J.J., Gentner, D. & Lombrozo, T. (2019) Explanation recruits comparison in a category-learning task. Cognition. 185, 21-38. [PDF]

Leung, W., & Williams, J. J. (2019). Enhancing education with instructor-in-the-loop algorithms. Paper presented at the Human+AI Modeling & Design workshop at the annual ACM CHI Conference on Human Factors in Computing

2018

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]

Rafferty, A., Ying, H., & Williams, J. J. (2018) Bandit assignment for educational experiments: Benefits to students versus statistical power. Proceedings 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]

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]

2017

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]

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]

2016

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 ]

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 Assessment. 6th International Learning Analytics & Knowledge Conference. [PDF]

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]

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., 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]

2015

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]

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 presented at the Spring 2015 Conference of the Society for Research on Educational Effectiveness, Washington, D. C.

Technology for In Vivo Educational Experiments. (in progress draft manuscript). 

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]

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]

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 

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.

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]

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]

2014

Williams, J. J., Li, N, Kim, J., Whitehill, J., Maldonado, S., Pechenizkiy, M., Chu, L., & Heffernan, N. (Working Paper). The MOOClet Framework: Improving Online Education through Experimentation and Personalization of Modules. [PDF from SSRN] [Google Doc]

Williams, J. J. (2014). How online educational resources provide novel affordances for conducting practical interventions and doing psychology experiments. Stanford Psychological Interventions in Educational Settings (PIES) group, Stanford, CA. [SlideShare]

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. & Williams, B.A. (2014). Online A/B Tests & Experiments: A Practical But Scientifically Informed Introduction. Course presented at ACM CHI Conference on Human Factors in Computing Systems. Toronto, Canada. [Materials]

Doing experiments by embedding Qualtrics into EdX 

The method outlined here can ensure control over which condition a user receives at different points in the course (from different Qualtrics surveys) because it assumes the final digit of the anonymous user ID is random, pulls that into Qualtrics, and assigns to experimental condition based on that.

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]

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., 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]

Ostrow, K., Williams, J. J., & Heffernan, N. (in press). The Future of Adaptive Learning: Infusing Educational Technology with Sound Science. Teachers College Record.

2013

Williams, J. J. (2013). Enhancing Educational Research & Practice using Experiments on Online Educational Resources. Pittsburgh Science of Learning Center LearnLab Summer School, Pittsburgh, PA. [SlideShare

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

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

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]

Williams, J.J. & Williams, B. A. (2013). Using Randomized Experiments as a Methodological and Conceptual Tool for improving the Design of Online Learning Environments. Paper presented at the Data Driven Education Workshop at the Conference on Neural Information Processing Systems.

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., & 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. (2013). Explanation and prior knowledge interact to guide learning. Cognitive Psychology, 66, 55–84. [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.

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]

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

2010

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

2008

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]