2024
Christie, S. T., Cook, C., & Rafferty, A. N. (2024) Uncertainty-preserving deep knowledge tracing with state-space models. In Proceedings of the 17th International Conference on Educational Data Mining (EDM2024). [extended abstract] [PDF]
Kumar, H., Xiao, R., Lawson, B., Musabirov, I., Shi, J., Wang, X., Luo, H., Williams, J. J., Rafferty, A. N., Stamper, J., & Liut, M. Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S ’24). [PDF]
Rafferty, A. N., Liben-Nowell, D., Musicant, D. R., Farley, E., Lyman, A., & May, A. (2024) Playing with Matches: Adopting Gale–Shapley for Managing Student Enrollments Beyond CS2. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE). [PDF]
Nurollahian, S., Rafferty, A. N., Brown, N., & Wiese, E. (2024) Growth in Knowledge of Programming Patterns: A Comparison Study of CS1 vs. CS2 Students. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE). [PDF]
Kumar, H., Li, T., Shi, J., Musabirov, I., Kornfield, R., Meyerhoff, J., Bhattacharjee, A., Karr, C., Mohr, D., Rafferty, A. N., Villar, S., Deliu, N., Williams, J. J. (2024) Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health. In Proceedings of the Thirty- Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24). [PDF]
2023
S. Thomas Christie, Hayden Johnson, Carson Cook, Garron Gianopulos, and Anna N. Rafferty. 2023. LENS: Predictive Diagnostics for Flexible and Efficient Assessments. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S '23). Association for Computing Machinery, New York, NY, USA, 14–24. https://doi.org/10.1145/3573051.3593392 [PDF]
McCormick, S., Davenport, J. L., Rafferty, A. N., Raysor, S., Yani, J., & Yaron, D. (2023). ChemVLab+: Integrating Next Generation Science Standards Practices with Chemistry. Journal of Chemical Education, 100(6), 2116–2131. doi:10.1021/acs.jchemed.2c01106
Nurollahian, S., Rafferty, A. N., & Wiese, E. (2023). Improving Assessment of Programming Pattern Knowledge through Code Editing and Revision. Proceedings of the 45th International Conference on Software Engineering. Software Engineering Education and Training Track (SEET). Received Best SEET Paper award. [PDF]
2022
Yang, Zhi-Han, Zhang, Shiyue, & Rafferty, A. N. Adversarial bandits for drawing generalizable conclusions in non-adversarial experiments: an empirical study. Proceedings of the 15th International Conference on Educational Data Mining (pp. 353--360).
Zavaleta Bernuy, A. Han, Z., Shaikh, H., Zheng, Q. Y., Lim, L.-A., Rafferty, A. N., Petersen, A., & Williams, J. J. How can Email Interventions Increase Students' Completion of Online Homework? A Case Study Using A/B Comparisons. Proceedings of LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 107-118).
Liben-Nowell, D. & Rafferty, A. N. (2022). Student Motivations and Goals for CS1: Themes and Variations. Proceedings of the 53rd ACM Technical Symposium on Computer Science Education.
Wiese, E.S., Rafferty, A. N., & Pyper J. (2022). Readable vs. Writable code: A Survey of Intermediate Students' Structure Choices. Proceedings of the 53rd ACM Technical Symposium on Computer Science Education. [PDF]
2021
Jansen, R.A., Rafferty, A.N. & Griffiths, T.L. (2021) A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers. Nature Human Behavior. https://doi.org/10.1038/s41562-021-01057-0 [Full text]
Singla, A., Rafferty, A. N., Radanovic, G., & Heffernan, N. (2021). Reinforcement Learning for Education: Opportunities and Challenges. Archived in the Computing Research Repository (CoRR): https://arxiv.org/abs/2107.08828
Reza, M., Kim, J., Bhattacharjee, A., Rafferty, A. N. & Williams, J. J. (2021). The MOOClet Framework: Unifying Experimentation, Dynamic Improvement, and Personalization in Online Courses. L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale (pp. 15-26). [PDF]
Zavaleta Bernuy, A., Zheng, Q. Y., Shaikh, H., Nogas, J., Rafferty, A. N., Petersen, A. & Williams, J. J. (2021). Using Adaptive Experiments To Rapidly Help Students. Proceedings of 22nd International Conference on Artificial Intelligence in Education (AIED). [PDF]
Wiese, E. S., Rafferty, A. N., & Moseke, Garrett. (2021). Students' Misunderstanding of the Order of Evaluation in Conjoined Conditions. Proceedings of the 29th IEEE/ACM International Conference on Program Comprehension 2021, Education Track. [PDF]
2020
Rafferty, A. N., Jansen R. A., & Griffiths, T. L. (2020). Assessing Mathematics Misunderstandings via Bayesian Inverse Planning. Cognitive Science. [PDF]
Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2020). A rational model of sequential self-assessment. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. [PDF]
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][Link to code repository] Correction: The original proceedings paper incorrectly stated we use traditional contextual Thompson sampling, when in fact we used a more exploitative version. The PDF linked here has all results updated with the traditional contextual Thompson sampling.
Chang, T. A. & Rafferty, A. N. (2020). Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages. Proceedings of the 5th Workshop on Representation Learning for NLP (pp. 7-16) [PDF]
2019
Rafferty, A. N., Ying, H., & Williams, J. J. (2019). Statistical consequences of using multi-armed bandits to conduct adaptive educational experiments. 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. [Poster]
Wiese, E. S., Rafferty, A. N., Kopta, D. & MacHardy, J. (2019) Replicating Novices' Struggles with Coding Style. Proceedings of the 27th IEEE/ACM International Conference on Program Comprehension 2019, Replications Track. [PDF]
Wiese, E. S., Rafferty, A. N., & Fox, A. (2019) Linking Code Readability, Structure, and Comprehension among Novices: It's Complicated. Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE 2019). [PDF]
2018
Davenport, J. L., Rafferty, A. N., & Yaron, D. J. (2018). Whether and how authentic contexts using a virtual chemistry lab support learning. Journal of Chemical Education. doi:10.1021/acs.jchemed.8b00048 (open access)
Rafferty, A. N., Ying, H., & Williams, J. J. (2018). Bandit assignment for educational experiments: Benefits to students versus statistical power. 17th International Conference on Artificial Intelligence in Education (AIED 2018). [PDF][Poster]
Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2018). Modeling the Dunning-Kruger Effect: A Rational Account of Inaccurate Self-Assessment. Proceedings of the 40th Annual Conference of the Cognitive Science Society. [PDF]
Williams, J. J., Rafferty, A. N., Tingley, D., Ang, A., Lasecki, W., & Kim, J. (2018). Enhancing Online Problems Through Instructor-Centered Tools for Randomized Experiments. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2018). [PDF]
Zhu, X., Singla, A., Zilles, S., \& Rafferty, A. N. (2018). An Overview of Machine Teaching. Archived in the Computing Research Repository (CoRR): http://arxiv.org/abs/1801.05927
2017
Wiese, E. S., Rafferty, A. N., & Linn, M. C. (2017). Eliciting Middle School Students’ Ideas About Graphs Supports Their Learning from a Computer Model. Proceedings of the 39th Annual Conference of the Cognitive Science Society. [PDF]
Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2017). Algebra is not like trivia: Evaluating self-assessment in an online math tutor. Proceedings of the 39th Annual Conference of the Cognitive Science Society. [PDF]
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). [extended abstract] [PDF]
Walker, C. M., Lombrozo, T., Williams, J. J., Rafferty, A. N. and Gopnik, A. (2017). Explaining Constrains Causal Learning in Childhood. Child Development, 80(1), 229-246. doi: 10.1111/cdev.12590.
2016
Rafferty, A. N., Brunskill, E., Griffiths, T. L. & Shafto, P. (2016). Faster Teaching via POMDP Planning. Cognitive Science, 40(6), 1290-1332.doi:10.1111/cogs.12290.
Gerard, L. F., Ryoo, K., McElhaney, K. W., Liu, O. L., Rafferty, A. N., & Linn, M. C. (2016). Automated Guidance for Student Inquiry. Journal Of Educational Psychology, 108(1), 60-81. doi:10.1037/edu0000052.
Rafferty, A. N., Jansen, R. A., & Griffiths, T. L. (2016) Using Inverse Planning for Personalized Feedback. Proceedings of the 9th International Conference on Educational Data Mining (pp. 472-477). [PDF]
Williams, J. J., Kim, J., Rafferty, A. N., Maldonado, S., Gajos, K. Z., Lasecki, W. S., & Heffernan, N. (2016). AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning. Proceedings of the Third (2016) ACM Conference on Learning@ Scale. [PDF]
2015
Rafferty, A. N. and Griffiths, T. L. (2015). Interpreting freeform equation solving. Proceedings of the 17th International Conference on Artificial Intelligence in Education. [PDF]
Rafferty, A. N., LaMar, M. M., & Griffiths, T. L. (2015). Inferring learners' knowledge from their actions. Cognitive Science, 39, 584-618. [PDF]
Linn, M. C., Eylon, B. S., Rafferty, A. N., & Vitale, J. M. (2015). Designing Instruction to Improve Lifelong Inquiry Learning. Eurasia Journal of Mathematics, Science & Technology Education, 11(2), 217-225. [PDF]
2014
Rafferty, A. N., Zaharia, M., and Griffiths, T. L. (2014). Optimally designing games for behavioural research. Proceedings of the Royal Society Series A, 470. [PDF]
Linn, M. C., Gerard, L. F., Ryoo, K., Liu, L., and Rafferty, A. N. (2014). “Computer-guided inquiry to improve science learning.” Science, 344, 155-156. [Education Forum paper] [PDF]
Neumann, R., Rafferty, A. N., and Griffiths, T. L. (2014). “A bounded rationality account of wishful thinking.” Proceedings of the 36th Annual Conference of the Cognitive Science Society. [PDF]
Rafferty, A. N. and Griffiths, T. L. (2014) “Diagnosing Algebra Understanding via Bayesian Inverse Planning.” Proceedings of the 7th International Conference on Educational Data Mining (p. 351- 352). [extended abstract] [PDF]
Rafferty, A. N., Gerard, L., McElhaney, K., and Linn, M. C. (2014) “Promoting Student Learning Through Automated Formative Guidance on Chemistry Drawings.” Proceedings of the International Conference of the Learning Sciences (ICLS) 2014 (p. 386-393). [PDF]
2013
Rafferty, A. N., Griffiths, T. G. and Ettlinger, M. (2013) “Greater learnability is not sufficient to produce cultural universals.” Cognition, 129, 70-87. [PDF]
Rafferty, A. N., Davenport, J., and Brunskill, E. (2013) “Estimating Student Knowledge from Paired Interaction Data.” Proceedings of The 6th International Conference on Educational Data Mining (EDM 2013). [PDF]
Rafferty, A. N., Gerard, L., McElhaney, K., Linn, M. C. (2013) “Automating Guidance for Students Chemistry Drawings.” Proceedings of Formative Feedback in Interactive Learning Environments (AIED Workshop). [PDF]
2012
Rafferty, A. N., Zaharia, M., and Griffiths, T. L. (2012) “Optimally Designing Games for Cognitive Science Research.” Proceedings of The 34th Annual Conference of the Cognitive Science Society (p. 893-898). [PDF]
Rafferty, A. N., LaMar, M. M., and Griffiths, T. L. (2012) “Inferring learners knowledge from observed actions.” Proceedings of The 5th International Conference on Educational Data Mining (EDM 2012). Winner of Best Poster Award. [PDF]
Davenport, J., Rafferty, A. N., Timms, M., Yaron, D., Karabinos, M. (2012) “ChemVLab+: Evaluating a Virtual Lab Tutor for High School Chemistry.” Proceedings of The 10th International Conference of the Learning Sciences (ICLS 2012). [PDF]
2011
Rafferty, A. N., Brunskill, E., Griffiths, T. L., and Shafto, P. (2011) “Faster teaching by POMDP planning.” Proceedings of The 15th International Conference on Artificial Intelligence in Education (AIED2011) (p. 280-287). [PDF]
Rafferty, A. N., Griffiths, T. L., and Ettlinger, M. (2011) “Exploring the relationship between learnability and linguistic universals.” Proceedings of The 2nd Workshop on Cognitive Modeling and Computational Linguistics at ACL 2011. [PDF]
2010
Rafferty, Anna N. and Thomas L. Griffiths. (2010) "Optimal language learning: The importance of starting representative." Proceedings of The 32nd Annual Conference of the Cognitive Science Society. [PDF]
2009 and before
Rafferty, A. N., Griffiths, T. L., and Klein, D. (2009) "Convergence Bounds for Language Evolution by Iterated Learning." Proceedings of The 31st Annual Conference of the Cognitive Science Society. [PDF]
Ramage, D., Rafferty, A. N., and Manning, C. D. (2009) "Random Walks for Text Semantic Similarity." Proceedings of ACL-IJCNLP TextGraphs-4 Workshop 2009. [PDF]
Rafferty, A. N. and Manning, C. D. (2008) "Parsing Three German Treebanks: Lexicalized and Unlexicalized Baselines." Proceedings of Workshop on Parsing German, ACL-HLT 2008. [PDF]
de Marneffe, M.-C., Rafferty, A. N., and Manning, C. D. (2008) "Finding Contradictions in Text." Proceedings of ACL-HLT 2008. [PDF]
Rafferty, A. N. and Yudelson, M. (2007) "Applying Learning Factors Analysis to Build Stereotypic Student Models." Proceedings of The 13th International Conference on Artificial Intelligence in Education (AIED2007) . Winner of Best Paper Award for the Young Researcher Track. [PDF]
de Marneffe, M.-C., MacCartney, B., Grenager, T., Cer, D., Rafferty, A. N., and Manning, C. D. (2006) "Learning to distinguish valid textual entailments." Proceedings of The Second PASCAL Challenges Workshop. [PDF]