Workshop Papers & Misc

Workshop Papers

WP35 Baral, S., Lucy, L., Knight, R., Ng, A., Soldaini, L., Heffernan, N., & Lo, K. (2025). DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students’ Hand-Drawn Math Images. In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS'24. PDF | https://drawedumath.org/ 

WP34 DiCicco, M., Worden, M., Olsen, C., Gangaram, N., Reichman, D., Heffernan, N. (2024). The Karp Dataset. The Thirty-Eight Annual Conference on Neural Information Processing Systems (NeurIPS 2024). PDF.

WP33 Haim, A.,  Worden, E., and Heffernan, N. (submitted)  The Effectiveness of AI Generated, On-Demand Assistance within Online Learning Platforms. The Fifth Annual Workshop on A/B Testing and Platform-Enabled Learning Research, held at the Learning at Scale (L@S). PDF.

WP32  Wang, A., Prihar, E., & Heffernan, N. (2023)  Assessing the Quality of Large Language Models in Generating Mathematics Explanations. Presented at the The Fourth Annual Workshop on A/B Testing and Platform-Enabled Learning Research held at the Learning @ Scale Conference.  PDF

WP31 Haim, A., Shaw, S.T., & Heffernan, N. (2023). How to Open Science: Promoting Principles and Reproducibility Practices within the Learning Analytics Community. The 13th International Learning Analytics and Knowledge Conference, March 13-17, 2023. Arlington, TX. Submitted Paper. Final Paper. 

WP30 Sales, A., Prihar, E., Gagnon-Bartsch, J., Gurung, A & Heffernan, N. (2022). The More Powerful A/B Testing using Auxiliary Data and Deep Learning. Conference on Digital Experimentation (CODE 2022). 

WP 29 Syed, M., Prihar, E., Haim, A., Sales, A., Shaw, S., & Heffernan, N. (2021). Common Interests and Trends in Online Educational Experiments [Paper presentation]. Conference on Digital Experimentation (MIT CODE), Cambridge, Massachusetts. Paper available here.  

WP 28 Shen, J. T., Yamashita, M., Prihar, E., Heffernan, N., Wu, X., Graff, B., & Lee, D. (2021). MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education. In NeurIPS 2021 Math AI for Education Workshop. Best Paper Winner

WP 27 Sales, A., Prihar, E., Heffernan, N. (2021). Causal Inference in Educational Data Mining. (2021). Part of Educational Data Mining 2021. Blinded Review Copy.  

WP26 Choi, H., Brooks, C., Hayward, C., Kitto, K., Gasevic, D., Pardo, A., Winne, P., and Heffernan, N. (2021). Engineering Learning Analytics Technology Environments (ELATE): Understand iteration between data and theory, and design and deployment. (LAK). blinded copy.  

WP25 Botelho, A. F., Erickson, J. A., Alphonsus, A. G., & Heffernan, N. T. (2020, March) Providing Directed Feedback Through QUICK-Comments. In the 10th International Conference on Learning Analytics and Knowledge (LAK) Workshop on Learning Analytic Services to Support Personalized Learning and Assessment at Scale, Online Workshop. PDF. Slides.

WP24 Doroudi, S., Williams, J., Kim, J., Patikorn, T., Ostrow, K., Selent, D., Heffernan, N. T., Hills, T., & Rosé, C. (2018). Crowdsourcing and Education: Towards a Theory and Praxis of Learnersourcing. In Kay, J. and Luckin, R. (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 2. London, UK: International Society of the Learning Sciences. ICLS-Link

WP23 Wilson, K., Xiong, X., Khajah, M., Lindsey, R. V., Zhao, S., Karklin, K., Van Inwegen, E., Han, B., Ekanadham, C., Beck, J., Heffernan, N., & Mozer, M., (2016) Estimating student proficiency: Deep learning is not the panacea. Submission to the NIPS 2016 Workshop on Machine Learning for Education.

WP22  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 Experiments. Proceedings of the Second ACM Conference on Learning@Scale. 

WP21 Williams, J. J.,  Schultz, S., & Heffernan, N. T. (2015) Adaptively Personalizing Instruction through Collaborative Development of MOOClets by Instructors, Education, Psychology and Machine Learning Researchers. Learning with MOOCs II workshop that will be held at Teachers College, Columbia University on October 2-3, 2015.  There were two peer reviews but an unpublished acceptance rates so I listed down in this section.

WP20 Patvarczki, J., Almeida, S., Beck, J. &  Heffernan, N. (2008). Lessons Learned from Scaling Up a Web-Based Intelligent Tutoring System. Lecture Notes in Computer Science, Intelligent Tutoring Systems, 5091, 766-770. 

WP19 Razzaq, L., Heffernan, N.T. (2008). Towards Designing a User-Adaptive Web-Based E-Learning System. In Mary Czerwinski, Arnold M. Lund, Desney S. Tan (Eds.): Extended Abstracts Proceedings of the 2008 Conference on Human Factors in Computing Systems, 3525-3530. Florence, Italy.

WP18 Pardos, Z., Heffernan, N. T., Anderson, B., & Heffernan-Lindquist, C. (2007). The effect of model granularity on student performance prediction using Bayesian networks. The Educational Data Mining Workshop held at the International User Modeling Conference 2007. Corfu, Greece. (Work later led to CP18)

WP17 Pardos, Z., Feng, M. Heffernan, N. T., Heffernan-Lindquist, C. & Ruiz, C. (2007). Analyzing fine-grained skill models using Bayesian and mixed effect methods. In the Educational Data Mining Workshop held at the 13th Conference on Artificial Intelligence in Education. This is a longer version of CP18.

WP16 Lloyd, N., Heffernan, N. & Ruiz, C. (2007). Predicting student engagement in intelligent tutoring systems using teacher expert knowledge. In the Educational DataMining Workshop held at the 13th Conference on Artificial Intelligence in Education.

WP15 Feng, M., Heffernan, N. T., Mani, M., & Heffernan, C. (2006). Using mixed-effects modeling to compare different grain-sized skill models. In Beck, J., Aimeur, E., & Barnes, T. (Eds). Educational Data Mining: Papers from the AAAI Workshop. Menlo Park, CA: AAAI Press, 57-66. Technical Report WS-06-05. (Work later led to PP8)

WP14 Pardos, Z. A., Heffernan, N. T., Anderson, B., & Heffernan C. (2006). Using fine-grained skill models to fit student performance with Bayesian networks. Workshop in Educational Data Mining held at the Eighth International Conference on Intelligent Tutoring Systems. Taiwan. 2006. (Work later led to PP8)

WP13 Feng, M., Heffernan, N. T., & Koedinger, K. R. (2005). Looking for sources of error in predicting student's knowledge. In Beck. J. (Eds). Educational Data Mining: Papers from the 2005 AAAI Workshop. Menlo Park, California: AAAI Press, 54-61. Technical Report WS-05-02.

WP12 Feng, M., & Heffernan, N. (2005). Informing teachers live about student learning: Reporting in the Assistment system. Workshop on Usage Analysis in Learning Systems held at the 12th International Conference on Artificial Intelligence in Education. Amsterdam. (Work later led to J3 and J5)

WP11 Nuzzo-Jones, G., Walonoski, J.A., Heffernan, N.T. & Livak, T. (2005). The eXtensible tutor architecture: A new foundation for ITS. Workshop on Adaptive Systems for Web-Based Education: Tools and Reusability held at the 12th Annual Conference on Artificial Intelligence in Education. Amsterdam, 1-7. (Work later led to BC1 and PP4)

WP10 Turner, T.E., Macasek, M.A., Nuzzo-Jones, G., Heffernan, N..T & Koedinger, K. (2005). The Assistment builder: A Rapid development tool for ITS. In a workshop on Adaptive Systems for Web-Based Education: Tools and Reusability, held at the 12th Annual Conference on Artificial Intelligence in Education Amsterdam. (Work later led to BC1, PP4 and CP14)

WP9   Freyberger, J., Heffernan, N., & Ruiz, C. (2004). Using association rules to guide a search for best fitting transfer models of student learning. In Beck, Baker, Corbett, Kay, Litman, Mitrovic & Rigger (Eds.) Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes. Held at the 7th Annual Intelligent Tutoring Systems Conference, Maceio, Brazil. Lecture Notes in Computer Science. 

WP8   Livak, T., Heffernan, N. T., Moyer, D. (2004). Using cognitive models for computer generated forces and human tutoring.13th Annual Conference on (BRIMS) Behavior Representation in Modeling and Simulation. Simulation Interoperability Standards Organization. Arlington, VA.

WP7   Razzaq, L. & Heffernan, N. T (2004). Tutorial dialog in an equation solving intelligent tutoring system. Workshop on “Dialog-based Intelligent Tutoring Systems: State of the art and new research directions” at the 7th Annual Intelligent Tutoring Systems Conference, Maceio, Brazil, 33-42.

WP6   Koedinger, K. R., Aleven, V., & Heffernan, N. T. (2003). Toward a rapid development environment for cognitive tutors. The 12th Annual Conference on Behavior Representation in Modeling and Simulation. Simulation Interoperability Standards Organization. (Work later led to CP7)

WP5   Heffernan, N. T., (2002). Web-based evaluation showing both motivational and cognitive benefits of the Ms. Lindquist tutor. SIGdial endorsed Workshop on Empirical Methods for Tutorial Dialogue Systems which was part of the International Conference on Intelligent Tutoring System 2002,1-8. Also appeared in a NSF-DFG sponsored workshop on Collaboration between German and American researchers in instructional technology. Tampa, Florida, May 5-7, 2002. (Work later led to CP8)

WP4   Heffernan, N. T, Koedinger, K. (2001). The design and formative analysis of a dialog-based tutor. Workshop on Tutorial Dialogue Systems held as part of the 2001 Artificial Intelligence in Education, 23-34. (Work later led to CP8)

WP3   Heffernan, N. T., & Koedinger, K. R. (2000). Building a 3rd generation ITS for symbolization: Adding a tutorial model with multiple tutorial strategies. Workshop entitled “Learning Algebra with the computer, a transdisciplinary workshop.” Held at Intelligent Tutoring Systems 2000 Conference, 12-22. Lecture Notes in Computer Science 1839, Berlin: Springer. (Work later led to CP8)

WP2   Heffernan, N. T. (2000). Adding a cognitive model of human tutor to an intelligent tutoring systems. Intelligent Tutoring System Conference- Young Researchers Track. (Work later led to D1)

WP1   Heffernan, N. T. (1998). Intelligent tutoring systems have forgotten the tutor: Adding a cognitive model of human tutors. Abstract at the 1998 Computer Human Interaction Conference’s Doctoral Consortium. (Work later led to D1)

Unknown Acceptance Rate

U27 Kalarickal, M. & Heffernan, N. (2026). Evaluating Pedagogical Styles of LLM-Generated Hint Sets. In Proceedings of the 27th International Conference on Artificial Intelligence in Education (DC Track). Submitted PDF. 

U26 Croteau, E. & Heffernan, N. (2026). Seeing Like a Student: Difficulty and Error Alignment of MLLMs on Visual Math Problems. In Proceedings of the 27th International Conference on Artificial Intelligence in Education (DC Track). Submitted PDF. 

U25 Lee, M. P., Frenk, A., Gupta, K. A., Pham, T. T., Croteau, E., Heffernan, N. T. (Submitted). Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Conceptual Drift. Journal of Educational Data Mining (EDM '25), Palermo, Italy. Draft.  (Not yet accepted)

U24 Croteau, E. & Heffernan, N. (2026). Beyond Seeing: Alternative Representations for Image-Dependant Mathematics. In Proceedings of the 19th International Conference on Educational Data Mining (DC Track). Submitted PDF. 

U23 Lee, M. & Heffernan, N. (2025). Concept Drift Detection for Knowledge Tracing. In Proceedings of the 18th International Conference on Educational Data Mining (DC Track). PDF

U22 Lim, W.C. & Heffernan, N. (2025). Evaluating the Impact of LLM-Generated Assignment Report Summaries in Intelligent Tutoring Systems.  In Proceedings of the 26th International Conference on Artificial Intelligence in Education (DC Track). PDF

U21 Worden, E., Baral, S., Yu, D., Santorelli, C., & Heffernan, N. (2025). Few-shot Is All You Need, a Framework for RAG-Based LLM Feedback. In Proceedings of the 26th International Conference on Artificial Intelligence in Education (DC Track). PDF

U20 Lee, Morgan P. & Heffernan, N. (2025). Improving Student Support Personalization with Historical Data and Theoretically Informed Feature Choice. In Proceedings of the 26th International Conference on Artificial Intelligence in Education (DC Track). PDF

U19 Siedahmed, A., Vanacore, K., Lee, M. P., & Heffernan, N. T. (2024). Evaluating the Effectiveness of Hints and Explanations Across Schools with Different Student Demographics. In The Proceedings of the Educational Data Mining Conference (EDM '24). Paper.

U18 Wei, X., Wortman, A., Cheng, L., Heffernan, N., Heffernan, C., Murphy, A., Zepeda, C., Motz, B., Jankowski, H., & Roschelle, J. (2024, March). Language and mathematics learning: A comparative study of digital learning platforms. Digital Promise. doi.org/10.51388/20.500.12265/206

U17 Prihar, E., Moore, A. & Heffernan, N. (2022). Identifying Explanations Within Student-Tutor Chat Logs. DC paper at EDM2022. View

U16 Baral, S. (2022). Improving Automated Assessment and Feedback for Student Open-Responses in Mathematics. EDM 2022 Doctoral Consortium. 

U16 Prihar, E., Moore, A., Heffernan, N. (2022). Identifying Explanations Within Student-Tutor Chat Logs. EDM 2022 Doctoral Consortium. 

U15 Gurung, A., Heffernan, N. (2022). Exploring Fairness in Automated Grading and Feedback Generation of Open-Response Math Problems. AIED 2022 Doctoral Consortium. 

U14 Haim, A., Prihar, E., & Heffernan, N. (2022). Toward Improving Effectiveness of Crowdsourced, On-Demand Assistance From Educators in Online Learning Platforms. AIED 2022 Doctoral Consortium.

U13 Singla, A., Rafferty, A, Radanovic, G & Heffernan, N. (2021) Reinforcement Learning for Education: Opportunities and Challenges.  An overview of what happened at a RL4ED.org 2021 EDM Workshop. https://arxiv.org/abs/2107.08828

U12 Goldstein, D.S., Heffernan, C., Heffernan, N.T., Pellegrino, J.W., Goldman, S.R., & Stoelinga, T.M. (April 2016). Mapping skills and knowledge in the Connected Mathematics Project 2 Curriculum. Poster presented at Annual Meeting of the American Educational Research Association, Washington, DC. 

U11 Goldstein, D.S., Pellegrino, J.W., Goldman, S.R., Stoelinga, T.M., Heffernan, N.T., & Heffernan C. (April 2016). Improving mathematical learning outcomes through applying principles of spaced practice and assessment with feedback. Poster presented at Annual Meeting of the American Educational Research Association, Washington, DC.

U10 McGuire, P., Logue, M., Mason, C., Tu, S., Heffernan, C., Heffernan, N., Ostrow, K. & Li, Y. (2016, accepted). To See or Not To See: Putting Image-Based Feedback in Question. Interactive lecture at the International Society for Technology in Education Conference. Denver, CO. 

U9 Trivedi, S.,, Pardos, Zachary A. , &  Heffernan, N. T. (2023) The Utility of Clustering in Prediction Tasks. https://doi.org/10.48550/arXiv.1509.06163 

U8 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, 313-318. 

U7 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). The Social Science Research Network.

U6 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.

U5 Williams, J., J., Li, N., Kim, J., Whitehill, J., Maldonado, S., Pechenizkiy, M., Chu, L., & Heffernan, N. (2015). MOOClets: A Framework for Improving Online Education through Experimental Comparison and Personalization of Modules (Working Paper No. 2523265). The Social Science Research Network: 

U4 Kelly, K., Heffernan, N., Heffernan, C., Goldman, S., Pellegrino, J., & Soffer-Goldstein, D. (2014). Improving student learning in math through web-based homework review. In Liljedahl, P., Nicol, C., Oesterle, S., & Allan, D. (Eds.). (2014). Proceedings of the Joint Meeting of PME 38 and PME-NA 36 (Vol. 3). Vancouver, Canada: PME, 417-424.

U3 Pellegrino, J., Goldman, S., Soffer-Goldstein, D., Stoelinga, T., Heffernan, N., & Heffernan, C. (2014).  Technology Enabled Assessment:Adapting to the Needs of Students and Teachers. American Educational Research Association (AERA 2014) Conference.  

U2  Soffer,-Goldstein, D., Das, V., Pellegrino, J., Goldman, S., Heffernan, N., Heffernan, C., & Dietz, K. (2014). Improving Long-term Retention of Mathematical Knowledge through Automatic Reassessment and Relearning. American Educational Research Association (AERA 2014) Conference. Division C - Learning and Instruction / Section 1c: Mathematics. (peer reviewed but unknown rate Nominated for the Best Poster of the Session. The paper and data are here :   https://sites.google.com/site/assistmentsdata/arrs 

U1 Heffernan, N., Heffernan, C., Dietz, K., Soffer, D., Pellegrino, J. W., Goldman, S. R. &  Dailey, M. (2012). Cognitively-Based Instructional Design Principles: A Technology for Testing their Applicability via Within-Classroom Randomized Experiments. AERA 2012.

Workshops & Tutorials I Helped Organize

WS25 Vanacore, K., Botelho, A., Closser, A., Sales, A., & Heffernan, N. (2025). CausalEDM: Linking Innovations in Instructional Design and the Complex Behaviors that Underlie Learning Processes and Outcomes. In Proceddings of the 18th International Conference on Educational Data Mining. 

WS24 Murphy, A., Fancsali, S., Ritter, S., Heffernan, N., Malick, D.B., Roschelle, J., McNamara, D., Williams, J.J., Stamper, J., Bier, N., Carver, J., & Kizilcic, R. (2025). In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25), July 21–23, 2025, Palermo, Italy. ACM, New York, NY, USA.

WS23 Haim, A., Hutt, S., Shaw, S. T., & Heffernan, N. T. (2024). Promoting Open Science in Artificial Intelligence: An Interactive Tutorial On Licensing, Data, and Containers. In Proceedings of The 25th International Conference on Artificial Intelligence In Education (AIED '24), Recife, Brazil, July 8–12, 2024. PDF.

WS22 Ritter, S., Fancsali, S. E., Murphy, A., Heffernan, N., Motz, B., Mallick, D. B., Roschelle, J., McNamara, D., Williams, J. J. (2024). Fifth Annual Workshop on A/B Testing and Platform-Enabled Learning Research. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24). Association for Computing Machinery, New York, NY, USA, 565–566. PDF.

WS21 Heffernan, N., Wang, R., MacLellan, C., Hellas, A., Li, C., Walkington, C., Littenberg-Tobias, J., Joyner, D., Moore, S., Singla, A., Pardos, Z., Pankiewicz, M., Kim, J., Sonkar, S., Cohn, C., Botelho, A., Lan, A., Jiang, L., Worden, E. (2024). Leveraging Large Language Models for Next-Generation Educational Technologies. In Proceedings of the 17th International Conference on Educational Data Mining (EDM '24), 1037--1039. https://doi.org/10.5281/zenodo.12730049

WS20 Botelho, A. F., Closser, A. H., Sales, A. C., Heffernan, N. T., Vanacore, K. P. (2024). Causal Inference in Educational Data Mining. In Proceedings of the 17th International Conference on Educational Data Mining (EDM '24), 1034--1036. https://doi.org/10.5281/zenodo.12730047

WS19 Haim, A., Hutt, St., Shaw, S. T., Heffernan, N. T. (2024). Promoting Open Science in Educational Data Mining: An Interactive Tutorial on Licensing, Data, and Containers. In Proceedings of the 17th International Conference on Educational Data Mining (EDM '24), 1017--1020. Paper.

WS18 Haim, A., Shaw, S.T., & Heffernan, N. (2023d, March 13). How to Open Science: Promoting Principles and Reproducibility Practices within the Learning Analytics Community. The 13th International Learning Analytics and Knowledge Conference, March 13-17, 2023. Arlington, TX. Final Paper  https://osf.io/kyxba/  Its in this

WS17 Haim, A., Shaw, S.T., & Heffernan, N. (2023e, July 3rd, ): How to Open Science: Promoting Principles and Reproducibility Practices Within the Artificial Intelligence in Education Community https://doi.org/10.1007/978-3-031-36336-8_11 

WS16 Haim, A., Shaw, S.T., & Heffernan, N. (2023f, July 14rd): How to Open Science: Promoting Principles and Reproducibility Practices with the Educational Data Mining Community. Pages 582-584. https://doi.org/10.5281/zenodo.8115776 

WS15 Haim, A., Shaw, S.T., & Heffernan, N. (2023g, July 23) How to Open Science: Promoting Principles and Reproducibility Practices within the Learning @ Scale Community. Pages 248-250. https://doi.org/10.1145/3573051.3593398

WS14 LAK (March 13-17, 2023): Participatory Co-Design of Platform-Embedded Learning Experiments. Fancsali, S., Ritter, S., Malick, D.B., Motz, B., Heffernan, N., Baker, R., Kizilcec, R., Roschelle, J., & McNarmara, D. The 13th International Learning Analytics and Knowledge Conference, March 13-17, 2023. Arlington, TX. Final Paper.

WS13 L@S (June 1-3, 2022): Third Annual Workshop on A/B Testing and Platform-Enabled Learning Research by Ritter, S., Heffernan, N., Williams, J.J., Lomas, D., Motz, B., Mallick, D.B., Bicknell, K., McNamara, D., Kizilcec, R.F., Roschelle, J., Baraniuk, R., & Baker, R. L. L@S, June 1-3, 2022. PDF. https://doi.org/10.1145/3491140.3528288

WS12 AAAI (March 1, 2022): RL4ED Workshop on Reinforcement Learning for Education by Heffernan, N., Lan, A., Rafferty, A., & Singla, A.

WS11 EDM (June 29-July 2, 2021): Reinforcement Learning for Education: Opportunities and Challenges by Singla, A., Rafferty, A, Radanovic, G & Heffernan, N. https://arxiv.org/abs/2107.08828

WS10  EDM (June 29-July 2, 2021): Causal Inference in Educational Data Mining Causal inference in Educational Data Mining by Sales, A., Prihar, E., & Heffernan, N. https://sites.google.com/umich.edu/causaledm21

WS9 L@S (June 22-25, 2021): Second Workshop on Educational A/B Testing at Scale by Ritter, S., Heffernan, N., Williams, J. J., Lomas, D., & Bicknell, K. https://doi.org/10.1145/3430895.3460876

WS8  LAK (April 12-16, 2021): ELATE: Engineering Learning Analytics Technology Environments: Understanding iteration between data and theory, and design and deployment by Choi, H., Brooks, K., Hayward, C., Heffernan, N., Gasevic, D., Kitto, K., Pardo, A., & Winne, P. https://cic.uts.edu.au/events/lak21

WS7  AAAI (Feb. 2-9, 2021): TIPCE 2021 Imagining Post-COVID Education with AI program committee member

WS6  NeurIPS (Dec. 6-12, 2020): Minimizing Bias in Machine Learning

WS5 NeurIPS (Dec. 16-20, 2020): Advances and Opportunities: Machine Learning for Education by Garg, K., Heffernan, N., & Meyers, K. https://nips.cc/Conferences/2020/Schedule?showEvent=16104 

WS4  SIGKDD (Aug. 2020): Recent Advances in Multimodal Educational Data Mining in K-12 Education in KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Pages 3549–3550. https://doi.org/10.1145/3394486.3406471 

WS3 L@S (Aug. 12, 2020): Artificial Intelligence for Video-based Learning at Scale. Was asked to give a keynote talk for this workshop. Pages 215–217. https://dl.acm.org/doi/abs/10.1145/3386527.3405937 

WS2  L@S (Aug. 12-14, 2020): Educational A/B Testing at Scale with over 100 participants by Ritter, S., Heffernan, N., Williams, J. J., Settles, B., Grimaldi, P., & Lomas, D. Pages 219–220. https://dl.acm.org/doi/abs/10.1145/3386527.3405933

WS1  Heffernan was asked to give the keynote to the Artificial Intelligence in Education 2020 Conference where he explained ASSISTments and his vision for crowdsourcing. https://www.youtube.com/watch?v=S-AydzWsjeU&feature=youtu.be