Publications

Please visit Dr. Heffernan's Google Scholar and DBLP pages. According to Google Scholar, Dr. Heffernan's 200+ papers have been cited 7,000+ times (as of June 2020). 

Dr. Heffernan has published 20+ randomized controlled experiments.  Over 10 external researchers have used ETRIALS to run their own studies. 

Dr. Heffernan is proud of the fact that he practices open science and gives out data sets; he is aware of over 100 other papers that have used these data sets to publish their own discoveries

Note: His OSF is osf.io/uyxdc while his OIRCD id is https://orcid.org/0000-0002-3280-288X.  Cristina Heffernan's OSF account is osf.io/fgvpy and her ORCID is https://orcid.org/0009-0009-4114-9892

Note: In the following sections, WPI students, who are co-authors, have their names in italics. 

Highlighted Publications

Dr. Heffernan is most well-known for ASSISTments, an online learning platform that provides assistance and assessment to teachers and students. The following paper provides a general overview of ASSISTments:  

SRI International found ASSISTments to be effective in increasing student achievement. (Dr. Heffernan is not an author) and got the study approved by the What Works Clearinghouse.  

Dr. Heffernan is the director of E-TRIALS (formerly the ASSISTments Testbed), a platform for conducting open science for education researchers.

Heffernan has embraced the idea of using ASSISTments as a crowd-sourcing look, taking ideas from teachers and trying to see who to give what to each student. 

An earlier example of crowdsourcing is this one:

ASSISTments is an accurate assessor of student knowledge and can predict state test scores by accounting for hints needed and attempts made.

ASSISTments has a demonstrated capacity to adapt to users.

The following papers assess the impacts of treatment effects:

Dr. Heffernan uses Bayesian Networks to Model Student Knowledge.

Dr. Heffernan is also well known his work detecting “gaming” behaviors.

Journal Articles.  

J37 Cheng, L., Croteau, E., Baral, S., Heffernan, C., & Heffernan, N. (2024). Facilitating Student Learning With a Chatbot in an Online Math Learning Platform. Journal of Educational Computing Research, 0(0). Author's Copy https://doi.org/10.1177/07356331241226592 

J36 Li, H., Wanli, C., Baral, S., & Heffernan, N. T. (submitted). Exploring the Multi-Modality Bias Arising from Privacy Leakage in an Automatic Feedback System Enhanced by Generative Artificial Intelligence. British Journal of Educational Technology.

J35 Lu, X., Wang, W., Motz, B. A., Ye, W., & Heffernan, N. T. (2023). Immediate text-based feedback timing on foreign language online assignments: How immediate should immediate feedback be? Computers and Education Open, 5. https://doi.org/10.1016/j.caeo.2023.100148. Author copy

J35 Sales, A.C., Gagnon-Bartsch, J.A., Prihar, E.B., & Heffernan, N.T. (2023). Using Auxiliary Data to Boost Precision in the Analysis of A/B Tests on an Online Educational Platform: New Data and New Results. Journal of Educational Data Mining. Accepted. https://arxiv.org/abs/2306.06273 

J34 Gagnon-Bartsch, J. A., Sales, A.C.,*, Wu, E.,  Botelho,  A. F.,  Erickson,  J. A., Miratrix,  L. W., & Heffernan, N. T. (Accepted 2023). Precise unbiased estimation in randomized experiments using auxiliary observational data. Journal of Casual Inference. https://arxiv.org/abs/2105.03529 

J33 Huang, W., Labille, K., Wu, X., Lee, D. & Heffernan, N. (2022). Achieving User-Side Fairness in Contextual Bandits. Human-Centric Intelligent Systems. Springer.  https://link.springer.com/article/10.1007/s44230-022-00008-w 

J32 Lu, X., Sales, A., & Heffernan, N. T. (2021). Immediate Versus Delayed Feedback on Learning: Do People’s Instincts Really Conflict With Reality?. Journal of Higher Education Theory and Practice, 21(16). https://doi.org/10.33423/jhetp.v21i16.4925 

J31 Lu, X., Wang, W., Motz, B., Ye, W., & Heffernan, N. T. (submitted for publication). How Immediate Should Immediate Feedback Be? Immediate Feedback Timing and its Effects on Learning Performance of Written Homework Assignments. Computers & Education Open.

J30 Botelho, A., Baral, S., Erickson, J., Benachamardi, P., & Heffernan, N. (2023). Leveraging natural language processing to support automated assessment and feedback for student open responses in mathematics. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12793     

J29 McCarthy, K. S., Crossley, S. A., Meyers, K., Boser, U., Allen, L. K., Chaudhri, V. K., Collins-Thompson, K., D’Mello, S., De Choudhury, M., Garg, K., Goel, A., Gosha, K., Heffernan, N., Hooper, M. A., Hyman, E., Jarratt, D. C., Khalil, D., Kizilcec, R. F., Litman, D., Malatinszky, A., Marks, K., McNamara, D. S., Menko, R., Palermo, C., Porcaro, D., Roscoe, R., Shapiro, S., Khanh-Phoung, T., Trumbore, A. M., White, C., Wong, W., Yang, D., & Zampieri, M. (in press). Toward more effective and equitable learning: Identifying barriers and solutions for the future of online education. Technology, Mind, & Behavior.  

J28 Patikorn, T., Baker, R. S., & Heffernan, N. T. (2020). ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface. JEDM | Journal of Educational Data Mining, 12(2), i-xi. https://doi.org/10.5281/zenodo.4008048  This is part of a Special Issue that Heffernan et al organized . The competition web site where 70+ researchers participated is here

J27 Lu, X., Ostrow, K., & Heffernan, N. (2020). Save Your Strokes: Chinese Handwriting Practice Makes for Ineffective Use of Instructional Time in Second Language. AERA Open.  Volume 6(2)  https://doi.org/10.1177/2332858419890326

J26  Graesser, A. C., Hu, X., Nye, B. D., VanLehn, K.,Kumar, R., Heffernan, C., Heffernan, C., et al. (2018). ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics. International Journal of STEM Education, 5: 15. 

J25  Kai, S., Almeda, M. V., Baker, R., Heffernan, C., & Heffernan, N. (2018). Decision Tree Modeling of Wheel- Spinning and Productive Persistence in Skill Builders. JEDM | Journal of Educational Data Mining, 10(1), 36-71.  Errata and New version 

J24 McGuire, P., Tu, S., Logue., M., Mason, C., Ostrow, K. (2017). Counterintuitive effects of online feedback in middle school math: results from a randomized controlled trial in ASSISTments. Educational Media International. Taylor Francis. Vol Pages 1-14. (Heffernan was not an author, but he helped these scientists use his platform.)

J23 Inventado, P., Scupelli, P., Ostrow, K., Heffernan, N., Almeda, V., & Slater, (2018). Contextual Factors Affecting Hint Utility, International Journal of STEM Education, 5(1), 13. 

J21 Ostrow, K.S., Heffernan, N.T., & Williams, J.J. (2017). Tomorrow’s EdTech Today: Establishing a Learning Platform as a Collaborative Research Tool for Sound Science. Teachers College Record Volume 119 Number 3, 2017, p. 1-36. ID Number: 21779

J20 Heffernan, N.T., Ostrow, K.S., Kelly, K., Selent, D., Van Inwegen, E.G., Xiong, X., & Williams, J.J. (2016). The Future of Adaptive Learning: Does the Crowd Hold the Key? International Journal of Artificial Intelligence in Education. Springer New York. DOI: 10.1007/s40593-016-0094-z 

J19  Heffernan, N. & Heffernan, C. (2014). The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching. International Journal of Artificial Intelligence in Education. 24 (4), 470-497. DOI 10.1007/s40593-014-0024-x. The Special Issue focused on landmark systems.

J18  Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., Heffernan, C. (2014). Population validity for Educational Data Mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501. DOI: 10.1111/bjet.12156

J17  Pardos, Z.A., Gowda, S. M., Baker, R. S.J.D., & Heffernan, N. T. (2012). The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software. ACM’s Knowledge Discovery and Datamining Explorations, 13(2), 37-44.

J16  Gong, Y, Beck, J. E., Heffernan, N. T. (2011). How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis. International Journal of Artificial Intelligence in Education. 21, 27-46.

J15  Pardos, Z., Dailey, M. & Heffernan, N. (2011). Learning what works in ITS from non-traditional randomized controlled trial data. The International Journal of Artificial Intelligence in Education. 21, 47-63.

J14  Koedinger, K., McLaughlin, E. & Heffernan, N. (2010). A Quasi-Experimental Evaluation of an On-line Formative Assessment and Tutoring System. Journal of Educational Computing Research. Baywood Publishing. 4, 489-510 

J13  Baker, R., Goldstein, A., Heffernan, N. (2011). Detecting the Moment of Learning. International Journal of Artificial Intelligence in Education, 21(1-2), 5-25. (Get the data here)

J12  Broderick, Z., O’Connor, C., Mulcahy, C., Heffernan, N. & Heffernan, C. (2011). Increasing Parent Engagement in Student Learning Using an Intelligent Tutoring System.  Journal of Interactive Learning Research, 22(4), 523-550. Chesapeake, VA: AACE. 

J11 Militello, M., & Heffernan, N. (2009). Which one is "just right"? What educators should know about formative assessment systems. International Journal of Educational Leadership Preparation, 4(3), 1-8.

J10  Feng, M., Heffernan, N.T., Heffernan, & C., Mani, M. (2009). Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models. IEEE Transactions on Learning Technologies, 2(2), 79-92. Featured Best Article of 8 articles in the edition (Based on PP8 and WP15)

J9 Razzaq, L., Patvarczki, J., Almeida, S.F., Vartak, M., Feng, M., Heffernan, N.T. and Koedinger, K. (2009). The ASSISTment builder: Supporting the Life-cycle of ITS Content Creation. IEEE Transactions on Learning Technologies Special Issue on Real-World Applications of Intelligent Tutoring Systems. 2(2) 157-166 (Based on YRP5)

J8 Feng, M., Heffernan, N.T., & Koedinger, K.R. (2009). Addressing the assessment challenge in an Intelligent Tutoring System that tutors as it assesses. The Journal of User Modeling and User-Adapted Interaction. 19, 243-266. (Based on CP15) Best Paper of the Year (See Award #20 above). Mentioned in National Ed. Tech Plan (See Award #19).

J7  Mendicino, M., Razzaq, L. & Heffernan, N. T. (2009). Improving Learning from Homework Using Intelligent Tutoring Systems. Journal of Research on Technology in Education (JRTE). 41(3), 331-346.

J6  Baker, R., Walonoski, J., Heffernan, T., Roll, I., Corbett, A. & Koedinger, K. (2008). Why students engage in "Gaming the System" behavior in interactive learning environments Journal of Interactive Learning Research (JILR).19(2), 185-224 (Based on CP12 and PP5)

J5  Razzaq, L., Heffernan, N., Feng, M., & Pardos Z. (2007). Developing Fine-Grained Transfer Models in the ASSISTment System. Journal of Technology, Instruction, Cognition, and Learning. 5(3), 289-304.

J4  Feng, M., & Heffernan, N.T. (2007). Towards live informing and automatic analyzing of student learning: Reporting in the Assistment system. Journal of Interactive Learning Research (JILR) 18(2), 207-230. (Based on W12)

J3  Feng, M., & Heffernan, N.T. (2006). Informing teachers live about student learning: Reporting in the Assistment system Technology, Instruction, Cognition, and Learning Journal. 3(1-2), 63. (Based on W12)

J2  Heffernan, N. T., Koedinger, K. & Razzaq, L. (2008). Expanding the model-tracing architecture: A 3rd generation intelligent tutor for Algebra symbolization. The International Journal of Artificial Intelligence in Education. 18(2), 153-178 (Builds upon CP8 and CP1-4).

J1  Heffernan, N. T., & Koedinger, K. R. (2002). Results from a Web-Based Tutor for Writing Algebra Expressions for Word-Problems Sciences et Techniques Educatives. 9(1-2), 11-35. (This French language journal has translated our paper into French. My translation of this Journal’s title is "Educational Sciences and Technology.") http://www.inrp.fr/atief/ste/ste-res-vol9.htm (Based on D1)

Book Chapters


BC12 Cheng, L., Prihar, E., Baral, S., Gurung, A., Botelho, A. T., Haim A., Heffernan, C., Patikorn, T., Sales, A., & Heffernan, N. T. (2023). Authoring Tools for Crowdsourcing from Teachers to Enhance Intelligent Tutoring Systems. In Sinatra, A.M., Graesser, A.C., Hu, X., Townsend, L.N. and Rus, V. (Eds.), Design Recommendations for Intelligent Tutoring Systems: Volume 11 - Professional Career Education, Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. ISBN 978-0-9977258-5-8. Available at: https://gifttutoring.org/documents/167 

BC11  Rodrigo, M.M.T., Vassileva, J.,...Heffernan, N., & et al. (2023). The Great Challenges and Opportunities of the Next 20 Years. In Benedict du Boulay , Antonija Mitrovic, and Kalina Yacef's (eds.). The Handbook of Artificial Intelligence in Education.   

BC10 Prihar, E. & Heffernan, N. (2023). Crowdsourcing Paves the Way for Personalized Learning. In B. du Boulay, A. Mitrovic, K. Yacef, (Eds.), Handbook of Artificial Intelligence in Education. (pp. 632-635). Edward Elgar Publishing. 

BC9  Lu, X., Ostrow, K. S., Yang, Q., & Heffernan, N. T. (2022 accepted for publication; passed review). Save Your Strokes: Further Studies on the efficiency Efficiency of Learning Chinese Words Without Practicing Handwriting Practices. In Chu, C., Coss, M., & Zhang, P. N. (Eds.),  Transforming L2 Hanzi Teaching & Learning in the Age of Digital Writing: Theory and Pedagogy ( Chinese name of the book is《电写时代汉字教学的理论与实践》). Routledge, UK.

BC8  Kelly, K. & Heffernan, N. (2021). Technology enhanced formative assessment increased the efficacy of the homework review.  In Jiao & Lissitz (Eds), Enhancing Effective Instruction and Learning Using Assessment Data.  MARC  ISBN 978-1-64802-626-3.

BC7  Ostrow, K.S. & Heffernan, N.T. (2019). Advancing the State of Online Learning: Stay Integrated, Stay Accessible, Stay Curious. In Robert Feldman (Ed.) Learning Science: Theory, Research, and Practice. McGraw Hill. p. 201-227. ISBN 1260458008. PDF 

BC6 Botelho, A. & Heffernan, N., (2019). Crowdsourcing feedback to support teachers and students. In Sinatra, A.M., Graesser, A.C., Hu, X., Brawner, K., and Rus, V. (Eds.). (2019). Design Recommendations for Intelligent Tutoring Systems: Volume 7 - Self-Improving Systems. Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-7-3. Available at: https://gifttutoring.org/documents/  Page 101-108.

BC5 Heffernan, N., Militello, M, Heffernan, C., & Decoteau, M. (2012). Effective and meaningful use of educational technology: three cases from the classroom. In C. Dede & J. Richards (Eds.). Digital Teaching Platforms, 88-102. Columbia, NY: Teachers College Press.

BC4 Razzaq, L. & Heffernan, N. (2010). Open content Authoring Tools. In Nkambou, Bourdeau & Mizoguchi (Eds.) Advanced in Intelligent Tutoring Systems.pp 425-439. Berlin: Springer Verlag.

BC3 Pardos, Z. A., Heffernan, N. T., Anderson, B., Heffernan, L. C. (2010). Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks. Chapter in C. Romero, S. Ventura, S. R. Viola, M. Pechenizkiy and R. S. J. Baker. Handbook of Educational Data Mining. Boca Raton, Florida: Chapman & Hall/CRC Press.

BC2 Feng, M., Heffernan, N.T., & Koedinger, K.R. (2010). Student Modeling in an Intelligent Tutoring System. In Stankov, Glavinc, and Rosic. (Eds.) Intelligent Tutoring Systems in E-learning Environments: Design, Implementation and Evaluation, 208-236. Hershey, PA: Information Science Reference. (Based on W10, W11 and W12).

BC1 Razzaq, Feng, Heffernan, Koedinger, Nuzzo-Jones, Junker, Macasek, Rasmussen, Turner & Walonoski. (2007). A Web-based authoring tool for intelligent tutors: Assessment and instructional assistance. In Nadia Nedjah, Luiza deMacedo Mourelle, Mario Neto Borges and Nival Nunesde Almeida (Eds). Intelligent Educational Machines. Intelligent Systems Engineering Book Series, 23-49. Berlin: Springer Verlag. 

Conference Papers

Strictly Reviewed Conferences (Acceptance rates in the 30% range or below)

Note: Unlike most other disciplines where journal papers are more prestigious than conference papers, in Computer Science as a discipline, conference publications are often more difficult to get accepted and are more prestigious than most journal publications. These conference proceedings are stringently peer-reviewed, with at least three reviewers. The acceptance rate is usually in the 30% to 39% range. (The Educational Data Mining conference in 2010 was unusual in that they accepted 42% of the papers, but that is non-standard.) I have started labeling the acceptance rates on new papers to make that easier to understand.


CP117 Feng, M., Huang, C., & Collins, K. (2023, June). Promising Long Term Effects of ASSISTments Online Math Homework Support. In International Conference on Artificial Intelligence in Education, pp. 212-217. Cham: Springer Nature Switzerland. 


CP116 Feng, M., Heffernan, N., Collins, K., Heffernan, C., & Murphy, R. (2023). Implementing and Evaluating ASSISTments Online Math Homework Support At large Scale over Two Years: Findings and Lessons Learned.  AIED2023. Submitted paper. Final paper


CP115 Haim, A., Gyurcssan, R., Baxter, C., Shaw, S., & Heffernan, N. (2023). How to Open Science: Developing and Testing Reproducibility Metrics on the Educational Data Mining Conference. Submitted Paper. Final Paper

CP114 Prihar, E., Sales, A., & Heffernan, N. (2023, June). A Bandit you can Trust. In UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP '23), June 26--29, 2023, Limassol, Cyprus. ACM, New York, NY, USA 10 Pages. https://doi.org/10.1145/3565472.3592955  Submitted Paper. Final Paper

CP113 Prihar, E., Haim, A., Shen, T., Sales, A., Lee, D., & Wu, X. (2023). Investigating the Impact of Skill-Related Videos on Online Learning. In Proceedings of the Tenth ACM Conference on Learning@Scale (L@S '23), July 20-22, 2023, Copenhagen, Denmark. ACM, New York, NY, USA, 10 pages. Submitted PDF. Final PDF.


CP112  Gurung, A., Lee, M. P., Baral, S., Sales, A. C., Vanacore, K. P., McReynolds, A. A., Kreisberg, H., Heffernan,  C., Haim, A,  & Heffernan, N. T. (2023) How Common are Common Wrong Answers? Crowdsourcing Remediation at Scale. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S ’23), July 20–22, 2023, Copenhagen, Denmark. ACM, New York, NY, USA, 11 pages. Submitted PDF. Final PDF. https://doi.org/10.1145/3573051.3593390 Neil's Slides

CP111 Haim, A., Gyurcsan, R., Baxter, C., Shaw, S., & Heffernan, N. (revising). How to Open Science: Analyzing the Open Science Statement Compliance of the Learning@Scale Conference. In Proceedings of the Tenth ACM Conference on Learning@Scale (L@S '23), July 20-22, 2023, Copenhagen, Denmark. ACM, New York, NY, USA, 8 pages. Link to paper

CP110 Gurung, A., Baral, S., Vanacore, K. P., McReynolds, A. A., Kreisberg, H., Botelho, A. F., Shaw, S. T., & Heffernan, N. T. (2023). Identification, Exploration, and Remediation: Can Teachers Predict Common Wrong Answers? In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK 2023), March 13–17, 2023, Arlington, TX, USA. ACM, New York, NY, USA, 16 pages. Submitted paper. Slides. https://doi.org/10.1145/3576050.3576109 

CP109 Vanacore, K.P., Gurung, A., McReynolds, A.A., Liu, A., Shaw, S.T., & Heffernan, N.T. (2023). Impact of Non-Cognitive Interventions on Student Learning Behaviors and Outcomes: An analysis of seven large-scale experimental inventions. In LAK ’23: Learning Analytics & Knowledge. ACM, New York, NY, USA. Final Version. Slides. https://doi.org/10.1145/3576050.3576073  

CP108 Haim, A., Shaw, S., & Heffernan, N. (2023). How to Open Science: A Principle and Reproducibility Review of the Learning Analytics and Knowledge Conference.  In LAK ’23: International Conference on Learning Analytics & Knowledge, March 13–17, 2023, Arlington, TX. ACM, New York, NY, USA. Accepted Versionhttps://doi.org/10.1145/3576050.3576071 

CP107 Gurung, A., Botelho, A.F., Thompson, R., Sales, A.C., Baral, S., & Heffernan, N. (2022). Considerate, Unfair, or Just Fatigued? Examining Factors that Impact Teacher. Iyer, S. et al. (Eds.) (2022). Proceedings of the 30th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education. 

CP106 Botelho, A., Prihar, E., & Heffernan, N. (2022). Deep Learning or Deep Ignorance? Comparing Untrained Recurrent Models in Educational Contexts.  AIED2022

CP105 Prihar, E., Syed, M., Ostrow, K., Shaw, S., Sales, A., & Heffernan, N. (2022). Exploring Common Trends in Online Educational Experiments. Proceedings of the 15th International Educational Data Mining Conference. Held in Durham, England., July 2022. Winner of "Best Data Set" Award.  

CP104 Zhang, M., Baral, S., Heffernan, N. & Lan, A. (2022). Automatic Short Math Answer Grading via In-context Meta-learning. Accepted to EDM2022. pdf

CP103 Prihar, E., Haim, A., Sales, A., & Heffernan, N. (2022). Automatic Interpretable Personalized Learning. Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S ’22), June 1–3, 2022, New York City, NY, USA.11 pages. PDF. https://doi.org/10.1145/3491140.3528267  Nominated for Best Paper and won "Best Data Set" for releasing a valuable dataset that lets external researchers try out their personalization models. 

CP102 Baral, S., Botelho, A., Erickson, J.A., Benachamardi, P., & Heffernan, N. (2021). Improving Automated Scoring of Student Open Responses in Mathematics.   In Hsiao,  Sahebi, Bouchet & Vie (eds). Proceedings of the 14th International Conference on Educational Data Mining (EDM2021). Pages 130-138. PDF. Video. Best Full Paper Nominee.

CP101 Sales, A., Prihar, E., Heffernan, N., & Pane, J. (2021). Estimating the Intelligent Tutor Effects on Specific Posttest Problems.  In Hsiao,  Sahebi, Bouchet & Vie (eds). Proceedings of the 14th International Conference on Educational Data Mining (EDM2021). Page 206-215/ https://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_246.pdf 

CP100 Prihar, E., Patikorn, T., Botelho, A., Sales, A., & Heffernan, N. (2021). Towards Personalizing Students' Education with Crowdsourced Tutoring. Learning@Scale 2021. Pages 37–45 https://doi.org/10.1145/3430895.3460130  Camera Ready Copy

CP99 Shen, J.T., Yamashita, M., Prihar, E., Heffernan, N., Wu, X., McGrew, S., & Lee, D. (2021). Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT. 22nd International Conference on Artificial Intelligence in Education (24% acceptance rate). Pages 408- 419. https://doi.org/10.1007/978-3-030-78292-4_33. Blinded Review Copy

CP98  Gurung, A., Botelho, A.F., & Heffernan, N. (2021). Examining Student Effort on Help Through Response Time Decomposition. The 11th International Learning Analytics and Knowledge Conference (LAK21). Pages 292–301. https://doi.org/10.1145/3448139.3448167  

CP97 Karumbaiah, S., Lan, A., Nagpal, S., Baker, R., Botelho, A., & Heffernan, N. (2021). Using Past Data to Warm Start Active Machine Learning: Does Context Matter? The 11th International Conference on Learning Analytics & Knowledge (LAK).  Pages 151–160 https://doi.org/10.1145/3448139.3448154 Blinded Copy

CP96 Huang, W., Labille, K., Wu, X.,  Lee, D. &  Heffernan, N. (2021). Fairness-aware Bandit-based Recommendation. 2021 IEEE International Conference on Big Data (Big Data), 1273-1278. Retrieved  from https://pike.psu.edu/publications/bigdata21.pdf  

CP95 Patikorn, T. & Heffernan, N. T. (2020, August 12). Effectiveness of Crowd-Sourcing On-Demand Tutoring from Teachers in Online Learning Platforms. Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S). Pages 115–124. https://doi.org/10.1145/3386527.3405912. Best Student Paper Awardee. Press. His recorded talk. Project Website

CP94 Ghosh, A., Heffernan, N., & Lan, A. (2020). Context-Aware Attentive Knowledge Tracing. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug. 2020.

CP93 Varatharaj, A., Botelho, A., Lu, X., & Heffernan, N. (2020). Supporting Teacher Assessment in Chinese Language Learning Using Textual and Tonal Features. In Bittencourt et al, The 21st Proceedings of the International Conference on Artificial Intelligence in Education (AIED). pp. 562–573. doi: 10.1007/978-3-030-52237-7_45 Original Version Submitted. Final

CP92 Erickson, J. A., Botelho, A. F., McAteer, S., Varatharaj, A., & Heffernan, N. T. (2020). The Automated Grading of Student Open Responses in Mathematics. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK ’20), March 23–27, 2020, Frankfurt, Germany. ACM, New York, NY, USA, 10 pages. 

CP91 Botelho, A.F., Varatharaj, A., Van Inwegen, E. & Heffernan, N. T. (2019). Refusing to Try: Characterizing Early Stopout on Student Assignments. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge. ACM pp. 391-400. 

CP90 Yang, T., Studer, C., Baker, R., Heffernan, N. & Lan, A. (2019). Active Learning for Student Affect Detection. In Desmarais, Lynch, Merceron & Nkambou (Eds) Proceedings of the 12th International Conference on Educational Data Mining(EDM2019) ISBN: 978-1-7336736-0-0. pp. 208-217. (21% acceptance rate) 

CP89 Botelho, A. F., Baker, R. S., Ocumpaugh, J., & Heffernan, N. T. (2018). Studying Affect Dynamics and Chronometry Using Sensor-Free Detectors. In Boyer & Yudelson’s (Eds) Proceedings of the Eleventh International Conference on Educational Data Mining. pp 157-166. (Acceptance rate = 16%) [Won Best Student Paper Award

CP89 Sales, A., Botelho, A. F., Patikorn, T., & Heffernan, N. T. (2018, July). Using Big Data to Sharpen Design-Based Inference in A/B Tests. In Boyer & Yudelson’s (Eds) Proceedings of the Eleventh International Conference on Educational Data Mining, 479-485. Retrieved from EDM

CP88 Ostrow, K & Heffernan, N. (2018). Testing the Validity and Reliability of Intrinsic Motivation Inventory Subscales within ASSISTments. Proceedings of the Nineteenth International Conference on Artificial Intelligence in Education. Pp 381-394. 

CP87 Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2017). Improving Sensor-Free Affect Detection Using Deep Learning. In E. Andre' et al (Eds.) Proceedings of the Eighteenth International Conference on Artificial Intelligence in Education. Pp 40-51. 

CP86 Slater, S., Ocumpaugh, J., Almeda, M., Allen, L., Heffernan, N., & Baker, R. (2017). Using Natural Language Processing Tools to Develop Complex Models of Student Engagement. Affective Computing and Intelligent Interaction, At San Antonio, TX, US.

CP85 Inventado, P. S., Scupelli, P., Heffernan, C., & Heffernan, N. (2017). Feedback Design Patterns for Math Online Learning Systems. EuroPLoP’17. (July 12-16 2017), 15 pages.

CP84 Slater, S., Baker, R., Almeda, M, Bowers, A., & Heffernan, N. (2017). Using Correlational Topic Modeling for Automated Topic Identification in Intelligent Tutoring Systems. Learning Analytics and Knowledge (LAK 2017).

CP83  Zhong, X., Sun, Z., Xiong, H., Heffernan, N., Islam, M. M. (2017). Learning Curve Analysis Using Intensive Longitudinal and Cluster-Correlated Data. Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems. CAS October 30 – November 1, 2017, Chicago, Illinois, USA. 

CP82 Heffernan, N., Heffernan, C., Li, Y., Logue, M.E., Mason, C., McGuire, P., Ostrow, K., & Tu, S. (2016). To See or Not to See: Putting Image-Based Feedback in Question. International Society for Technology in Education (ISTE). Denver. Listen & Learn: Research Paper.

CP81 Williams, J. J., Kim, J., Rafferty, A., Maldonado, S., Gajos, K. Z., Lasecki, W. S. & Heffernan, N. T. (2016). Axis: Generating explanations at scale with learnsourcing and machine learning. Proceedings of the Third (2016) ACM Conference on Learning @ Scale pp 379-388. (Acceptance Rate = 23%). 

CP80 Ostrow, K. S., Selent, D., Wang, Y., VanInwegen, E. G., Heffernan, N. T. & Williams, J. J. (2016). The assessment of learning infrastructure (ALI): the theory, practice and scalability of automated assessment. In the Proceedings of the Sixth International Conference on Learning Analytics & Knowledge pp 279-288. (Acceptance Rate = 30%)

CP79 Slater, S. Ocumpaugh, J., Baker, R., Scupelli, P., Inventado, P. & Heffernan, N. (2016). Semantic Features of Math Problems: Relationships to Student Learning and Engagement. In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining. pp 223-230. (Acceptance Rate = 27%) 

CP78  Selent, D. & Heffernan, N. T. (2015). When More Intelligent Tutoring in the Form of Buggy Messages Does Not Help. In Conati, Heffernan, Mitrovic & Verdejo (Eds) The 17th Proceedings of the Conference on Artificial Intelligence in Education, Madrid, Spain. Springer, 768-771.

CP77 Van Inwegen, E., Adjei, S., Wang, Y., & Heffernan, N.T. (2015). Using Partial Credit and Response History to Model User Knowledge. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0 pp 313-319. (Acceptance Rate = 36%)

CP76 Lang, C., Heffernan, N., Ostrow, K. & Wang, Y. (2015). The Impact of Incorporating Student Confidence Items into an Intelligent Tutor: A Randomized Controlled Trial. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0 pp 144-149. (Acceptance Rate = 36%)

CP75 San Pedro, M.O., Snow, E., Baker, R.S., McNamara, D., & Heffernan, N. (2015). Exploring Dynamical Assessments of Affect, Behavior, and Cognition and Math State Test Achievement. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0 pp 85-91. (Acceptance Rate = 36%)

CP74 Ostrow, K., Heffernan, N.T., Heffernan, C., & Peterson, Z. (2015) Blocking vs. Interleaving: A Conceptual Replication Examining Single-Session Effects within Middle School Math Homework. In Conati, Heffernan, Mitrovic & Verdejo (Eds) The 17th Proceedings of the Conference on Artificial Intelligence in Education, Madrid, Spain. Springer, 388-347 (Acceptance Rate = 28%) 

CP73  Ostrow, K., Donnelly, C. Adjei, S. & Heffernan, N. T. (2015) Improving Student Modeling Through Partial Credit and Problem Difficulty. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (L@S 2015). ACM, New York DOI 10.1145/2724660.2724667 pp 11-20. (Acceptance Rate = 25%)

CP72 Botelho, A., Wan, H., Heffernan, N. T. (2015) The Prediction of Student First Response Using Prerequisite Skills.  In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (L@S 2015). ACM, New York pp 39-45. (Acceptance Rate = 25%)

CP71 San Pedro, M., Ocumpaugh, J., Baker, R., & Heffernan, N. (2014). Predicting STEM and Non-STEM College Major Enrollment from Middle School Interaction with Mathematics Educational Software. In John Stamper et al. (Eds) Proceedings of the 7th International Conference on Educational Data Mining, 276-279. 

CP70 Ostrow, K., & Heffernan, N. T. (2014). Testing the Multimedia Principle in the Real World: A Comparison of Video vs. Text Feedback in Authentic Middle School Math Assignments. In John Stamper et al. (Eds) Proceedings of the 7th International Conference on Educational Data Mining, 296-299.

CP69 Feng, M., Roschelle, J., Heffernan, N., Fairman, J. & Murphy, R. (2014). Implementation of an Intelligent Tutoring System for Online Homework Support in an Efficacy Trial. In Stefan Trausan-Matu, et al. (Eds) The Proceeding of the International Conference on Intelligent Tutoring 2014. LNCS 8474. pp 561-566. (Acceptance Rate = 42%. A longer version is here.)

CP68  Hawkins, W., Heffernan, N. Baker, R. (2014). Learning Bayesian Knowledge Tracing parameters with a Knowledge Heuristic and Empirical Probabilities. In Stefan Trausan-Matu, et al. (Eds) International Conference on Intelligent Tutoring 2014. LNCS 8474. (Acceptance Rate = 42%). 

CP67 Wang, Y. & Heffernan, N. (2014). The Effect of Automatic Reassessment and Relearning on Assessing Student Long-term Knowledge in Mathematics. In Stefan Trausan-Matu, et al. (Eds) International Conference on Intelligent Tutoring 2014. pp 490-495. LNCS 8474. (Acceptance Rate = 42%). Author Copy  Data

CP66 Feng, M., Roschelle, J., Murphy, R. & Heffernan, N. (2014). Using Analytics for Improving Implementation Fidelity in a Large Scale Efficacy Trial. International Conference of the Learning Sciences 2014.

CP65 San Pedro, M., Baker, R., Bowers, A. & Heffernan, N. (2013). Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School. In S. D’Mello, R. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM2013). Memphis, TN, 177-184.

CP64  Hawkins, W., Baker, R. S. J. d., & Heffernan, N. T., (2013). Which is more responsible for boredom in intelligent tutoring systems: students (trait) or problems (state)? Affective Computing and Intelligent Interaction. Geneva, 618-623. 

CP63  Hawkins, W., Heffernan, N., Wang, Y. & Baker, S.J.d., (2013). Extending the Assistance Model: Analyzing the Use of Assistance over Time. In S. D’Mello, R. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM2013). Memphis, TN, 59-66. 

CP62 San Pedro, M., Baker, R., Gowda, S., & Heffernan, N. (2013). Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System. In Lane, Yacef, Mostow & Pavlik (Eds) The Artificial Intelligence in Education Conference. Springer-Verlag, 41-50.

CP61 Wang, Y. & Heffernan, N. (2013). Extending Knowledge Tracing to allow Partial Credit: Using Continuous versus Binary Nodes. In Lane, Yacef, Mostow & Pavlik (Eds) The Artificial Intelligence in Education Conference.  Springer-Verlag, 181-188.

CP60 Song, F., Trivedi, S., Wang, Y., Sárközy, G., & Heffernan, N. (2013). Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods. In Boonthum-Denecke, Youngblood (Eds) Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013, St. Pete Beach, Florida. May 22-24, 2013. AAAI Press 2013, 527-532.

CP59 Kehrer, P., Kelly, K. & Heffernan, N. (2013).  Does Immediate Feedback While Doing Homework Improve Learning. In Boonthum-Denecke, Youngblood (Eds) Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013, St. Pete Beach, Florida. May 22-24, 2013. AAAI Press 2013. p 542-545.

CP58  Kelly, K., Heffernan, N., D'Mello, S., Namias, J., & Strain, A. (2013). Adding Teacher-Created Motivational Video to an ITS. In Boonthum-Denecke, Youngblood (Eds) Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013, St. Peters Beach, Florida, 503-508.

CP57  Pardos, Z. & Heffernan, N. (2012). Tutor Modeling vs. Student Modeling. Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference. Invited talk. Florida Artificial Intelligence Research Society (FLAIRS 2012). St. Petersburg Beach, Florida pp 420-425.

CP56  Qiu, Y., Pardos, Z. & Heffernan, N. (2012). Towards data driven user model improvement. Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference. Florida Artificial Intelligence Research Society (FLAIRS 2012), 462-465.

CP55  Pardos, Z., Trivedi, S., Heffernan, N. & Sarkozy, G. (2012). Clustered Knowledge Tracing. 11th International Conference on Intelligent Tutoring Systems, 404-410.

CP54  Wang, Y. & Heffernan, N. (2012). The Student Skill Model. 11th International Conference on Intelligent Tutoring Systems. Springer. pp 399-404.

CP53  Gong, Y., Beck, J. & Heffernan, N. (2012). WEBsistments: Enabling an Intelligent Tutoring System to Excel at Explaining Why Other Than Showing How; 11th International Conference on Intelligent Tutoring Systems. Springer. pp 268-273.

CP52 Wang, Y. & Heffernan, N. (2012). Leveraging First Response Time into the Knowledge Tracing Model.  5th International Conference on Educational Data Mining, 176-179.

CP51 Trivedi, S. Pardos, Z., Sarkozy, G. & Heffernan, N. (2012). Co-Clustering by Bipartite Spectral Graph Partitioning for Out-Of-Tutor Prediction. 5th International Conference on Educational Data Mining, 33-40.

CP50  Gowda, S., Baker, R.S.J.d., Pardos, Z., Heffernan, N. (2011). The Sum is Greater than the Parts: Ensembling Student Knowledge Models in ASSISTments. Proceedings of the KDD 2011 Workshop on KDD in Educational Data.

CP49 Qiu, Y., Qi, Y., Lu, H., Pardos, Z. & Heffernan, N. (2011). Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining, 139-148.

CP48 Trivedi, S., Pardos, Z., Sarkozy, G. & Heffernan, N. (2011). Spectral Clustering in Educational Data Mining. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining, 129-138.

CP47  Bahador, N., Pardos, Z., Heffernan & Baker, R. (2011). Less is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining, 101-110.

CP46   Pardos, Z., Gowda, S., Baker, R. & Heffernan, N. (2011). Ensembling Predictions of Student Post-Test Scores for an Intelligent Tutoring System. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining, 189-198.

CP45   Baker, R., Pardos, Z., Gowda, S., Nooraei, B., & Heffernan, N. (2011). Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems. In Konstant et al (Eds.) 20th International Conference on User Modeling, Adaptation and Personalization (UMAP 2011), 13-24.

CP44  Pardos, Z. & Heffernan, N. (2011). KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model. In Konstant et al (Eds.) 20th International Conference on User Modeling, Adaptation and Personalization (UMAP 2011), 243-254. 

CP43  Trivedi, S., Pardos, Z. & Heffernan, N. (2011). Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions In Biswas et al. (Eds) Proceedings of the Artificial Intelligence in Education Conference 2011, 328–336.

CP42  Singh, R., Saleem, M., Pradhan, P., Heffernan, C., Heffernan, N., Razzaq, L. Dailey, M. O'Connor, C. & Mulchay, C. (2011). Feedback during Web-Based Homework: The Role of Hints In Biswas et al. (Eds) Proceedings of the Artificial Intelligence in Education Conference 2011, 328–336.

CP41 Wang, Y. & Heffernan, N. (2011). The "Assistance" Model: Leveraging How Many Hints and Attempts a Student Needs. The 24th International FLAIRS Conference. pp 549-554 Nominated for Best Student Paper.

CP40   Pardos, Z. & Heffernan, N. (2010). Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. In Paul De Bra, Alfred Kobsa, David Chin, (Eds.) The 18th Proceedings of the International Conference on User Modeling, Adaptation and Personalization, 255-266.

CP39 Feng, M. & Heffernan, N. (2010). Can We Get Better Assessment From a Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It Too (Student Learning During the Test). In Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. (Eds.) Proceedings of the 3rd International Conference on Educational Data Mining, 41-50.

CP38  Pardos, Z. & Heffernan, N. (2010). Navigating the parameter space of Bayesian Knowledge Tracing models: Visualization of the convergence of the Expectation Maximization algorithm. In Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. (Eds.) Proceedings of the 3rd International Conference on Educational Data Mining, 161-170.

CP37  Gong, Y., Beck, J, Heffernan, N. (2010). Using Multiple Dirichlet distributions to improve parameter plausibility Educational Data Mining 2010. In Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. (Eds.) Proceedings of the 3rd International Conference on Educational Data Mining, 61-70.

CP36  Weitz, R., Salden, R, Kim, R. & Heffernan, N. T. (2010) Comparing Worked Examples and Tutored Problem Solving: Pure vs. Mixed Approaches. 32nd Annual Conference of the Cognitive Science Society, 2877-2881. Retrieved Oct. 10, 2014 from http://csjarchive.cogsci.rpi.edu/proceedings/2010/papers/0676/paper0676.pdf

CP35   Pardos, Z. A., Dailey, M. D., Heffernan, N. T. In Press (2010). Learning what works in ITS from non-traditional randomized controlled trial data. In Aleven, V., Kay, J & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 2. Springer-Verlag, Berlin, 41-50. Nominated for Best Student Paper.

CP34   Razzaq, L. & Heffernan, N. (2010). Hints: Is It Better to Give or Wait to be Asked? In Aleven, V., Kay, J & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 1. Springer, 349-358.

CP33 Gong, Y., Beck, J., Heffernan, N. & Forbes-Summers, E. (2010). The impact of gaming (?) on learning at the fine-grained level. In Aleven, V., Kay, J & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 1. Springer, 194-203.

CP32 Gong, Y., Beck, J. & Heffernan, N. (2010). Comparing Knowledge Tracing and Performance Factor Analysis by Using Multiple Model Fitting. In Aleven, V., Kay, J & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 1. Springer-Verlag, Berlin, 35-44. Nominated for Best Student Paper.

CP31   Baker, R., Goldstein, A. & Heffernan, N. (2010). Detecting the Moment of Learning. In Aleven, V., Kay, J., & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 1. Springer, 25-33. Nominated for Best Paper.

CP30   Sao Pedro, M., Gobert, J., Heffernan, N., & Beck, J. (2009). In N.A. Taathen & H. van Rjin (Eds.) Comparing Pedagogical Approaches for Teaching the Control of Variables Strategy. Proceedings of the 31st Annual Conference of the Cognitive Science Society Austin, TX: Cognitive Science Society.

CP29   Pardos, Z.A., Heffernan, N.T. (2009). Determining the Significance of Item Order In Randomized Problem Sets. In Barnes, Desmarais, Romero & Ventura (Eds.) Proc. of the 2nd International Conference on Educational Data Mining, 111-120. Won Best Paper First-Authored by a Student.

CP28   Feng, M., Beck, J., & Heffernan, N. (2009). Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning. In Barnes, Desmarais, Romero & Ventura (Eds) Proc. of the 2nd International Conference on Educational Data Mining, 51-60.

CP27  Gong, Y., Rai, D. Beck, J. & Heffernan, N. (2009). Does Self-Discipline impact students’ knowledge and learning? In Barnes, Desmarais, Romero & Ventura (Eds) Proc. of the 2nd International Conference on Educational Data Mining, 61-70. ISBN: 978-84-613-2308-1.

CP26   Pardos, Z. & Heffernan, N. (2009). Detecting the Learning Value of Items in a Randomized Problem Set. In Dimitrova, Mizoguchi, du Boulay & Graesser (Eds.) Proceedings of the 2009 Artificial Intelligence in Education Conference. IOS Press, 499-506.

CP25   Razzaq, L. & Heffernan, N. (2009). To Tutor or Not to Tutor: That is the Question. In Dimitrova, Mizoguchi, du Boulay & Graesser (Eds.) Proceedings of the 2009 Artificial Intelligence in Education Conference. IOS Press. pp. 457-464. Honorable Mention for Best Paper First Authored by a Student.

CP24   Feng, M., Heffernan, N. & Beck, J. (2009). Using Learning Decomposition to Analyze Instructional Effectiveness in the ASSISTment System. Proceedings of the 2009 Artificial Intelligence in Education Conference. IOS Press, 523-530.

CP23   Feng, M., Beck, J,. Heffernan, N. & Koedinger, K. (2008). Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test? In Baker & Beck (Eds.). Proceedings of the 1st International Conference on Education Data Mining. Montreal, Canada, 107-116.

CP22 Feng, M., Heffernan, N., Beck, J, & Koedinger, K. (2008). Can we predict which groups of questions students will learn from? In Baker & Beck (Eds.). Proceedings of the 1st International Conference on Education Data Mining. Montreal, Canada, 218-225.

CP21   Pardos, Z. A., Beck, J., Ruiz, C. & Heffernan, N. T. (2008). The Composition Effect: Conjunctive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS. In Baker & Beck (Eds.) Proceedings of the First International Conference on Educational Data Mining. Montreal, Canadam, 147-156. 

CP20   Razzaq, L., Mendicino, M. & Heffernan, N. (2008). Comparing classroom problem-solving with no feedback to web-based homework assistance. In Woolf, Aimeur, Nkambou, and Lajoie (Eds.) Proceeding of the 9th International Conference on Intelligent Tutoring Systems, 426-437.

CP19   Razzaq, L., Heffernan, N. T., Lindeman, R. W. (2007). What Level of Tutor Interaction is Best? In Luckin & Koedinger (Eds.) Proceedings of the 13th Conference on Artificial Intelligence in Education, 222-229.

CP18   Pardos, Z. A., Heffernan, N. T., Anderson, B. & Heffernan, C. (2007). The effect of model granularity on student performance prediction using Bayesian networks. The International User Modeling Conference 2007, 435-439. (Based on W14 and W18)

CP17   Feng, M., Heffernan, N. T., Mani, M., & Heffernan, C. (2007). Assessing students’ performance longitudinally: Item difficulty parameter vs. skill learning tracking. The National Council on Educational Measurement 2007 Annual Conference, Chicago. (Based upon WP15)

CP16   Feng, M., Heffernan, N. & Koedinger, K.R. (2006a). Predicting state test scores better with intelligent tutoring systems: developing metrics to measure assistance required. In Ikeda, Ashley & Chan (Eds.). Proceedings of the Eighth International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin, 31-40.

CP15   Feng, M., Heffernan, N. T., & Koedinger, K. R. (2006b).  Addressing the Testing Challenge with a Web-Based Assessment System that Tutors as it Assesses   Proceedings of the Fifteenth International World Wide Web Conference (WWW-06). New York, NY: ACM Press. ISBN:1-59593-332-9, 307-316. Nominated for Best Student Paper. Later turned into this journal paper  

CP14   Heffernan N.T., Turner T. E., Lourenco A.L.N., Macasek M.A., Nuzzo-Jones G., & Koedinger K.R. (2006). The ASSISTment builder: Towards an analysis of cost effectiveness of ITS creation. Proceedings of the 19th International FLAIRS Conference, Melbourne Beach, Florida, USA, 515-520. (Based on W10)

CP13   Razzaq, L. & Heffernan, N.T. (2006). Scaffolding vs. hints in the Assistment system. In Ikeda, Ashley & Chan (Eds.). Proceedings of the Eight International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin, 635-644.

CP12   Walonoski, J. & Heffernan, N.T. (2006a). Detection and analysis of off-task gaming behavior in intelligent tutoring systems. In Ikeda, Ashley & Chan (Eds.). Proceedings of the Eight International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin, 382-391. 

CP11   Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N.T., Koedinger, K. R., Junker, B., Ritter, S., Knight, A., Aniszczyk, C., Choksey, S., Livak, T., Mercado, E., Turner, T.E., Upalekar. R, Walonoski, J.A., Macasek. M.A. & Rasmussen, K.P. (2005). The Assistment project: Blending assessment and assisting. In C.K. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.) Proceedings of the 12th Artificial Intelligence in Education, Amsterdam: ISO Press, 555-562.

CP10   Rose C., Donmez P., Gweon G., Knight A., Junker B., Cohen W., Koedinger K., Heffernan N.T. (2005). Automatic and semi-automatic skill coding with a view towards supporting on-line Assessment. In Looi, McCalla, Bredeweg, & Breuker (Eds.) The 12th Annual Conference on Artificial Intelligence in Education 2005, Amsterdam. ISO Press, 571-578.

CP9 Croteau, E., Heffernan, N. T. & Koedinger, K. R. (2004). Why are Algebra word problems difficult? Using tutorial log files and the power law of learning to select the best fitting cognitive model. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems. Berlin: Springer-Verlag, 240-250.

CP8 Heffernan, N. T. & Croteau, E. (2004). Web-Based Evaluations Showing Differential Learning for Tutorial Strategies Employed by the Ms. Lindquist Tutor. In James C. Lester, Rosa Maria Vicari, Fábio Paraguaçu (Eds.) Proceedings of 7th Annual Intelligent Tutoring Systems Conference, Maceio, Brazil, 491-500.

CP7 Jarivs, M., Nuzzo-Jones, G. & Heffernan. N. T. (2004). Applying machine learning techniques to rule generation in intelligent tutoring systems. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) In James C. Lester, Rosa Maria Vicari, Fábio Paraguaçu (Eds.) Proceedings of 7th Annual Intelligent Tutoring Systems Conference, Maceio, Brazil, 541-553.

CP6 Koedinger, K. R., Aleven, V., Heffernan. T., McLaren, B. & Hockenberry, M. (2004). Opening the door to non-programmers: Authoring intelligent tutor behavior by demonstration. In James C. Lester, Rosa Maria Vicari, Fábio Paraguaçu (Eds.) Proceedings of 7th Annual Intelligent Tutoring Systems Conference,e, Maceio, Brazil,162-173.

CP5 Heffernan, N. T. (2003). Web-based evaluations showing both cognitive and motivational benefits of the Ms. Lindquist tutor In F. Verdejo and U. Hoppe (Eds) 11th International Conference Artificial Intelligence in Education. Sydney, Australia. IOS Press,115-122.

CP4 Heffernan, N. T., & Koedinger, K. R.(2002). An intelligent tutoring system incorporating a model of an experienced human tutor. In Stefano A. Cerri, Guy Gouardères, Fábio Paraguaçu (Eds.): 6th International Conference on Intelligent Tutoring System. Biarritz, France. Springer Lecture Notes in Computer Science, 596-608.

CP3 Heffernan, N. T., & Koedinger, K. R. (2000). Intelligent tutoring systems are missing the tutor: Building a more strategic dialog-based tutor. In C.P. Rose & R. Freedman (Eds.) Proceedings of the AAAI Fall Symposium on Building Dialogue Systems for Tutorial Applications. Menlo Park, CA: AAAI Press, 14- 19.

CP2 Heffernan, N. T. & Koedinger, K. R. (1998). A developmental model for algebra symbolization: The results of a difficulty factors assessment. In M. Gernsbacher & S. Derry (Eds.) Proceedings of the Twentieth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum, 484-489.

CP1 Heffernan, N. T. & Koedinger, K.R. (1997). The composition effect in symbolizing: The role of symbol production vs. text comprehension. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum, 307-312. [Marr prize winner for Best Student Paper].

Short Papers

In computer science, there are many times where a full paper gets downgraded to a "short" paper. For instance, at the Educational Data Mining Conference, a full paper is typically 8-10 pages and has an acceptance rate of 30% (those papers are listed above). These "Short Papers" listed below typically have an acceptance rate of 50% so they should not be considered in the same league as the work listed above (e.g., In 2011, the acceptance rates for short papers was 46%, which is considerably higher than the 33% acceptance rate for “Full Papers”).

SP31 Baral, S., Santhanam, A., Botelho, A.F., Santhanam, A., Gurung, A., Cheng, L., & Heffernan, N. (2023). Auto-scoring Student Responses with Images in Mathematics. In The Proceedings of the 16th International Conference on Educational Data Mining. Submitted paper. Final paper.  

SP30 Prihar, E., Vanacore, K., Sales, A., & Heffernan, N. (2023). Effective Evaluation of Online Learning Interventions with Surrogate Measures. In The Proceedings of the 16th International Conference on Educational Data Mining. Submitted paper. Final paper.

SP29 Lee, M.P., Croteau, E., Gurung, A., Botelho, A.F., & Heffernan, N. (2023). Knowledge Tracing Over Time: A Longitudinal Analysis. In The Proceedings of the 16th International Conference on Educational Data Mining. Submitted paper. Final paper

SP28 Baral, S., Seetharaman, K., Botelho, A.,  Wang, A., Heineman, G.,& Heffernan, N. (2022)  Enhancing auto-scoring of student open-responses in the presence of mathematical terms and expressions. AIED2022. Submitted longer version - Shorter Final version  Talk

SP27 Prihar,E. & Heffernan, N. (2021). A Novel Algorithm for Aggregating Crowdsource Opinions.  In Hsiao,  Sahebi, Bouchet & Vie (eds). Proceedings of the 14th International Conference on Educational Data Mining (EDM2021).  Pages 547-552. https://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_63.pdf 

SP26 Razzaq, R., Ostrow, K. &  Heffernan, N. (2020) Effect of Immediate Feedback on Math Achievement in Secondary Education AIED2020.  In Bittencourt et al, The 21st Proceedings of the International Conference on Artificial Intelligence in Education (AIED). pp. 263-267. doi: https://doi.org/10.1007/978-3-030-52240-7_48  Longer Blinded Version 

SP25 Patikorn, T., Deisadze, D., Grande, L., Yu, Z., & Heffernan, N. (2019). Generalizability of Methods for Imputing Mathematical Skills Needed to Solve Problems from Texts. In International Conference on Artificial Intelligence in Education (pp. 396-405). Springer, Cham. 

SP24 Sales, A. C., Botelho, A. F., Wu, E., Gagnon-Bartsch, J., Miratrix, L., Patikorn, T. & Heffernan, N. T. (2018). Residualization Methods to Better Estimate Treatment Effects in Randomized Controlled Trials. Presented at the Conference on Digital Experimentation (CODE) held at MIT. Abstract. View recorded talk here. Here in youtube.

SP23 Sales, A., Patikorn, T. & Heffernan, N. T. (2018) Bayesian Partial Pooling to Improve Inference Across A/B Tests in EDM. Published in the Proceeding of the Educational Data Mining Conference (EDM2018) Pp 521-524. Retrieved from http://educationaldatamining.org/files/conferences/EDM2018/EDM2018_Preface_TOC_Proceedings.pdf 

SP22  Patikorn, T, Selent, D., Beck, J., Heffernan, N., & Zhou, J. (2017). Using a Single Model Trained Across Multiple Experiments to Improve the Detection of Treatment Effects. Conference of Educational Data Mining 2017. Pp 202-207.  

SP21 Zhao, S. & Heffernan, N. (2017) Estimating Individual Treatment Effects from Educational Studies with Residual Counterfactual Networks. In the 10th International Conference on Educational Data Mining (EDM 2017).  

SP20 Zhang, L., Xiong, X., Zhao, S., Botelho, A. & Heffernan, N. (2017) Incorporating Rich Features into Deep Knowledge Tracing. In the Proceedings of the Forth (2017) ACM Conference on Learning @ Scale. Cambridge, MA. (L@S2017) ,169-172. (Acceptance Rate = 44%) PDF (A longer version is available here)

SP19 Yin, B., Patikorn, T., Botelho, A., Heffernan, N. (2017) Observing Personalizations in Learning: Identifying Heterogeneous Treatment Effects Using Causal Trees. In the Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale.(L@S2017) , 299-302. Cambridge, MA. (Acceptance Rate = 44%) 

SP18 Zhao, S., Zhang, Y., Xiong, X., Botelho, A. F., & Heffernan, N. T. (2017) A Memory-Augmented Neural Model for Automated Grading. In the Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale.(L@S2017)   Cambridge, MA. Pages 189-172 (Acceptance Rate = 44%) 

SP17 Lu, X., Xiong, X. & Heffernan, N. (2017) Experimenting Choices of Video and Text Feedbacks in Authentic Foreign Language Assignments at Scale. In the Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale.(L@S2017) , 335-338. (Acceptance Rate 44%)

SP16 Inventado, P. S., VanInwegen, E., Ostrow, K., Scupelli, P., Heffernan, N., Baker, R., Slater, S. & Ocumpaugh, J. (2016) Design Subtleties Driving Differential Attrition. The 6th International Learning Analytics & Knowledge Conference pp 284-289. 

SP15 Ostrow, K.S. & Heffernan, N. T. (2016) Studying Learning at Scale with the ASSISTments TestBed Proceedings of the Third (2016) ACM Conference on Learning @Scale, 333-334.

SP14  Inventado, P., Scupelli, P., Van Inwegen, E., Ostrow, K., Heffernan, N., Ocumpaugh, J., Baker, R., Slater, S. & Almeda, M. (2016) Hint availability slows completion times in summer work. In: Barnes, T, Chi, M, Feng, M (Eds.) In Proceedings of the 9th International Conference on Educational Data Mining, (pp. 388–393). (Acceptance Rate = 50%)

SP13 Botelho, A., Adjei, S. & Heffernan, N. (2016) Modeling Interactions Across Skills: A Method to Construct and Compare Models Predicting the Existence of Skill Relationships. In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining, 292-297.

SP12 Wang, Y., Ostrow, K., Beck, J. & Heffernan, N. T. (2016) Enhancing the efficiency and reliability of group differentiation through partial credit. In the Proceedings of the Sixth International Conference on Learning Analytics & Knowledge LAK2016 , 454-458 (Acceptance Rate = Not released but maybe 50%). Materials from the study.   

SP11 Adjei, S., Boethello, A. & Heffernan, N. (2016) Predicting student performance on post-requisite skills using prerequisite skill data: an alternative method for refining prerequisite skill structures In the Proceedings of the Sixth International Conference on Learning Analytics & Knowledge LAK2016 , 469-473 (Acceptance Rate = Not released but maybe 50%). 

SP10 Ostrow, K, Donnelly, C. & Heffernan, N. (2015) Optimizing Partial Credit Algorithms to Predict Student Performance. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0, 404-407. (Acceptance Rate = 50%)

SP9 Castro, F., Adjei, S., Colombo, T., & Heffernan, N.T. (2015) Building Models to Predict Hint-or-Attempt Actions of  Students. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0, 476-479. (Acceptance Rate = 50%)

SP8 Wang, Y., Heffernan, N, & Heffernan, C. (2015). Towards better affect detectors: effect of missing skills, class features and common wrong answers. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 31-35.   

SP7 Van Inwegen, E., Adjei, S., Wang, Y., & Heffernan, N. (2015) An analysis of the impact of action order on future performance: the fine-grain action model. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 320-324. 

SP6 San Pedro, M.O., Baker, R., Heffernan, N., Ocumpaugh, J. (2015) Exploring College Major Choice and Middle School Student Behavior, Affect and Learning: What Happens to Students Who Game the System? Proceedings of the 5th International Learning Analytics and Knowledge Conference, 36-40. 

SP5  Adjei, S., Selent, D., Heffernan, N., Pardos, Z., Broaddus, A., Kingston, N. (2014). Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps. In John Stamper et al. (Eds) Proceedings of the 7th International Conference on Educational Data Mining, 413-414. 

SP4  Selent, D. & Heffernan, N. (2014). Reducing Student Hint Use by Creating Buggy Messages from machine Learned Incorrect Processes. In Stefan Trausan-Matu, et al. (Eds) International Conference on Intelligent Tutoring 2014.  LNCS 8474. (Acceptance Rate = 66%)

SP3  Gu, J., Wang, Y. & Heffernan, N. (2014). Personalizing Knowledge Tracing: Should We Individualize Slip, Guess, Prior or Learn rate? In Stefan Trausan-Matu, et al. (Eds) International Conference on Intelligent Tutoring 2014.  LNCS 8474. (Acceptance Rate = 66%)

SP2  Kelly, K.,  Heffernan, N., Heffernan, C.,  Goldman, S., Pellegrino, G. & Soffer, D. (2013). Estimating the Effect of Web-Based Homework. In Lane, Yacef, Mostow & Pavlik (Eds) The Artificial Intelligence in Education Conference, 824-827. (Watch the videos and related archived material  here: http://web.cs.wpi.edu/~nth/PublicScienceArchive/Kelly.htm and permanently archived at http://www.webcitation.org/6E6lv54G8

SP1  Feng, M., Heffernan, N., Pardos, Z. & Heffernan, C.(2011). Establishing the value of dynamic assessment in an online tutoring system. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining, 295-300.

Published 3-4 Page Papers

(aka “Poster”) in Prestigious Conferences (Acceptance rates of  50-60%).

PP49 Prihar, E., Lee, M., Hopman, M., Kalai, A., Vempala, S., Wang, A., Wickline, G., & Heffernan, N. (2023). Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models.  In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_45  Submitted PDF Final PDF

PP48 Baral, S., Botelho, A.F., Santhanam, A., Gurung, A., Erickson, J., & Heffernan, N. (2023). Investigating Patterns of Tone and Sentiment in Teacher Written Feedback Messages. Accepted to AIED 2023. Submitted PDF Final PDF

PP47 Sales, A., Prihar, E., Gagnon-Bartsch, J., Gurung, A., & Heffernan, N. (2022). More Powerful A/B Testing Using Auxiliary Data and Deep Learning.  AIED2022. Submitted Version (blind)

PP46 Haim, A. & Heffernan, N. (2022). Student Perception on the Effectiveness of On-Demand Assistance in Online Learning Platforms. EDM Accepted Version

PP45 Rivera-Bergollo, R., Baral, S., Botelho, A., & Heffernan, N. (2022). Leveraging Auxiliary Data from Similar Problems to Improve Automatic Open Response Scoring. Accepted Version. EDM PDF

PP44 Erickson, J.A., Botelho, A., Peng, Z., Huang, R., Kasal, M.V., Heffernan, N. (2021) Is it Fair? Automated Open Response Grading. In Hsiao,  Sahebi, Bouchet & Vie (eds). Proceedings of the 14th International Conference on Educational Data Mining (EDM21).  Page 682-687. 

PP43 Botelho, A., Baker, R. & Heffernan, N. (2019) Machine-Learned or Expert-Engineered Features? Exploring Feature Engineering Methods in Detectors of Disengaged Behavior and Affect. In Desmarais, Lynch, Merceron & Nkambou (Eds) Proceedings of the 12th International Conference on Educational Data Mining(EDM2019)  ISBN: 978-1-7336736-0-0. pp. 508-511

PP42 Varatharaj, A., Botelho, A., Lu, W. & Heffernan, N. (2019) Hao Fa Yin: Developing Automated Audio Assessment Tools for a Chinese Language Course. In Desmarais, Lynch, Merceron & Nkambou (Eds) Proceedings of the 12th International Conference on Educational Data Mining(EDM2019)  ISBN: 978-1-7336736-0-0. pp. 663-667

PP41 Hulse, T., Harrison, A., Ostrow, K. S., Botelho, A. F., & Heffernan, N. T. (2018). Starters and Finishers: Predicting Next Assignment Completion From Student Behavior During Math Problem Solving. In Proceedings of the Eleventh International Conference on Educational Data Mining, 525-528.

PP40 Yin, B., Botelho, A. F., Patikorn, T., Heffernan, N. T., & Zou, J. (2017, June). Causal Forest vs. Naïve Causal Forest in Detecting Personalization: An Empirical Study in ASSISTments. In Proceedings of the Tenth International Conference on Educational Data Mining, 388-389. ACM

PP39 Patikorn, T., Heffernan, N., & Zhou, J. (2017). An Offline Evaluation Method for Individual Treatment Rules and How to Find Heterogeneous Treatment Effects. Conference of Educational Data Mining 2017. 

PP38 Patikorn, T., Selent, D., Heffernan, N. T., Yin, B., Botelho, A. (2016) ASSISTments Dataset for a Data Mining Competition to Improve Personalized Learning. Poster at MIT Conference on Digital Experimentation. CODE 2016.

PP37 Williams, J. J., Botelho, A., Sales, A., Heffernan, N. & Lang, C. (2016) Discovering 'Tough Love' Interventions Despite Dropout In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining, 650-651.

PP36 Kelly, K. & Heffernan, N. (2016) Optimizing the Amount of Practice in an On-Line Platform. A “Work in progress” category presented at Learning at Scale 2016. Pp. 145-148. (Acceptance Rate = 59%) 

PP35  Selent, D., Patikorn, T. & Heffernan, N. T. (2016) ASSISTments Dataset from Multiple Randomized Controlled Experiments. A “Work in progress” category presented at Learning at Scale 2016. At ACM Digital Library, 181-184. (Acceptance Rate = 59%)

PP34 Wang, Y., Ostrow, K., Adjei, S. & Heffernan, N. (2016) The Opportunity Count Model: A Flexible Approach to Modeling Student Performance. A “Work in progress” category presented at Learning at Scale 2016. At ACM Digital Library. (Acceptance Rate = 59%)

PP33  Williams, J. W. & Heffernan, N. T. (2015) A Methodology for Discovering how to Adaptively Personalize to Users using Experimental Comparisons. Submitted to UMAP Late Breaking Results. Not final draft. (Acceptance Rate = 50%)

PP32  Selent, D. & Heffernan, N. T. (2015) When More Intelligent Tutoring in the Form of Buggy Messages Does Not Help  In Conati, Heffernan, Mitrovic & Verdejo (Eds) The 17th Proceedings of the Conference on Artificial Intelligence in Education, Madrid, Spain. Springer, 768-771.

PP31 Jiang, Y., Baker, R., Paquette, L., San Pedro, M. & Heffernan, N. T. (2015) Learning, Moment-by-Moment and Over the Long Term. In Conati, Heffernan, Mitrovic & Verdejo (Eds) The 17th Proceedings of the Conference on Artificial Intelligence in Education, Madrid, Spain. Springer, 654-657.

PP30 Ostrow, K. & Heffernan, N. T. (2015) The Role of Student Choice Within Adaptive Tutoring. In Conati, Heffernan, Mitrovic & Verdejo (Eds) The 17th Proceedings of the Conference on Artificial Intelligence in Education, Madrid, Spain. Springer, 752-755.

PP29 Adjei, S. A., & Heffernan, N. (2015)  Improving Learning Maps Using an Adaptive Testing System: PLACEMENTS. In Conati, Heffernan, Mitrovic & Verdejo (Eds) The 17th Proceedings of the Conference on Artificial Intelligence in Education, Madrid, Spain. Springer, 517-520.(Longer Version)

PP28  Van Inwegen, E., Wang, Y., Adjei, S. & Heffernan, N.T. (2015) The Effect of the Distribution of Predictions of User Models. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0. pp 620-621.

PP27  Botelho, A., Adjei, S., Wan, H. & Heffernan, N.T. (2015) Predicting Student Aptitude Using Performance History. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0. pp 622-623.

PP26  Kelly, K., Wang, Y., Thompson, T. & Heffernan, N.T. (2015) Defining Mastery: Knowledge Tracing Versus N- Consecutive Correct Responses, Madrid, Spain. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0, 630-631.

PP25 Kelly, K., Arroyo, I., & Heffernan, N. (2013). Using ITS Generated Data to Predict Standardized Test Scores. In S. D’Mello, R. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM2013). Memphis, TN, 322-323.

PP24 Duong, H., Zhu, L., Wang,Y. and Neil Heffernan. (2013). A prediction model that uses the sequence of attempts and hints to better predict knowledge: “Better to attempt the problem first, rather than ask for a hint.” In S. D’Mello, R. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM2013). Memphis, TN, 316-317.

PP23 Adjei, S., Salehizadeh, S. M. A., Wang, Y., & Heffernan, N.T. (2013). Do students really learn an equal amount independent of whether they get an item correct or wrong? In S. D’Mello, R. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM2013). Memphis, TN, 304-305.

PP22 Wang, Y., & Heffernan, N. T. (2013). A Comparison of Two Different Methods to Individualize Students and Skills. In Lane, Yacef, Mostow & Pavlik (Eds.) The Artificial Intelligence in Education Conference. Springer-Verlag, 836-840

PP21 Heffernan, N. T., Heffernan, C. L., Dietz, K., Soffer, D. A., Goldman, S. R., & Pellegrino, J. W. (September, 2012). Spacing Practice, Assessment and Feedback to Promote Learning and Retention. Presented at the Scientific Research on Educational Effectiveness Fall 2012 Conference in Washington, D.C.

PP20 Wang, Y. & Heffernan, N. (2011). Towards Modeling Forgetting and Relearning in ITS: Preliminary Analysis of ARRS Data. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining, 351-352. 

PP19 Goldstein, A., Baker, R. & Heffernan, N. (2010). Pinpointing Learning Moments; A finer grain P(J) model In Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. (Eds.) Proceedings of the 3rd International Conference on Educational Data Mining, 289-290. (Related to CP31) 

PP18 Wang, Y., Heffernan, N. & Beck, J. (2010). Representing Student Performance with Partial Credit. In Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. (Eds.) Proceedings of the 3rd International Conference on Educational Data Mining, 335-336.   

PP17 Rai, D., Beck, J., & Heffernan, N. (2010). Mily’s World: A Coordinate Geometry Learning Environment with Game-Like Properties. In Aleven, V., Kay, J & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 2. Springer, 399-401. 

PP16 Feng, M. & Heffernan, N. T. (2010). Can We Get Better Assessment From a Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It Too (Student Leaning During the Test)? In Aleven, V., Kay, J & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 2. Springer, 309-311. (Lead to CP38) 

PP15 Feng, M., Heffernan, N., Koedinger, K. (2010). Using Data Mining Findings to Aid Searching for Better Skill Models. In Aleven, V., Kay, J & Mostow, J. (Eds) Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part 2 Springer, 312-314. 

PP14 Patvarczki, J., Mani, M. & Heffernan, N. T. (2009). Performance driven database design for scalable web applications. In J. Grundspenkis, T. Morzy & G. Vossen (Eds) Advances in Databases and Information Systems Springer-Verlag: Berlin, 43-58 

PP13 Shrestha, P., Wei, X., Maharjan, A., Razzaq, L., Heffernan, N.T., & Heffernan, C., (2009). Are Worked Examples an Effective Feedback Mechanism During Problem Solving? In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, 1294-1299.

PP12 Kim, R, Weitz, R., Heffernan, N. & Krach, N. (2009). Tutored Problem Solving vs. “Pure”: Worked Examples In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, 3121-3126). Austin, TX: Cognitive Science Society.

PP11 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: ACM 2008.

PP10 Patvarczki, J., Almeida, J. F., Beck, J. E., & Heffernan, N. T. (2008). Lessons Learned from Scaling Up a Web-Based Intelligent Tutoring System. In Woolf & Aimeur (Eds.) Proceeding of the 9th International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin.Volume 5091, 766-770.

PP9 Guo, Y., Heffernan, N. T., & Beck, J. E. (2008). Trying to Reduce Bottom-out hinting: Will telling student how many hints they have left help?. In Woolf & Aimeur (Eds.) Proceeding of the 9th International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin. Volume 5098, 774-778.

PP8 Pardos, Z., Feng, M. & Heffernan, N. T. & Heffernan-Lindquist, C. (2007). Analyzing fine-grained skill models using bayesian and mixed effect methods. In Luckin & Koedinger (Eds.) Proceedings of the 13th Conference on Artificial Intelligence in Education. IOS Press, 626-628. (Based on W17)

PP7 Weitz, R., Heffernan, N. T., Kodaganallur, V. & Rosenthal, D. (2007). The distribution of student errors across schools: An initial study. In Luckin & Koedinger (Eds.) Proceedings of the 13th Conference on Artificial Intelligence in Education. IOS Press. pp 671-673.

PP6 Kardian, K. & Heffernan, N.T. (2006). Knowledge engineering for intelligent tutoring systems: Assessing semi-automatic skill encoding methods. In Ikeda, Ashley & Chan (Eds.) Proceedings of the Eight International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin, 735-737. A longer version was published as WPI-CS-TR-06-06 (pdf). 

PP5 Walonoski, J. & Heffernan, N. (2006b). Prevention of off-task gaming behavior in intelligent tutoring systems. In Ikeda, Ashley & Chan (Eds.). Proceedings of the 8th International Conference on Intelligent Tutoring Systems. 2006, LNCS 4053. Springer-Verlag: Berlin, 722-724. [Winner of the Best 3-page Paper Award out of 40 such papers]. A longer version is here.

PP4 Nuzzo-Jones, G., Walonoski, J.A., Heffernan, N.T. & Livak, T. (2005). The eXtensible tutor architecture: A new foundation for ITS. In Looi, McCalla, Bredeweg, & Breuk http://www.lcc.uma.es/~eva/waswbe05/papers/nuzzo.pdf er (Eds.) Proceedings of the 12th Artificial Intelligence in Education, 555-562. Amsterdam: ISO Press, 902-904. (Based on W11)

PP3 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 Looi, McCalla, Bredeweg, & Breuker (Eds.) Proceedings of the 12th Artificial Intelligence in Education, Amsterdam: ISO Press, 929-931. A longer version appears in Heffernan et al. 2006. (Based on W10)

PP2 Koedinger, K. R., Aleven, V., & Heffernan, N. T. (2003). Toward a rapid development environment for cognitive tutors. In F. Verdejo and U. Hoppe (Eds) 11th International Conference Artificial Intelligence in Education. Sydney, Australia. IOS Press, 455-457.

PP1 Razzaq, L. & Heffernan, N. T (2004). Tutorial dialog in an equation solving intelligent tutoring system. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of 7th Annual International Intelligent Tutoring Systems Conference, Berlin: Springer-Verlag, 851-853. [Winner of the Best Poster Award].

Workshop Papers


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 ConferencePDF

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


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.

Dissertation

Heffernan, N. T (2001). Intelligent tutoring systems have forgotten the tutor: Adding a cognitive model of human tutors Dissertation. Computer Science Department, School of Computer Science, Carnegie Mellon University. Technical Report CMU-CS-01-127. (Pieces published as CP1, CP8, CP5, CP4, and CP3)

Workshops & Tutorials I Helped Organize


WP18 LAK (March 13-14, 2023). How to Open Science: Promoting Principles and Reproducibility Practices within the Learning Analytics Community by Haim, A., Shaw, S. T. & Heffernan, N. T. https://doi.org/10.17605/osf.io/kyxba

WP17 AIED (July 3rd, 2023): How to Open Science: Promoting Principles and Reproducibility Practices Within the Artificial Intelligence in Education Community by Haim, A., Shaw, S. ., & Heffernan, N. T. https://doi.org/10.1007/978-3-031-36336-8_11

WP16 EDM (July 14, 2023): How to Open Science: Promoting Principles and Reproducibility Practices with the Educational Data Mining Community by Haim, A., Shaw, S. T., & Heffernan, N. T. Pages 582-584. https://doi.org/10.5281/zenodo.8115776

WP15 L@S (July 20, 2023): How to Open Science: Promoting Principles and Reproducibility Practices within the Learning @ Scale Community by Haim, A., Shaw, S. T., & Heffernan, N. T. Pages 248-250. https://doi.org/10.1145/3573051.3593398

WP14 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