Poster Papers

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

PP54 Zhang, S., Worden, E., Leite, W., Heffernan, N.T., & Botelho, A.F. (2026). Would a Short Explanation Immediately Help? Examining Causal Effects of Students' Help-Seeking in Math. In Proceedings of the 19th International Conference on Educational Data Mining. 

PP53 Sales, A., Mann, C., Gagnon-Bartsch, J., & Heffernan, N. (2025). Effect Estimates Using Publicly Available School-Level Data in a Cluster-Randomized Educational Experiment. In Proceedings of the 18th International Conference on Educational Data Mining, July 20–23, 2025, Palermo, Italy. ACM, New York, NY, USA. PDF

PP52 Feng, M., Brezack, N., & Heffernan, N. (2025). Scaling What Works Smartly: A Cost Analysis of an AI-Enhanced Tool for Middle School Math Learning. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25), July 21–23, 2025, Palermo, Italy. ACM, New York, NY, USA. Submitted PDF

PP51 Lim, W.C., Eroshenko, I., Khumwang, W., Chan, P-C., & Heffernan, N.T. (2025). Levering LLMs for Assignment Report Summaries to Support Teacher Insights in Intelligent Tutoring Systems. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25), July 21–23, 2025, Palermo, Italy. ACM, New York, NY, USA. Final PDF

PP50 Baral, S., Worden, E., Lim, W., Luo, Z., Santorelli, C., & Gurung, A. (2024) Automated Assessment in Math Education: A Comparative Analysis of LLMs for Open-Ended Responses. In The Proceedings of the  Educational Data Mining Conference (EDM '24). PDF.

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].