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.

2026

CP132 Worden, E., Dang, L., Lim, W.-C., Miller, S., Zhang, J., Haim, A., Sales, A., Gurung, A., & Heffernan, N. (2026). A Large Scale Randomized Control Trial Showing LLM Generated Feedback Helps Low-Knowledge Middle School Math Students with Short-Term Learning. In Proceedings of the Thirteenth ACM Conference on Learning @ Scale (L@S ’26), June 29–July 3, 2026, Seoul, Republic of Korea. ACM, New York, NY, USA, 12 pages. Submitted version.

CP131 Lim, W.-C., Worden, E., Sales, A., & Heffernan, N. T. (2026). LLM-Generated Summaries for Teachers: A Randomized Field Experiment in a Digital Learning Platform. In Proceedings of the Thirteenth ACM Conference on Learning @ Scale (L@S ’26), June 29–July 3, 2026, Seoul, Republic of Korea. ACM, New York, NY, USA, 12 pages. Submitted version. https://doi.org/10.1145/3774398.3811606 (URL will not be active until June 28, 2026).

CP 130 Ikram, F., Kumar, N.A., Lu, J., McNichols, H., Walkington, C., Heffernan, N., & Lan, A.S. (2026). A Multi-Agent Approach to Validate and Refine LLM-Generated Personalized Math Problems. In The 27th International Conference on AI in Education. [Submitted Version]

CP129 Worden, E., Lee, M., Siedahmed, A., Sales, A., Zhang, J., Shraga, R., & Heffernan, N.T. (2026). Short, Long, or Affective: Evaluating LLM-Generated Feedback Styles for Student Learning. In The 27th International Conference on AI in Education. [Submitted Version]

CP128 Siedahmed, A., Ocumpaugh, J., Ferris, Z., Kodwani, D., Worden, E., & Heffernan, N. (2025). Nonstandard English and the Automated Scoring of Open-Ended Math Problems. In Proceedings of the 18th International Conference on Educational Data Mining, July 20–23, 2025, Palermo, Italy. ACM, New York, NY, USA. PDF.

2025

CP127 Zengilowski, A., Schuetze, B.A., Siedahmed, A., Yan, V.X., & Heffernan, N. (2025). Encouraging Megacognitive Reflection through Prompts in a Computer-Based Learning Platform: Failure to Find a Benefit in a Large-Scale Randomized Trial. 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 Paper

CP126 Matlen, B., Bartel, A., Davenport, J., Rohrer, D., Heffernan, C., Shaw, S., & Heffernan, N. (2025). Scaling Learning Interventions: A Case Study in Interleaved Math Practice. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25), July 21–23, 2025, Palermo, Italy. ACM, New York, NY, USA. Accepted Paper.

CP 125 Worden, E., Vanacore, K., Haim, A., & Heffernan, N. (2025). Scaling Effective AI-Generated Explanations for Middle School Mathematics in Online Learning Platforms. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S ’25), July 21–23, 2025, Palermo, Italy. ACM, New York, NY, USA, 10 pages. Final Submission. https://doi.org/10.1145/3698205.3729546

CP 124 Worden, E., Baral, S., Yu, D., Santorelli, C., & Heffernan, N. (2025). Few-shot Is All You Need, A Framework for RAG-Based LLM Feedback. (Submitted to AIED). Main DC

CP123 Baral, S., Lucy. L., Knight, R., Ng, A., Soldaini, L., Heffernan, N. T., & Lo, K. (2025). DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students' Hand-Drawn Math Images. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technology (Volume I: Long Paper), pages 6902-6920. Received an Outstanding Paper Award. PDF | https://drawedumath.org/

CP122 Williamson, K., Heffernan, N., Fath, S., & Kizilcec, R. F. (2025). Algorithm Appreciation in Education: Educators Prefer Complex over Simple Algorithms. In The 15th International Learning Analytics & Knowledge Conference (LAK '25). Paper.

CP121 Worden, E., Gurung, A., Baral, S., Lee, M., Heffernan, N. (Submitted). Can LLMs Predict Common Errors in Math? Towards Scaling the Identification of Common Wrong Answers. In The 15th International Learning Analytics & Knowledge Conference (LAK '25). Draft.

2024

CP120 Vanacore, K., Gurung, A., Sales, A., & Heffernan, N. T. (2024, March). The Effect of Assistance on Gamers: Assessing The Impact of On-Demand Hints & Feedback Availability on Learning for Students Who Game the System. In Proceedings of the 14th Learning Analytics and Knowledge Conference, 462-472. https://doi.org/10.1145/3636555.3636904

CP119 Li, H., Li, C., Xing, W., Baral, S., & Heffernan, N. (2024, March). Automated Feedback for Student Math Responses Based on Multi-Modality and Fine-Tuning. In Proceedings of the 14th Learning Analytics and Knowledge Conference, 763-770. https://doi.org/10.1145/3636555.3636860

CP118 Gurung, A., Vanacore, K., Mcreynolds, A. A., Ostrow, K. S., Worden, E., Sales, A. C., & Heffernan, N. T. (2024, March). Multiple Choice vs. Fill-In Problems: The Trade-off Between Scalability and Learning. In Proceedings of the 14th Learning Analytics and Knowledge Conference 507-517. https://doi.org/10.1145/3636555.3636908

2023

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., Shaw, S., & Heffernan, N. (2023a). 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. https://doi.org/10.1145/3576050.3576071

CP114 Haim, A., Gyurcsan, R., Baxter, C., Shaw, S., & Heffernan, N. (2023b). 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. https://dl.acm.org/doi/abs/10.1145/3573051.3596166

CP113 Haim, A., Gyurcssan, R., Baxter, C., Shaw, S., & Heffernan, N. (2023c).

How to Open Science: Debugging Reproducibility within the Educational Data Mining Conference. Presented at The Educational Data Mining Conference (EDM23). https://educationaldatamining.org/EDM2023/proceedings/2023.EDM-long-papers.10/2023.EDM-long-papers.10.pdf

CP112 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.

CP111 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.

CP110 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

CP109 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

CP108 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

2022

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.

2021

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

2020

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.

2019

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)

2018

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.

2017

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.

2016

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%)

2015

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%)

2014

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.

2013

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.

2012

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.

2011

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].

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