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”).

SP37 Worden, E., Heffernan, C., Heffernan, N., & Sonkar, S. (2026). FoundationalASSIST: Dataset for Foundational Knowledge Tracing & Pedagogical Grounding of Large Language Models. In The 27th International Conference on Artificial Intelligence in Education. [Submitted version] 

SP36 Wang, A., Prihar, E., Haim, A., & Heffernan, N. (2024). Can Large Language Models Generate Middle School Mathematics Explanations Better Than Human Teachers? In Artificial Intelligence in Education Conference (AIED '24), 242-250. Paper.

SP35 Li, H., Xing, W., Li, C., Zhu, W., Heffernan, N. 2024. Positive Affective Feedback Mechanisms in an Online Mathematics Learning Platform. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24). Association for Computing Machinery, New York, NY, USA, 371–375. Paper.

SP34  Zambrano, A. F., Baker, R. S., Baral, S., Heffernan, N. T., Lan, A.. (2024). From Reaction to Anticipation: Predicting Future Affect. In Proceedings of the 17th International Conference on Educational Data Mining (EDM'24), 566--574. Paper.

SP33 Lee, M., Siedahmed, A., & Heffernan, N. (2024). Expert Features for a Student Support Recommendation Contextual Bandit Algorithm. In Proceedings of the 14th Learning Analytics and Knowledge Conference (LAK '24). Association for Computing Machinery, New York, NY, USA, 864–870. https://doi.org/10.1145/3636555.3636909 

SP32 Zhang, M., Heffernan, N., Lan, A. (2023) Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. Educational Data Mining. https://files.eric.ed.gov/fulltext/ED630868.pdf 

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 (EDM '18) (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. In The 10th International Conference on Educational Data Mining (EDM '17)(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.