Papers that use ASSISTments Open Released Datasets


Papers that use ASSISTments data that Neil Heffernan is not an author of.   Neil is proud of the fact that we give away our data to allow others to use, and possibly correct our published result.  


A25 Shirly Montero, Akshit Arora, Sean Kelly, Brent Milne, Michael Mozer (2018).Does Deep Knowledge Tracing Model Interactions Among Skills? 11th International Conference on Educational Data Mining, EDM 2018.

A24 Pardos, Z, Wang, Q, & Trivedi, S. (2012)  The real world significance of performance prediction EDM 2012: 192-195.

A23 Ritwick Chaudhry, Harvineet Singh, Pradeep Doggay, Shiv Kumar Saini (2018). Modeling Hint-Taking Behavior and Knowledge State of Students with Multi-Task Learning.11th International Conference on Educational Data Mining. EDM 2018.

A22 Ruixue Liu, Erin Walker, &, Erin Solovey. (2017) . Toward Neuroadaptive Personal Learning Environments.  In The First Biannual Neuroadaptive Technology Conference. Retrieved from  http://neuroadaptive.org/files/NAT17_Berlin_Conference_Programme.pdf#page=63  

A21  Song Y., Cai H., Zheng X., Qiu Q., Jin Y., Zhao X. (2017) FTGWS: Forming Optimal Tutor Group for Weak Students Discovered in Educational Settings. In: Benslimane D., Damiani E., Grosky W., Hameurlain A., Sheth A., Wagner R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science, vol 10438. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-64468-4_33

A20 Song Y., Jin Y., Zheng X., Han H., Zhong Y., Zhao X. (2015) PSFK: A Student Performance Prediction Scheme for First-Encounter Knowledge in ITS. In: Zhang S., Wirsing M., Zhang Z. (eds) Knowledge Science, Engineering and Management. Lecture Notes in Computer Science, vol 9403. Springer, Cham. pp 639-650. https://doi.org/10.1007/978-3-319-25159-2_58Author Copy 

A19 Sha L. and Hong P. (2017) Neural Knowledge Tracing. In: Frasson C., Kostopoulos G. (eds) Brain Function Assessment in Learning. BFAL 2017. Lecture Notes in Computer Science, vol 10512. Springer, Cham

A18 Pardos, Z.A., Dadu, A. (2017) Imputing KCs with Representations of Problem Content and Context. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP'17). Bratislava, Slovakia. ACM. Pp. 148-155. http://dl.acm.org/authorize?N31523 

A17 Rochelle, J., Feng, M., Murphy, R., Mason, C. & Fairman, J. (2017) Rigor and Relevance in an Efficacy Study of an Online Mathematics Homework Intervention Intervention .  Paper presented at The Society for Research on Educational Effectiveness Spring Conference.  Presented March 2nd 2017.  Slides

A16 Zhang, J., Shi, X.,  King, I., & Yeung, D. (2016) Dynamic Key-Value Memory Network for Knowledge Tracing. Retrieved from https://arxiv.org/pdf/1611.08108.pdf 

A15 Rochelle, J., Feng, M., Murphy, R. & Mason, C. (2016). Online Mathematics Homework Increases Student Achievement.  AERA OPEN. October-December 2016, Vol. 2, No. 4, pp. 1–12.   DOI: 10.1177/2332858416673968

A14 Feng, M. & Roschelle, J. (2016) Predicting Students' Standardized Test Scores Using Online Homework. L@S 2016: 213-216  

A13  Khajah, M., Lindsey, R., & Mozer, M. (2016) How Deep is Knowledge Tracing?   In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining.  pp 94-101

A12Wilson, K., Karklin, Y., Han, B., &  Ekanadham, C. (2016) Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation.  In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining.  pp 539-544.

A11Xiong, X.,  Zhao, S., Vaninwegen, E. & Beck, J. (2016) Going Deeper with Deep Knowledge Tracing.  In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining.  pp 94-101

A10 Feng, M., Roschelle, J., Mason, C. & Bhanot, R. (2016) Investigating Gender Difference on Homework in Middle School Mathematics.  In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining. pp 364-369.

A9  Xing, W., & Goggins, S. (2015, March). Learning analytics in outer space: a Hidden Naïve Bayes model for automatic student off-task behavior detection. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 176-183). ACM.

A8  Wan, H. & Beck, J. (2015) Considering the influence of prerequisite performance onwheel spinning.  In Romero, C. and Pechenizkiy, M. (eds.) Proceedings of the 8th International Conference Educational Data Mining. Madrid, Spain.

A7  Tang, S., Gogel, H., McBride, E., Pardos, Z.A. (2015) Desirable Difficulty and Other Predictors of Effective Item Orderings. In Romero, C. and Pechenizkiy, M. (eds.) Proceedings of the 8th International Conferenceon Educational Data Mining. Madrid, Spain. Pages 416-419.

A6  Piech, C.,  Spencer, J., Huang, J.,   Ganguli, S.,   Sahami, M.,  Guibas, L. &  Sohl-Dickstein, J. (2015) Deep Knowledge Tracing.  Neural Information Processing Systems (NIPS) 2015 Retrieved from http://arxiv.org/pdf/1506.05908.pdf  

A5  Tan, Ling, Sun, Xiaoxun, & Kho, Siek Toon (2014).  Can Engagement be Compared? Measuring Academic Engagement for Comparison In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining. pp. 213-216.

A4  Galyardt, A. & Goldin, I. (2014). Recent-Performance Factors AnalysisIn Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining.  pp. 411-412 [pdf]

A3  Feng, M. (2014)Towards Uncovering the Mysterious World of Math Homework.  Proceedings of the 7th International Conference on Educational Data Mining. EDM 2014.  pp 425-426.

A2  Schultz, S. & Arroyo, I. (2014). Tracing Knowledge and Engagement in Parallel in an Intelligent Tutoring System.  In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining. pp. 312-315.

A1  Pardos, Z, Wang, Q, & Trivedi, S. (2012)  The real world significance of performance prediction EDM 2012: 192-195.


Heffernan, N., (2019)