Papers That Use ASSISTments Data

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.  If you know of a paper I should add here please email me as I would love to know.  

A16 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 

A15 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

A14 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 

A13 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.

A12 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

A11 Feng, M. & Roschelle, J. (2016) Predicting Students' Standardized Test Scores Using Online Homework. L@S 2016: 213-216  

A10  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
A9 Wilson, 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.
A8  Xiong, 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
A7 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.
A6  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. 
A5  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.
A4  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  
A3  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.
A2  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]
A1    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.


Papers that use ASSISTments to run studies (typically a randomized controlled trials).  Neil is not an author of.   Neil is proud of the fact that we  let others propose studies to be run on our shared scientific instrument.

RCT1  Fyfe, E.  (2016) Providing feedback on computer-based algebra homework in middle-school classrooms. Computers in Human Behavior 63. 568-574

RCT2  Koedinger, K. &  McLaughlin, E. (2016) Closing the Loop with Quantitative Cognitive Task Analysis.  In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining.  Pp 412-417. 

RCT3 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. 


Other recent  randomized controlled trials where Heffernan contributed but the first author is not at WPI.

RCT1 Inventado, P., Scupelli, P., Van Inwegen, E., Ostrow, K., Heffernan, N., Ocumpaugh, J., Baker, R., Slater, S. & Almeda, M. (2016) Availability Slows Completion Times in Summer Work.  In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining.  pp 388-394

RCT2 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. 


 

Papers that describe how a professor at UCCS used ASSISTments in an Math Methods classroom

A1      McGuire, P. (2013). Using online error analysis items to support preservice teachers’ pedagogical content knowledge in mathematics. Contemporary Issues in Technology and Teacher Education13(3). Retrieved from http://www.citejournal.org/vol13/iss3/mathematics/article1.cfm