We have solicited input from the field on topics that researchers would like to be able to run inside of the ASSISTments Testbed. The Following table reflect Dr Heffernan categorization of the group of researchers listed in bold with a representive publication.
Adams, D. M., McLaren, B. M., Durkin, K., Mayer, R. E., Rittle-Johnson, B., Isotani, S., & Van Velsen, M. (2014). Using erroneous examples to improve mathematics learning with a web-based tutoring system. Computers in Human Behavior, 36, 401-411.
Aleven, V.A., Koedinger, K.R. (2002) An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive science 26 (2), 147-179 https://www.sciencedirect.com/science/article/pii/S0364021302000617
Baker, R.S.J.d., Corbett, A.T., Koedinger, K.R., Evenson, S.E., Roll, I., Wagner, A.Z., Naim, M., Raspat, J., Baker, D.J., Beck, J. (2006) Adapting to When Students Game an Intelligent Tutoring System. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 392-401.
Bernacki, M.L., Byrnes, J.P. & Cromley, J.G. (2012). The effects of achievement goals and self-regulated learning behaviors on reading comprehension in technology-enhanced learning environments. Contemporary Educational Psychology. 37 (2): 148-161.
Booth, J.L., Lange, K.E., Koedinger, K.R. & Newton, K.J. (2013). Using example problems to improve student learning in algebra: Differentiating between correct and incorrect examples. Learning and Instruction. 25: 24-34.
Butler, A.C., & Roediger, H.L., III. (2007). Testing improves long-term retention in a simulated classroom setting. European Journal of Cognitive Psychology. 19: 514-527.
Choi, H.N. Dowell, C. Brooks, S. Teasley (2019). Social Comparison in MOOCs: Perceived SES, Opinion, and Message Formality. 9th International Conference on Learning Analytics and Knowledge 2019 (LAK19). March, 2019. Tempe, AZ.
Clinton, V., Walkington, C., & Sparks, A. (2019). The effect of modifying the language of mathematics story problems on problem-solving measures. Presented at The 2019 Annual Meeting of the American Educational Research Association, Toronto, Canada.
Cordes, S., Williams, C.L., & Meck, W.H. (2007). Common representations of abstract quantities. Current Directions in Psychological Science. 16 (3): 156-161.
Hurst, M. A., Cordes, S. (2018) Labeling Common and Uncommon Fractions Across Notation and Education. Proceeding of Cognitive Science. 1841-1846 Retrieved from http://mindmodeling.org/cogsci2018/papers/0354/index.html
Fyfe, E.R., Rittle-Johnson, B. & DeCaro, M.S. (2012). The effects of feedback during exploratory mathematics problem solving: Prior knowledge matters. Journal of Educational Psychology. 104: 1094- 1108.
Kelly, K., Heffernan, N., Heffernan, C., Goldman, S., Pellegrino, G. & Soffer, D. (2013). Estimating the Effect of Web-Based Homework. In Lane, Yacef, Motow & Pavlik (Eds) The Artificial Intelligence in Education Conference. pp. 824-827.
Kizilcec, R., Saltarelli, A., Reich, J. & Cohen, G. (2017) Closing global achievement gaps in MOOCs. Science 355. American Association for the Advancement of Science. Pp. 251-252. Retrieved from http://science.sciencemag.org/content/sci/355/6322/251.full.pdf
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.
Mozer, M, & Pashler, H. (2009) Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory. Neural Information Processing Systems Conference (NIPS). Retrieved from https://papers.nips.cc/paper/3731-predicting-the-optimal-spacing-of-study-a-multiscale-context-model-of-memory
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology. 45 (3): 487-501.
Ogan, A., Finkelstein, S., Walker, E., Carlson, R. Cassel, J., (2012). Rudeness and rapport: Insults and learning gains in peer tutoring. International Conference on Intelligent Tutoring Systems, 11-21. Retrieved from: http://articulab.hcii.cs.cmu.edu/wordpress/wp-content/uploads/2015/09/ITS2012.pdf
Rafferty, A.N. & Griffiths, T.L. (2014). Diagnosing algebra understanding via bayesian inverse planning.” In Proceedings of the 7th International Conference on Educational Data Mining. 351- 352.
Roediger, H.L. & Karpicke, J.D. (2006). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science. 1 (3): 181-210.
Roll, I., Holmes, N.G., Day, J., & Bonn, D. (2012). Evaluating metacognitive scaffolding in guided invention activities. Instructional Science. Springer Netherlands. 40 (4): 691-710.
Shute, V.J. (2008). Focus on formative feedback. Review of Educational Research, 78, 153-189.
Stamper, J., Eagle, M., Barnes, T. & Croy, M. (2013). Experimental evaluation of automatic hint generation for a logic tutor. In Biswas, Bull, Kay, & Mitrovic Proceedings of the 15th International Conference on Artificial Intelligence in Education. Springer Berlin Heidelberg. 6738: 345-352.
Walker, E., Rummel, N., & Koedinger, K.R. (2011). Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity. International Journal of Computer-Supported Collaborative Learning. Springer US. 6 (2): 279-306.
Walkington, C. (2013). Using learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105 (4): 932-945.
Ostrow & Heffernan (2018) Results of a survey of interested researchers.