| Using data-driven discovery of better student models to improve student learning KR Koedinger, JC Stamper, EA McLaughlin, T Nixon Artificial Intelligence in Education: 16th International Conference, AIED …, 2013 | 140 | 2013 |
| Group communication analysis: A computational linguistics approach for detecting sociocognitive roles in multiparty interactions NMM Dowell, TM Nixon, AC Graesser Behavior research methods 51, 1007-1041, 2019 | 137 | 2019 |
| A Method for Finding Prerequisites Within a Curriculum. A Vuong, T Nixon, B Towle EDM, 211-216, 2011 | 91 | 2011 |
| Reducing the Knowledge Tracing Space. S Ritter, TK Harris, T Nixon, D Dickison, RC Murray, B Towle International Working Group on Educational Data Mining, 2009 | 87 | 2009 |
| Avoiding problem selection thrashing with conjunctive knowledge tracing K Koedinger, PI Pavlik Jr, J Stamper, T Nixon, S Ritter Educational data mining 2011, 2010 | 55 | 2010 |
| Predicting standardized test scores from cognitive tutor interactions S Ritter, A Joshi, S Fancsali, T Nixon Educational Data Mining 2013, 2013 | 46 | 2013 |
| Revealing the learning in learning curves RC Murray, S Ritter, T Nixon, R Schwiebert, RGM Hausmann, B Towle, ... International Conference on Artificial Intelligence in Education, 473-482, 2013 | 35 | 2013 |
| Optimal and worst-case performance of mastery learning assessment with bayesian knowledge tracing S Fancsali, T Nixon, S Ritter Educational Data Mining 2013, 2013 | 30 | 2013 |
| Better data beats big data M Yudelson, S Fancsali, S Ritter, S Berman, T Nixon, A Joshi Educational data mining 2014, 2014 | 24 | 2014 |
| The Complex Dynamics of Aggregate Learning Curves. T Nixon, S Fancsali, S Ritter EDM, 338-339, 2013 | 14 | 2013 |
| Computational linguistic analysis of learners' discourse in computer-mediated group learning environments NMM Dowell, T Nixon US Patent 11,170,177, 2021 | 13 | 2021 |
| Simulated students, mastery learning, and improved learning curves for real-world cognitive tutors SE Fancsali, T Nixon, A Vuong, S Ritter AIED 2013 Workshops Proceedings Volume 4, 11, 2013 | 13 | 2013 |
| Toward “hyper-personalized” Cognitive Tutors SE Fancsali, S Ritter, J Stamper, T Nixon AIED 2013 Workshops Proceedings Volume 7, 71-79, 2013 | 13 | 2013 |
| Generalizing and extending a predictive model for standardized test scores based on cognitive tutor interactions A Joshi, S Fancsali, S Ritter, T Nixon, S Berman Educational Data Mining 2014, 2014 | 6 | 2014 |
| Fair blame assignment in student modeling KR Koedinger, PI Pavlik Jr, J Stamper, T Nixon, S Ritter Proceedings of the 4th International Conference on Educational Data Mining …, 2011 | 5 | 2011 |
| Predicting the effects of skill model changes on student progress D Dickison, S Ritter, T Nixon, TK Harris, B Towle, RC Murray, ... International conference on intelligent tutoring systems, 300-302, 2010 | 4 | 2010 |
| Learning curve disaggregation by student mastery S Ritter, T Nixon, R Murray, R Schwiebert, S Fancsali US Patent App. 14/326,327, 2015 | 3 | 2015 |
| Via: Using GOMS to improve authorware for a virtual internship environment AC Graesser, TM Nixon, AJ Hampton, SE Franklin, JB Love End-user considerations in educational technology design, 205-216, 2018 | 2 | 2018 |
| Exploring Optimal Conditions of Instructional Guidance in an Algebra Tutor. HS Lee, JR Anderson, SR Berman, J Ferris-Glick, A Joshi, T Nixon, ... Society for Research on Educational Effectiveness, 2013 | 2 | 2013 |
| Massively Scalable EDM with Spark. T Nixon EDM, 615, 2016 | | 2016 |