Prior studies

Drs. Baker, Heffernan, & Ocumpaugh have conducted several studies related to student affect (as inferred by Baker's team).

1. Paper reporting on affect measures available in the data set:
   • San Pedro, M., Baker, R., Gowda, S., & Heffernan, N. (2013). Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System. In Lane, Yacef, Mostow & Pavlik (Eds) The Artificial Intelligence in Education Conference. Springer-Verlag. pp. 41-50.
2. First prediction paper, showing that ASSISTments data (and the variables available in this data set) predict state test scores:
   • Pardos, Z.A., Baker, R.S.J.d., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2014) Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End‐of‐Year Learning Outcomes. Journal of Learning Analytics, 1(1), 107–128.
     1. First appeared as Pardos, Z.A., Baker, R.S.J.d., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2013) Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge, 117-124.
3. Second prediction paper, showing that these variables predict who enrolls in college several years after using ASSISTments:
   • San Pedro, M., Baker, R., Bowers, A. & Heffernan, N. (2013) Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School. In S. D'Mello, R. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining
4. Third prediction paper, showing that these variables can predict college major:
   • San Pedro, M., Ocumpaugh, J., Baker, R., & Heffernan, N. (2014) Predicting STEM and Non-STEM College Major Enrollment from Middle School Interaction with Mathematics Educational Software. In John Stamper et al. (Eds) Proceedings of the 7th International Conference on Educational Data Mining. pp. 276-279. A longer version is here
5. Additional paper, exploring role of gaming the system in college major:
   • 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. pp 36-40.
6. Paper exploring the degree to which affect models generalize across students from urban, suburban, and rural areas:
   • 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. OI: 10.1111/bjet.12156
7. Recent papers presenting enhancements to affective models:
   • 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. pp 31-35. See data here and here.
   • Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2017, June). Improving Sensor-Free Affect Detection Using Deep Learning. Proceedings of the Eighteenth International Conference on Artificial Intelligence in Education .
8. If you are interested in the method used to collect measures of behavior and affect (which were used to create affect models), you may want to look at the BROMP training manual.
   • Ocumpaugh, J., Baker, R.S., Rodrigo, M.M.T. (2015) Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP) 2.0 Technical and Training Manual. Technical Report. New York, NY: Teachers College, Columbia University. Manila, Philippines: Ateneo Laboratory for the Learning Sciences.