Bridging the Bridge to Algebra

The purpose of this project was to develop an automated tutorial intervention that was intended to improve learning in a pre-algebra curriculum. In particular, the goal of this intervention was to help children learn new complex math skills by attending to prerequisite math skills.

Measuring and Optimizing the Influence of Prerequisite Skills on a Pre-Algebra Curriculum

Papers available here at People ​link above.

  • Pavlik Jr., P. I., Cen, H., Wu, L., & Keodinger, K. R. (2008). Using item-type performance covariance to improve the skill model of an existing tutor. In R. S. J. d. Baker & J. E. Beck (Eds.), Proceedings of the 1st International Conference on Educational Data Mining. Montreal, Canada.
  • Pavlik Jr., P. I., Cen, H., & Koedinger, K. R. (2009). Learning factors transfer analysis: Using learning curve analysis to automatically generate domain models. In T. Barnes, M. Desmarais, C. Romero & S. Ventura (Eds.), Proceedings of the The 2nd International Conference on Educational Data Mining (pp. 121-130). Cordoba, Spain.
  • Pavlik Jr., P. I., Cen, H., & Koedinger, K. R. (2009). Performance factors analysis -- A new alternative to knowledge tracing. In V. Dimitrova & R. Mizoguchi (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence in Education. Brighton, England.
  • Pavlik Jr., P. I., & Toth, J. (2010). How to Build Bridges between Intelligent Tutoring System Subfields of Research. In J. Kay, V. Aleven & J. Mostow (Eds.), Proceedings of the 10th International Conference on Intelligent Tutoring Systems, Part II (pp. 103-112). Pittsburgh, PA: Springer, Heidelberg.
  • Yudelson, M., Pavlik Jr., P. I., & Koedinger, K. R. (2011). Towards better understanding of transfer in cognitive models of practice. Proceedings of the 3rd International Conference on Educational Data Mining (pp. 373-374), Eindhoven, Netherlands.
  • Yudelson, M., Pavlik Jr., P. I., & Koedinger, K. R. (2011). User modeling – a notoriously black art. In J. Konstan, R. Conejo, J. Marzo & N. Oliver (Eds.), User modeling, adaption and personalization (Vol. 6787, pp. 317-328): Springer Berlin / Heidelberg.
  • Pavlik Jr., P. I., Yudelson, M., & Koedinger, K. (2011). Using contextual factors analysis to explain transfer of least common multiple skills. In G. Biswas, S. Bull, J. Kay & A. Mitrovic (Eds.), Artificial intelligence in education (Vol. 6738, pp. 256-263): Springer Berlin / Heidelberg.
  • Koedinger, K., Pavlik Jr., P. I., Stamper, J., Nixon, T., & Ritter, S. (2011). Fair blame assignment in student modeling. Proceedings of the 3rd International Conference on Educational Data Mining (pp. 91-100), Eindhoven, Netherlands.
  • Pavlik Jr., P. I., Yudelson, M., & Koedinger, K. R. (2015). A Measurement Model of Microgenetic Transfer for Improving Instructional Outcomes. International Journal of Artificial Intelligence in Education, 1-34. 

Details of grant at US Department of Education.

Prezi's

Pavlik Jr., P. I., & Toth, J. (2010). How to Build Bridges between Intelligent Tutoring System Subfields of Research. In J. Kay & V. Aleven (Eds.), Proceedings of the 10th International Conference on Intelligent Tutoring Systems. Pittsburgh, PA.

Pavlik Jr., P. I. (2010). Integrating Perceptual Factors into Applied Learning Research. In E. Albro (Ed.), Symposium: Perceptual Characteristics and Concept Mastery: What Makes a Difference? Boston: American Psychology Association 22nd Annual Convention.