IES 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.

• 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.

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.

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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.

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