Faculty and Staff Profiles

Associate Professor, Cognitive Area Graduate Director, and Director of the Optimal Learning Lab.

Website - http://optimallearning.org/

• Certificate Cognitive Neuroscience - Carnegie Mellon University - 2005
• PhD Cognitive Psychology - Carnegie Mellon University - 2005
• BA Economics - University of Michigan - 1992

• Associate Professor, Institute for Intelligent Systems and Psychology - University of Memphis - 2017-current
• Assistant Professor, Institute for Intelligent Systems and Psychology - University of Memphis - August 2011-2017
• Systems Scientist, Human Computer Interaction Institute - Carnegie Mellon University - July 2008 – August 2011
• Postdoctoral Researcher, Human Computer Interaction Institute - Carnegie Mellon University - September 2005 – July 2008

• Best Paper - Automated Search for Logistic Knowledge Tracing Models - Proceedings of The 16th International Conference on Educational Data Mining, India - 2023
• PI Millionaire Achievement - University of Memphis - 2020
• First-Time Principal Investigator Award - University of Memphis - 2013
• Best Paper - An ACT-R model of the spacing effect - Proceedings of the Fifth International Conference of Cognitive Modeling, Germany - 2003

• Psych 7213/8213 Cognitive Science - University of Memphis
• Psych 7222/8222: Human Memory - University of Memphis
• Psych 4305: Mind, Brain, and Intelligence - University of Memphis
• Psych 7302/8302: Advanced Statistics in Psychology I - University of Memphis
• Psych 7514/8514: Cognitive Science Seminar, "Generalization and discrimination of categories and concepts in the transfer of learning" - University of Memphis
• Psych 7503/8503: Seminar in Experimental Psychology, “Adaptive Learner Modeling” - University of Memphis
• Research Method for the Learning Sciences (with Ken Koedinger), 85-748 - Carnegie Mellon University

• PhD Candidate - Hannah-Joy Simms - current
• PhD Candidate - Meng Cao - current
• PhD Candidate - Wei Chu - current
• Masters General Psychology Candidate - Blake Telfer - current
• Masters General Psychology Candidate - James Haner - current
• Masters Gerneral Psychology Prgorqam - Kaitlyn Peperone - 2020
• PhD - Jaclyn Maass - 2017
• PhD - Kenneth Barideaux - 2017
• PhD - Chanda Murphy - 2017
• Masters General Psychology Candidate - Clayton Estey - 2015
• PhD - Henry Hua - 2015
• Masters General Psychology Candidate - Shardae Dawkins - 2015
• Masters General Psychology Candidate - Adam Boyd - 2014

• Designed and published the R package LKT (Logistic Knowledge Tracing) a general application for student modeling. - 2019-current - University of Memphis - NSF and Dept of Education
• Design of the MoFaCT system to supersede and replace FaCT system. New system is more powerful and easier to use. - 2015-current - University of Memphis - University of Memphis and Dept of Education
• Design and creation of FaCT system for mathematically optimized practice scheduling online and experimental research on learning in the laboratory - 2005-2015 - Carnegie Mellon and University of Memphis - Multiple sources

• Improving STEM K-12 learning using optimal spaced retrieval in Podsie, an existing adaptive educational technology platform (CoPI) - NSF: subcontract with Carnegie Mellon University - 226K - 08/01/23-07/31/27
• Using Adaptive Practice to Improve Recall and Understanding in Postsecondary Anatomy and Physiology (PI) - IES: US Department of Education - $1,173,820 - 2019-2023
• The Learner Data Institute: Harnessing The Data Revolution To Make The Learning Ecosystem More Effective, Efficient, and Engaging (CoPI) - NSF - $2,605,586 - 2020-2023
• Schmidt University-Industry Postdoc Fellowships - Eric Schmidt Educational Foundation (CMU subcontract) - 200K - 2018-2019 (2 years)
• Building a Scalable Infrastructure for Data-Driven Discovery and Innovation in Education (PI) - NSF subcontract with Carnegie Mellon University - 750K - 2015-2019
• Shareable Knowledge Objects (SKO) as Enhanced, Portable ITS Modules (Co-PI) - Office of Naval Research - $1,477,402 - 2012-2014
• Generalized Intelligent Framework for Tutors (Senior Scientist) - Army Research Laboratory - $1,289,545 - 2012-2016
• Motivational Effects in Vocabulary Learning: Difficulty and Strategy Use (PI) - NSF: Pittsburgh Science of Learning Center - approx $70K - 11/2009-8/2011
• Bridging the Bridge to Algebra: Measuring and Optimizing the Influence of Prerequisite Skills on a Pre-Algebra Curriculum (PI) - IES: US Department of Education - $1,121,000 - 8/2007–8/2012
• Providing Optimal Support for Robust Learning of Syntactic Constructions in ESL (Co-PI) - NSF: Pittsburgh Science of Learning Center - approx $50K - 9/2006–8/2007
• Postdoctoral Research Grant with Kenneth Koedinger (PI) - Privately funded by Ronald Zdrojkowski - $411,000 - 8/2005–8/2008
• Dynamics of Metacognition and Motivation (PI) - NSF: Pittsburgh Science of Learning Center, Subcontract from Carnegie Mellon University - $56,279 - 10/2012–10/2013

• Memphis Comic and Fantasy Convention, Geek 101 demonstrations - Students from regional schools - 2022 -  
• 69th Pittsburgh Regional Science and Engineering Fair - Regional High School Students - April 4, 2008 - Business and Industry sponsored
• 70th Pittsburgh Regional Science and Engineering Fair - Regional High School Students - April 3, 2009 - Business and Industry sponsored

• Journal of Education Data Mining - Editorial Board - 2020-2024
• University of Memphis - Cognitive Area Graduate Director - 2016-current
• University of Memphis - Diversity,Equity, and Inclusion Department Committee - 2018-2022
• University of Memphis - Gradschoolmatch.com coordinator - 2015- 2018
• University of Memphis - SONA (Psychology Subject Pool) administrator - 2012-2013
• University of Memphis - CRISTAL Representative for Institute of Intelligent Systems - 2012-2017
• University of Memphis - ACAD (College Readiness Course) University Planning Committee - 2014
• 6th International Conference on Educational Data Mining, Memphis - Local Co-Chair - 2013
• 16th International Conference on Artificial Intelligence in Education, Memphis - Local Co-Chair and Proceedings Editor - 2013
• 3rd International Conference on Educational Data Mining, Carnegie Mellon - Program Committee Co-Chair - 2010
• Transactions on Learning Technologies - Associate Editor - 2014-2019
• National Science Foundation: Perception, Action and Cognition - Grant review - ad hoc
• Archives of Scientific Psychology - Journal review - ad hoc
• Perceptual and Motor Skills - Journal review - ad hoc
• Applied Cognitive Psychology - Journal review - ad hoc
• Developmental Science - Journal review - ad hoc
• PLOS ONE - Journal review - ad hoc
• Journal of Experimental Psychology: General - Journal review - ad hoc
• Cognitive Science - Journal review - ad hoc
• Psychological Review (with Lynne Reder) - Journal review - ad hoc
• Journal of Machine Learning Research (KDD cup issue) - Journal review - ad hoc
• Journal of Educational Data Mining - Journal review - ad hoc
• International Journal of Artificial Intelligence in Education - Journal review - ad hoc
• Learning and Instruction - Journal review - ad hoc
• Journal of Experimental Psychology: Applied - Journal review - ad hoc
• Memory and Cognition - Journal review - ad hoc
• Journal of Educational Psychology - Journal review - ad hoc
• User Modeling and User-Adapted Interaction - Journal review - ad hoc
• Learning and Instruction - Journal review - ad hoc
• Transactions on Learning Technologies - Journal review - ad hoc
• 16th International Conference on Artificial Intelligence in Education, Memphis - Program Committee - 2013
• 6th International Conference on Educational Data Mining, Memphis - Program Committee - 2013
• Cognitive Science Conference - Program Committee - 2014
• Society for Research in Educational Effectiveness - Conference review - ad hoc
• Proceedings of the National Academy of Sciences - Conference review - ad hoc
• International Conference of Cognitive Modeling - Conference review - ad hoc
• Cognitive Science Society Conference - Conference review - ad hoc
• International Conference of Educational Data Mining - Conference review - ad hoc
• International Conference on Development and Epigenetic Robotics - Conference review - ad hoc
• ACM Conference on Human Factors in Computing Systems (CHI) - Conference review - ad hoc
• Artificial Intelligence in Education (AIED) Conference - Conference review - ad hoc
• Institute for Intelligent Systems Cognitive Science Seminar (UofM) - Speaker - Approx once yearly since 2011
• Psychology Dept. Graduate First Year Colloquium (UofM) - Speaker - Approx once yearly since 2011
• Works in Progress Symposium (UofM) - Local conference review - Approx once yearly since 2011
• Optimal Learning Lab - to discuss research reports and provide methods tutorials - Director - Since 2011 weekly meetings
• Cognitive Science Society - Member - 2002–2011
• Association for Behavior Analysis - Member - 2007-2008
• Psychonomic Society - Member - 2007-2009, 2013-current
• International Artificial Intelligence in Education Society - Member - 2008-current
• International Educational Data Mining Society - Member - 2008-current

• RAND Corporation - 2022
• McGraw Hill - 2017-2018
• K12 Inc. - 2009-2010
• Drill Sargent Website - 2010-2011
• Kaplan Inc. - 2012
• Podsie.org - 2020-Current

• Pavlik Jr, P. I., Maass, J. K., & Kim, J. W. (2017). Assessment of Forgetting. In R. Sottilare, A. Graesser, X. Hu, & G. Goodwin (Eds.), Design Recommendations for Intelligent Tutoring System-Volume 5: Assessment Methods (Vol. 5, pp. 203-208). Book chapter
• Goldin, I., Pavlik Jr, P. I., & Ritter, S. (2016). Discovering domain models in learning curve data. In R. A. Sottilare, A. C. Graesser, X. Hu, A. Olney, B. D. Nye, & A. M. Sinatra (Eds.), Design Recommendations for Intelligent Tutoring Systems: Volume 4-Domain Modeling (Vol. 4, pp. 115-126). Book chapter
• Olney, A. M., Brawner, K. W., Pavlik Jr., P. I., & Koedinger, K. R. (2015). Emerging Trends in Automated Authoring. In R. A. Sottilare, A. Graesser, H. Xiangen, & K. W. Brawner (Eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems: Authoring Tools (Volume 3). Army Research Labs/ University of Memphis. Book chapter
• Pavlik Jr., P. I., Hu, X., & Morrison, D. M. (2014). Issues Regarding the Use of Natural Language Discourse In Intelligent Tutoring Systems. In R. A. Sottilare, A. Graesser, X. Hu, & H. K. Holden (Eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems: Instructional Management (Vol. 2, pp. 185-187). Army Research Labs/ University of Memphis. Book chapter
• Pavlik Jr., P. I., Brawner, K. W., Olney, A., & Mitrovic, A. (2013). A Review of Learner Models Used in Intelligent Tutoring Systems In R. A. Sottilare, A. Graesser, X. Hu, & H. K. Holden (Eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems: Learner Modeling (Vol. 1, pp. 39-68). Army Research Labs/ University of Memphis. Book chapter
• Pavlik Jr., P. I. (2007). Timing is an order: Modeling order effects in the learning of information. In F. E., Ritter, J. Nerb, E. Lehtinen, & T. O'Shea (Eds.), In order to learn: How order effects in machine learning illuminate human learning (pp. 137–150). Oxford University Press. Book chapter
• Lane, H. C., Yacef, K., Mostow, J., & Pavlik Jr., P. I. (2013). Artificial Intelligence in Education: 16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013. Proceedings: Springer Publishing Company, Incorporated. Proceedings volume
• Baker, R. S. J. d., Merceron, A., & Pavlik Jr., P. I. (2010). Proceedings of the 3rd International Conference on Educational Data Mining: International Educational Data Mining Society. Proceedings volume

• Pavlik Jr., P. I. (2003). Review of Dynamical Cognitive Science. Brain and Cognition, 51, 155-156. Book review
• Pavlik Jr., P. I. (2001). Hybrid modeling of cognition: A review of The Atomic Components of Thought. Brain and Cognition, 47, 570-573. Book review

• Cao, M., Pavlik Jr, P. I., Chu, W., & Zhang, L. (2024). Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Training Sequences on Category Learning. In B. Paassen & C. D. Epp (Eds.), Proceedings of The 17th International Conference on Educational Data Mining. Short paper

• Cao, M., & Pavlik Jr, P. I. (2024). Exploring the Effects of Category Similarity, Spacing, and Block Size on Category Learning. Frontiers in Psychology. Journal article impact factor 2.6
• Stamper, J., Moore, S., Rose, C., Pavlik Jr, P. I., & Koedinger, K. (2024). LearnSphere: A Learning Data and Analytics CyberInfrastructure. Journal of Educational Data Mining. Journal article impact factor 2.3
• Chu, W., & Pavlik Jr, P. I. (2024). Distributed Retrieval Practice and Picture Illustrations: Improving Initial Aural Foreign Vocabulary Learning. Journal of Applied Research in Memory and Cognition. Journal article impact factor 4.2
• Zhang, L., Pavlik, P. I., Hu, X., Cockroft, J. L., Wang, L., & Shi, G. (2023). Exploring the Individual Differences in Multidimensional Evolution of Knowledge States of Learners. In 5th International Conference, AIS 2023, (pp. 265-284). Springer Nature Switzerland. Paper
• Pavlik Jr, P. I., & Eglington, L. G. (2023). Automated Search for Logistic Knowledge Tracing Models. In F. Mingyu, K. Tanja, & T. Partha (Eds.), Proceedings of The 16th International Conference on Educational Data Mining (pp. 17-27). https://doi.org/10.5281/zenodo.8115673 Paper 17% acceptance rate
• Chu, W., & Philip, I. P., Jr. (2023). The Predictiveness of PFA is Improved by Incorporating the Learner’s Correct Response Time Fluctuation. In F. Mingyu, K. Tanja, & T. Partha (Eds.), Proceedings of The 16th International Conference on Educational Data Mining (pp. 244-250). https://doi.org/10.5281/zenodo.8115643 Short paper 28% acceptance rate
• Cao, M., & Pavlik Jr, P. I. (2022). A Variant of Performance Factors Analysis Model for Categorization. In A. Mitrovic & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (pp. 763-766). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.6852974 Poster proceedings
• Pavlik Jr., P. I., & Zhang, L. (2022). Using autoKC and Interactions in Logistic Knowledge Tracing. In Proceedings of The Third Workshop of the Learner Data Institute, The 15th International Conference on Educational Data Mining (EDM 2022) (pp. 1-6). Short paper
• Pavlik Jr., P. I., & Eglington, L. (2021a). The Mobile Fact and Concept Textbook System (MoFaCTS) Computational Model and Scheduling System. In 22nd International Conference on Artificial Intelligence in Education (AIED 2021) Third Workshop on Intelligent Textbooks (pp. 1-15). In CEUR workshop proceedings (Vol. 2895). Paper
• Pavlik Jr, P. I., Eglington, L., & Zhang, L. (2021). Automatic Domain Model Creation and Improvement. In C. Lynch, A. Merceron, M. Desmarais, & R. Nkambou (Eds.), Proceedings of The 14th International Conference on Educational Data Mining (pp. 672-676). International Educational Data Mining Society. Poster proceedings
• Pavlik Jr., P. I., & Eglington, L. (2021b). Modeling the EdNet Dataset with Logistic Regression. In 35th AAAI Conference on Artificial Intelligence, Imagining Post-COVID Education with AI Workshp (pp. 1-5). Short paper
• Pavlik Jr., P. I., Olney, A. M., Banker, A., Eglington, L., & Yarbro, J. (2020). The Mobile Fact and Concept Textbook System (MoFaCTS). In 21st International Conference on Artificial Intelligence in Education (AIED 2020) Second Workshop on Intelligent Textbooks (pp. 35–49). In CEUR workshop proceedings (Vol. 2674). Paper
• Cao, M., Pavlik Jr, P. I., & Bidelman, G. M. (2019). Incorporating Prior Practice Difficulty into Performance Factor Analysis to Model Mandarin Tone Learning. In C. Lynch, A. Merceron, M. Desmarais, & R. Nkambou (Eds.), Proceedings of the 12th International Conference on Educational Data Mining (pp. 516-519). Poster proceedings
• Hampton, A. J., Nye, B. D., Pavlik, P. I., Swartout, W. R., Graesser, A. C., & Gunderson, J. (2018). Mitigating Knowledge Decay from Instruction with Voluntary Use of an Adaptive Learning System. In Proceedings of the 19th International Conference on Artificial Intelligence in Education (pp. 119-133). Springer International Publishing. Paper 23% acceptance rate
• Pavlik Jr, P. I., Zimmerman, N., & Riedesel, M. (2018). Large Scale Search for Optimal Logistic Knowledge Tracing Features. In K. E. Boyer & M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 584-587). Educational Data Mining Society. Poster proceedings
• Fang, Y., Shubeck, K. T., Lippert, A., Cheng, Q., Shi, G., Feng, S., Gatewood, J., Chen, S., Cai, Z., Pavlik Jr., P. I., Frijters, J. C., Greenberg, D., & Graesser, A. C. (2018). Clustering the Learning Patterns of Adults with Low Literacy Interacting with an Intelligent Tutoring System. In K. E. Boyer & M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 348-354). Educational Data Mining Society. Short paper 41% acceptance rate
• Shi, G., Pavlik Jr., P. I., & Graesser, A. (2017). Using an Additive Factor Model and Performance Factor Analysis to Assess Learning Gains in a Tutoring System to Help Adults with Reading Difficulties. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings for the 10th International Conference on Educational Data Mining (pp. 475-476). Poster proceedings
• Olney, A. M., Pavlik, P. I., & Maass, J. K. (2017). Improving Reading Comprehension with Automatically Generated Cloze Item Practice. In E. André, R. Baker, X. Hu, M. M. T. Rodrigo, & B. du Boulay (Eds.), Proceedings of Artificial Intelligence in Education: 18th International Conference (pp. 262-273). Springer International Publishing. https://doi.org/10.1007/978-3-319-61425-0_22 Paper 30% acceptance rate
• Fang, Y., Nye, B., Pavlik Jr., P. I., Xu, Y., Graesser, A., & Hu, X. (2017). Online Learning Persistence and Academic Achievement. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings for the 10th International Conference on Educational Data Mining (pp. 312-316). Short paper 42% acceptance rate
• Maass, J. K., & Pavlik Jr, P. I. (2016). Modeling the Influence of Format and Depth during Effortful Retrieval Practice. In T. Barnes, M. Chi, & M. Feng (Eds.), The 9th International Conference on Educational Data Mining (pp. 143-150). Paper 28% acceptance rate
• Pavlik Jr., P. I., Kelly, C., & Maass, J. K. (2016). Using the mobile fact and concept training system (MoFaCTS). In A. Micarelli & J. Stamper (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems (pp. 247-253). Springer. Short paper 42% acceptance rate
• Maass, J. K., Pavlik Jr., P. I., & Hua, H. (2015). How Spacing and Variable Retrieval Practice Affect the Learning of Statistics Concepts. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), 17th International Conference on Artificial Intelligence in Education (Vol. 9112, pp. 247-256). Springer International Publishing. Paper
• Nye, B. D., Windsor, A., Pavlik Jr., P. I., Olney, A., Hajeer, M., Graesser, A. C., & Hu, X. (2015). Evaluating the Effectiveness of Integrating Natural Language Tutoring into an Existing Adaptive Learning System. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), 17th International Conference on Artificial Intelligence in Education (Vol. 9112, pp. 743-747). Springer International Publishing. https://doi.org/10.1007/978-3-319-19773-9_106 Poster proceedings
• Forsyth, C., Graesser, A. C., Samei, B., Walker, B., & Pavlik Jr., P. I. (2014). Predicting performance behaviors during question generation in a game-like intelligent tutoring system. In J. Polman, A. Kyza, K. O'Neill, & I. Tabak (Eds.), Proceedings of the International Conference of Learning Sciences (pp. 1611-1612). International Society of the Learning Sciences. Poster proceedings
• Forsyth, C., Graesser, A., Pavlik Jr, P. I., Millis, K., & Samei, B. (2014). Discovering Theoretically Grounded Predictors of Deep vs. Shallow Level Learning. In J. Stamper, Z. A. Pardos, M. Mavrikis, & B. McLaren (Eds.), Proceedings of 7th International Conference on Educational Data Mining (pp. 229-232). Short paper 41% acceptance rate
• Pavlik Jr., P. I., Hua, H., Williams, J., & Bidelman, G. M. (2013). Modeling and Optimizing Forgetting and Spacing Effects during Musical Interval Training. In S. K. D'Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the  6th International Conference of Educational Datamining (pp. 145-152). Paper 25% acceptance rate
• Maass, J. K., & Pavlik Jr., P. I. (2013). Using Learner Modeling to Determine Effective Conditions of Learning for Optimal Transfer. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (Vol. 7926, pp. 189-198). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_20 Paper 33% acceptance rate
• Forsyth, C. M., Graesser, A. C., Walker, B., Millis, K., Pavlik Jr., P. I., & Halpern, D. (2013). Didactic Galactic: Types of Knowledge Learned in a Serious Game. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (Vol. 7926, pp. 832-835). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_124 Poster proceedings
• Pavlik Jr, P. I., Hua, H., Williams, J., & Bidelman, G. M. (2013, July 6–9). Modeling the effect of spacing on musical interval training. Proceedings of 6th International Conference on Educational Data Mining, Memphis, TN. Short paper 45% acceptance rate
• Pavlik Jr., P. I., Maass, J. K., Rus, V., & Olney, A. M. (2012). Facilitating Co-adaptation of Technology and Education through the Creation of an Open-source Repository of Interoperable Code. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K.-K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 677-678). Springer. Poster proceedings
• Forsyth, C. M., Pavlik Jr., P. I., Graesser, A. C., Cai, Z., Germany, M.-l., Millis, K., Butler, H., Halpern, D. F., & Dolan, R. P. (2012). Learning gains for core concepts in a serious game on scientific reasoning. In K.Yacef, O. Zaïane, H. Hershkovitz, M. Yudelson, & J. Stamper (Eds.), Proceedings of the 5th International Conference on Educational Data Mining (pp. 172-175). International Educational Data Mining Society. Short paper 46% acceptance rate
• Pavlik Jr., P. I., Yudelson, M., & Koedinger, K. R. (2011). Using contextual factors analysis to explain transfer of least common multiple skills. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), 15th International Conference Artificial Inteligence in Education (Vol. 6738, pp. 256–263). Springer. https://doi.org/http://doi:10.1007/978-3-642-21869-9_34 Paper 32% acceptance rate
• Koedinger, K. R., Pavlik Jr., P. I., Stamper, J., Nixon, T., & Ritter, S. (2011). Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero , & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 91–100). Paper 33% acceptance rate
• Yudelson, M., Pavlik Jr., P. I., & Koedinger, K. R. (2011a). Towards better understanding of transfer in cognitive models of practice. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero , & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 373–374). Poster proceedings
• Yudelson, M., Pavlik Jr., P. I., & Koedinger, K. R. (2011b). 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. https://doi.org/10.1007/978-3-642-22362-4_27 Paper 22% acceptance rate
• Pavlik Jr., P. I., & Wu, S. (2011). A dynamical system model of microgenetic changes in performance, efficacy, strategy use and value during vocabulary learning. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero , & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 277–282). Short paper 46% acceptance rate
• 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). Springer. Paper 38% acceptance rate
• Pavlik Jr., P. I. (2010). Data Reduction Methods Applied to Understanding Complex Learning Hypotheses. In R. S. J. d. Baker, A. Merceron, & P. I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (pp. 311-312). Poster proceedings
• Pavlik Jr., P. I., Cen, H., & Koedinger, K. R. (2009a). Learning factors transfer analysis: Using learning curve analysis to automatically generate domain models. In T. Barnes, M. C. Desmarais, C. Romero, & S. Ventura (Eds.), Proceedings of the 2nd International Conference on Educational Data Mining (pp. 121–130). Paper 37% acceptance rate
• Pavlik Jr., P. I., Cen, H., & Koedinger, K. R. (2009b). Performance factors analysis -- A new alternative to knowledge tracing. In V. Dimitrova, R. Mizoguchi, B. d. Boulay, & A. Graesser (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 531–538). IOS Press. https://doi.org/http://doi:10.3233/978-1-60750-028-5-531 Paper 29% acceptance rate
• Frishkoff, G., Levin, L., Pavlik Jr., P. I., Idemaru, K., & de Jong, N. (2008). A Model-based Approach to Second-Language Learning of Grammatical Constructions. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Conference of the Cognitive Science Society (pp. 916-921). Paper 74% acceptance rate (poster presentation)
• Pavlik Jr., P. I., Cen, H., Wu, L., & Koedinger, K. R. (2008). Using item-type performance covariance to improve the skill model of an existing tutor. In R. S. Baker & J. E. Beck (Eds.), Proceedings of the 1st International Conference on Educational Data Mining (pp. 77–86). Paper 38% acceptance rate
• Koedinger, K. R., Pavlik Jr., P. I., McLaren, B. M., & Aleven, V. (2008). Is it better to give than to receive? The assistance dilemma as a fundamental unsolved problem in the cognitive science of learning and instruction. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Conference of the Cognitive Science Society (pp. 2155–2160). Paper 74% acceptance rate (poster presentation)
• Pavlik Jr., P. I., Bolster, T., Wu, S., Koedinger, K. R., & MacWhinney, B. (2008). Using optimally selected drill practice to train basic facts. In B. Woolf, E. Aimer, & R. Nkambou (Eds.), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (pp. 593–602). Paper 30% acceptance rate
• Pavlik Jr., P. I., Presson, N., Dozzi, G., Wu, S.-m., MacWhinney, B., & Koedinger, K. R. (2007). The FaCT (Fact and Concept Training) System: A new tool linking cognitive science with educators. In D. McNamara & G. Trafton (Eds.), Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society (pp. 1379–1384). Lawrence Erlbaum. Paper 69% acceptance rate (poster presentation)
• Pavlik Jr., P. I., Presson, N., & Koedinger, K. R. (2007). Optimizing knowledge component learning using a dynamic structural model of practice. In R. Lewis & T. Polk (Eds.), Proceedings of the Eighth International Conference of Cognitive Modeling (pp. 37–42). University of Michigan. Paper
• Pavlik Jr., P. I., & Anderson, J. R. (2004). An ACT-R model of memory applied to finding the optimal schedule of practice. In M. Lovett, C. Schunn, C. Lebiere, & P. Munro (Eds.), Proceedings of the Sixth International Conference of Cognitive Modeling (pp. 376-377). Carnegie Mellon University/University of Pittsburgh. Poster proceedings
• Pavlik Jr., P. I., & Anderson, J. R. (2003). An ACT-R model of the spacing effect. In F. Detje, D. Dorner, & H. Schaub (Eds.), Proceedings of the Fifth International Conference of Cognitive Modeling (pp. 177-182). Universitats-Verlag Bamberg. Paper

• Barideaux, K. J., & Pavlik, P. I. (2023). Enhancing memory recall during video lectures: does the visual display format matter? Educational Psychology, 43(6), 659-678. https://doi.org/10.1080/01443410.2023.2238143 Journal article

• Eglington, L. G., & Pavlik, P. I. (2023). How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences. International Journal of Artificial Intelligence in Education, 33(3), 497-518. https://doi.org/10.1007/s40593-022-00296-0 Journal article
• Pavlik Jr, P. I., & Eglington, L. G. (2023). Automated Search Improves Logistic Knowledge Tracing, Surpassing Deep Learning in Accuracy and Explainability. Journal of Educational Data Mining, 15(3), 58-86. https://doi.org/10.5281/zenodo.10363337 Journal article
• Scruggs, R., Baker, R. S., Pavlik, P. I., McLaren, B. M., & Liu, Z. (2023). How well do contemporary knowledge tracing algorithms predict the knowledge carried out of a digital learning game? Educational Technology Research and Development, 1-18. https://doi.org/10.1007/s11423-023-10218-z Journal article
• Banker, A. M., Pavlik Jr, P. I., Olney, A., & Eglington, L. G. (2022). Online Tutoring System (MoFaCTS) for Anatomy and Physiology: Implementation and Initial Impressions. HAPS Educator, 26(2), 44-54. https://doi.org/10.21692/haps.2022.012 Journal article
• Barideaux Jr, K. J., & Pavlik Jr, P. I. (2021). Can concept maps attenuate auditory distraction when studying with music? Applied Cognitive Psychology, 35(6), 1547-1558. https://doi.org/10.1002/acp.3889 Journal article
• Pavlik, P. I., Eglington, L. G., & Harrell-Williams, L. M. (2021). Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling. IEEE Transactions on Learning Technologies, 14(5), 624-639. https://doi.org/10.1109/TLT.2021.3128569 Journal article
• Eglington, L. G., & Pavlik Jr, P. I. (2020). Optimizing practice scheduling requires quantitative tracking of individual item performance. npj Science of Learning, 5(1), 15. https://doi.org/10.1038/s41539-020-00074-4 Journal article
• Eglington, L., & Pavlik Jr, P. I. (2019). Predictiveness of prior failures is modulated by trial duration. Journal of Educational Data Mining, 11, 1-19. https://doi.org/10.5281/zenodo.3554675 Journal article
• Graesser, A. C., Hu, X., Nye, B. D., VanLehn, K., Kumar, R., Heffernan, C., Heffernan, N., Woolf, B., Olney, A. M., Rus, V., Andrasik, F., Pavlik, P., Cai, Z., Wetzel, J., Morgan, B., Hampton, A. J., Lippert, A. M., Wang, L., Cheng, Q., Vinson, J. E., Kelly, C. N., McGlown, C., Majmudar, C. A., Morshed, B., & Baer, W. (2018). ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics. International Journal of STEM Education, 5(1), 15. https://doi.org/10.1186/s40594-018-0110-y Journal article
• Murphy, C. S., & Pavlik Jr, P. I. (2018). Effects of Spacing and Testing on Inductive Learning. Journal of Articles in Support of the Null Hypothesis, 14(2), 23-39. Journal article
• Nye, B. D., Pavlik, P. I., Windsor, A., Olney, A. M., Hajeer, M., & Hu, X. (2018). SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics. International Journal of STEM Education, 5(1), 12. https://doi.org/10.1186/s40594-018-0109-4 Journal article
• Shi, G., Lippert, A. M., Shubeck, K., Fang, Y., Chen, S., Pavlik, P., Greenberg, D., & Graesser, A. C. (2018). Exploring an intelligent tutoring system as a conversation-based assessment tool for reading comprehension. Behaviormetrika, 45(2), 615-633. https://doi.org/10.1007/s41237-018-0065-9 Journal article
• Koedinger, K. R., Yudelson, M. V., & Pavlik, P. I. (2016). Testing theories of transfer using error rate learning curves. Topics in Cognitive Science, published online. https://doi.org/10.1111/tops.12208 Journal article
• Thiessen, E. D., & Pavlik Jr, P. I. (2016). Modeling the role of distributional information in children’s use of phonemic contrasts. Journal of Memory and Language, 88, 117-132. https://doi.org/10.1016/j.jml.2016.01.003 Journal article
• Li, H., Graesser, A. C., Conley, M., Cai, Z., Pavlik, P. I., & Pennebaker, J. W. (2015). A New Measure of Text Formality: An Analysis of Discourse of Mao Zedong. Discourse Processes, 52(1), 1-28. https://doi.org/10.1080/0163853X.2015.1010191 Journal article
• Medimorec, S., Pavlik Jr, P. I., Olney, A., Graesser, A. C., & Risko, E. F. (2015). The Language of Instruction: Compensating for Challenge in Lectures. Journal of Educational Psychology, 107(4), 971-990. https://doi.org/10.1037/edu0000024 Journal article
• 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, 25, 346-379. https://doi.org/http://doi:10.1007/s40593-015-0039-y Journal article
• Forsyth, C. M., Graesser, A. C., Pavlik Jr., P. I., Cai, Z., Butler, H., Halpern, D. F., & Millis, K. (2013). Operation aries!: Methods, mystery, and mixed models: Discourse features predict affect and motivation in a serious game. Journal of Educational Data Mining, 5(1), 147-189. https://doi.org/10.5281/zenodo.3554615 Journal article
• Pavlik Jr., P. I. (2013). Mining the Dynamics of Student Utility and Strategy Use during Vocabulary Learning. Journal of Educational Data Mining, 5(1), 39-71. https://doi.org/10.5281/zenodo.3554609 Journal article
• Thiessen, E. D., & Pavlik Jr., P. I. (2013). iMinerva: A Mathematical Model of Distributional Statistical Learning. Cognitive Science, 37(2), 310-343. https://doi.org/10.1111/cogs.12011 Journal article
• Pavlik Jr., P. I., & Anderson, J. R. (2008). Using a model to compute the optimal schedule of practice. Journal of Experimental Psychology: Applied, 14(2), 101–117. https://doi.org/10.1037/1076-898X.14.2.101 Journal article
• Pavlik Jr., P. I. (2007). Understanding and applying the dynamics of test practice and study practice. Instructional Science, 35, 407–441. https://doi.org/10.1007/s11251-006-9013-2 Journal article
• Pavlik Jr., P. I., & Anderson, J. R. (2005). Practice and forgetting effects on vocabulary memory: An activation-based model of the spacing effect. Cognitive Science, 29(4), 559–586. https://doi.org/10.1207/s15516709cog0000_14 Journal article

• Telfer, B., Pavlik Jr, P. I., Sabatini, J., & Hollander, J. (2023). Assessment During Learning. 64th Annual Meeting of the Psychonomic Society, SanFrancisco, CA, USA. Poster
• Koedinger, K., Stamper, J., & Pavlik Jr., P. I. (2020). Reproducibility and Replication of Analytic Methods with LearnSphere. In EDM 2020 Tutorials. Tutorial
• Pavlik Jr, P. I. (2019). Instructional engineering for personalized adaptive practice systems EdCrunch 2019, Moscow, Russia. Conference keynote
• Pavlik Jr., P. I., Cao, M., & Eglington, L. (2019). Mathematically Modeling the Optimal Desirable Difficulty. 60th Annual Meeting of the Psychonomic Society, Montreal, Canada. Poster
• Cao, M., & Pavlik Jr, P. I. (2019). Using a Variant of the Performance Factors Analysis Model for Adaptive Training on Mandarin Tones Third International Conference on Artificial Intelligence and Adaptive Education 2019, Beijing, China. Talk
• Koedinger, K., Stamper, J., Carvalho, P. F., Pavlik Jr, P. I., & Eglington, L. (2019). Sharing and Reusing Data and Analytic Methods with LearnSphere. In C. Lynch, A. Merceron, M. Desmarais, & R. Nkambou (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 516-519). Tutorial
• Stamper, J., Koedinger, K., Rose, C., & Pavlik Jr., P. I. (2018). Sharing and Reusing Data and Analytic Methods with LearnSphere. In LAK 2018 Workshops. Workshop
• Koedinger, K., Liu, R., Stamper, J., Thille, C., Pavlik Jr., P. I., & O'Reilly, U.-M. (2017). Community Based Educational Data Repositories and Analysis Tools. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK 2017 Workshops (pp. 524-525). Workshop
• Liu, R., Koedinger, K., Stamper, J., & Pavlik Jr., P. I. (2017). Sharing and Reusing Data and Analytic Methods with LearnSphere. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings for the 10th International Conference on Educational Data Mining (pp. 475-476). Workshop
• Barideaux Jr., K. J., & Pavlik Jr., P. I. (2016, November). Examining the Effects of Studying with Music: Turn off the Verbal Music, Unless You're Studying a Concept Map. 57th Annual Meeting of the Psychonomic Society, Chicago. Poster
• Maass, J. K., & Pavlik Jr., P. I. (2016, November). Discerning Misconceptions through Retrieval Practice. 57th Annual Meeting of the Psychonomic Society, Chicago. Poster
• Stamper, J., Koedinger, K., Pavlik Jr., P. I., Rose, C., Liu, R., Eagle, M., Yudelson, M., & Veeramachaneni, K. (2016). Educational data analysis using LearnSphere workshop. In J. Rowe & E. Snow (Eds.), Proceedings of the EDM 2016 Workshops and Tutorials co-located with the 9th International Conference on Educational Data Mining. http://ceur-ws.org/Vol-1633/ Workshop
• Pavlik Jr., P. I., Maass, J. K., & Hua, H. (2015, November). Redundancy causes spacing effects. 56th Annual Meeting of the Psychonomic Society, Chicago. Poster
• Pavlik Jr, P. (2015, April). Ingredients for a theory of instruction Rice Workshop on Personalized Learning, Rice University, Houston, Texas. Workshop keynote
• Medimorec, S., Schaffer, K. V., Pavlik Jr, P. I., Olney, A., Graesser, A. C., & Risko, E. F. (2014, December). The Language of Lectures: Offsetting Challenging Words. In Canadian Journal of Experimental Psychology (Vol. 68, pp. 257-257). Canadian Psychological Society. Talk with abstract
• Forsyth, C. M., Graesser, A. C., Cai, Z., Pavlik Jr., P. I., Millis, K., & Halpern, D. (2013, April). Learner profiles emerge from a serious game teaching scientific inquiry Annual Meeting of the American Educational Research Association., San Francisco, CA. Talk
• Forsyth, C. M., Millis, K., Pavlik Jr., P. I., & Graesser, A. C. (2013, April). Assessing performance metrics within a serious game Annual Meeting of the American Educational Research Association., San Francisco, CA. Talk
• Barideaux Jr., K. J., Maass, J. K., & Pavlik Jr., P. I. (2013). A Comparison of Concept Maps and Text Summaries: The Benefits for Learning and Transfer. 54th Annual Meeting of the Psychonomic Society, Toronto. Poster
• Pavlik Jr., P. I., Yudelson, M., & Koedinger, K. R. (2011). A method for the microanalysis of pre-algebra transfer. Society for Research on Educational Effectiveness: Fall, Washington DC. Talk with abstract
• 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? American Psychology Association 22nd Annual Convention. Invited talk
• Pavlik Jr., P. I., & Koedinger, K. R. (2009). Understanding the advantages of retrieval for long-term retention using modeling. 50th Annual Meeting of the Psychonomic Society, Boston. Poster
• Pavlik Jr., P. I. (2008). Classroom testing of a discrete trial practice system. 34th Annual Meeting of the Association for Behavior Analysis, Chicago. Talk
• Pavlik Jr., P. I., Presson, N., & Hora, D. (2008). Using the FaCT System (Fact and Concept Training System) for Classroom and Laboratory Experiments. Inter-Science Of Learning Center Conference, Pittsburgh, PA. Talk with abstract
• Pavlik Jr., P. I. (2007). Understanding why practice should be fast and accurate. 33rd Annual Meeting of the Association for Behavior Analysis, San Diego, CA. Talk
• Pavlik Jr., P. I. (2006a). Transfer effects in Chinese vocabulary learning. In R. Sun (Ed.), Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 2579). Lawrence Erlbaum. Poster
• Pavlik Jr., P. I. (2006b). Understanding the effectiveness of direct instruction methods. 24th Annual Meeting of the California Association for Behavior Analysis, Burlingame, CA. Talk
• Pavlik Jr., P. I., & Anderson, J. R. (2004, November). Optimizing Paired-Associate Learning. 45th Annual Meeting of the Psychonomic Society, Minneapolis, MN. Poster
• Pavlik Jr., P. I., & Anderson, J. R. (2004). The memory consequences of study after successful recall. In K. D. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society (pp. 1615). Lawrence Erlbaum. Poster
• Pavlik Jr., P. I. (2004). A PDP Model of Spacing Effects in Memory. 22nd Annual Pittsburgh-CMU Psychology Conference, Pittsburgh, PA. Talk with abstract
• Pavlik Jr., P. I., & Anderson, J. R. (2002). Mental rotation transfer. In W. D. Gray & C. Schunn (Eds.), Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society (pp. 1029). Lawrence Erlbaum Associates. Poster
• Pavlik Jr., P. I., & Burns, S. (2001). Learning Mental Rotation: Exemplar Learning or Process Learning. Midwestern Psychological Association, Chicago, IL. Poster
• Invited, 2012 - 8th Annual PSLC LearnLab Summer School, In-Vivo Experimentation Track Lectures   
• Invited, 2011 - 7th Annual PSLC LearnLab Summer School, In-Vivo Experimentation Track Lectures 
• Invited, 2010 - 6th Annual PSLC LearnLab Summer School, In-Vivo Experimentation Track Lectures  
• Invited, 2009 - University of Phoenix’s National Research Center “Optimizing the Practice Schedule”  
• Invited, 2007 - Department of Modern Languages, Carnegie Mellon University, Graduate Seminar “Using a Cognitive Model to Schedule Vocabulary Practice for Second Language Learners”               
• Invited, 2006 - Department of Educational and School Psychology and Special Education, Pennsylvania State University “Using Cognitive Theory and Computational Modeling to Explain the Success of Direct Instruction and Precision Teaching”  
• Invited, 2004 - Department of Psychology, Northern Michigan University “Optimizing Paired-Associate Learning by Paying Attention to Individual and Item Differences”   
• Invited, 2003 - Tenth Annual ACT-R Summer School, Carnegie Mellon University “Unit 7: Base-Level Activation”   
• Invited, 2002 - Department of Psychology, Northern Michigan University “Paired-Associate Practice and Forgetting”