Books Published

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

Book Reviews

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

Refereed Journal Publications

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. https://doi.org/10.1007/s11423-023-10218-z

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

Eglington, L. G., & Pavlik, P. I. (2022). How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00296-0

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

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

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

Eglington, L., & Pavlik Jr, P. I. (2019). Predictiveness of prior failures is modulated by trial duration. Journal of Educational Data Mining, 11, 1-19. http://dx.doi.org/10.5281/zenodo.3554675

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

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

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. Journal article

Graesser, A., Hu, X., Nye, B., Vanlehn, K., Kumar, R., Heffernan, C., . . . Baer, W. (2018). ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics. International Journal of STEM Education, Special Issue. Journal article

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

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. 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. Journal article

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

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. 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. 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. Journal article

Olney, A. M., Brawner, K. W., Pavlik Jr., P. I., & Koedinger, K. R. (2015). Emerging Trends in Automated Authoring 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. (2013). Mining the Dynamics of Student Utility and Strategy Use during Vocabulary Learning. Journal of Educational Data Mining, 5(1), 39-71. Journal article

Thiessen, E. D., & Pavlik Jr., P. I. (2013). iMinerva: A Mathematical Model of Distributional Statistical Learning. Cognitive Science, 37(2), 310-343. Journal article

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 (Volume I) (pp. 39-68). Army Research Labs/ University of Memphis. Book chapter

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. 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. Journal article

Pavlik Jr., P. I. (2007a). Understanding and applying the dynamics of test practice and study practice. Instructional Science, 35, 407–441. Journal article

Pavlik Jr., P. I. (2007b). 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). New York: Oxford University Press. Book chapter

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. Journal article

Refereed Conference Publications

Chu, W. and P. I. Pavlik Jr (accepted). The Predictiveness of PFA is Improved by Incorporating the Learner’s Correct Response Time Fluctuation. Educational Data Mining 2023: 1-6.

Pavlik Jr, P. I. and L. G. Eglington (accepted). Automated Search for Logistic Knowledge Tracing Models. Educational Data Mining 2023: 1-10.

Cao, M. and P. I. Pavlik Jr (2022). A Variant of Performance Factors Analysis Model for Categorization. Proceedings of the 15th International Conference on Educational Data Mining. Durham, United Kingdom: 763-766.

Pavlik Jr., P. I. and L. Zhang (2022). Using autoKC and Interactions in Logistic Knowledge Tracing. Proceedings of The Third Workshop of the Learner Data Institute, The 15th International Conference on Educational Data Mining (EDM 2022). Durham, UK: 1-6.

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).

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).

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).

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).

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 11th International Conference on Educational Data Mining (pp. 516-519).

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).

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.

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 International Conference on Artificial Intelligence in Education (pp. 119-133). Springer International Publishing.

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.

Pavlik Jr., P. I., & Eglington, L. (2021). Modeling the EdNet Dataset with Logistic Regression. In 35th AAAI Conference on Artificial Intelligence, Imagining Post-COVID Education with AI Workshp (pp. 1-5).

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). Wuhan, China: Springer International Publishing. Paper

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). Wuhan, China. Short paper

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 15% 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). Switzerland: Springer. Short paper 42% acceptance rate

Maass, J., 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 29% acceptance rate

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. Poster 68% acceptance rate

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). Fulton County, GA: International Society of the Learning Sciences. Poster 52% acceptance rate

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

Maass, J. K., & Pavlik Jr., P. I. (2013a). 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. Paper 33% acceptance rate

Maass, J. K., & Pavlik Jr., P. I. (2013b). Utilizing Concept Mapping in Intelligent Tutoring Systems. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (Vol. 7926, pp. 880-883). Springer Berlin Heidelberg. Doctoral consortium 68% 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). Memphis, TN. Paper 25% acceptance rate

Forsyth, C. M., Pavlik Jr., P. I., Graesser, A. C., Cai, Z., Germany, M.-l., Millis, K., . . . 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). Chania, Greece: International Educational Data Mining Society. Short paper 46% 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). Eindhoven, the Netherlands. Short paper 46% acceptance rate

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. Paper 22% 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.), Artificial Intelligence in Education (Vol. 6738, pp. 256–263). Berlin, Germany: Springer. Paper 32% acceptance rate

Koedinger, K. R., Pavlik Jr., P. I., Stamper, J., Nixon, T., & Ritter, S. (2011). Fair blame assignment in student modeling. 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). Eindhoven, the Netherlands. Paper 33% 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). Pittsburgh, PA: Springer. Paper 38% acceptance rate

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). Cordoba, Spain. 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). Brighton, England. Paper 29% acceptance rate

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). Montreal, Canada. 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). Washington, D.C. 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). Montreal, Canada. Paper 30% 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). Washington, D.C. Paper 74% 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). Ann Arbor: University of Michigan. Paper unknown 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). Mahwah, NJ: Lawrence Erlbaum. Paper 69% acceptance rate (poster presentation)

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). Germany: Universitats-Verlag Bamberg. Paper unknown acceptance rate

Presentations - Conference (refereed *)

Eglington, L.G., Pavlik, P.I. (2020, November). Optimally efficient spaced practice using logistic regression and difficulty thresholds. Poster presented at the 61st Annual meeting of the Psychonomic Society. Virtual Conference. Poster

Pavlik Jr., P. I., Cao, M., & Eglington, L. (2019). Mathematically Modeling the Optimal Desirable Difficulty. Paper presented at the 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. Paper presented at the Third International Conference on Artificial Intelligence and Adaptive Education 2019, Beijing, China. Talk

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). Wuhan, China. Poster

Liu, R., Koedinger, K., Stamper, J., & Pavlik Jr., P. I. (2017). Workshop: 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). Wuhan, China. Workshop

Koedinger, K., Liu, R., Stamper, J., Thille, C., & Pavlik, P. (2017). Workshop: Community based educational data repositories and analysis tools Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 524-525). ACM. Workshop

Maass, J. K., & Pavlik Jr., P. I. (2016, November). Discerning Misconceptions through Retrieval Practice. Proceedings of the 57th Annual Meeting of the Psychonomic Society, Chicago. Poster

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. Proceedings of the 57th Annual Meeting of the Psychonomic Society, Chicago. Poster

Stamper, J., Koedinger, K., Pavlik Jr., P. I., Rose, C., Liu, R., Eagle, 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. Raleigh, NC. Workshop

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 Canadian Journal of Experimental Psychology (Vol. 68, pp. 257-257). Canadian Psychological Society. Talk

Forsyth, C., Millis, K., Pavlik Jr., P. I., & Graesser, A. C. (2013, April). Assessing performance metrics within a serious game. Paper presented at the Annual Meeting of the American Educational Research Association., San Francisco, CA. Talk

Forsyth, C., 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. Paper presented at the 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. Proceedings of the 54th Annual Meeting of the Psychonomic Society, Toronto. Poster

Forsyth, C., Graesser, A., 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. Poster 76% 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). Berlin: Springer. Poster 76% acceptance rate

Yudelson, M., Pavlik Jr., P. I., & Koedinger, K. R. (2011). 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). Eindhoven, the Netherlands. Poster 63% acceptance rate

Pavlik Jr., P. I., Yudelson, M., & Koedinger, K. R. (2011). A method for the microanalysis of pre-algebra transfer. Proceedings of the Society for Research on Educational Effectiveness: Fall, Washington DC. Talk with abstract

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). Pittsburgh. Poster 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? Boston: 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. Proceedings of the 50th Annual Meeting of the Psychonomic Society, Boston. Poster

Pavlik Jr., P. I. (2008). Classroom testing of a discrete trial practice system. Proceedings of the 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. Proceedings of the Inter-Science Of Learning Center Conference, Pittsburgh, PA. Talk with abstract

Pavlik Jr., P. I. (2007). Understanding why practice should be fast and accurate. Paper presented at the 33rd Annual Meeting of the Association for Behavior Analysis, San Diego, CA. Talk

Pavlik Jr., P. I. (2006a). Understanding the effectiveness of direct instruction methods. Paper presented at the 24th Annual Meeting of the California Association for Behavior Analysis, Burlingame, CA. Talk

Pavlik Jr., P. I. (2006b). Transfer effects in Chinese vocabulary learning. In R. Sun (Ed.), Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 2579). Mahwah, NJ: Lawrence Erlbaum. Poster with abstract

Pavlik Jr., P. I., & Anderson, J. R. (2004, November). Optimizing Paired-Associate Learning. Proceedings of the 45th Annual Meeting of the Psychonomic Society, Minneapolis, MN. Poster

Pavlik Jr., P. I. (2004, August). A PDP Model of Spacing Effects in Memory. Paper presented at the 22nd Annual Pittsburgh-CMU Psychology Conference, Pittsburgh, PA. Talk with abstract

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). Mahwah, NJ: Lawrence Erlbaum. Poster with abstract

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). Pittsburgh, PA: Carnegie Mellon University/University of Pittsburgh. Poster 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). Mahweh, NJ: Lawrence Erlbaum Associates. Poster with abstract

Pavlik Jr., P. I., & Burns, S. (2001). Learning Mental Rotation: Exemplar Learning or Process Learning. Paper presented at the Midwestern Psychological Association, Chicago, IL. Poster

Presentations - Universities/Industry (refereed *)

Pavlik Jr, P. (2015, April). Ingredients for a theory of instruction. Paper presented at the Rice Workshop on Personalized Learning, Rice University, Houston, Texas. Invited talk

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”