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ORCIDPhilip I. Pavlik Jr.
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Journal Articles
- 2023
[j6]Luke G. Eglington
, Philip I. Pavlik:
How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences. Int. J. Artif. Intell. Educ. 33(3): 497-518 (2023)- 2021
- [j5]Philip I. Pavlik, Luke G. Eglington, Leigh M. Harrell-Williams:
Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling. IEEE Trans. Learn. Technol. 14(5): 624-639 (2021) - 2016
- [j4]Kenneth R. Koedinger, Michael Yudelson, Philip I. Pavlik Jr.:
Testing Theories of Transfer Using Error Rate Learning Curves. Top. Cogn. Sci. 8(3): 589-609 (2016) - 2015
- [j3]Philip I. Pavlik, Michael Yudelson, Kenneth R. Koedinger:
A Measurement Model of Microgenetic Transfer for Improving Instructional Outcomes. Int. J. Artif. Intell. Educ. 25(3): 346-379 (2015) - 2013
- [j2]Erik D. Thiessen, Philip I. Pavlik Jr.:
iMinerva: A Mathematical Model of Distributional Statistical Learning. Cogn. Sci. 37(2): 310-343 (2013) - 2005
- [j1]Philip I. Pavlik Jr., John R. Anderson:
Practice and Forgetting Effects on Vocabulary Memory: An Activation-Based Model of the Spacing Effect. Cogn. Sci. 29(4): 559-586 (2005)
Conference and Workshop Papers
- 2024
- [c43]Meng Cao, Philip I. Pavlik Jr., Wei Chu, Liang Zhang:
Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Train-ing Sequences on Category Learning. EDM 2024 - [c42]Philip I. Pavlik Jr., Luke G. Eglington, Meng Cao, Wei Chu:
Logistic Knowledge Tracing Tutorial: Practical Educational Applications. EDM 2024 - 2023
- [c41]Wei Chu, Philip I. Pavlik Jr.:
The Predictiveness of PFA is Improved by Incorporating the Learner's Correct Response Time Fluctuation. EDM 2023 - [c40]Philip I. Pavlik Jr., Luke G. Eglington:
Automated Search for Logistic Knowledge Tracing Models. EDM 2023 - [c39]Liang Zhang, Philip I. Pavlik, Xiangen Hu, Jody L. Cockroft, Lijia Wang, Genghu Shi:
Exploring the Individual Differences in Multidimensional Evolution of Knowledge States of Learners. HCI (34) 2023: 265-284 - 2022
- [c38]Meng Cao, Philip I. Pavlik:
A Variant of Performance Factors Analysis Model for Categorization. EDM 2022 - 2021
- [c37]Philip I. Pavlik Jr., Luke G. Eglington:
The Mobile Fact and Concept Textbook System (MoFaCTS) Computational Model and Scheduling System. iTextbooks@AIED 2021: 93-107 - [c36]Philip I. Pavlik Jr., Luke Eglington, Liang Zhang:
Automatic Domain Model Creation and Improvement. EDM 2021 - [c35]Vasile Rus, Stephen E. Fancsali, Philip I. Pavlik Jr., Deepak Venugopal, Arthur C. Graesser, Steven Ritter, Dale Bowman, The L. D. I. Team:
The Learner Data Institute - Conceptualization: A Progress Report. EDM (Workshops) 2021 - 2020
- [c34]Philip I. Pavlik Jr., Andrew McGregor Olney, Amanda Banker, Luke Eglington, Jeffrey Yarbro:
The Mobile Fact and Concept Textbook System (MoFaCTS). iTextbooks@AIED 2020: 35-49 - 2019
- [c33]Meng Cao, Philip I. Pavlik Jr., Gavin M. Bidelman:
Incorporating Prior Practice Difficulty into Performance Factor Analysis to Model Mandarin Tone Learning. EDM 2019 - 2018
- [c32]Andrew J. Hampton, Benjamin D. Nye, Philip I. Pavlik, William R. Swartout, Arthur C. Graesser, Joseph Gunderson:
Mitigating Knowledge Decay from Instruction with Voluntary Use of an Adaptive Learning System. AIED (2) 2018: 119-133 - [c31]Ying Fang, Keith T. Shubeck, Anne Lippert, Qinyu Cheng, Genghu Shi, Shi Feng, Jessica Gatewood, Su Chen, Zhiqiang Cai, Philip I. Pavlik, Jan C. Frijters, Daphne Greenberg, Arthur C. Graesser:
Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System. EDM 2018 - 2017
- [c30]Andrew McGregor Olney, Philip I. Pavlik Jr., Jaclyn K. Maass:
Improving Reading Comprehension with Automatically Generated Cloze Item Practice. AIED 2017: 262-273 - [c29]Ying Fang, Benjamin Nye, Philip I. Pavlik Jr., Yonghong Xu, Arthur C. Graesser, Xiangen Hu:
Online Learning Persistence and Academic Achievement. EDM 2017 - [c28]Ran Liu, Kenneth R. Koedinger, John C. Stamper, Philip I. Pavlik Jr.:
Sharing and Reusing Data and Analytic Methods with LearnSphere. EDM 2017 - [c27]Genghu Shi, Philip I. Pavlik Jr., Arthur C. Graesser:
Using an Additive Factor Model and Performance Factor Analysis to Assess Learning Gains in a Tutoring System to Help Adults with Reading Difficulties. EDM 2017 - [c26]Ken Koedinger, Ran Liu, John C. Stamper, Candace Thille, Phil Pavlik:
Community based educational data repositories and analysis tools. LAK 2017: 524-525 - 2016
- [c25]Jaclyn K. Maass, Philip I. Pavlik Jr.:
Modeling the Influence of Format and Depth during Effortful Retrieval Practice. EDM 2016: 143-150 - [c24]Philip I. Pavlik Jr., Craig Kelly, Jaclyn K. Maass:
The Mobile Fact and Concept Training System (MoFaCTS). ITS 2016: 247-253 - 2015
- [c23]Jaclyn K. Maass, Philip I. Pavlik Jr., Henry Hua:
How Spacing and Variable Retrieval Practice Affect the Learning of Statistics Concepts. AIED 2015: 247-256 - [c22]Benjamin D. Nye, Alistair Windsor, Philip I. Pavlik, Andrew Olney, Mustafa H. Hajeer, Arthur C. Graesser, Xiangen Hu:
Evaluating the Effectiveness of Integrating Natural Language Tutoring into an Existing Adaptive Learning System. AIED 2015: 743-747 - 2014
- [c21]Srdan Medimorec, Philip I. Pavlik Jr., Andrew Olney, Arthur C. Graesser, Evan F. Risko:
Linguistic Features of Lectures: Offsetting Challenging Words. CogSci 2014 - [c20]Carol Forsyth, Arthur C. Graesser, Philip I. Pavlik Jr., Keith K. Millis, Borhan Samei:
Discovering Theoretically Grounded Predictors of Shallow vs. Deep- level Learning. EDM 2014: 229-232 - [c19]Carol Forsyth, Arthur C. Graesser, Borhan Samei, Breya Walker, Philip I. Pavlik:
Predicting Performance Behaviors during Question Generation in a Game-Like Intelligent Tutoring System. ICLS 2014 - 2013
- [c18]Jaclyn K. Maass, Philip I. Pavlik Jr.:
Using Learner Modeling to Determine Effective Conditions of Learning for Optimal Transfer. AIED 2013: 189-198 - [c17]Carol Forsyth, Arthur C. Graesser, Breya Walker, Keith K. Millis, Philip I. Pavlik Jr., Diane F. Halpern:
Didactic Galactic: Types of Knowledge Learned in a Serious Game. AIED 2013: 832-835 - [c16]Jaclyn K. Maass, Philip I. Pavlik Jr.:
Utilizing Concept Mapping in Intelligent Tutoring Systems. AIED 2013: 880-883 - [c15]Philip I. Pavlik, Henry Hua, Jamal Williams, Gavin M. Bidelman:
Modeling and Optimizing Forgetting and Spacing Effects during Musical Interval Training. EDM 2013: 145-152 - 2012
- [c14]Carol Forsyth, Philip I. Pavlik Jr., Arthur C. Graesser, Zhiqiang Cai, Mae-Lynn Germany, Keith K. Millis, Heather Butler, Diane F. Halpern, Robert P. Dolan:
Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning. EDM 2012: 172-175 - [c13]Philip I. Pavlik Jr., Jaclyn K. Maass, Vasile Rus, Andrew Olney:
Facilitating Co-adaptation of Technology and Education through the Creation of an Open-Source Repository of Interoperable Code. ITS 2012: 677-678 - 2011
- [c12]Philip I. Pavlik, Michael Yudelson, Kenneth R. Koedinger:
Using Contextual Factors Analysis to Explain Transfer of Least Common Multiple Skills. AIED 2011: 256-263 - [c11]Kenneth R. Koedinger, Philip I. Pavlik Jr., John C. Stamper, Tristan Nixon, Steven Ritter:
Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing. EDM 2011: 91-100 - [c10]Philip I. Pavlik Jr., Sue-mei Wu:
A Dynamical System Model of Microgenetic Changes in Performance, Efficacy, Strategy Use and Value during Vocabulary Learning. EDM 2011: 277-282 - [c9]Michael Yudelson, Philip I. Pavlik, Kenneth R. Koedinger:
Towards Better Understanding of Transfer in Cognitive Models of Practice. EDM 2011: 373-374 - [c8]Michael Yudelson, Philip I. Pavlik, Kenneth R. Koedinger:
User Modeling - A Notoriously Black Art. UMAP 2011: 317-328 - 2010
- [c7]Philip I. Pavlik Jr.:
Data Reduction Methods Applied to Understanding Complex Learning Hypotheses. EDM 2010: 311-312 - [c6]Philip I. Pavlik, Joe Toth:
How to Build Bridges between Intelligent Tutoring System Subfields of Research. Intelligent Tutoring Systems (2) 2010: 103-112 - 2009
- [c5]Philip I. Pavlik, Hao Cen, Kenneth R. Koedinger:
Performance Factors Analysis - A New Alternative to Knowledge Tracing. AIED 2009: 531-538 - [c4]Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinger:
Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models. EDM 2009: 121-130 - 2008
- [c3]Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koedinger:
Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor. EDM 2008: 77-86 - [c2]Philip I. Pavlik, Thomas Bolster, Sue-mei Wu, Kenneth R. Koedinger, Brian MacWhinney:
Using Optimally Selected Drill Practice to Train Basic Facts. Intelligent Tutoring Systems 2008: 593-602 - 2004
- [c1]Philip I. Pavlik Jr., John R. Anderson:
An ACT-R Model of Memory Applied to Finding the Optimal Schedule of Practice. ICCM 2004: 376-377
Editorship
- 2013
- [e2]H. Chad Lane, Kalina Yacef, Jack Mostow, Philip I. Pavlik:
Artificial Intelligence in Education - 16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013. Proceedings. Lecture Notes in Computer Science 7926, Springer 2013, ISBN 978-3-642-39111-8 [contents] - 2010
- [e1]Ryan Shaun Joazeiro de Baker, Agathe Merceron, Philip I. Pavlik Jr.:
Educational Data Mining 2010, The 3rd International Conference on Educational Data Mining, Pittsburgh, PA, USA, June 11-13, 2010. Proceedings. www.educationaldatamining.org 2010, ISBN 978-0-615-37529-8 [contents]
Informal and Other Publications
- 2024
- [i2]Meng Cao, Philip I. Pavlik Jr., Wei Chu, Liang Zhang:
Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Training Sequences on Category Learning. CoRR abs/2407.15020 (2024) - 2021
- [i1]Philip I. Pavlik Jr., Luke G. Eglington:
Modeling the EdNet Dataset with Logistic Regression. CoRR abs/2105.08150 (2021)
Coauthor Index
aka: Ken Koedinger
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