Philip I. Pavlik Jr.

According to our database, Philip I. Pavlik Jr. authored at least 51 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Training Sequences on Category Learning.

Meng Cao, Philip I. Pavlik Jr., Wei Chu, Liang Zhang

CoRR, 2024

Logistic Knowledge Tracing Tutorial: Practical Educational Applications.

Philip I. Pavlik Jr., Luke G. Eglington, Meng Cao, Wei Chu

Proceedings of the 17th International Conference on Educational Data Mining, 2024

Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Train-ing Sequences on Category Learning.

Meng Cao, Philip I. Pavlik Jr., Wei Chu, Liang Zhang

Proceedings of the 17th International Conference on Educational Data Mining, 2024

2023

How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences.

Luke G. Eglington, Philip I. Pavlik

Int. J. Artif. Intell. Educ., September, 2023

Exploring the Individual Differences in Multidimensional Evolution of Knowledge States of Learners.

Liang Zhang, Philip I. Pavlik, Xiangen Hu, Jody L. Cockroft, Lijia Wang, Genghu Shi

Proceedings of the Adaptive Instructional Systems, 2023

Automated Search for Logistic Knowledge Tracing Models.

Philip I. Pavlik Jr., Luke G. Eglington

Proceedings of the 16th International Conference on Educational Data Mining, 2023

The Predictiveness of PFA is Improved by Incorporating the Learner's Correct Response Time Fluctuation.

Wei Chu, Philip I. Pavlik Jr.

Proceedings of the 16th International Conference on Educational Data Mining, 2023

2022

A Variant of Performance Factors Analysis Model for Categorization.

Meng Cao, Philip I. Pavlik

Proceedings of the 15th International Conference on Educational Data Mining, 2022

2021

Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling.

Philip I. Pavlik, Luke G. Eglington, Leigh M. Harrell-Williams

IEEE Trans. Learn. Technol., 2021

Modeling the EdNet Dataset with Logistic Regression.

Philip I. Pavlik Jr., Luke G. Eglington

CoRR, 2021

The Learner Data Institute - Conceptualization: A Progress Report.

Vasile Rus, Stephen E. Fancsali, Philip I. Pavlik Jr., Deepak Venugopal, Arthur C. Graesser, Steven Ritter, Dale Bowman, The L. D. I. Team

Proceedings of the Joint Proceedings of the Workshops at the International Conference on Educational Data Mining 2021 co-located with 14th International Conference on Educational Data Mining (EDM 2021), 2021

Automatic Domain Model Creation and Improvement.

Philip I. Pavlik Jr., Luke Eglington, Liang Zhang

Proceedings of the 14th International Conference on Educational Data Mining, 2021

The Mobile Fact and Concept Textbook System (MoFaCTS) Computational Model and Scheduling System.

Philip I. Pavlik Jr., Luke G. Eglington

Proceedings of the Third International Workshop on Inteligent Textbooks 2021 Co-located with 22nd International Conference on Artificial Intelligence in Education (AIED 2021), 2021

2020

The Mobile Fact and Concept Textbook System (MoFaCTS).

Philip I. Pavlik Jr., Andrew McGregor Olney, Amanda Banker, Luke Eglington, Jeffrey Yarbro

Proceedings of the Second International Workshop on Intelligent Textbooks 2020 co-located with 21st International Conference on Artificial Intelligence in Education (AIED 2020), 2020

2019

Incorporating Prior Practice Difficulty into Performance Factor Analysis to Model Mandarin Tone Learning.

Meng Cao, Philip I. Pavlik Jr., Gavin M. Bidelman

Proceedings of the 12th International Conference on Educational Data Mining, 2019

2018

Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System.

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

Proceedings of the 11th International Conference on Educational Data Mining, 2018

Mitigating Knowledge Decay from Instruction with Voluntary Use of an Adaptive Learning System.

Andrew J. Hampton, Benjamin D. Nye, Philip I. Pavlik, William R. Swartout, Arthur C. Graesser, Joseph Gunderson

Proceedings of the Artificial Intelligence in Education - 19th International Conference, 2018

2017

Community based educational data repositories and analysis tools.

Ken Koedinger, Ran Liu, John C. Stamper, Candace Thille, Phil Pavlik

Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017

Using an Additive Factor Model and Performance Factor Analysis to Assess Learning Gains in a Tutoring System to Help Adults with Reading Difficulties.

Genghu Shi, Philip I. Pavlik Jr., Arthur C. Graesser

Proceedings of the 10th International Conference on Educational Data Mining, 2017

Sharing and Reusing Data and Analytic Methods with LearnSphere.

Ran Liu, Kenneth R. Koedinger, John C. Stamper, Philip I. Pavlik Jr.

Proceedings of the 10th International Conference on Educational Data Mining, 2017

Online Learning Persistence and Academic Achievement.

Ying Fang, Benjamin Nye, Philip I. Pavlik Jr., Yonghong Xu, Arthur C. Graesser, Xiangen Hu

Proceedings of the 10th International Conference on Educational Data Mining, 2017

Improving Reading Comprehension with Automatically Generated Cloze Item Practice.

Andrew McGregor Olney, Philip I. Pavlik Jr., Jaclyn K. Maass

Proceedings of the Artificial Intelligence in Education - 18th International Conference, 2017

2016

Testing Theories of Transfer Using Error Rate Learning Curves.

Kenneth R. Koedinger, Michael Yudelson, Philip I. Pavlik Jr.

Top. Cogn. Sci., 2016

The Mobile Fact and Concept Training System (MoFaCTS).

Philip I. Pavlik Jr., Craig Kelly, Jaclyn K. Maass

Proceedings of the Intelligent Tutoring Systems - 13th International Conference, 2016

Modeling the Influence of Format and Depth during Effortful Retrieval Practice.

Jaclyn K. Maass, Philip I. Pavlik Jr.

Proceedings of the 9th International Conference on Educational Data Mining, 2016

2015

A Measurement Model of Microgenetic Transfer for Improving Instructional Outcomes.

Philip I. Pavlik, Michael Yudelson, Kenneth R. Koedinger

Int. J. Artif. Intell. Educ., 2015

Evaluating the Effectiveness of Integrating Natural Language Tutoring into an Existing Adaptive Learning System.

Benjamin D. Nye, Alistair Windsor, Philip I. Pavlik, Andrew Olney, Mustafa H. Hajeer, Arthur C. Graesser, Xiangen Hu

Proceedings of the Artificial Intelligence in Education - 17th International Conference, 2015

How Spacing and Variable Retrieval Practice Affect the Learning of Statistics Concepts.

Jaclyn K. Maass, Philip I. Pavlik Jr., Henry Hua

Proceedings of the Artificial Intelligence in Education - 17th International Conference, 2015

2014

Predicting Performance Behaviors during Question Generation in a Game-Like Intelligent Tutoring System.

Carol Forsyth, Arthur C. Graesser, Borhan Samei, Breya Walker, Philip I. Pavlik

Proceedings of the Learning and Becoming in Practice: Proceedings of the 11th International Conference of the Learning Sciences, 2014

Discovering Theoretically Grounded Predictors of Shallow vs. Deep- level Learning.

Carol Forsyth, Arthur C. Graesser, Philip I. Pavlik Jr., Keith K. Millis, Borhan Samei

Proceedings of the 7th International Conference on Educational Data Mining, 2014

Linguistic Features of Lectures: Offsetting Challenging Words.

Srdan Medimorec, Philip I. Pavlik Jr., Andrew Olney, Arthur C. Graesser, Evan F. Risko

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

2013

iMinerva: A Mathematical Model of Distributional Statistical Learning.

Erik D. Thiessen, Philip I. Pavlik Jr.

Cogn. Sci., 2013

Modeling and Optimizing Forgetting and Spacing Effects during Musical Interval Training.

Philip I. Pavlik, Henry Hua, Jamal Williams, Gavin M. Bidelman

Proceedings of the 6th International Conference on Educational Data Mining, 2013

Utilizing Concept Mapping in Intelligent Tutoring Systems.

Jaclyn K. Maass, Philip I. Pavlik Jr.

Proceedings of the Artificial Intelligence in Education - 16th International Conference, 2013

Using Learner Modeling to Determine Effective Conditions of Learning for Optimal Transfer.

Jaclyn K. Maass, Philip I. Pavlik Jr.

Proceedings of the Artificial Intelligence in Education - 16th International Conference, 2013

Didactic Galactic: Types of Knowledge Learned in a Serious Game.

Carol Forsyth, Arthur C. Graesser, Breya Walker, Keith K. Millis, Philip I. Pavlik Jr., Diane F. Halpern

Proceedings of the Artificial Intelligence in Education - 16th International Conference, 2013

2012

Facilitating Co-adaptation of Technology and Education through the Creation of an Open-Source Repository of Interoperable Code.

Philip I. Pavlik Jr., Jaclyn K. Maass, Vasile Rus, Andrew Olney

Proceedings of the Intelligent Tutoring Systems - 11th International Conference, 2012

Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning.

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

Proceedings of the 5th International Conference on Educational Data Mining, 2012

2011

User Modeling - A Notoriously Black Art.

Michael Yudelson, Philip I. Pavlik, Kenneth R. Koedinger

Proceedings of the User Modeling, Adaption and Personalization, 2011

Towards Better Understanding of Transfer in Cognitive Models of Practice.

Michael Yudelson, Philip I. Pavlik, Kenneth R. Koedinger

Proceedings of the 4th International Conference on Educational Data Mining, 2011

A Dynamical System Model of Microgenetic Changes in Performance, Efficacy, Strategy Use and Value during Vocabulary Learning.

Philip I. Pavlik Jr., Sue-mei Wu

Proceedings of the 4th International Conference on Educational Data Mining, 2011

Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing.

Kenneth R. Koedinger, Philip I. Pavlik Jr., John C. Stamper, Tristan Nixon, Steven Ritter

Proceedings of the 4th International Conference on Educational Data Mining, 2011

Using Contextual Factors Analysis to Explain Transfer of Least Common Multiple Skills.

Philip I. Pavlik, Michael Yudelson, Kenneth R. Koedinger

Proceedings of the Artificial Intelligence in Education - 15th International Conference, 2011

2010

How to Build Bridges between Intelligent Tutoring System Subfields of Research.

Philip I. Pavlik, Joe Toth

Proceedings of the Intelligent Tutoring Systems, 10th International Conference, 2010

Data Reduction Methods Applied to Understanding Complex Learning Hypotheses.

Philip I. Pavlik Jr.

Proceedings of the Educational Data Mining 2010, 2010

2009

Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models.

Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinger

Proceedings of the Educational Data Mining, 2009

Performance Factors Analysis - A New Alternative to Knowledge Tracing.

Philip I. Pavlik, Hao Cen, Kenneth R. Koedinger

Proceedings of the Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, 2009

2008

Using Optimally Selected Drill Practice to Train Basic Facts.

Philip I. Pavlik, Thomas Bolster, Sue-mei Wu, Kenneth R. Koedinger, Brian MacWhinney

Proceedings of the Intelligent Tutoring Systems, 9th International Conference, 2008

Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor.

Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koedinger

Proceedings of the Educational Data Mining 2008, 2008

2005

Practice and Forgetting Effects on Vocabulary Memory: An Activation-Based Model of the Spacing Effect.

Philip I. Pavlik Jr., John R. Anderson

Cogn. Sci., 2005

2004

An ACT-R Model of Memory Applied to Finding the Optimal Schedule of Practice.

Philip I. Pavlik Jr., John R. Anderson

Proceedings of the International Conference on Cognitive Modelling, 2004