Philip Pavlik
Professor at Freelance
Memphis, Tennessee, United States
Summary
👤
Senior🎓
Top SchoolPhilip Pavlik is a cognitive psychologist and professor specializing in the mathematics of practice, forgetting, and conceptual learning, with over a decade of academic experience bridging basic memory research and educational applications. His work, informed by doctoral mentorship under John R. Anderson and postdoctoral training with Kenneth Koedinger, develops computational models to predict recall and optimize instruction across transferable and procedural knowledge. As a systems scientist at Carnegie Mellon and now faculty at the University of Memphis, he combines rigorous experimental methods, statistical modeling, and educational data mining—serving editorial roles that shape the field’s research standards. Notably, his research trajectory moved from fact-memory tasks to mapping knowledge dependency structures, aiming to make cognitive theory directly useful for instructional design.
12 years of coding experience
18 years of employment as a software developer
Certificate, Certificate in Business Sales, Certificate, Certificate in Business Sales at Washtenaw Community CollegeCertificate, Cognitive Neuroscience Certificate, Certificate, Cognitive Neuroscience Certificate at Center for the Neural Basis of CognitionB.A., Economics, B.A., Economics at University of MichiganPh.D., Cognitive Psychology, Ph.D., Cognitive Psychology at Carnegie Mellon UniversityNon-degree credit, Psychology Concentration, Non-degree credit, Psychology Concentration at Northern Michigan University
Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
benchmark6
r6
rstudio6
model-driven5
ide5
machine-learning4
deep-learning3
meteor3
algorithms3
svelte2
bayesian1
Programming languages (8)
Github contributions (5)
Optimal-Learning-Lab/LKT
Oct 2020 - Jan 2023
Contributions:40 commits, 107 pushes, 1 branch in 2 years 3 months
memphis-iis/MoFaCTS
Apr 2026 - Jul 2026
Web-based adaptive learning system for cognitive-model-driven practice and research.
Contributions:132 pushes, 2 branches in 2 months
model-drivenadaptive-learninglearning-sciencemeteorresearch-software
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