Optimal Learning Lab

Applying cognitive theory to optimize learning.
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Two core tools: MoFaCTS for adaptive practice and LKT for modeling and analysis.

MoFaCTS is the lab's adaptive practice platform, and LKT is its package and documentation layer for related modeling and analysis workflows.

Core Tool 1

MoFaCTS

What it does

MoFaCTS supports research on how practice should be selected, scheduled, and adapted across different kinds of learning content. It also serves as a practical system for delivering those ideas in usable instructional workflows.

A skinned MoFaCTS implementation also supports adaptive video learning through Cognivideo.

The MoFaCTS codebase is available under the Business Source License 1.1 (BUSL 1.1).

Open MoFaCTS MoFaCTS GitHub Cognivideo

Why it matters

  • Supports adaptive practice experiments
  • Tracks learner interaction history over time
  • Connects theory to implementation and reporting

Implementation contributors

Current: Rusty Haner, August White, Shelbi Kuhlmann (adaptive video collaborator; skinned MoFaCTS deployment via Cognivideo).

Earlier: Andrew Tackett, Craig Kelly, Nicholas Gordon, Justin Hiller, Edward Prins III, Wes Carter, James Antoine, Ian Burns, Kenneth Brown, Andrew DiMotta, Karlos Abel, Genc Doko, Lili Wu, Giancarlo Dozzi.

MoFaCTS-related publications and presentations

  • Cao, M., Pavlik, P. I., Jr., & Bidelman, G. M. (2024). Enhancing lexical tone learning for second language speakers: effects of acoustic properties in Mandarin tone perception. Frontiers in Psychology, 15, 1403816.
  • Barideaux, K. J., Jr., & Pavlik, P. I., Jr. (2023). Enhancing memory recall during video lectures: does the visual display format matter? Educational Psychology, 43(6), 659-678.
  • Pavlik, P. I., Jr., & Eglington, L. G. (2023). Automated Search for Logistic Knowledge Tracing Models. In Proceedings of the 16th International Conference on Educational Data Mining (pp. 17-27).
  • Banker, A. M., Pavlik, P. I., Jr., Olney, A., & Eglington, L. G. (2022). Online Tutoring System (MoFaCTS) for Anatomy and Physiology: Implementation and Initial Impressions. HAPS Educator, 26(2), 44-54.
  • Barideaux, K. J., Jr., & Pavlik, P. I., Jr. (2021). Can concept maps attenuate auditory distraction when studying with music? Applied Cognitive Psychology, 35(6), 1547-1558.
  • Pavlik, P. I., Jr., & Eglington, L. G. (2021). The Mobile Fact and Concept Textbook System (MoFaCTS) Computational Model and Scheduling System. In CEUR Workshop Proceedings (Vol. 2895, pp. 93-107).
  • Pavlik, P. I., Jr., Olney, A. M., Banker, A., Eglington, L., & Yarbro, J. (2020). The Mobile Fact and Concept Textbook System (MoFaCTS). In CEUR Workshop Proceedings (Vol. 2674, pp. 35-49).
  • Eglington, L. G., & Pavlik, P. I., Jr. (2019). Predictiveness of Prior Failures Is Improved by Incorporating Trial Duration. Journal of Educational Data Mining, 11(2), 1-19.
  • Murphy, C. S., & Pavlik, P. I., Jr. (2018). Effects of spacing and testing on inductive learning. Journal of Articles in Support of the Null Hypothesis, 15(1), 23-40.
  • Olney, A. M., Pavlik, P. I., Jr., & Maass, J. K. (2017). Improving reading comprehension with automatically generated cloze item practice. In Artificial Intelligence in Education (Lecture Notes in Computer Science, Vol. 10331, pp. 262-273).
  • Pavlik, P. I., Kelly, C., & Maass, J. K. (2016). The Mobile Fact and Concept Training System (MoFaCTS). In Lecture Notes in Computer Science (Vol. 9684, pp. 247-253).
  • Maass, J. K., & Pavlik, P. I. (2016). Modeling the Influence of Format and Depth during Effortful Retrieval Practice. In Proceedings of the 9th International Conference on Educational Data Mining (pp. 143-150).
  • Maass, J. K., Pavlik, P. I., Jr., & Hua, H. (2015). How spacing and variable retrieval practice affect the learning of statistics concepts. In Artificial Intelligence in Education (Lecture Notes in Computer Science, Vol. 9112, pp. 247-256).
  • Barideaux, K. J., Jr., Maass, J. K., & Pavlik, P. I., Jr. (2013). A comparison of concept maps and text summaries: The benefits for learning and transfer. 54th Annual Meeting of the Psychonomic Society.
Core Tool 2

LKT

What it does

The LKT package supports modeling and analysis workflows connected to the lab's work on knowledge tracing, learner history features, and interpretable predictive models for educational data.

LKT on CRAN LKT on GitHub LKT tutorial

Why it matters

  • Supports modeling and feature-based analysis
  • Provides reusable package-level methods and documentation
  • Complements the platform side of the lab's software work

Implementation contributors

Current: Philip I. Pavlik, Jr., Luke G. Eglington.

Earlier: Liang Zhang, Leigh Harrell-Williams, Michael Yudelson, Hao Cen, Kenneth R. Koedinger, and collaborators across EDM and AIED modeling projects.

LKT-related publications and presentations

  • Pavlik, P. I., Jr., & Eglington, L. G. (2023). Automated Search for Logistic Knowledge Tracing Models. In Proceedings of the 16th International Conference on Educational Data Mining (pp. 17-27).
  • Eglington, L. G., & Pavlik, P. I., Jr. (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.
  • Scruggs, R., Baker, R. S., Pavlik, P. I., Jr., 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, 71(3), 901-918.
  • 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.
  • Pavlik, P. I., Jr., Cen, H., & Koedinger, K. R. (2009). Performance factors analysis: A new alternative to knowledge tracing. In Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 531-538).
  • Pavlik, P., Cen, H., Wu, L., & Koedinger, K. (2008). Using item-type performance covariance to improve the skill model of an existing tutor. In Educational Data Mining 2008: 1st International Conference on Educational Data Mining, Proceedings (pp. 77-86).