| 2022 | AIED | Using Knowledge Tracing to Predict Students' Performance in Cognitive Training and Math. | Richard Scruggs, Jalal Nouri, Torkel Klingberg |
| 2022 | EDM | Using Neural Network-Based Knowledge Tracing for a Learning System with Unreliable Skill Tags. | Shamya Karumbaiah, Jiayi Zhang, Ryan Baker, Richard Scruggs, Whitney L. Cade, Margaret Clements, Shuqiong Lin |
| 2021 | AIED | Gaming and Confrustion Explain Learning Advantages for a Math Digital Learning Game. | J. Elizabeth Richey, Jiayi Zhang, Rohini Das, Juan Miguel L. Andres-Bray, Richard Scruggs, Michael Mogessie Ashenafi, Ryan S. Baker, Bruce M. McLaren |
| 2021 | EDM | A New Interpretation of Knowledge Tracing Models' Predictive Performance in Terms of the Cold Start Problem. | Rohini Das, Jiayi Zhang, Ryan S. Baker, Richard Scruggs |
| 2021 | EDM | The Cold Start Problem and Interpretation of Knowledge Tracing Models' Predictive Performance. | Jiayi Zhang, Rohini Das, Ryan S. Baker, Richard Scruggs |
| 2020 | ICCE | Extending Deep Knowledge Tracing: Inferring Interpretable Knowledge and Predicting PostSystem Performance. | Richard Scruggs, Ryan S. Baker, Bruce M. McLaren |
| 2019 | AIED | Confrustion in Learning from Erroneous Examples: Does Type of Prompted Self-explanation Make a Difference? | J. Elizabeth Richey, Bruce M. McLaren, Miguel Andres-Bray, Michael Mogessie Ashenafi, Richard Scruggs, Ryan S. Baker, Jon R. Star |
| 2018 | CHI | Labeling Implicit Computational Thinking in Pizza Pass Gameplay. | Elizabeth Rowe, Jodi Asbell-Clarke, Ryan Shaun Baker, Santiago Gasca, Erin Bardar, Richard Scruggs |