Richard Scruggs

According to our database, Richard Scruggs authored at least 11 papers between 2018 and 2022.

Collaborative distances:

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

2022

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

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

Using Knowledge Tracing to Predict Students' Performance in Cognitive Training and Math.

Richard Scruggs, Jalal Nouri, Torkel Klingberg

Proceedings of the Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners' and Doctoral Consortium, 2022

2021

Assessing implicit computational thinking in <i>Zoombinis</i> puzzle gameplay.

Elizabeth Rowe, Ma. Victoria Almeda, Jodi Asbell-Clarke, Richard Scruggs, Ryan Baker, Erin Bardar, Santiago Gasca

Comput. Hum. Behav., 2021

The development of students' computational thinking practices in elementary- and middle-school classes using the learning game, <i>Zoombinis</i>.

Jodi Asbell-Clarke, Elizabeth Rowe, Mia Victoria Almeda, Teon Edwards, Erin Bardar, Santiago Gasca, Ryan S. Baker, Richard Scruggs

Comput. Hum. Behav., 2021

The Cold Start Problem and Interpretation of Knowledge Tracing Models' Predictive Performance.

Jiayi Zhang, Rohini Das, Ryan S. Baker, Richard Scruggs

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

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

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

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

Proceedings of the Artificial Intelligence in Education - 22nd International Conference, 2021

2019

Extending Deep Knowledge Tracing: Inferring Interpretable Knowledge and Predicting Post-System Performance.

Richard Scruggs, Ryan S. Baker, Bruce M. McLaren

CoRR, 2019

More confusion and frustration, better learning: The impact of erroneous examples.

J. Elizabeth Richey, Juan Miguel L. Andres-Bray, Michael Mogessie Ashenafi, Richard Scruggs, Juliana Ma. Alexandra L. Andres, Jon R. Star, Ryan S. Baker, Bruce M. McLaren

Comput. Educ., 2019

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

Proceedings of the Artificial Intelligence in Education - 20th International Conference, 2019

2018

Labeling Implicit Computational Thinking in Pizza Pass Gameplay.

Elizabeth Rowe, Jodi Asbell-Clarke, Ryan Shaun Baker, Santiago Gasca, Erin Bardar, Richard Scruggs

Proceedings of the Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, 2018