Jonathan Rowe

4D Affect Detection: Improving Frustration Detection in Game-Based Learning with Posture-Based Temporal Data Fusion

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, pp. 144–156.

Collaborative inquiry play A design case to frame integration of collaborative problem solving with story-centric games

INFORMATION AND LEARNING SCIENCES, 120(9/10), 547–566.

ENGAGING ADOLESCENTS IN A SELF-ADAPTIVE PERSONALIZED BEHAVIOR CHANGE SYSTEM FOR ADOLESCENT PREVENTIVE HEALTHCARE

JOURNAL OF ADOLESCENT HEALTH, 64(2), S60–61.

Detecting and addressing frustration in a serious game for military training

International Journal of Artificial Intelligence in Education, 28(2), 152–193.

Play in the museum: Design and development of a game-based learning exhibit for informal science education

International Journal of Gaming and Computer-Mediated Simulations, 9(3), 96–113.

Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion

International conference on affective computing and intelligent, 184–189.

Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with CRYSTAL ISLAND

Computers in Human Behavior, 76, 641–655.

The AIIDE 2015 Workshop Program

AI Magazine, 37(2), 91–94.

Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with CRYSTAL ISLAND

Intelligent tutoring systems, its 2016, 9684, 240–246.

Deepstealth: Leveraging deep learning models for stealth assessment in game-based learning environments

Artificial intelligence in education, aied 2015, 9112, 277–286.

A supervised learning framework for modeling director agent strategies in educational interactive narrative

IEEE Transactions on Computational Intelligence and AI in Games, 6(2), 203–215.

Generalizability of goal recognition models in narrative-centered learning environments

User modeling, adaptation, and personalization, umap 2014, 8538, 278–289.

Leveraging semi-supervised learning to predict student problem-solving performance in narrative-centered learning environments

Intelligent tutoring systems, its 2014, 8474, 664–665.

Serious games go informal: a museum-centric perspective on intelligent game-based learning

Intelligent tutoring systems, its 2014, 8474, 410–415.

Reports on the 2011 AAAI Fourth Artificial Intelligence for Interactive Digital Entertainment Conference Workshops

AI Magazine, 33(1), 55–56.

Affect dynamics in military trainees using vMedic: From engaged concentration to boredom to confusion

Ocumpaugh, J., Andres, J. M., Baker, R., DeFalco, J., Paquette, L., Rowe, J., … Sottilare, R. Artificial intelligence in education, aied 2017, 10331, 238–249.

Archetype-driven character dialogue generation for interactive narrative

Rowe, J. P., Ha, E. Y., & Lester, J. C. Intelligent virtual agents, proceedings, 5208, 45–58.

Balancing learning and engagement in game-based learning environments with multi-objective reinforcement learning

Sawyer, R., Rowe, J., & Lester, J. Artificial intelligence in education, aied 2017, 10331, 323–334.

Improving student problem solving in narrative-centered learning environments: A modular reinforcement learning framework

Rowe, J. P., & Lester, J. C. Artificial intelligence in education, aied 2015, 9112, 419–428.

Is more agency better? The impact of student agency on game-based learning

Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. Artificial intelligence in education, aied 2017, 10331, 335–346.

Off-task behavior in narrative-centered learning environments

Rowe, J. P., McQuiggan, S. W., Robison, J. L., & Lester, J. C. Artificial intelligence in education - building learnning systems that care: from knowledge representation to affective modelling , 200, 99–106.

Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion

Mudrick, N. V., Taub, M., Azevedo, R., Rowe, J., & Lester, J. International conference on affective computing and intelligent, 184–189.

Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with CRYSTAL ISLAND

Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. Intelligent tutoring systems, its 2016, 0684, 240–246.

Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with crystal island

Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. Intelligent tutoring systems, its 2016, 9684, 240–246.