Roger Azevedo

Enhancing stealth assessment in game-based learning through goal recognition

Gupta, A., Min, W., Carpenter, D., Azevedo, R., & Lester, J. (2025, September 23). Journal of Research on Technology in Education, Vol. 9.

Contextualizing Plans: Aligning Students Goals and Plans During Game-Based Inquiry Science Learning

Wiedbusch, M., Dever, D., Goslen, A., Carpenter, D., Marano, C., Smith, K., & Azevedo, R. (2024, January 1). Lecture Notes in Computer Science, Vol. 14723, pp. 113–128.

A Theoretical and Evidence-Based Conceptual Design of MetaDash: An Intelligent Teacher Dashboard to Support Teachers' Decision Making and Students’ Self-Regulated Learning

Frontiers in Education, 6.

Multimodal learning analytics for game‐based learning

Emerson, A., Cloude, E. B., Azevedo, R., & Lester, J. (2020, July 3). British Journal of Educational Technology.

Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection

Geden, M., Emerson, A., Carpenter, D., Rowe, J., Azevedo, R., & Lester, J. (2020, October 20). International Journal of Artificial Intelligence in Education.

PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems

Kim, Y.-J., Taub, M., Azevedo, R., & Chi, M. (2019, December 24). Lecture Notes in Computer Science.

The Role of Achievement Goal Orientation on Metacognitive Process Use in Game-Based Learning

Cloude, E. B., Taub, M., Lester, J., & Azevedo, R. (2019, January 1). Lecture Notes in Computer Science.

"Let's set up some subgoals": Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance

IEEE Transactions on Learning Technologies, 11(1), 54–66.

Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology

Journal of Construction Engineering and Management, 145(1), 04018115.

Changes in Emotion and Their Relationship with Learning Gains in the Context of MetaTutor

Sinclair, J., Jang, E. E., Azevedo, R., Lau, C., Taub, M., & Mudrick, N. V. (2018, January 1). Lecture Notes in Computer Science.

Evaluating Adaptive Pedagogical Agents’ Prompting Strategies Effect on Students’ Emotions

Bouchet, F., Harley, J. M., & Azevedo, R. (2018, January 1). Lecture Notes in Computer Science.

Gaze-Enhanced Student Modeling for Game-based Learning

Emerson, A., Sawyer, R., Azevedo, R., & Lester, J. (2018, July 3).

How Are Students’ Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs?

Taub, M., Mudrick, N. V., Rajendran, R., Dong, Y., Biswas, G., & Azevedo, R. (2018, January 1). Lecture Notes in Computer Science.

How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems?

Taub, M., Azevedo, R., & Mudrick, N. V. (2018, January 1). Lecture Notes in Computer Science.

How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System?

Taub, M., & Azevedo, R. (2018, March 19). International Journal of Artificial Intelligence in Education.

Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH

Mudrick, N. V., Sawyer, R., Price, M. J., Lester, J., Roberts, C., & Azevedo, R. (2018, January 1). Lecture Notes in Computer Science.

Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning

Mudrick, N. V., Azevedo, R., & Taub, M. (2018, June 26). Computers in Human Behavior.

Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems

Cloude, E. B., Taub, M., & Azevedo, R. (2018, January 1). Lecture Notes in Computer Science.

The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with MetaTutor

Price, M. J., Mudrick, N. V., Taub, M., & Azevedo, R. (2018, January 1). Lecture Notes in Computer Science.

Emotion recognition with facial expressions and physiological signals

Zhong, B., Qin, Z., Yang, S., Chen, J., Mudrick, N., Taub, M., … Lobaton, E. (2017, November 1). 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1170–1177.

The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor

Lallé, S., Taub, M., Mudrick, N. V., Conati, C., & Azevedo, R. (2017, January 1). Lecture Notes in Computer Science.

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. (2017, October 1).

Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island

Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2017, February 7). Computers in Human Behavior.

Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment

Taub, M., Azevedo, R., Bradbury, A. E., Millar, G. C., & Lester, J. (2017, September 1). Learning and Instruction.

Are Pedagogical Agents’ External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems?

Azevedo, R., Martin, S. A., Taub, M., Mudrick, N. V., Millar, G. C., & Grafsgaard, J. F. (2016, January 1). Lecture Notes in Computer Science.

Can adaptive pedagogical agents' prompting strategies improve students' learning and self-regulation?

Intelligent tutoring systems, its 2016, 0684, 368–374.

Development and Testing of a Personalized Hazard-Recognition Training Intervention

Jeelani, I., Albert, A., Azevedo, R., & Jaselskis, E. J. (2016, October 26). Journal of Construction Engineering and Management, Vol. 143.

Examining the predictive relationship between personality and emotion traits and students' agent-directed emotions: Towards emotionally-adaptive agent-based learning environments

User Modeling and User-Adapted Interaction, 26(2-3), 177–219.

Self-regulated learning processes vary as a function of epistemic beliefs and contexts: Mixed method evidence from eye tracking and concurrent and retrospective reports

Learning and Instruction, 42, 31–46.

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. (2016, January 1). Lecture Notes in Computer Science.

Using eye-tracking to determine the impact of prior knowledge on self-regulated learning with an adaptive hypermedia-learning environment

Intelligent tutoring systems, its 2016, 0684, 34–47.

Using multi-channel data to assess, understand, and support affect and metacognition with intelligent tutoring systems

Intelligent tutoring systems, its 2016, 0684, 543–544.

A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system

Computers in Human Behavior, 48, 615–625.

Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues

Educational Psychologist, 50(1), 84–94.

Examining the predictive relationship between personality and emotion traits and learners' agent-direct emotions

Artificial intelligence in education, aied 2015, 9112, 145–154.

Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system

Computers in Human Behavior, 52, 338–348.

Team regulation in a simulated medical emergency: An in-depth analysis of cognitive, metacognitive, and affective processes

Instructional Science, 43(3), 401–426.

The curious case of climate change: Testing a theoretical model of epistemic beliefs, epistemic emotions, and complex learning

Learning and Instruction, 39, 168–183.

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments?

Computers in Human Behavior, 39, 356–367.

Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning

Metacognition and Learning, 9(2), 217–228.

Metacognition

Cambridge Handbook of the Learning Sciences, 2nd Edition, 63–87.

Note-taking within MetaTutor: Interactions between an intelligent tutoring system and prior knowledge on note-taking and learning

Educational Technology Research and Development: ETR & D, 62(5), 507–528.

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.

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.