Roger Azevedo

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.

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

INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 31(1), 1–23.

Multimodal learning analytics for game-based learning

BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 51(5), 1505–1526.

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

MULTIMEDIA MODELING (MMM 2020), PT II, Vol. 11962, pp. 63–75.

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

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

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

INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 29(1), 1–28.

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

COMPUTERS IN HUMAN BEHAVIOR, 96, 223–234.

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

ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, Vol. 11626, pp. 36–40.

"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.

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

INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 202–211.

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

INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 33–43.

Gaze-Enhanced Student Modeling for Game-based Learning

PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18), pp. 63–72.

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

INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 233–242.

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

INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 223–232.

Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH

INTELLIGENT TUTORING SYSTEMS, ITS 2018, 10858, 140–149.

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

INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 44–53.

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

INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 170–179.

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

LEARNING AND INSTRUCTION, 54, 93–103.

Development and Testing of a Personalized Hazard-Recognition Training Intervention

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 143(5).

Emotion recognition with facial expressions and physiological signals

2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1170–1177.

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

ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017, Vol. 10331, pp. 149–161.

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.

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

INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 197–207.

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

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

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

INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 240–246.

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.