Michelle Taub

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.

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

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

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.

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.

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.

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.

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.

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.

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.