STEM Learning and Learning Environments The Effectiveness of Intelligent Virtual Humans in Facilitating Self-Regulated Learning in STEM with MetaTutor
Effective Years: 2014-2018
The investigators will research how characteristics of intelligent virtual humans (IVHs) support the ability of students to reflect on and, therefore, improve their learning in undergraduate biology. To date, research has shown mixed effectiveness when human avatars are used in learning technologies. To remedy that, the researchers will first study how expert human tutors use verbal and facial cues in reacting to students' cognitive, affective, metacognitive, and motivational (CAMM) processes. Then, they will use these data to build an enhanced intelligent virtual human tutor (by altering software called "MetaTutor"). The project will advance the field's ability to build more effective intelligent tutors and advance understanding of self-regulated learning. The researchers propose to experimentally study the effectiveness of the enhanced IVHs on learners' self-regulatory processes and other learning outcomes. Data will be collected on both a natural face and a natural face that has been morphed and presented as a virtual human. The facial and verbal expressions are meant to provide learners with an additional information source they can use to monitor and regulate their ongoing self-regulatory processes, including making accurate emotional appraisals. In addition to the facial data, the researchers will collect self-report data, trace data using a variety of sensors, learning outcomes (e.g., pretest and posttest), and knowledge construction activities (e.g., summaries of content, notes, quizzes). Finally, the project will be disseminated in the form of journal publications, conference presentations, and an enhanced version of MetaTutor.
21 Publications Related to This Project:
- Understanding and Reasoning About Real-Time Cognitive, Affective, and Metacognitive Processes to Foster Self-Regulation with Advanced Learning Technologies
- Transitioning Self-Regulated Learning Profiles in Hypermedia-Learning Environments
- Using Data Visualizations to Foster Emotion Regulation During Self-Regulated Learning with Advanced Learning Technologies: A Conceptual Framework
- Let's Set Up Some Subgoals: Understanding Human-Pedagogical Agent Collaborations and Their Implications for Learning and Prompt and Feedback Compliance
- How are Students' Emotions Related to the Accuracy of Cognitive and Metacognitive Processes During Learning with an Intelligent Tutoring System?
- Integrating Metacognitive Judgments and Eye Movements Using Sequential Pattern Mining to Understand Processes Underlying Multimedia Learning
- Motivation Matters: Interactions Between Achievement Goals and Agent Scaffolding for Self-Regulated Learning Within an Intelligent Tutoring System
- How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes During Learning with an Intelligent Tutoring System?
- Impact of Learner-Centered Affective Dynamics on Metacognitive Judgements and Performance in Advanced Learning Technologies
- Evaluating Adaptive Pedagogical Agents' Prompting Strategies Effect on Students' Emotions
- Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems
- How are Students' Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSS?
- How Do Different Levels of Au4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems?
- The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with Metatutor
- Identifying How Metacognitive Judgments Influence Student Performance During Learning with Metatutorivh
- The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with Metatutor
- Using Eye-Tracking to Determine the Impact of Prior Knowledge on Self-Regulated Learning with an Adaptive Hypermedia-Learning Environment
- Are There Benefits of Using Multiple Pedagogical Agents to Support and Foster Self-Regulated Learning in an Intelligent Tutoring System?
- Are Pedagogical Agents' External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems?
- Predicting Co-Occurring Emotions from Eye-Tracking and Interaction Data in Metatutor
- Learners' Gaze Behaviors and Metacognitive Judgments with an Agent-Based Multimedia Environment
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