STEM Learning and Learning Environments MetaDash: A Teacher Dashboard Informed by Real-Time Multichannel Self-Regulated Learning Data
Effective Years: 2017-2025
The project is supported by the Education and Human Resource Core Research program, which supports fundamental research in STEM learning and learning environments. It is important to find new ways to support students' ability to construct, analyze, critique, and use models of STEM phenomena. Given that teachers are the main mediator of any educational innovation, it is imperative to support STEM teachers to effectively engage students in critical thinking skills. This project involves the research and development of MetaDash, a teacher dashboard that provides information regarding students' cognitive, affective, metacognitive, and motivational self-regulatory learning processes during STEM instruction. The dashboard is informed by multi-modal channels that synthesize information such as student facial expressions, eye gaze behavior, electrodermal activity, and verbalizations. MetaDash will impact current teacher training by providing real-time student data (both individual student and aggregated) to enhance instructional decision-making. Research methodology centers around the design and testing of Metadash as an intelligent, multichannel data visualization tool that displays key aspects of students' learning processes and knowledge construction in real time. The research approach investigates (1) how and when to present the multi-channel input based on human-computer-interaction design principles and informed by teacher usability studies; (2) how to optimize statistical approaches to handle unstructured data from multiple sources; and (3) how to create behavioral signatures for constructs such as self-regulation, motivation and frustration using multi-modal measures such as eye-tracking and facial expression. The ultimate goal of MetaDash is to foster STEM learning by supporting teachers' and students' monitoring and control of CAMM SRL processes. As such, MetaDash will advance current dashboards by providing teachers with: (1) multichannel STEM learning and cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) data collected from students; and (2) individual student and aggregate data to accelerate teachers' decision-making.
24 Publications Related to This Project:
- The Challenge of Measuring Processes and Outcomes While Learning from Multiple Representations with Advanced Learning Technologies
- Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
- Lessons Learned and Future Directions of Metatutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning with an Intelligent Tutoring System
- Multiple Negative Emotions During Learning with Digital Learning Environments - Evidence on Their Detrimental Effect on Learning from Two Methodological Approaches
- Using Data Visualizations to Foster Emotion Regulation During Self-Regulated Learning with Advanced Learning Technologies: A Conceptual Framework
- Investigating Pedagogical Agents' Scaffolding of Self-Regulated Learning in Relation to Learners' Subgoals
- 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?
- Measuring Self-Regulated Learning and the Role of AI: Five Years of Research Using Multimodal Multichannel Data
- Analyzing Multimodal Multichannel Data About Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges
- How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes During Learning with an Intelligent Tutoring System?
- A Complex Systems Approach to Analyzing Pedagogical Agents' Scaffolding of Self-Regulated Learning Within an Intelligent Tutoring System
- Introduction to the Special Issue: The Role of Metacognition in Complex Skills-Spotlights on Problem Solving, Collaboration, and Self-Regulated Learning
- Reflections on the Field of Metacognition: Issues, Challenges, and Opportunities
- Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems
- Evaluating Adaptive Pedagogical Agents' Prompting Strategies Effect on Students' Emotions
- 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
- Analytical Approaches for Examining Learners' Emerging Self-Regulated Learning Complex Behaviors with an Intelligent Tutoring System
- Leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems
- Effectiveness of System-Facilitated Monitoring Strategies on Learning in an Intelligent Tutoring System
- Revealing Data Feature Differences Between System- And Learner-Initiated Self-Regulated Learning Processes Within Hypermedia
- Learners' Gaze Behaviors and Metacognitive Judgments with an Agent-Based Multimedia Environment
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