STEM Learning and Learning Environments Improving Science Problem Solving with Adaptive Game-Based Reflection Tools
Effective Years: 2017-2022
The project is supported by the Education and Human Resources Core Research program, which supports fundamental research in STEM learning and learning environments. Reflection plays a critical role in student learning and encompasses a broad range of cognitive and metacognitive processes enabling students to (1) think critically about their learning processes, (2) integrate new information with prior knowledge, (3) form and adapt learning strategies, (4) view concepts and knowledge from multiple perspectives, (5) generate self-explanations to enrich conceptual understanding, (6) compare learning processes and artifacts to those created by experts and peers, and (7) make metacognitive judgments about knowledge. The National Research Council recently concluded that systematic reflection is essential for deep learning and effective educational practice. This project investigates a suite of theoretically grounded, adaptive game-based reflection tools to scaffold students' cognitive and metacognitive reflection with the overarching objective of improving middle school students' science problem-solving processes. These reflection tools are integrated as part of the existing Crystal Island learning environment. In studying middle school students' cognitive and metacognitive reflective processes, three key research questions will be addressed: 1) How do embedded and retrospective reflection scaffolds promote improved learning outcomes including science problem-solving skills, science content knowledge, metacognitive awareness, and reflection skills? 2) How can learning analytics be leveraged to extend models of reflection and self-regulated learning for problem solving within game-based learning environments? and 3) How can we create adaptive scaffolding for game-based learning environments that best foster reflection during and following science problem solving? The project builds upon a theoretical framework of self-regulated learning to inform the development of embedded and retrospective scaffold tools, gathering both process data (e.g., log data, eye tracking, think-aloud) and outcome data (e.g., pre- post- learning, transfer, metacognition, self-efficacy) to inform design principles and descriptive models of the role of reflection in students' problem-solving processes. Key project outcomes include an empirically grounded theoretical framework for reflection-enhanced learning and learning analytic techniques that yield predictive models of reflection in science problem solving.
29 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
- Identifying the Effects of Scaffolding on Learners' Temporal Deployment of Self-Regulated Learning Operations During Game-Based Learning Using Multimodal Data
- Capturing Sequences of Learners' Self-Regulatory Interactions with Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis
- 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
- Game-Based Learning Analytics for Supporting Adolescents' Reflection
- From Product to Process Data: Game Mechanics for Science Learning
- Investigating Student Reflection During Game-Based Learning in Middle Grades Science
- Investigating Pedagogical Agents' Scaffolding of Self-Regulated Learning in Relation to Learners' Subgoals
- How are Students' Emotions Related to the Accuracy of Cognitive and Metacognitive Processes During Learning with an Intelligent Tutoring System?
- The Agency Effect: The Impact of Student Agency on Learning, Emotions, and Problem-Solving Behaviors in a Game-Based Learning Environment
- 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
- Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection
- 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
- 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
- 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
- Tracing Emerging Complexity of Scientific Reasoning Actions During Game-Based Learning
- Analytical Approaches for Examining Learners' Emerging Self-Regulated Learning Complex Behaviors with an Intelligent Tutoring System
- Scaffolding Self-Regulated Learning in Game-Based Learning Environments Based on Complex Systems Theory
- 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
- Automated Analysis of Middle School Students' Written Reflections During Game-Based Learning
- Learners' Gaze Behaviors and Metacognitive Judgments with an Agent-Based Multimedia Environment
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