STEM Learning and Learning Environments Supporting Student Planning with Open Learner Models in Middle Grades Science
Effective Years: 2018-2023
This project is supported by the Education and Human Resources Core Research (ECR) program, which supports fundamental research in STEM learning and learning environments. Self-regulated learning requires learners to set learning goals, plan how to achieve them, monitor the success of their plans, and make needed changes. Critical gaps exist in our understanding of how and to what extent middle school students can engage in self-regulated learning during science inquiry activities. Analogous gaps exist in our understanding of how to help students become self-regulated learners. Computer-based learning tools might help fill these gaps, but these tools are usually "closed," meaning that how the software interprets students' knowledge and progress is hidden from the student. In contrast, "open" learning tools provide students with understandable, visual representations of their knowledge and progress. Such "open learner models" may be useful for helping students develop self-regulated learning skills. For example, by providing easy-to-understand representations of student progress, open learner models may support the self-regulated learning processes of goal setting and planning. This project focuses on the design, development, and investigation of open learner models for student goal setting and planning in middle school science. The project will explore how students engage in goal-directed learning behaviors using data from eye tracking, log files, and think-aloud exercises. It is anticipated that the project results can advance the goal of high quality STEM learning experiences for all students. A major project goal is improving students' problem-solving abilities and learning outcomes through a theoretically grounded, data-driven open learner model. The project is designed to integrate an open learner model into the Future Worlds science learning environment for middle school ecosystems education. A culminating between-subjects experiment will be conducted to compare a baseline version of Future Worlds that does not have an open learner model (control condition) with a version of Future Worlds that has an embedded open learner model (experimental condition). The project will use state-of-the-art artificial intelligence computational frameworks to recognize students' goals and plans from observations of their problem-solving activities in an online learning environment. It is hypothesized that the open learner model will yield better student learning outcomes, with improved science problem-solving skills, increased science content knowledge, increased metacognitive awareness, and enhanced science self-efficacy. The project is designed to make significant contributions to both theory and practice of self-regulated learning. The project's aim is to make contributions to foundational knowledge and theory by advancing our understanding of how to improve science learning with open learner models: (1) With a focus on student goal setting and planning in technology-rich learning environments, the project aims to formulate an empirically-based theoretical framework for open learner model-enhanced learning that addresses both cognitive and metacognitive components of middle grades science education. (2) By conceptualizing goal setting and planning to account for self-regulated learning in the context of science problem solving, the project is designed to create a rich framework that connects goal setting and planning to students' problem solving and metacognitive processes. (3) By expanding to fine-grained process data, the project is intended to make methodological contributions that will enable the field to go beyond self-report measures, which have long dominated research on self-regulated learning. (4) The project plans to produce learning analytic techniques that yield predictive models of goal setting and planning in science problem solving, as well as open learner models to effectively support student goal setting and problem solving. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
20 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
- From Product to Process Data: Game Mechanics for Science Learning
- Investigating Pedagogical Agents' Scaffolding of Self-Regulated Learning in Relation to Learners' Subgoals
- Leveraging Student Planning in Game-Based Learning Environments for Self-Regulated Learning Analytics
- Llm-Based Student Plan Generation for Adaptive Scaffolding in Game-Based Learning Environments
- 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
- Tracing Emerging Complexity of Scientific Reasoning Actions During Game-Based Learning
- Contextualizing Plans: Aligning Students Goals and Plans During Game-Based Inquiry Science 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
- Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning
- 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
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