Self-Regulated Learning in the Digitally Enhanced Science Classroom: Toward an Early Warning System
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| Title: | Self-Regulated Learning in the Digitally Enhanced Science Classroom: Toward an Early Warning System |
|---|---|
| Language: | English |
| Authors: | Marcus Kubsch (ORCID |
| Source: | Educational Psychology Review. 2025 37(2). |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 38 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education Elementary Education Grade 7 Grade 8 |
| Descriptors: | Inquiry, Science Instruction, Electronic Books, Workbooks, Physics, Artificial Intelligence, Prediction, Data Collection, Cognitive Processes, Metacognition, Affective Behavior, Productivity, Self Management, Foreign Countries, At Risk Students, Middle School Students, Grade 7, Grade 8, Technology Uses in Education |
| Geographic Terms: | Germany |
| DOI: | 10.1007/s10648-025-10011-9 |
| ISSN: | 1040-726X 1573-336X |
| Abstract: | Recent research underscores the importance of inquiry learning for effective science education. Inquiry learning involves self-regulated learning (SRL), for example when students conduct investigations. Teachers face challenges in orchestrating and tracking student learning in such instruction; making it hard to adequately support students. Using AI methods such as machine learning (ML), the data that is generated when students interact in technology-enhanced classrooms can be used to track their learning and subsequently to inform teachers so that they can better support student learning. This study implemented digital workbooks in an inquiry-based physics unit, collecting cognitive, metacognitive, and affective data from 214 students. Using ML methods, an early warning system was developed to predict students' learning outcomes. Explainable ML methods were used to unpack these predictions and analyses were conducted for potential biases. Results indicate that an integration of cognitive, metacognitive, and affective data can predict students' productivity with an accuracy ranging from 60 to 100% as the unit progresses. Initially, affective and metacognitive variables dominate predictions, with cognitive variables becoming more significant later. Using only affective and metacognitive data, predictive accuracies ranged from 60 to 80% throughout. Bias was found to be highly dependent on the ML methods being used. The study highlights the potential of digital student workbooks to support SRL in inquiry-based science education, guiding future research and development to enhance instructional feedback and teacher insights into student engagement. Further, the study sheds new light on the data needed and the methodological challenges when using ML methods to investigate SRL processes in classrooms. |
| Abstractor: | As Provided |
| Notes: | https://osf.io/uv8tn |
| Entry Date: | 2025 |
| Accession Number: | EJ1466628 |
| Database: | ERIC |
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| Abstract: | Recent research underscores the importance of inquiry learning for effective science education. Inquiry learning involves self-regulated learning (SRL), for example when students conduct investigations. Teachers face challenges in orchestrating and tracking student learning in such instruction; making it hard to adequately support students. Using AI methods such as machine learning (ML), the data that is generated when students interact in technology-enhanced classrooms can be used to track their learning and subsequently to inform teachers so that they can better support student learning. This study implemented digital workbooks in an inquiry-based physics unit, collecting cognitive, metacognitive, and affective data from 214 students. Using ML methods, an early warning system was developed to predict students' learning outcomes. Explainable ML methods were used to unpack these predictions and analyses were conducted for potential biases. Results indicate that an integration of cognitive, metacognitive, and affective data can predict students' productivity with an accuracy ranging from 60 to 100% as the unit progresses. Initially, affective and metacognitive variables dominate predictions, with cognitive variables becoming more significant later. Using only affective and metacognitive data, predictive accuracies ranged from 60 to 80% throughout. Bias was found to be highly dependent on the ML methods being used. The study highlights the potential of digital student workbooks to support SRL in inquiry-based science education, guiding future research and development to enhance instructional feedback and teacher insights into student engagement. Further, the study sheds new light on the data needed and the methodological challenges when using ML methods to investigate SRL processes in classrooms. |
|---|---|
| ISSN: | 1040-726X 1573-336X |
| DOI: | 10.1007/s10648-025-10011-9 |
