Implement early-warning systems in real classrooms using easily accessible data and transparent machine learning techniques¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 2 studies (1 review, 1 associational), q2 · 0 of 2 report an effect size

Description¶

The article recommends that SRL support tools such as early-warning systems can be implemented in authentic classrooms without heavy technological investment. As the authors state, "The findings support the implementation of early-warning systems in real classrooms using easily accessible data and transparent machine learning techniques without requiring heavy technological investments." Predictive and explanatory models together allow teachers to understand not just who needs support, but why, while maintaining teachers' agency in human-AI interactions.

Design Implications¶

Context¶

Requirements¶

• Accurate predictive models combined with interpretable explanatory models so teachers can understand who needs support and why

Constraints¶

• Predictive power and model explainability must be balanced, and fairness across subgroups (e.g., gender, parental education background) must be examined

Target Learners¶

• Students in inquiry-based science classrooms

Target Learning Goals¶

• Timely identification of students at risk and pedagogically meaningful SRL support

Claims¶

• In an AI-based early warning system, the predictive importance of CAM dimensions shifted over an instructional unit, with affect/metacognition self-reports leading early and cognitive artifacts gaining accuracy toward the end [+W]

Related Strategies¶

• Deploy ML-based early-warning prediction to shift retention support from reactive to proactive intervention
• Use an early warning system based on linked school and human services data to target resources and lessen risks before they become more serious
• Build early-warning indicators and leverage online-learning-system process data to support students before gaps widen in Grade 3
• Use explainable machine learning early warning systems to help teachers identify struggling inquiry learners early

Examples¶

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Key Sources¶

• Joni Lämsä, Susanne de Mooij, Martine Baars, Inge Molenaar, Sanna Järvelä. (2026). Self-Regulated Learning, Multimodal Data, and Analysis Grid: Where Are We Now and Where Are We Going? Educational Psychology Review. https://doi.org/10.1007/s10648-025-10113-4