Build early-warning indicators and leverage online-learning-system process data to support students before gaps widen in Grade 3¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The authors recommend creating indicators to alert educators to students needing different levels of support, especially for first graders starting below grade level, to minimize widening gaps before Grade 3. They propose embedding data collection in classroom routines, aggregating student-level usage data to school-level measures, and developing process measures beyond lessons completed and time spent, "such as the consistency of student access to the online learning system and student response time once lessons are assigned".

Design Implications¶

Context¶

Requirements¶

• Access to online learning system data that can be aggregated to school level
• Educator routines that allow embedding data collection with minimal disruption

Constraints¶

• If all students use the tool regularly through embedded instruction, student-level usage variation disappears and only school-level aggregation remains informative

Target Learners¶

• Early elementary students, particularly first graders placing below grade level

Target Learning Goals¶

• Sustaining mathematics growth across the school year and preventing gap widening by Grade 3

Related Strategies¶

• Use positive-deviance school identification and engagement process indicators to target supports and future research on growth variation
• Use an early warning system based on linked school and human services data to target resources and lessen risks before they become more serious
• Combine in-school and out-of-school data sources to understand and support students flagged as at risk
• Use findings on strategy–time-management links to develop early warning systems and interventions promoting effective time management and strategy use in flipped classrooms
• Use school-level data systems to identify which students need which types of support for dropout prevention
• Invest early: identify learning problems and intervene before third or sixth grade rather than waiting for promotional gates
• Use kindergarten-entry test data for early identification so educators can intervene before third grade
• Use longitudinal student-level data systems to diagnose dropout scope and identify at-risk students before key transitions
• Use KEAs to strengthen early learning systems: family engagement, p–3 alignment, and aggregated data for equitable investment
• Implement early-warning systems in real classrooms using easily accessible data and transparent machine learning techniques
• Use explainable machine learning early warning systems to help teachers identify struggling inquiry learners early

Examples¶

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Key Sources¶

• Hiroyuki Yamada. (2024). New Normal in Early Elementary Mathematics Learning: Part III - Learning from Variation in Grade 3. E4 Center, Northwestern University. https://e4.northwestern.edu/2024/12/19/new-normal-in-early-elementary-mathematics-learning-part-iii-learning-from-variation-in-grade-3/