Schools and districts should choose screener benchmarks that match their goals and resources for defining and serving at-risk students¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The brief recommends that schools and districts deliberately align benchmark selection with their local definition of risk. It states that 'schools and districts must carefully consider their goals and resources in choosing how to use them to identify and address student needs,' because benchmark scores link to a wide range of the MCAS scale. If the risk cut score is too low, needy students go unserved; if too high, resources are spent unnecessarily.

Design Implications¶

Context¶

Requirements¶

• A local definition of what 'risk' means and what instructional supports and resources are available to respond

Constraints¶

• Data included in the analysis are described by the authors as somewhat limited

Target Learners¶

• Students in kindergarten through grade 3 in Massachusetts grantee schools and districts

Target Learning Goals¶

• Early literacy and reading proficiency; identification of students at risk of reading difficulty

Related Strategies¶

• Align selected data directly to the purpose or problem the data use is meant to serve
• Frame local risk definitions in MCAS terms: benchmarks near Partially Meeting identify students most in need, while benchmarks nearer Meeting Expectations identify students needing support to meet MCAS goals
• Pair norm-referenced metrics with evidence of progress toward grade-level expectations in accountability decisions
• Use multiple evidence sources alongside benchmark scores when identifying students for intensive intervention
• Provide more intensive literacy interventions and supports for at-risk students in later grades
• Screen multilingual students in multiple languages where possible, choosing screener languages informed by students' instructional contexts and home language use
• Teachers should use the identified moderators as criteria when selecting CALL applications and predicting student performance

Examples¶

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Key Sources¶

• Lemke, M., Murphy, D., Soo Ping Chow, A., Spencer, H., & Zhang, A. (2023). Comparing early literacy screening assessment benchmarks in Massachusetts. WestEd. https://eric.ed.gov/?id=ED638508