Use postsecondary readiness indicators in an early warning system and combine cultural responsiveness with tiered interventions to address disparities¶

Strategy · All strategies
Evidence · 1 claim (1 mixed) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The report recommends that Arkansas integrate the studied indicators into an early warning system or student success plans to systematically identify students needing extra support, and that leaders address disparities by combining cultural responsiveness with tiered interventions. The authors state that "Research suggests that one strategy for ensuring equitable access to high-quality learning environments is to combine cultural responsiveness with tiered interventions that become increasingly targeted for identified students." They also suggest integrating indicators that use data collected multiple times per year.

Design Implications¶

Context¶

Requirements¶

• Subsequent analyses would be required to ensure appropriate time points from the indicators are integrated into models aiming to enable more contemporaneous decisionmaking

Constraints¶

• The findings should not be interpreted as causal; predictive strength information can help prioritize indicators for monitoring but does not establish intervention effects

Target Learners¶

• Arkansas middle and high school students, particularly those from historically marginalized groups

Target Learning Goals¶

• Postsecondary readiness and success, including ACT proficiency, college enrollment, and college persistence

Claims¶

• Fewer substantial differences between student groups occurred for behavioral indicators than for academic indicators of postsecondary readiness and success [~W]

Related Strategies¶

• Use Early Warning Indicator (EWI) and Multi-Tiered Systems of Support (MTSS) for continuous improvement
• Raise student awareness of requirement flexibility and support options for meeting CCR graduation requirements
• Use early-session timing signals, interpreted relative to proficiency, to identify students needing adaptive support
• Use explainable machine learning early warning systems to help teachers identify struggling inquiry learners early

Examples¶

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Key Sources¶

• Hester, C., Lazarev, V., Zacamy, J., Nardi, C., & Feygin, A. (2022). Student group differences in Arkansas' indicators of postsecondary readiness and success. Regional Educational Laboratory Southwest. https://ies.ed.gov/ncee/edlabs/regions/southwest/rel_2023-145.asp