Performance Indicators in Math: Implications for Brief Experimental Analysis of Academic Performance

Background/Context A primary argument that supports charter school policy assumes students favor schools with high academic performance ratings, leading to systemic school improvement. Previous research challenges this assumption but has limited generalizability because geographic and enrollment constraints limit student choice sets. Purpose/Objective This study examines student enrollment patterns within cyber charter schools in Pennsylvania, a state where elected policymakers tend to view choice as a means for school improvement. Cyber charter schools are advantageous to study in this context because they have fewer enrollment barriers, helping researchers account for constraints found in previous studies. Research Design Using consecutive years of student-level enrollment data, we use descriptive statistics and multinomial logistic regression analyses to answer the following questions: Is a particular cyber charter school more popular if it displays relatively higher performance on academic indicators? To what extent do enrollments in the highest performing cyber charter school relate to the demographics of students and school environments that they left? Findings/Results The findings suggest that despite the more accessible choice sets inherent in the cyber charter school sector, academic performance indicators still are not linked to popularity within the sector. Enrollment clustering persists along student demographics and feeder district traits. Conclusions/Recommendations These findings suggest that even in the cyber charter school sector where key enrollment restrictions are removed, inequitable enrollment patterns persist. These findings continue to challenge basic assumptions used in school choice policy framing. Policymakers should consider this evidence when and if they design and implement charter school policy, creating policy that accounts for inequitable enrollments that occur under current policy logic.

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Brief Experimental Analysis of Math Interventions: A Synthesis of Evidence

Assessment for Effective Intervention ◽

10.1177/1534508419883937 ◽

2019 ◽

pp. 153450841988393

Author(s):

Nicole M. McKevett ◽

Robin S. Codding

Keyword(s):

Experimental Analysis ◽

Reading Interventions ◽

Brief Experimental Analysis ◽

Quick Method ◽

Math Intervention ◽

Math Interventions ◽

Computational Fluency ◽

Effective Interventions ◽

And Performance ◽

Performance Interventions

Brief experimental analysis (BEA) is a quick method used to identify the function of student learning difficulties and match effective interventions to students’ needs. Extensive work has been done to explore the use of this methodology to determine effective reading interventions; however, a smaller number of published studies have examined the use of BEAs in math. The purpose of the current review was to identify all studies that have used BEA methodology in math. Fifteen studies that included 63 participants and used BEA methodology to identify the most effective math intervention for students were located. Results of the synthesis indicate that the majority of BEAs compared skill and performance interventions on computational fluency; however, the methodology across the included studies varied. Strengths and limitations of the research, in addition to implications for research and practice, are discussed.

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