Comparing AimswebPlus to the Benchmark Assessment System for Universal Screening in Upper Elementary Grades

Author:

Klingbeil David A.1ORCID,Van Norman Ethan R.2ORCID,Osman David J.3,Berry-Corie Kimberly4,Carberry Caroline K.5,Kim Jessica S.1

Affiliation:

1. University of Wisconsin-Madison, Madison, Wisconsin, USA

2. Lehigh University, Bethlehem, Pennsylvania, USA

3. Gibson Consulting Group, Austin, Texas, USA

4. Round Rock Independent School District, Round Rock, Texas, USA

5. The University of Texas at Austin, Austin, Texas, USA

Abstract

Early identification of students needing additional support is a foundational component of Multi-Tiered Systems of Support (MTSS). Due to the resource-intensive nature of implementing MTSS, it is critical that universal screening procedures are maximally accurate and efficient. The purpose of this study was to compare the classification accuracy of aimswebPlus reading scores to the Benchmark Assessment System scores. We used data from a mid-size city in Texas to retrospectively compare the classification accuracy between fall aimswebPlus reading composites to the Benchmark Assessment System scores when predicting student performance on the statewide reading test. When classification decisions were made based on the vendor-recommended cut-scores, both measures were insufficiently sensitive for screening in MTSS. Following aimswebPlus’ recommended method for establishing local-cut scores improved the sensitivity of decisions, but the specificity values were well below minimally acceptable levels. Limitations, directions for future research, and implications for practice are discussed.

Publisher

SAGE Publications

Subject

General Psychology,Clinical Psychology,Education

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Do Mathematics and Reading Skills Impact Student Science Outcomes?;Journal of Learning Disabilities;2024-07-26

2. Rapid online assessment of reading and phonological awareness (ROAR-PA);Scientific Reports;2024-05-04

3. An Intelligent Classification Method for Online Teaching Student Performance Data Based on Improved Decision Trees;2024 6th International Conference on Computer Science and Technologies in Education (CSTE);2024-04-19

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