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ERIC Number: EJ1165153
Record Type: Journal
Publication Date: 2018
Pages: 18
Abstractor: As Provided
ISSN: ISSN-1537-7903
Identifying Students at Risk: An Examination of Computer-Adaptive Measures and Latent Class Growth Analysis
Keller-Margulis, Milena; McQuillin, Samuel D.; CastaƱeda, Juan Javier; Ochs, Sarah; Jones, John H.
Journal of Applied School Psychology, v34 n1 p18-35 2018
Multitiered systems of support depend on screening technology to identify students at risk. The purpose of this study was to examine the use of a computer-adaptive test and latent class growth analysis (LCGA) to identify students at risk in reading with focus on the use of this methodology to characterize student performance in screening. Participants included 3,699 students in Grades 3-5. Three time points of administration (fall, winter, and spring) of the computer-adaptive reading measure were selected. LCGA results indicated 6-7 classes, depending on grade, informed by level and growth in student performance that significantly predicted failure on the statewide test administered at the end of the year. The lowest-performing classes had failure rates above 90% across all grades. The results indicate that identifying homogeneous groups of learners through LCGA may be valuable as an approach to determining students who need additional instruction. Practical implications and future directions are discussed.
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Publication Type: Journal Articles; Reports - Research
Education Level: Grade 3
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Texas
Grant or Contract Numbers: N/A