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Direct linkBuzhardt, Jay; Greenwood, Charles R.; Jia, Fan; Walker, Dale; Schneider, Naomi; Larson, Anne L.; Valdovinos, Maria; McConnell, Scott R. – Exceptional Children, 2020
Data-driven decision making (DDDM) helps educators identify children not responding to intervention, individualize instruction, and monitor response to intervention in multitiered systems of support (MTSS). More prevalent in K-12 special education, MTSS practices are emerging in early childhood. In previous reports, we described the Making Online…
Descriptors: Data Analysis, Decision Making, Special Education, Infants
Buzhardt, Jay; Greenwood, Charles R.; Jia, Fan; Walker, Dale; Schneider, Naomi; Larson, Anne L.; Valdovinos, Maria; McConnell, Scott R. – Grantee Submission, 2020
Data-driven decision making (DDDM) helps educators identify children not responding to intervention, individualize instruction, and monitor response to intervention in multitiered systems of support (MTSS). More prevalent in K-12 special education, MTSS practices are emerging in early childhood. In previous reports, we described the Making Online…
Descriptors: Data Analysis, Decision Making, Special Education, Infants
Ilana Umansky; Hanna Dumont – Annenberg Institute for School Reform at Brown University, 2019
Prior research has shown that EL classification is consequential for students, however, less is known about how EL classification impacts students' outcomes. In this study, we examine one hypothesized mechanism: teacher perceptions. Using nationally-representative data (ECLS-K:2011), we use coarsened exact matching to estimate the effect of EL…
Descriptors: English Learners, Classification, Teacher Attitudes, Teacher Student Relationship
