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Filling an Information Gap in Preschool MTSS and RTI Decision Making
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Greenwood, Charles R.; Carta, Judith J.; Schnitz, Alana G.; Irvin, Dwight W.; Jia, Fan; Atwater, Jane – Exceptional Children, 2019
A tenet of multitiered systems of support and response to intervention (MTSS-RTI) is that lack of response to instructional intervention is explained by classroom experiences and behaviors given opportunities to learn. We investigated the potential of filling this information gap in MTSS-RTI decision making using ecobehavioral observation to…
Descriptors: Response to Intervention, Decision Making, Access to Information, Preschool Children
Filling an Information Gap in Preschool MTSS and RTI Decision Making
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PDF on ERIC Download full text
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Greenwood, Charles R.; Carta, Judith J.; Schnitz, Alana G.; Irvin, Dwight W.; Jia, Fan; Atwater, Jane – Grantee Submission, 2019
A tenet of multitiered systems of support and response to intervention (MTSS/RTI) is that lack of response to instructional intervention is explained by classroom experiences and behaviors given opportunities to learn. We investigated the potential of filling this information gap in MTSS/RTI decision making using ecobehavioral observation to…
Descriptors: Response to Intervention, Decision Making, Access to Information, Preschool Children
Technology to Guide Data-Driven Intervention Decisions: Effects on Language Growth of Young Children at Risk for Language Delay
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Buzhardt, 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
Technology to Guide Data-Driven Intervention Decisions: Effects on Language Growth of Young Children at Risk for Language Delay
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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