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Riggleman, Samantha – Journal of Special Education Technology, 2021
Social-emotional development in early childhood (EC) is an important factor to their later development and adjustment. While all young children display unwanted behaviors at some time during development, challenging behaviors that occur across settings and over a period of time should be identified and intervened; thus, data collection efforts…
Descriptors: Early Childhood Education, Behavior Change, Social Emotional Learning, Child Development
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Deshais, Meghan A.; Fisher, Alyssa B.; Kahng, SungWoo – Education and Treatment of Children, 2018
We conducted a preliminary investigation of a randomized dependent group contingency to decrease disruptive behavior during hallway transitions. Two first-graders, identified by their classroom teacher, participated in this study. A multiple baseline across transitions was used to evaluate the effects of the randomized dependent group contingency…
Descriptors: Student Behavior, Behavior Problems, Discipline Problems, Prevention
Lindström, Esther R.; Gesel, Samantha A.; Lemons, Christopher J. – Intervention in School and Clinic, 2019
Students with severe and persistent academic or behavioral challenges may benefit from data-based individualization (DBI). Starting with an evidence-based standard protocol and systematic progress monitoring, teachers can evaluate growth and implement individualized interventions to meet students' needs. Specifically, this article addresses the…
Descriptors: Students with Disabilities, Behavior Problems, Evidence Based Practice, Student Evaluation
Mires, Carolyn B.; Lee, David L. – Beyond Behavior, 2017
Calvin is a student who will not stay in his seat. He calls out constantly. Calvin does not complete his class work, and his homework is rarely returned. Do you have a student like Calvin? Does he fail to turn in homework, or act disrespectfully toward teachers and peers? Easy to implement, the Daily Behavior Report Card is an empirically based…
Descriptors: Student Behavior, Behavior Modification, Family School Relationship, Intervention
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Sorensen, Lucy C. – Educational Administration Quarterly, 2019
Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…
Descriptors: At Risk Students, Dropouts, Data Collection, Data Analysis