Search

Adapting interventions based on learner progress is paramount to the effectiveness of interventions in special education and applied behavior analysis. Although there is some research on effective methods for training practitioners to make general instructional decisions (e.g., modify an intervention) based on graphed performance data, research on training individuals to make specific decisions (e.g., how to modify an intervention) is more limited. Our purpose in this study was to evaluate the effects of a training package, consisting of a brief online training and a visual decision-making model, for increasing preservice teachers' and behavior analysts' accuracy in making specific instructional decisions based on graphed performance data. In a multiple baseline across participants design, all participants increased their decision-making accuracy on novel graphs during assessment sessions and maintained accuracy at 1-month follow-up. The implications of these findings for training and future research on data-based decision-making are discussed.

Adapting interventions based on learner progress is paramount to the effectiveness of interventions in special education and applied behavior analysis. Although there is some research on effective methods for training practitioners to make general instructional decisions (e.g., modify an intervention) based on graphed performance data, research on training individuals to make specific decisions (e.g., how to modify an intervention) is more limited. Our purpose in this study was to evaluate the effects of a training package, consisting of a brief online training and a visual decision-making model, for increasing preservice teachers' and behavior analysts' accuracy in making specific instructional decisions based on graphed performance data. In a multiple baseline across participants design, all participants increased their decision-making accuracy on novel graphs during assessment sessions and maintained accuracy at 1-month follow-up. The implications of these findings for training and future research on data-based decision-making are discussed., Author(s): Katie Wolfe [sup.1], Meka N. McCammon [sup.1], Lauren M. LeJeune [sup.1], Ashley K. Holt [sup.1] Author Affiliations: (1) grid.254567.7, 0000 0000 9075 106X, Department of Educational Studies, University of [...]