Computer-Programmed Decision Trees for Assessing Teacher Noticing

• In Event: 15.016 - Advancements in Human Scoring: Bringing Together Multiple Approaches

Abstract

Scoring of teacher noticing responses is typically burdened with rater bias and reliance upon interrater consensus. The authors sought to make the scoring process more objective, equitable, and generalizable. The development process began with a description of response characteristics for each professional noticing component disconnected from the specific context but allowing for the integration of context-specific relevant elements. The descriptions were transformed into a decision tree through which the raters need only make binary decisions at each node. Finally, the scoring process was streamlined through the use of a JavaScript-based scoring assistant guiding the rater through the decision tree.

Authors

• Edna O. Schack, Morehead State University

• David Dueber, University of Kentucky

• Jonathan Norris Thomas, University of Kentucky

• Molly H. Fisher, University of Kentucky

• Cindy Jong, University of Kentucky