ERIC Number: ED613864
Record Type: Non-Journal
Publication Date: 2019-Apr-5
Pages: 9
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-
EISSN: N/A
Computer-Programmed Decision Trees for Assessing Teacher Noticing
Schack, Edna O.; Dueber, David; Thomas, Jonathan Norris; Fisher, Molly H.; Jong, Cindy
AERA Online Paper Repository, Paper presented at the Annual Meeting of the American Educational Research Association (Toronto, Canada, Apr 5-9, 2019)
Scoring of teachers' 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.
Descriptors: Models, Teacher Evaluation, Observation, Bias, Computer Assisted Testing, Interrater Reliability, Scoring Rubrics
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Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: 1043656; 1043667; 1043831