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Title: Using Hierarchical Logistic Regression Analysis to Investigate Equity in Classroom Discourse
Authors: Orner, Aviv
Lefstein, Adam
Keywords: Learning Sciences
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Orner, A. & Lefstein, A. (2022). Using hierarchical logistic regression analysis to investigate equity in classroom discourse. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 977-980). International Society of the Learning Sciences.
Abstract: This paper presents a novel methodology for examining equity in classroom discourse. Coding discourse data at the level of the exchange (rather than the utterance) affords investigation of the relationships between teachers' expectations, teaching patterns, students’ social backgrounds and participation patterns. Using a hierarchical logistic regression model, we found that the likelihood of students’ answers being developed by the teacher in this particular classroom was greater for Jewish students, for boys, for students from higher socioeconomic backgrounds, for answers given following teacher nomination, and for relevant responses. In contrast to previous studies, the teacher’s perception of the student’s learning ability and the type of question posed had no statistically significant effect on the likelihood of the teacher developing the student’s answer. The study contributes to our understanding of the mechanisms of educational injustice, to the development of methods for investigating equity in classroom discourse, and – thereby – to its rectification.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2022

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