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Cited by 296

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Teachers’ Analyses of Classroom Video Predict Student Learning of Mathematics: Further Explorations of a Novel Measure of Teacher Knowledge

Kersting, Nicole B., Givvin, Karen B., Sotelo, Francisco L., Stigler, James W.·2010·Journal of Teacher Education

Abstract

This study explores the relationship between teacher knowledge and student learning in the area of mathematics by developing and evaluating an innovative approach to assessing teacher knowledge. This approach is based on teachers’ analyses of classroom video clips. Teachers watched 13 video clips of classroom instruction and then provided written comments on the interactions of the teacher, students, and content. The quality of teachers’ analyses, coded using an objective rubric, are shown to be reliable and valid, relating both to another widely used measure of teacher knowledge and to teachers’ own students’ learning (from pre- to posttest).

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Metadata

Authors
Kersting, N. B., Givvin, K. B., Sotelo, F. L., & Stigler, J. W.
Venue
Journal of Teacher Education
Published
2009-12-30
Volume
61
Issue
1-2
Pages
172-181
ISSN
  • 0022-4871 (ISSN-L)
  • 1552-7816
OpenAlex
W2044662025
Cited by
296
References
25
FWCI
38.03
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Teachers’ Analyses of Classroom Video Predict Student Learning of Mathematics: Further Explorations of a Novel Measure of Teacher Knowledge. https://app.cochat.ai/papers/10.1177/0022487109347875
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Kersting, N. B., Givvin, K. B., Sotelo, F. L., & Stigler, J. W. (2010). Teachers’ Analyses of Classroom Video Predict Student Learning of Mathematics: Further Explorations of a Novel Measure of Teacher Knowledge. Journal of Teacher Education. https://doi.org/10.1177/0022487109347875

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.