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Measuring Complex Features of Science Instruction: Developing Tools to Investigate the Link Between Teaching and Learning
There is a growing national recognition that teachers and teaching are at the heart of successful educational reform. However, few tools exist for measuring classroom instruction. The primary purpose of this article is to describe methods we developed to measure and study teaching, specifically whil...
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Published in: | The Journal of the learning sciences 2009-04, Vol.18 (2), p.285-322 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | There is a growing national recognition that teachers and teaching are at the heart of successful educational reform. However, few tools exist for measuring classroom instruction. The primary purpose of this article is to describe methods we developed to measure and study teaching, specifically while teachers were using a multimedia intervention for promoting scientific problem solving. Lessons were videotaped, and coding schemes were developed to measure 2 aspects of teaching: (a) the lesson's organization, particularly whole-class instruction used to introduce problems and share students' work; and (b) the nature of tasks and questions given to students. Results showed that the coding schemes were reliable and that they detected differences in instruction across teachers. Qualitative analyses were consistent with the quantitative findings. The codes also captured features of teaching that would have been difficult to detect or verify with qualitative observations alone. Finally, we explored how these measures could be used with student outcome data to examine the relationship between teaching and learning in future studies. We argue that quantitative measures of instruction serve many purposes, not the least of which is allowing researchers to explore the relationship between teaching and student learning at a high degree of granularity. |
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ISSN: | 1050-8406 1532-7809 |
DOI: | 10.1080/10508400902797941 |