Computer assessment of interview data using latent semantic analysis

Behavior Research Methods
Gregory Dam, Stefan Kaufmann

Abstract

Clinical interviews are a powerful method for assessing students' knowledge and conceptualdevelopment. However, the analysis of the resulting data is time-consuming and can create a "bottleneck" in large-scale studies. This article demonstrates the utility of computational methods in supporting such an analysis. Thirty-four 7th-grade student explanations of the causes of Earth's seasons were assessed using latent semantic analysis (LSA). Analyses were performed on transcriptions of student responses during interviews administered, prior to (n = 21) and after (n = 13) receiving earth science instruction. An instrument that uses LSA technology was developed to identify misconceptions and assess conceptual change in students' thinking. Its accuracy, as determined by comparing its classifications to the independent coding performed by four human raters, reached 90%. Techniques for adapting LSA technology to support the analysis of interview data, as well as some limitations, are discussed.

References

Sep 10, 2004·Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc·Arthur C GraesserMax M Louwerse

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Citations

Dec 4, 2014·Journal of Psycholinguistic Research·Rumen Iliev, Anastasia Smirnova
Feb 15, 2018·Frontiers in Psychology·M Teresa AngueraSusana Sanduvete-Chaves
Nov 26, 2019·Topics in Cognitive Science·Andrew Shtulman, Cristine H Legare
Aug 10, 2017·Animals : an Open Access Journal From MDPI·Daniela LunaTamara A Tadich

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