Insights into students' conceptual understanding using textual analysis: A case study in signal processing

Goncher, Andrea M., Jayalath, Dhammika, & Boles, Wageeh (2016) Insights into students' conceptual understanding using textual analysis: A case study in signal processing. IEEE Transactions on Education. (In Press)

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Abstract

Concept inventory tests are one method to evaluate conceptual understanding and identify possible misconceptions. The multiple-choice question format, offering a choice between a correct selection and common misconceptions, can provide an assessment of students' conceptual understanding in various dimensions. Misconceptions of some engineering concepts exist due to a lack of mental frameworks, or schemas, for these types of concepts or conceptual areas. This study incorporated an open textual response component in a multiple-choice concept inventory test to capture written explanations of students' selections. The study's goal was to identify, through text analysis of student responses, the types and categorizations of concepts in these explanations that had not been uncovered by the distractor selections. The analysis of the textual explanations of a subset of the discrete-time signals and systems concept inventory questions revealed that students have difficulty conceptually explaining several dimensions of signal processing. This contributed to their inability to provide a clear explanation of the underlying concepts, such as mathematical concepts. The methods used in this study evaluate students' understanding of signals and systems concepts through their ability to express understanding in written text. This may present a bias for students with strong written communication skills. This study presents a framework for extracting and identifying the types of concepts students use to express their reasoning when answering conceptual questions.

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ID Code: 93219
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Concept inventories, conceptual understanding, signal processing, text analysis, HERN
DOI: 10.1109/TE.2016.2515563
ISSN: 0018-9359
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 IEEE
Deposited On: 25 Feb 2016 05:19
Last Modified: 20 May 2016 06:05

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