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Test of Understanding Graphs in Kinematics: Item Objectives Confirmed by Clustering Eye Movement Transitions

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Title: Test of Understanding Graphs in Kinematics: Item Objectives Confirmed by Clustering Eye Movement Transitions
Language: English
Authors: Klein, P. (ORCID 0000-0003-3023-1478), Becker, S. (ORCID 0000-0002-2461-0992), Küchemann, S., Kuhn, J. (ORCID 0000-0002-6985-3218)
Source: Physical Review Physics Education Research. Jan-Jun 2021 17(1).
Availability: American Physical Society. One Physics Ellipse 4th Floor, College Park, MD 20740-3844. Tel: 301-209-3200; Fax: 301-209-0865; e-mail: assocpub@aps.org; Web site: http://prst-per.aps.org
Peer Reviewed: Y
Page Count: 6
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Foreign Countries, Science Tests, Multiple Choice Tests, Graphs, Motion, Mechanics (Physics), Test Items, Eye Movements, High School Students, Problem Solving, Scores, Item Analysis, Multivariate Analysis
Geographic Terms: Germany
DOI: 10.1103/PhysRevPhysEducRes.17.013102
ISSN: 2469-9896
Abstract: The test of understanding graphs in kinematics (TUG-K) has widely been used to assess students' understanding of this subject. The TUG-K poses different objectives to the test takers such as (1) the selection of a graph from a textual description, (2) the selection of corresponding graphs, and (3) the selection of a textual description from a graph. Whether test takers follow these task requirements is usually inferred from evaluating the test scores as correct or incorrect, yet the process of how students actually interact with the different tasks remains unknown. Recent studies have shown that eye tracking can provide rich insight into student's interaction with multiple-choice tasks. In the current work, we analyzed the eye movement patterns of N = 115 high school students while solving the TUG-K. Each question was divided into a question area (Q) and an option area (O), then gaze transitions between Q and O and between different options were calculated. A cluster analysis using the transition metrics revealed three item groups, containing the aforementioned objectives of the items. The clusters remain stable for different subsamples of our dataset, for instance, considering only the correct or only the incorrect responses, or considering high- or low-confidence responses. We conclude that eye movements can reflect task demands on a procedural level well beyond the classical methods of evaluating test scores, eventually making eye tracking an additional method for item analysis that can be utilized to confirm or explore test and item structures.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1293094
Database: ERIC
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  Data: Test of Understanding Graphs in Kinematics: Item Objectives Confirmed by Clustering Eye Movement Transitions
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  Data: <searchLink fieldCode="AR" term="%22Klein%2C+P%2E%22">Klein, P.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3023-1478">0000-0003-3023-1478</externalLink>)<br /><searchLink fieldCode="AR" term="%22Becker%2C+S%2E%22">Becker, S.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2461-0992">0000-0002-2461-0992</externalLink>)<br /><searchLink fieldCode="AR" term="%22Küchemann%2C+S%2E%22">Küchemann, S.</searchLink><br /><searchLink fieldCode="AR" term="%22Kuhn%2C+J%2E%22">Kuhn, J.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6985-3218">0000-0002-6985-3218</externalLink>)
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  Data: American Physical Society. One Physics Ellipse 4th Floor, College Park, MD 20740-3844. Tel: 301-209-3200; Fax: 301-209-0865; e-mail: assocpub@aps.org; Web site: http://prst-per.aps.org
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  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
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  Data: 10.1103/PhysRevPhysEducRes.17.013102
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  Data: 2469-9896
– Name: Abstract
  Label: Abstract
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  Data: The test of understanding graphs in kinematics (TUG-K) has widely been used to assess students' understanding of this subject. The TUG-K poses different objectives to the test takers such as (1) the selection of a graph from a textual description, (2) the selection of corresponding graphs, and (3) the selection of a textual description from a graph. Whether test takers follow these task requirements is usually inferred from evaluating the test scores as correct or incorrect, yet the process of how students actually interact with the different tasks remains unknown. Recent studies have shown that eye tracking can provide rich insight into student's interaction with multiple-choice tasks. In the current work, we analyzed the eye movement patterns of N = 115 high school students while solving the TUG-K. Each question was divided into a question area (Q) and an option area (O), then gaze transitions between Q and O and between different options were calculated. A cluster analysis using the transition metrics revealed three item groups, containing the aforementioned objectives of the items. The clusters remain stable for different subsamples of our dataset, for instance, considering only the correct or only the incorrect responses, or considering high- or low-confidence responses. We conclude that eye movements can reflect task demands on a procedural level well beyond the classical methods of evaluating test scores, eventually making eye tracking an additional method for item analysis that can be utilized to confirm or explore test and item structures.
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  Data: As Provided
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  Data: EJ1293094
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        Value: 10.1103/PhysRevPhysEducRes.17.013102
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      – Text: English
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Multiple Choice Tests
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      – SubjectFull: Motion
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      – SubjectFull: Germany
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      – TitleFull: Test of Understanding Graphs in Kinematics: Item Objectives Confirmed by Clustering Eye Movement Transitions
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