Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions

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) t...

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Autores principales:	P. Klein, S. Becker, S. Küchemann, J. Kuhn
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Special aspects of education LC8-6691 Physics QC1-999
Acceso en línea:	https://doaj.org/article/032a213d51394c5f8f1714ed3502fd86
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spelling	oai:doaj.org-article:032a213d51394c5f8f1714ed3502fd862021-12-02T13:34:32ZTest of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions10.1103/PhysRevPhysEducRes.17.0131022469-9896https://doaj.org/article/032a213d51394c5f8f1714ed3502fd862021-03-01T00:00:00Zhttp://doi.org/10.1103/PhysRevPhysEducRes.17.013102http://doi.org/10.1103/PhysRevPhysEducRes.17.013102https://doaj.org/toc/2469-9896The 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.P. KleinS. BeckerS. KüchemannJ. KuhnAmerican Physical SocietyarticleSpecial aspects of educationLC8-6691PhysicsQC1-999ENPhysical Review Physics Education Research, Vol 17, Iss 1, p 013102 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Special aspects of education LC8-6691 Physics QC1-999
spellingShingle	Special aspects of education LC8-6691 Physics QC1-999 P. Klein S. Becker S. Küchemann J. Kuhn Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions
description	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.
format	article
author	P. Klein S. Becker S. Küchemann J. Kuhn
author_facet	P. Klein S. Becker S. Küchemann J. Kuhn
author_sort	P. Klein
title	Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions
title_short	Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions
title_full	Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions
title_fullStr	Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions
title_full_unstemmed	Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions
title_sort	test of understanding graphs in kinematics: item objectives confirmed by clustering eye movement transitions
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/032a213d51394c5f8f1714ed3502fd86
work_keys_str_mv	AT pklein testofunderstandinggraphsinkinematicsitemobjectivesconfirmedbyclusteringeyemovementtransitions AT sbecker testofunderstandinggraphsinkinematicsitemobjectivesconfirmedbyclusteringeyemovementtransitions AT skuchemann testofunderstandinggraphsinkinematicsitemobjectivesconfirmedbyclusteringeyemovementtransitions AT jkuhn testofunderstandinggraphsinkinematicsitemobjectivesconfirmedbyclusteringeyemovementtransitions
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Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions

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