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Lan Yang; Leheng Huang; Xianqiu Wu; Jianwen Xiong; Lei Bao; Yang Xiao – Physical Review Physics Education Research, 2024
In physics education, a number of studies have developed assessments of teachers' knowledge of student understanding (KSU) of specific physics concepts with modified versions of existing concept inventories, in which teachers were asked to predict the popular incorrect answers from students. The results provide useful but indirect information to…
Descriptors: Preservice Teachers, Knowledge Level, Science Education, Scientific Concepts
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Yasuda, Jun-ichiro; Hull, Michael M.; Mae, Naohiro – Physical Review Physics Education Research, 2023
We aim to graphically analyze the depth of conceptual understanding behind the Force Concept Inventory (FCI) responses of students, focusing on three questions (questions 1, 15, and 28). In our study, we created and implemented subquestions to clarify and quantify the students' reasoning steps in reaching their responses to the original FCI…
Descriptors: Scientific Concepts, Concept Formation, Misconceptions, Visual Aids
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Vy Le; Jayson M. Nissen; Xiuxiu Tang; Yuxiao Zhang; Amirreza Mehrabi; Jason W. Morphew; Hua Hua Chang; Ben Van Dusen – Physical Review Physics Education Research, 2025
In physics education research, instructors and researchers often use research-based assessments (RBAs) to assess students' skills and knowledge. In this paper, we support the development of a mechanics cognitive diagnostic to test and implement effective and equitable pedagogies for physics instruction. Adaptive assessments using cognitive…
Descriptors: Physics, Science Education, Scientific Concepts, Diagnostic Tests
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Wheatley, Christopher; Wells, James; Pritchard, David E.; Stewart, John – Physical Review Physics Education Research, 2022
The Force Concept Inventory (FCI) is a popular multiple-choice instrument used to measure a student's conceptual understanding of Newtonian mechanics. Recently, a network analytic technique called module analysis has been used to identify responses to the FCI and other conceptual instruments that are preferentially selected together by students;…
Descriptors: Physics, Science Instruction, Concept Formation, Scientific Concepts
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Hansen, John; Stewart, John – Physical Review Physics Education Research, 2021
This work is the fourth of a series of papers applying multidimensional item response theory (MIRT) to widely used physics conceptual assessments. This study applies MIRT analysis using both exploratory and confirmatory methods to the Brief Electricity and Magnetism Assessment (BEMA) to explore the assessment's structure and to determine a…
Descriptors: Item Response Theory, Science Tests, Energy, Magnets
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Eaton, Philip; Frank, Barrett; Willoughby, Shannon – Physical Review Physics Education Research, 2020
Items that are chained, or blocked, together appear on many of the conceptual assessments utilized for physics education research. However, when items are chained together there is the potential to introduce local dependence between those items, which would violate the assumption of item independence required by classical test theory,…
Descriptors: Science Instruction, Physics, Motion, Scientific Concepts
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Yasuda, Jun-ichiro; Hull, Michael M.; Mae, Naohiro – Physical Review Physics Education Research, 2022
This paper presents improvements made to a computerized adaptive testing (CAT)-based version of the FCI (FCI-CAT) in regards to test security and test efficiency. First, we will discuss measures to enhance test security by controlling for item overexposure, decreasing the risk that respondents may (i) memorize the content of a pretest for use on…
Descriptors: Adaptive Testing, Computer Assisted Testing, Test Items, Risk Management
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Fabian Kieser; Peter Wulff; Jochen Kuhn; Stefan Küchemann – Physical Review Physics Education Research, 2023
Generative AI technologies such as large language models show novel potential to enhance educational research. For example, generative large language models were shown to be capable of solving quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory (FCI). Given the importance of such concept inventories for…
Descriptors: Physics, Science Instruction, Artificial Intelligence, Computer Software
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Kortemeyer, Gerd – Physical Review Physics Education Research, 2023
Massive pretrained language models have garnered attention and controversy due to their ability to generate humanlike responses: Attention due to their frequent indistinguishability from human-generated phraseology and narratives and controversy due to the fact that their convincingly presented arguments and facts are frequently simply false. Just…
Descriptors: Artificial Intelligence, Physics, Science Instruction, Introductory Courses
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Jannis Weber; Thomas Wilhelm – Physical Review Physics Education Research, 2024
Students experience many difficulties learning the fundamental relationships in Newtonian mechanics, partly due to preexisting mental models that originate from their everyday lives. These preconceptions often persist even after instruction in mechanics and lead to a supposed incompatibility between physics lessons in school and personal…
Descriptors: Physics, Science Instruction, Scientific Concepts, Mechanics (Physics)
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Eaton, Philip; Willoughby, Shannon – Physical Review Physics Education Research, 2020
As targeted, single-conception curriculum research becomes more prevalent in physics education research (PER), the need for a more sophisticated statistical understanding of the conceptual surveys used becomes apparent. Previously, the factor structure of the Force Concept Inventory (FCI) was examined using exploratory factor analysis (EFA) and…
Descriptors: Item Response Theory, Factor Analysis, Factor Structure, Models
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Wells, James; Henderson, Rachel; Stewart, John; Stewart, Gay; Yang, Jie; Traxler, Adrienne – Physical Review Physics Education Research, 2019
Module analysis for multiple-choice responses (MAMCR) was applied to a large sample of Force Concept Inventory (FCI) pretest and post-test responses (N[subscript pre] = 4509 and N[subscript post] = 4716) to replicate the results of the original MAMCR study and to understand the origins of the gender differences reported in a previous study of this…
Descriptors: Physics, Misconceptions, Science Tests, Scientific Concepts
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Stoen, Siera M.; McDaniel, Mark A.; Frey, Regina F.; Hynes, K. Mairin; Cahill, Michael J. – Physical Review Physics Education Research, 2020
The Force Concept Inventory (FCI) can serve as a summative assessment of students' conceptual knowledge at the end of introductory physics, but previous work has suggested that the knowledge measured by this instrument is not a unitary construct. In this article, we consider the idea that FCI performance may reflect a number of student attributes…
Descriptors: Physics, Scientific Concepts, Student Characteristics, Calculus
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Stewart, John; Drury, Byron; Wells, James; Adair, Aaron; Henderson, Rachel; Ma, Yunfei; Perez-Lemonche, Ángel; Pritchard, David – Physical Review Physics Education Research, 2021
This study reports an analysis of the Force Concept Inventory (FCI) using item response curves (IRC)--the fraction of students selecting each response to an item as a function of their total score. Three large samples (N = 9606, 4360, and 1439) of calculus-based physics students were analyzed. These were drawn from three land-grant institutions…
Descriptors: Physics, Science Instruction, Scientific Concepts, Item Response Theory
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Reinhard, Aaron; Felleson, Alex; Turner, Paula C.; Green, Maxwell – Physical Review Physics Education Research, 2022
We studied the impact of metacognitive reflections on recently-completed work as a way to improve the retention of newly learned problem-solving techniques. Students video recorded themselves talking through problems immediately after finishing them, completed ongoing problem-solving strategy maps or problem-sorting exercises, and filled out…
Descriptors: Metacognition, Problem Solving, Retention (Psychology), Video Technology
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