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Showing 1 to 15 of 28 results Save | Export
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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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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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Yangqiuting Li; Chandralekha Singh – Physical Review Physics Education Research, 2023
In this study, we investigated female and male students' sense of belonging and their academic performance measured by the Force Concept Inventory (FCI) and course grades in a calculus-based introductory physics course at a large public university in the United States. We find that compared to male students, female students' sense of belonging was…
Descriptors: Sense of Community, Predictor Variables, Introductory Courses, Physics
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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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Li, Qiuye; Xu, Shaorui; Chen, Yilin; Lu, Chuting; Zhou, Shaona – Physical Review Physics Education Research, 2022
In science education, it is vital for teachers to consider students' academic and emotional needs. Teachers' prediction of students' learning states has been commonly regarded as an indicator to measure that competence to understand students. This study aimed to explore the outcome and the process of prediction to reflect teachers' pedagogical…
Descriptors: Eye Movements, Attention Control, Preservice Teachers, Prediction
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Dunleavy, Spencer; Kestin, Greg; Callaghan, Kristina; McCarty, Logan; Deslauriers, Louis – Physical Review Physics Education Research, 2022
The typical introductory physics lecture requires students to consolidate and assimilate a large quantity of complex information that is often novel to them. This can leave students overwhelmed, slow the pace of their learning, and lower their motivation. We find that carefully designed multimedia summaries in the form of one-minute videos and…
Descriptors: Physics, Science Instruction, Teaching Methods, Undergraduate Students
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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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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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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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Aiken, John M.; De Bin, Riccardo; Lewandowski, H. J.; Caballero, Marcos D. – Physical Review Physics Education Research, 2021
Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large datasets and machine learning techniques. In physics education research (PER), this increased focus has recently been shown through the 2019 Physical Review…
Descriptors: Physics, Science Education, Educational Research, Statistical Analysis
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Shoji, Yoshihiko; Munejiri, Shuji; Kaga, Eiko – Physical Review Physics Education Research, 2021
In this study, we evaluate the validity of question 29 (Q.29) in the Force Concept Inventory (FCI), which requires an understanding of atmospheric pressure. At least in some Japanese physics classes, the rate of the correct answer to Q.29 is reduced between the pretest and post-test. To examine this deterioration of a student's response, we…
Descriptors: Science Tests, Physics, Test Validity, Pretests Posttests
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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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Eaton, Philip – Physical Review Physics Education Research, 2021
A performance gap between the genders has been observed on the Force Concept Inventory (FCI) almost since its introduction. Many studies have sought to characterize this gender gap, however, few have tested the consistency of the factor structure across the genders. This study fills in this gap by offering the first piece of evidence that the…
Descriptors: Error of Measurement, Factor Structure, Gender Differences, Achievement Gap
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Mason, Andrew J. – Physical Review Physics Education Research, 2021
This paper is a follow-up to a previous study, in which students (predominately life science majors) were found to self-express achievement goals with regard to a pre-lab problem-solving exercise in an algebra-based introductory physics course. In this paper, the same sampled population was also asked in the same feedback survey to discuss what…
Descriptors: Majors (Students), Physics, Science Instruction, Introductory Courses
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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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