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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
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
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
Salima Aldazharova; Gulnara Issayeva; Samat Maxutov; Nuri Balta – Contemporary Educational Technology, 2024
This study investigates the performance of GPT-4, an advanced AI model developed by OpenAI, on the force concept inventory (FCI) to evaluate its accuracy, reasoning patterns, and the occurrence of false positives and false negatives. GPT-4 was tasked with answering the FCI questions across multiple sessions. Key findings include GPT-4's…
Descriptors: Physics, Science Tests, Artificial Intelligence, Problem Solving
Achmad Samsudin; Aldi Zulfikar; Duden Saepuzaman; Andi Suhandi; Adam Hadiana Aminudin; Supriyadi Supriyadi; Bayram Costu – Journal of Turkish Science Education, 2024
This study aims to reconstruct Grade 11 students' misconceptions about force through the Conceptual Change Model (CCM) making use of Predict, Discuss, Explain, Observe, Discuss, Explore, Explain (PDEODEE) tasks. This study was conducted using a mixedmethod approach. The participants in this study were 65 students (33 students in the experiment…
Descriptors: Scientific Concepts, Concept Formation, Misconceptions, Foreign Countries
Timothy K. Osborn – ProQuest LLC, 2024
Concept inventories are widely used in physics education research, yet numerous studies show that concept inventories measure dramatically different learning gains in different demographic groups. Prior studies show that the performance gaps between demographic groups are already present in their preinstruction responses, suggesting that the…
Descriptors: Science Education, College Science, Physics, College Students
Ferrarelli, Paola; Iocchi, Luca – Technology, Knowledge and Learning, 2021
Novel technology has been applied to improve students' learning abilities in different disciplines. The research in this field is still finding suitable methodologies, tools, and evaluation mechanisms to devise learning frameworks with high impact on students' performance. This article describes an instructional method to perform Newtonian physics…
Descriptors: Physics, Scientific Concepts, Programming, Robotics
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
Pawl, Andrew – Physics Teacher, 2020
Examination of my students' individual gains on the Force Concept Inventory (FCI) over the course of several semesters led to the realization that student pretest knowledge on certain key questions appeared to be correlated to enhanced gain during the class. Acting under the hypothesis that early intervention aimed at helping the class perform…
Descriptors: Scientific Concepts, Physics, Science Instruction, Teaching Methods
Bahtaji, Michael Allan A. – Journal of Baltic Science Education, 2023
Exploring students' physics conceptions, science engagement and misconceptions in physics together with their association to certain variables (gender, educational background, and program of study) are essential in improving physics teaching and learning. However, few studies have been conducted to explore these three measures and almost none…
Descriptors: Physics, Science Education, Scientific Concepts, Misconceptions
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
Kim, Hong-Jeong; Im, Sungmin – Asia-Pacific Science Education, 2021
This study investigates pre-service teachers' beliefs about learning physics and explores how beliefs correlate with learning achievement as evidenced by conceptual understanding and grades in a year-long physics course. To investigate beliefs about learning physics, 14 second-year pre-service teachers in a teacher training program in South Korea…
Descriptors: Preservice Teachers, Student Attitudes, Physics, Science Instruction
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
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
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