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Karl Lundengård; Peter Johnson; Phil Ramsden – International Journal for Technology in Mathematics Education, 2024
Formative feedback is important in learning. Automating the provision of specific, objective, constructive feedback to large cohorts requires complex algorithms that most teachers do not have time to develop, suggesting that a community effort is needed to create a library of specialised algorithms. We present an exemplar algorithm for a class of…
Descriptors: Automation, Feedback (Response), Algorithms, Science Education
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Axel Langner; Lea Sophie Hain; Nicole Graulich – Journal of Chemical Education, 2025
Often, eye-tracking researchers define areas of interest (AOIs) to analyze eye-tracking data. Although AOIs can be defined with systematic methods, researchers in organic chemistry education eye-tracking research often define them manually, as the semantic composition of the stimulus must be considered. Still, defining appropriate AOIs during data…
Descriptors: Organic Chemistry, Science Education, Eye Movements, Educational Research
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Wenchao Ma; Miguel A. Sorrel; Xiaoming Zhai; Yuan Ge – Journal of Educational Measurement, 2024
Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of…
Descriptors: Models, Misconceptions, Diagnostic Tests, Ability
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Atanu Bhattacharya; Kalyan Dasgupta; Binoy Paine – Journal of Chemical Education, 2024
In this paper, we present a computational chemistry project that demonstrates the quantum dynamics of a free particle, using both classical and quantum computing algorithms. This project can be used in a computational quantum chemistry course in which the instructor introduces quantum computing. Students write their own programs to simulate the…
Descriptors: Chemistry, Science Education, Quantum Mechanics, Computer Science
Jessa Henderson – ProQuest LLC, 2024
Algorithms may be better at prediction than humans in a variety of contexts, but they are not perfect. A deeper understanding of the ways in which educators use and question algorithmic advice within their professional domain is needed. Educators are a particularly unique professional group, in comparison with the other groups studied in the…
Descriptors: Algorithms, Literacy, High School Teachers, Science Teachers
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Jyoti Wadmare; Dakshita Kolte; Kapil Bhatia; Palak Desai; Ganesh Wadmare – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This paper highlights an innovative and impactful online operating system algorithms e-learning tool in engineering education. Background: Common teaching methodologies make it difficult to teach complex algorithms of operating systems. This paper presents a solution to this problem by providing simulations of different complex…
Descriptors: Engineering, Science Education, Material Development, Computer Simulation
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Sy-Miin Chow; Jungmin Lee; Jonathan Park; Prabhani Kuruppumullage Don; Tracey Hammel; Michael N. Hallquist; Eric A. Nord; Zita Oravecz; Heather L. Perry; Lawrence M. Lesser; Dennis K. Pearl – Journal of Statistics and Data Science Education, 2024
Personalized educational interventions have been shown to facilitate successful and inclusive statistics, mathematics, and data science (SMDS) in higher education through timely and targeted reduction of heterogeneous training disparities caused by years of cumulative, structural challenges in contemporary educational systems. However, the burden…
Descriptors: Individualized Instruction, Instructional Design, Science Education, Higher Education
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Peter Hu; Yangqiuting Li; Chandralekha Singh – Physical Review Physics Education Research, 2024
Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name…
Descriptors: Quantum Mechanics, Computer Science Education, Inquiry, Teaching Methods
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John Pace; John Hansen; John Stewart – Physical Review Physics Education Research, 2024
Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on…
Descriptors: Artificial Intelligence, Identification, At Risk Students, Physics