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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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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
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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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Ezberci-Çevik, Ebru; Kurnaz, Mehmet Altan – Malaysian Online Journal of Educational Technology, 2022
In this study, it is aimed to reveal the models related to star subject as one of the concepts of astronomy of prospective science teachers before and after the current instruction through model analysis. This modeling situation is expressed as a Grounded Mental Model (GMM), since there will be a mental modeling that is revealed according to what…
Descriptors: Schemata (Cognition), Astronomy, Science Teachers, Preservice Teachers
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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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Tom Bleckmann; Gunnar Friege – Knowledge Management & E-Learning, 2023
Formative assessment is about providing and using feedback and diagnostic information. On this basis, further learning or further teaching should be adaptive and, in the best case, optimized. However, this aspect is difficult to implement in reality, as teachers work with a large number of students and the whole process of formative assessment,…
Descriptors: Concept Mapping, Formative Evaluation, Automation, Feedback (Response)
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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
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Pei, Bo; Xing, Wanli; Zhu, Gaoxia; Antonyan, Kristine; Xie, Charles – Education and Information Technologies, 2023
Infrared (IR) technologies have been universally acknowledged as a valuable pedagogical tool for exploring novel and abstract scientific subjects in science education. This study explores the roles of IR images played in middle school students' Evidence-based Reasoning (EBR) process in support of the understanding of the heat radiation process.…
Descriptors: Technology Integration, Spectroscopy, Science Education, Science Instruction
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Karimov, Ayaz; Saarela, Mirka; Kärkkäinen, Tommi – International Educational Data Mining Society, 2023
Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…
Descriptors: Student Attitudes, Feedback (Response), Educational Games, Information Retrieval
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Reid, Norman; Yang, Mei-Jung – Research in Science and Technological Education, 2002
Offers a simple classification of problems and seeks to explore the many factors that may be important in the successful solving of problems. Considers the place of procedures and algorithms. Solving open-ended problems is extremely important in education and offering learners experience with this in a group work context is a helpful way forward.…
Descriptors: Algorithms, Chemistry, Problem Solving, Science Education
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Niaz, Mansoor – Science Education, 1995
Describes a study with the main objective of constructing models based on strategies students use to solve chemistry problems and to show that these models form sequences of progressive transitions termed "problemshifts" that increase the explanatory/heuristic power of the model. Results implies that the relationship between algorithmic…
Descriptors: Algorithms, Chemistry, Concept Formation, Models
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Gfeller, Mary K.; Niess, Margaret L.; Lederman, Norman G. – School Science and Mathematics, 1999
Examines solutions presented by preservice teachers for solving graphical and numerical problems involving the arithmetic mean. Participants presented two methods: algorithmic computation and balancing deviations about the mean. A significant difference was found between science and mathematics preservice teachers in the use of balancing…
Descriptors: Algorithms, Higher Education, Mathematics Education, Preservice Teacher Education
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Coulter, David – School Science and Mathematics, 1981
A study to investigate one of the mechanisms teachers may use to convince themselves incorrectly that students have learned science concepts requiring formal operational ability is presented. The investigation indicates instructors may actually teach and test for memorization of algorithms rather than understanding. (MP)
Descriptors: Algorithms, Chemistry, Educational Research, Learning Theories
Kokoski, Teresa M.; Housner, Lynn Dale – 1994
The present study was an exploratory study of the content-specific knowledge structures of three teacher educators (two in math and one in science). Pathfinder, a method for eliciting associative memory networks, was used to describe the knowledge structures of the teacher educators. Pathfinder was also used to determine changes in knowledge…
Descriptors: Academic Achievement, Algorithms, Cognitive Structures, Higher Education
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