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Huiying Dai; So Hee Yoon – International Journal of Web-Based Learning and Teaching Technologies, 2024
The multimedia simulation teaching mode introduces students into virtual scenes for learning. Whether it is enhancing students' interest in learning or enhancing their physical fitness, it is a new teaching mode. This article discusses the establishment of a BP neural network model to study the prediction of students' physical fitness and conducts…
Descriptors: Physical Education, Physical Fitness, Prediction, Adolescents
Harikesh Singh; Li-Minn Ang; Dipak Paudyal; Mauricio Acuna; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava – Technology, Knowledge and Learning, 2025
Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of…
Descriptors: Artificial Intelligence, Computer Software, Computer Simulation, Prediction
Tong, Wangyu; Wang, Youxue; Su, Qinghua; Hu, Zhongbo – Education and Information Technologies, 2022
Compared with the application of Digital Twin (DT) in the industrial field, the application of DT in the field of education is still in its infancy. In this paper, a Digital Twin Campus (DTC) for teaching and learning is proposed. It is argued that DTC possesses two characteristics. First, DTC has a wide variety of employment orientations for…
Descriptors: Computer Simulation, Technology Uses in Education, Instructional Materials, Benchmarking
Ates, Hüseyin; Garzón, Juan – Education and Information Technologies, 2023
Many studies show that augmented reality (AR) provides multiple benefits to science education, including learning gains, motivation to learn, and collaborative learning. However, while using AR largely depends on the teachers' willingness, existing literature lacks studies that identify teachers' intentions to use this technology. This study…
Descriptors: Models, Intention, Technology Uses in Education, Computer Simulation
Serrano-Mamolar, Ana; Miguel-Alonso, Ines; Checa, David; Pardo-Aguilar, Carlos – Comunicar: Media Education Research Journal, 2023
At present, the use of eye-tracking data in immersive Virtual Reality (iVR) learning environments is set to become a powerful tool for maximizing learning outcomes, due to the low-intrusiveness of eye-tracking technology and its integration in commercial iVR Head Mounted Displays. However, the most suitable technologies for data processing should…
Descriptors: Student Evaluation, Computer Simulation, Eye Movements, Technology Integration
Kotlyar, Igor; Sharifi, Tina; Fiksenbaum, Lisa – International Journal of Artificial Intelligence in Education, 2023
Teamwork skills are commonly evaluated by human assessors, which can be logistically challenging and resource intensive. Technological advancements provide an opportunity for a new assessment method -- virtual behavioural simulations with self-scoring algorithms. This study explores whether a rule-based algorithm can match human assessors at…
Descriptors: Algorithms, Undergraduate Students, Computer Simulation, Evaluation
Samsudin, Achmad – International Journal of Technology in Education and Science, 2023
The purpose of this study is to analyze the mental model of improvement and changes on light wave concepts with Conceptual Change based on Virtual Media (CC-VM) x POE strategy. The method used is mixed methods with embedded design. The sample consisted of 30 students (with an age range of 16-17 years) in one of the schools in Subang, Indonesia.…
Descriptors: Foreign Countries, Concept Formation, Schemata (Cognition), Computer Simulation
Abdu, Rotem; Slakmon, Benzi – Computers in the Schools, 2023
Computer simulations are considered efficient in supporting exploratory learning. This paper highlights instruction challenges and undesirable consequences on student learning in exploratory learning with computer simulations in classroom situations. A classroom with students who explore using simulations contains ideas explicated on several…
Descriptors: Observation, Cooperative Learning, Computer Simulation, Mathematics Education
Mohammed Jebbari; Bouchaib Cherradi; Soufiane Hamida; Abdelhadi Raihani – Education and Information Technologies, 2024
With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of…
Descriptors: MOOCs, Algorithms, Computer Simulation, COVID-19
Wolf, Mark E.; Norris, J. Widener; Fynewever, Herb; Turney, Justin M.; Schaefer, Henry F., III – Journal of Chemical Education, 2022
Over the past half century, computational chemistry has evolved from a niche field to a ubiquitous pillar of modern chemical research. Driven by the increased demand for computational chemistry in research settings, the undergraduate curriculum has evolved alongside to ensure that students are well-equipped for modern research. Toward this end,…
Descriptors: Science Instruction, Science Laboratories, Chemistry, Computer Simulation
Natalia Spitha; Yujian Zhang; Samuel Pazicni; Sarah A. Fullington; Carla Morais; Amanda Rae Buchberger; Pamela S. Doolittle – Chemistry Education Research and Practice, 2024
The Beer-Lambert law is a fundamental relationship in chemistry that helps connect macroscopic experimental observations (i.e., the amount of light exiting a solution sample) to a symbolic model composed of system-level parameters (e.g., concentration values). Despite the wide use of the Beer-Lambert law in the undergraduate chemistry curriculum…
Descriptors: Chemistry, Science Instruction, Undergraduate Students, Scientific Principles
Liu, Zheyu; Yu, Ping; Liu, Jiale; Pi, Zhongling; Cui, Weijin – British Journal of Educational Technology, 2023
Virtual reality, as an excellent supportive instructional technology, has gained increasing attention from educators and professionals, where desktop-based virtual reality (DVR) is broadly adopted due to its affordability and accessibility. However, when evaluating students' learning experiences such as flow experiences in DVR environments, most…
Descriptors: Self Control, STEM Education, Computer Simulation, Undergraduate Students
Kaste, Joshua A. M.; Green, Antwan; Shachar-Hill, Yair – Biochemistry and Molecular Biology Education, 2023
The modeling of rates of biochemical reactions--fluxes--in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and flux balance analysis)…
Descriptors: Science Instruction, Teaching Methods, Prediction, Metabolism
Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
Sprenger, David A.; Schwaninger, Adrian – British Journal of Educational Technology, 2023
The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to…
Descriptors: Video Technology, Demonstrations (Educational), Prediction, Intention