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Tamar Fuhrmann; Leah Rosenbaum; Aditi Wagh; Adelmo Eloy; Jacob Wolf; Paulo Blikstein; Michelle Wilkerson – Science Education, 2025
When learning about scientific phenomena, students are expected to "mechanistically" explain how underlying interactions produce the observable phenomenon and "conceptually" connect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing…
Descriptors: Mechanics (Physics), Thinking Skills, Scientific Concepts, Concept Formation
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Dvir, Michal; Ben-Zvi, Dani – Mathematical Thinking and Learning: An International Journal, 2023
Growing scholarship on the pedagogical applications of statistical modeling is currently taking place to create adaptations of this practice to introduce novices to statistics. These are intended to promote novices' reasoning, and are typically void of formal mathematical procedures and calculations. In this article, we define the potential…
Descriptors: Teaching Methods, Statistics Education, Novices, Correlation
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Bradley Bowen; Bryanne Peterson – Journal of STEM Education: Innovations and Research, 2024
Modeling and simulation activities are common in secondary technology and engineering education classrooms. Virtual simulations are used to integrate engineering design into classroom instruction. The performance outcomes of a student's final virtual design usually depend on the level of knowledge application. When applying the learned content to…
Descriptors: Models, Middle Schools, STEM Education, Secondary School Curriculum
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Dong, Yi; Biswas, Gautam – International Educational Data Mining Society, 2017
This paper discusses a novel approach for developing more refined and accurate learner models from student data collected from Open Ended Learning Environments (OELEs). OELEs provide students choice in how they go about constructing solutions to problems, and students exhibit a variety of learning behaviors in such environments. Building accurate…
Descriptors: Student Behavior, Models, Monte Carlo Methods, Learning Processes
Amrein-Beardsley, Audrey; Geiger, Tray – Phi Delta Kappan, 2017
Houston's experience with the Educational Value-Added Assessment System (R) (EVAAS) raises questions that other districts should consider before buying the software and using it for high-stakes decisions. Researchers found that teachers in Houston, all of whom were under the EVAAS gun, but who taught relatively more racial minority students,…
Descriptors: Value Added Models, School Districts, Computer Software, Educational Technology
Sampson, Demetrios G., Ed.; Spector, J. Michael, Ed.; Ifenthaler, Dirk, Ed.; Isaias, Pedro, Ed. – International Association for Development of the Information Society, 2014
These proceedings contain the papers of the 11th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2014), October 25-27, 2014, which has been organized by the International Association for Development of the Information Society (IADIS) and endorsed by the Japanese Society for Information and Systems in…
Descriptors: Conference Papers, Teaching Methods, Technological Literacy, Technology Uses in Education
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Duncan, Susan; Papers, Jerry; Franzen, Woody; Otto, Pat – Science Scope, 2006
Vertical connections, constructed using inquiry, give students the skills to reach new heights in both their academic and local communities. In this article, the authors present inquiry projects, developed by middle level teachers, to ensure that students use higher-level thinking skills to improve the community. Each project is connected to the…
Descriptors: Biodiversity, Investigations, Field Studies, Thinking Skills
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection