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Wen, Cai-Ting; Chang, Chia-Jung; Chang, Ming-Hua; Fan Chiang, Shih-Hsun; Liu, Chen-Chung; Hwang, Fu-Kwun; Tsai, Chin-Chung – Instructional Science: An International Journal of the Learning Sciences, 2018
This study investigated students' modeling progress and strategies in a problem-solving simulation game through content analysis, and through supervised and unsupervised lag sequential analysis (LSA). Multiple data sources, including self-report models and activity logs, were collected from 25 senior high school students. The results of the…
Descriptors: Educational Games, Simulation, Problem Solving, Models
Chen, Chen; Schneps, Matthew H.; Sonnert, Gerhard – Journal of Science Education and Technology, 2016
Teachers choosing between different models to facilitate students' understanding of an abstract system must decide whether to adopt a model that is simplified and striking or one that is realistic and complex. Only recently have instructional technologies enabled teachers and learners to change presentations swiftly and to provide for learning…
Descriptors: High School Students, Secondary School Science, Science Education, Models
Duncan, Ravit Golan; Choi, Jinnie; Castro-Faix, Moraima; Cavera, Veronica L. – Science & Education, 2017
Learning progressions (LPs) are hypothetical models of how learning in a domain develops over time with appropriate instruction. In the domain of genetics, there are two independently developed alternative LPs. The main difference between the two progressions hinges on their assumptions regarding the accessibility of classical (Mendelian) versus…
Descriptors: Genetics, Learning Processes, Sequential Learning, Sequential Approach
Tretter, Thomas R.; Thornburgh, William R.; Duckwall, Mark – Science and Children, 2016
Supporting elementary student understandings of ideas related to Earth's Place in the Universe (ESS1) can be challenging, especially given the large time and distance scales involved with many of the concepts. However, with effective use of crosscutting concepts and science and engineering practices, important concepts within this content domain…
Descriptors: Astronomy, Space Sciences, Elementary School Students, Scientific Concepts
Khourey-Bowers, Claudia – Science Scope, 2009
The purpose of this article is to share a learning-cycle sequence of lessons designed to convey the particulate nature of matter through use of physical models and analogical thinking. This activity was adapted from Conceptual Chemistry, a long-running professional development program for teachers of grades 4-9. Conceptual Chemistry's approach is…
Descriptors: Chemistry, Scientific Concepts, Scientific Principles, Scientific Literacy

Poole, Richard L. – Journal of Chemical Education, 1989
Presents a method to teach stoichiometry where a problem is broken down into four factors and two cycles. Offers grams of known, moles of known, moles of unknown, and grams of unknown as the factors. Uses factor labeling as the second cycle. (MVL)
Descriptors: Chemical Analysis, Chemical Nomenclature, Educational Strategies, Instruction
Saari, Heikki – International Journal of Science Education, 2003
The purpose of this study was to construct and study the impact of a research-based sequence for teaching the concept of modelling to seventh-grade science students. We identified students' notions of models and the aspects of school science to be addressed regarding the model concept, which were then taken into account when we planned the…
Descriptors: Control Groups, Science Activities, Educational Research, Science Instruction
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