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Ardiansyah, Roni; Harlita, H.; Ramli, Murni – Journal of Biological Education Indonesia (Jurnal Pendidikan Biologi Indonesia), 2021
Strengthening Learning Progression (LP) for students' reasoning abilities is important, especially learning about diseases in Indonesia. This study aimed to map the learning progression of disease in Indonesia, compare and analyze its similarities and differences with the LP designed by National Research Council (NRC). This qualitative research to…
Descriptors: Foreign Countries, Diseases, Teaching Methods, Comprehension
Ferguson, Joseph Paul; Tytler, Russell; White, Peta – International Journal of Science Education, 2022
Reporting on a Grade 4 teaching and learning sequence, we highlight foundational constructs of measurement and data modelling which are fundamental to competence development in both science and mathematics. The sequence involved students generating and representing measures of their teacher's arm-span, with a focus on the invention and refinement…
Descriptors: Aesthetics, Data Analysis, Educational Philosophy, Teaching Methods
Chen, Bodong; Resendes, Monica; Chai, Ching Sing; Hong, Huang-Yao – Interactive Learning Environments, 2017
As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish "productive" threads of knowledge-building discourse. A database of Grade 1-6 knowledge-building discourse was first coded for…
Descriptors: Elementary Education, Knowledge Level, Databases, Coding
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior
Bussey, Thomas J. – ProQuest LLC, 2013
Biochemistry education relies heavily on students' ability to visualize abstract cellular and molecular processes, mechanisms, and components. As such, biochemistry educators often turn to external representations to provide tangible, working models from which students' internal representations (mental models) can be constructed, evaluated, and…
Descriptors: Biochemistry, Science Instruction, Science Teachers, Teacher Attitudes
Hanuscin, Deborah L.; Lee, Michele H. – Journal of Elementary Science Education, 2008
The learning cycle has been embraced as a teaching approach that is consistent with the goals of the "National Science Education Standards" (National Research Council, 1996). Science teacher educators may be disappointed to find, however, that preservice teachers may fail to grasp this model, even after extensive instruction (e.g., Settlage,…
Descriptors: Preservice Teachers, Learning Processes, Teaching Models, Science Instruction
Glaser, Robert, Ed. – 1965
This collection of 17 papers relating behavioral science theory to the process of experimental education grew out of a 1963 National Education Association symposium on research in programed instruction. Perspectives and the technology of programing are described in this updated and supplemental successor to the source book, "Teaching Machines…
Descriptors: Behavioral Science Research, Computer Assisted Instruction, Conferences, Discovery Learning
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

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