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Brandin Conrath; Amy Voss Farris; Scott McDonald – Journal of Science Education and Technology, 2025
The changing landscape of geoscience learning has initiated growing interest in engaging science learners with climate data. One approach to teaching climate is the application of broadly accessible digital science curricula, which often include data tools such as visualizations, data representations, and simulations embedded within digital…
Descriptors: Earth Science, Wildlife, Science Education, Climate
Egiebor, Esohe E.; Foster, Ellen J. – Journal of Geography, 2019
This article describes the findings of a qualitative phenomenographic study that explored students' engagement from the perspective of the students. It describes how the participants perceived their engagement when they learned social studies using GIS Story Maps. Qualitative data collection involved classroom observations, student-written…
Descriptors: Student Attitudes, Geography Instruction, Social Studies, Geographic Information Systems
Logan, Tracy; Lowrie, Tom – Australian Primary Mathematics Classroom, 2013
Tracy Logan and Tom Lowrie argue that while little attention is given to visual imagery and spatial reasoning within the Australian Curriculum, a significant proportion of National Assessment Program--Literacy and Numeracy (NAPLAN) tasks require high levels of visuospatial reasoning. This article includes teaching ideas to promote visuospatial…
Descriptors: Mathematics Instruction, Visual Perception, Spatial Ability, Elementary School Mathematics
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