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Neal, Jennifer Watling; Neal, Zachary P. – School Psychology, 2022
Understanding educators' networks can inform the field of school psychology by offering insight into how the structure of social relationships supports the implementation of school-based programs. However, the difficulties of collecting and modeling network data remain barriers to using network methods in school psychology. To overcome these…
Descriptors: Data Collection, Visualization, Models, Social Networks
Bartholomew, Scott R.; McGraw, Tim; Fauber, Daphne; Charlesworth, Jon; Weitlauf, John – Technology and Engineering Teacher, 2020
Technological advances, artificial intelligence innovations, and widespread computing have all combined to necessitate a new generation of knowledge workers where data becomes a ubiquitous part of decision making (Sutton, 2006). Teaching today's students through the application of this "new" knowledge to long-established fields…
Descriptors: Sanitation, Water, Agriculture, High School Students
Taylor, Katie Headrick; Silvis, Deborah; Kalir, Remi; Negron, Anthony; Cramer, Catherine; Bell, Adam; Riesland, Erin – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2019
A project called Mobile City Science (MCS), a partnership between the University of Washington, New York Hall of Science, the Digital Youth Network, and two high schools, leverages young people's proclivity for on-the-move digital engagement to re-place and mobilize learning through public, community settings that youth identify as being relevant…
Descriptors: Educational Technology, Technology Uses in Education, College School Cooperation, High Schools
Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich – International Journal of Computer-Supported Collaborative Learning, 2016
Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…
Descriptors: Cooperative Learning, Information Retrieval, Data Collection, Data Analysis
Gal, Ya'akov; Uzan, Oriel; Belford, Robert; Karabinos, Michael; Yaron, David – Journal of Chemical Education, 2015
A process for analyzing log files collected from open-ended learning environments is developed and tested on a virtual lab problem involving reaction stoichiometry. The process utilizes a set of visualization tools that, by grouping student actions in a hierarchical manner, helps experts make sense of the linear list of student actions recorded in…
Descriptors: Virtual Classrooms, Laboratory Experiments, Online Courses, Electronic Learning
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Baumgartner, Erin; Zabin, Chela J. – Science Teacher, 2006
The study of "zonation", the distribution of plants and animals into distinct spatial areas, is a great way to introduce students to basic ecological concepts. Students can conduct methodical, quantifying surveys of zones in areas as diverse as mudflats, beaches, forests, wetlands, and fields. Students collect data from these areas with field…
Descriptors: Sampling, Ecology, Plants (Botany), Animals