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Wang, Karen D.; Cock, Jade Maï; Käser, Tanja; Bumbacher, Engin – British Journal of Educational Technology, 2023
Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry…
Descriptors: Data Use, Educational Environment, Science Process Skills, Inquiry
Data Literacy on the Road: Setting up a Large-Scale Data Literacy Initiative in the Databuzz Project
Seymoens, Tom; Van Audenhove, Leo; Van den Broeck, Wendy; Mariën, Ilse – Journal of Media Literacy Education, 2020
This paper presents "the DataBuzz Project." "DataBuzz" is a high-tech, mobile educational lab, which is housed in a 13-meter electric bus. Its specific goal is to increase the data literacy of different segments of society in the Brussels region through inclusive and participatory games and workshops. In this paper, we will…
Descriptors: Data Analysis, Literacy, Program Descriptions, Laboratories
Tenison, Caitlin; Anderson, John R. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2016
A focus of early mathematics education is to build fluency through practice. Several models of skill acquisition have sought to explain the increase in fluency because of practice by modeling both the learning mechanisms driving this speedup and the changes in cognitive processes involved in executing the skill (such as transitioning from…
Descriptors: Skill Development, Mathematics Skills, Learning Processes, Markov Processes
Problem Analysis: Examining the Selection and Evaluation of Data during Problem-Solving Consultation
Newell, Markeda L.; Newell, Terrance S. – Psychology in the Schools, 2011
The purpose of this study was to analyze how school psychologists engaged in problem analysis during problem-solving consultation. Five aspects of the problem analysis process were examined: 1) the types of questions participants asked during problem identification, 2) the types of data participants requested, 3) the frequency of requests for each…
Descriptors: School Psychologists, Identification, Problem Solving, Simulated Environment
Undergraduate Non-Science Majors' Descriptions and Interpretations of Scientific Data Visualizations
Swenson, Sandra Signe – ProQuest LLC, 2010
Professionally developed and freely accessible through the Internet, scientific data maps have great potential for teaching and learning with data in the science classroom. Solving problems or developing ideas while using data maps of Earth phenomena in the science classroom may help students to understand the nature and process of science. Little…
Descriptors: Majors (Students), Public Colleges, Maps, Internet
Raychaudhuri, Debasree – International Journal of Mathematical Education in Science and Technology, 2008
In this note we develop a framework that makes explicit the inherent dynamic structure of certain mathematical definitions by means of the four facets of context-entity-process-object. These facets and their interrelations are then used to capture and interpret specific aspects of student constructions of the concept of solution to first order…
Descriptors: Mathematics Education, Calculus, Models, Definitions

Eisenberg, Michael B.; Spitzer, Kathleen L. – Catholic Library World, 1991
Addresses the implications for education of the expansion of information technology and proposes a model for teaching information skills within the context of an overall process. A model called the "Big Six Skills" is presented, and its application to information problems related to school, life, and work are explained. (10 references)…
Descriptors: Course Integrated Library Instruction, Data Interpretation, Elementary Secondary Education, Information Literacy