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Showing all 9 results Save | Export
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Liza Bondurant; Stephanie Somersille – Mathematics Teacher: Learning and Teaching PK-12, 2024
This article describes an activity and resource from The New York Times that can be used to help learners cultivate critical statistical literacy. Critical statistical literacy involves understanding, interpreting, and questioning statistical information to make informed decisions (Casey et al., 2023; Franklin et al., 2015; Weiland, 2017). It is a…
Descriptors: Statistics Education, Teaching Methods, Newspapers, Decision Making
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Chia-Yu Hsu; Izumi Horikoshi; Rwitajit Majumdar; Hiroaki Ogata – Educational Technology & Society, 2024
This study focuses on the problem that the process of building learning habits has not been clearly described. Therefore, we aim to extract the stages of learning habits from log data. We propose a data model to extract stages of learning habits based on the transtheoretical model and apply the model to the learning logs of self-directed extensive…
Descriptors: Habit Formation, Behavior Change, Learning Analytics, Data Interpretation
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Bussani, Andrea; Comici, Cinzia – Physics Teacher, 2023
Data analysis and interpretation has always played a fundamental role in the scientific curricula of high school students. The spread of digitalization has further increased the number of learning environments whereby this topic can be effectively taught: as a matter of fact, the ever-growing diffusion of data science across diverse sectors of…
Descriptors: Learning Analytics, High Schools, Data Interpretation, Data Science
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Bowen, G. Michael; Bartley, Anthony – Science Activities: Projects and Curriculum Ideas in STEM Classrooms, 2020
School science is often very different from "real world" science. One important difference, and possibly the main one, is that in school science the relationships between variables have often been sanitized -- essentially "cleaned up" -- so that there is very little (and often no) variation in the data from the relationship…
Descriptors: Science Instruction, Data, Science Activities, Authentic Learning
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Lawrimore, Cassie; Surber, Emily A. – Proceedings of the Interdisciplinary STEM Teaching and Learning Conference, 2018
Students often struggle with the relationship between mathematical graphs and the data they represent. To truly understand types of evolutionary selection, students need to be proficient with several different skills in math, science, and literacy contexts. With math, students must be able to identify variables, design appropriate graphs based on…
Descriptors: Graphs, Evolution, High School Students, Biology
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O'Brien, Joe; Peavey, Scott; Fuller, Molly – Social Studies, 2016
Learning about people from long ago and far away poses a challenge for students because such people seem so distant and different. The lack of easily comprehensible text-based primary sources compounds this problem. Using a built environment as a primary source makes people from the distant past more accessible, concrete and exciting. Broadly…
Descriptors: Buildings, World History, Physical Environment, Middle School Students
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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
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Martin, Caitlin K.; Nacu, Denise; Pinkard, Nichole – Journal of Learning Analytics, 2016
Online environments can cultivate what have been referred to as 21st century skills and capabilities, as youth contribute, pursue, share, and interact around work and ideas. Such environments also hold great potential for addressing digital divides related to the development of such skills by connecting youth in areas with fewer resources and…
Descriptors: Data Collection, Data Interpretation, Creativity, Socialization
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Smith, Amy; Molinaro, Marco; Lee, Alisa; Guzman-Alvarez, Alberto – Science Teacher, 2014
For students to be successful in STEM, they need "statistical literacy," the ability to interpret, evaluate, and communicate statistical information (Gal 2002). The science and engineering practices dimension of the "Next Generation Science Standards" ("NGSS") highlights these skills, emphasizing the importance of…
Descriptors: STEM Education, Statistics, Statistical Analysis, Learning Modules