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Matías Bustamante-Valdés; Danilo Díaz-Levicoy – International Electronic Journal of Mathematics Education, 2024
This study aims to analyze the graphical comprehension of 5th and 6th grade students in multigrade rural primary education in Chile. The theoretical framework considers levels of reading and semiotic levels in statistical graphs. A qualitative methodology of descriptive level is followed, using content analysis method for the responses given to a…
Descriptors: Rural Schools, Foreign Countries, Multigraded Classes, Grade 5
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Ozaltun Celik, Aytug – Journal of Pedagogical Research, 2022
Interpreting statistical graphs and making inferences based on the graphs are a precursor for formal statistical inferences. To support student inferences, both teachers and future teachers should have adequate knowledge regarding students' thinking on graphs as well as their potential misinterpretations and difficulties in interpreting graphs. In…
Descriptors: Middle School Teachers, Middle School Students, Mathematics Instruction, Secondary School Mathematics
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Ozmen, Zeynep Medine; Guven, Bulent; Kurak, Yasin – Eurasian Journal of Educational Research, 2020
Purpose: Previous research focused on graphical skills of the students, which remains a gap that exists, and there has not been comprehensive research on students' graphical literacy abilities. The present study aims to picture graphical literacy levels of the 8th grade students concerning the "reading," "interpreting,"…
Descriptors: Middle School Students, Grade 8, Knowledge Level, Graphs
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Boda, Phillip A.; Bathia, Shruti; Linn, Marcia C. – Journal of Research in Science Teaching, 2021
Integrating sophisticated graphical analysis skills as they learn science is essential for K-12 students and emphasized in current standards. In this study, we iteratively designed a Graphing Integration Inventory (GII) over a three-year period, while also supporting students to develop their capabilities to use graphs to learn science content in…
Descriptors: Middle School Students, Graphs, Teaching Methods, Science Instruction
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Vitale, Jonathan M.; Applebaum, Lauren; Linn, Marcia C. – Cognition and Instruction, 2019
Graphs illustrating complex scientific relationships require students to integrate multiple concepts and visual features into a coherent understanding. We investigate ways to support students in integrating their understanding of density concepts through a graph that is linked to a simulation depicting the relationship between mass, volume, and…
Descriptors: Graphs, Scientific Concepts, Concept Formation, Grade 8
Lewis, Felicia Wider – ProQuest LLC, 2016
This thesis examined middle school students' current understanding of variability using a constructed response item assessment question. Variability is an essential concept in the teaching and learning of statistics. However, many students have difficulty with the concept of variability especially when constructing boxplots. Using a framework…
Descriptors: Middle School Students, Knowledge Level, Statistics, Mathematical Concepts
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Kar, Tugrul – International Electronic Journal of Elementary Education, 2016
This study examined prospective middle school mathematics teachers' problem-posing skills by investigating their ability to associate linear graphs with daily life situations. Prospective teachers were given linear graphs and asked to pose problems that could potentially be represented by the graphs. Their answers were analyzed in two stages. In…
Descriptors: Middle School Teachers, Mathematics Teachers, Preservice Teachers, Knowledge Level
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Tunç-Pekkan, Zelha – Educational Studies in Mathematics, 2015
It is now well known that fractions are difficult concepts to learn as well as to teach. Teachers usually use circular pies, rectangular shapes and number lines on the paper as teaching tools for fraction instruction. This article contributes to the field by investigating how the widely used three external graphical representations (i.e., circle,…
Descriptors: Mathematics Instruction, Mathematical Concepts, Teaching Methods, Graphs
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
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Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
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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
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