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Showing 1 to 15 of 66 results Save | Export
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Lonneke Boels; Alex Lyford; Arthur Bakker; Paul Drijvers – Frontline Learning Research, 2023
Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how…
Descriptors: Data Interpretation, Interpretive Skills, Statistical Distributions, Graphs
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Saskia Schreiter; Markus Vogel – Educational Studies in Mathematics, 2025
The ability to interpret and compare data distributions is an important educational goal. Inherent in the statistical concept of distribution is the need to focus not only on individual data points or small groups of data points (so-called local view), but to perceive a distribution as a whole, allowing to recognize global features such as center,…
Descriptors: Eye Movements, Statistical Distributions, Data Interpretation, Data Analysis
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Lonneke Boels; Arthur Bakker; Wim Van Dooren; Paul Drijvers – Educational Studies in Mathematics, 2025
Many students persistently misinterpret histograms. This calls for closer inspection of students' strategies when interpreting histograms and case-value plots (which look similar but are different). Using students' gaze data, we ask: "How and how well do upper secondary pre-university school students estimate and compare arithmetic means of…
Descriptors: Secondary School Students, Learning Strategies, Data Interpretation, Graphs
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Delport, Danri H. – Teaching Statistics: An International Journal for Teachers, 2021
It is said that a picture is worth a thousand words, but what about graphs? Although graphs have the potential to bring data to life, numerous studies show that learners struggle with graphical comprehension. Furthermore, many textbook examples on graphs are boring and appear meaningless to students. Students want to know more about something…
Descriptors: Statistics Education, Introductory Courses, Graphs, Teaching Methods
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Dunn, Peter K.; Marshman, Margaret – Australian Mathematics Education Journal, 2021
This is the fourth in a series of statistical articles for mathematics teachers. In this article, the authors discuss topics in General Mathematics in Unit 2 Topic 1 (Univariate data analysis and the statistical investigation process) and topics in Essential Mathematics, Unit 2 Topic 1 (Representing and comparing data).
Descriptors: Mathematics Education, Mathematics Instruction, Data Analysis, Graphs
Naian Yin – ProQuest LLC, 2020
Online networks, as digital images reflecting every aspects of real world especially under the current context of information technology, have become the most massive and opulent data source these days, where vast amount of information keeps being produced and transmitted. Their rapid expansion in scale and data volume draws wide attention and…
Descriptors: Sampling, Networks, Topology, Computer Mediated Communication
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Donegan, Sarah; Dias, Sofia; Tudur-Smith, Catrin; Marinho, Valeria; Welton, Nicky J. – Research Synthesis Methods, 2018
Background: Meta-regression results must be interpreted taking into account the range of covariate values of the contributing studies. Results based on interpolation or extrapolation may be unreliable. In network meta-regression (NMR) models, which include covariates in network meta-analyses, results are estimated using direct and indirect…
Descriptors: Graphs, Networks, Meta Analysis, Regression (Statistics)
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Chaput, J. Scott; Crack, Timothy Falcon; Onishchenko, Olena – Journal of Statistics and Data Science Education, 2021
How accurately can final-year students majoring in statistics, physics, and finance label the vertical axis of a normal distribution, explain their label, identify units, and answer a question about the impact of horizontal-axis rescaling? Our survey finds that only 27 out of 148 students surveyed (i.e., 18.2%) could label the vertical axis of the…
Descriptors: Undergraduate Students, Advanced Students, Business Administration Education, Mathematics Skills
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González, Orlando – Statistics Education Research Journal, 2021
Many studies have reported on the influence of teachers' conceptions of variability on different aspects of their professional knowledge for teaching statistics and their classroom practices. However, research on these kind of conceptions is still scarce, particularly in Latin American countries like Venezuela. In an effort to help fill this gap,…
Descriptors: Statistics Education, Mathematics Teachers, Mathematical Concepts, Knowledge Level
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Diwakar, Rekha – Practical Assessment, Research & Evaluation, 2017
Many existing methods of statistical inference and analysis rely heavily on the assumption that the data are normally distributed. However, the normality assumption is not fulfilled when dealing with data which does not contain negative values or are otherwise skewed--a common occurrence in diverse disciplines such as finance, economics, political…
Descriptors: Statistical Inference, Statistical Distributions, Research, Foreign Countries
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Page, Robert B.; Espinosa, James; Mares, Chris A.; Del Pilar, Joselyn; Shelton, G. Robert – Journal of College Science Teaching, 2018
Education is frequently cited as the path to an informed citizenry with an optimistic economic outlook. Consequently, it is not surprising that there is an initiative to extend postsecondary educational opportunities to underserved and at-risk demographics. A challenge facing educators that serve at-risk populations is the tension between…
Descriptors: At Risk Students, STEM Education, Academic Achievement, Statistical Distributions
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Sarkar, Jyotirmoy; Rashid, Mamunur – Teaching Statistics: An International Journal for Teachers, 2016
The sample mean is sometimes depicted as a fulcrum placed under the Dot plot. We provide an alternative geometric visualization of the sample mean using the empirical cumulative distribution function or the cumulative histogram data.
Descriptors: Geometric Concepts, Geometry, Numbers, Statistical Distributions
Humphrey, Patricia B.; Taylor, Sharon; Mittag, Kathleen Cage – Teaching Statistics: An International Journal for Teachers, 2014
Students often are confused about the differences between bar graphs and histograms. The authors discuss some reasons behind this confusion and offer suggestions that help clarify thinking.
Descriptors: Graphs, Statistical Distributions, Mathematics Instruction, Statistics
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Lem, Stephanie; Baert, Kathy; Ceulemans, Eva; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim – Educational Psychology, 2017
The ability to interpret graphs is highly important in modern society, but has proven to be a challenge for many people. In this paper, two teaching methods were used to remediate one specific misinterpretation: the area misinterpretation of box plots. First, we used refutational text to explicitly state and invalidate the area misinterpretation…
Descriptors: Graphs, Teaching Methods, Misconceptions, Statistical Data
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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