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Showing 1 to 15 of 27 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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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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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
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Lem, Stephanie; Kempen, Goya; Ceulemans, Eva; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim – International Journal of Science and Mathematics Education, 2015
Box plots are frequently misinterpreted and educational attempts to correct these misinterpretations have not been successful. In this study, we used two instructional techniques that seemed powerful to change the misinterpretation of the area of the box in box plots, both separately and in combination, leading to three experimental conditions,…
Descriptors: Mathematical Concepts, Mathematics Instruction, Teaching Methods, Graphs
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Schild, Anne H. E.; Voracek, Martin – Research Synthesis Methods, 2015
Research has shown that forest plots are a gold standard in the visualization of meta-analytic results. However, research on the general interpretation of forest plots and the role of researchers' meta-analysis experience and field of study is still unavailable. Additionally, the traditional display of effect sizes, confidence intervals, and…
Descriptors: Graphs, Visualization, Meta Analysis, Data Interpretation
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Kaplan, Jennifer J.; Gabrosek, John G.; Curtiss, Phyllis; Malone, Chris – Journal of Statistics Education, 2014
Histograms are adept at revealing the distribution of data values, especially the shape of the distribution and any outlier values. They are included in introductory statistics texts, research methods texts, and in the popular press, yet students often have difficulty interpreting the information conveyed by a histogram. This research identifies…
Descriptors: Statistical Distributions, Graphs, Undergraduate Students, Misconceptions
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Prodromou, Theodosia; Pratt, Dave – Technology, Knowledge and Learning, 2013
This paper presents a case study of students (age 14-15) as they attempt to make sense of distribution, adopting a range of causal meanings for the variation observed in the animated computer display and in the graphs generated by a "BasketBall" simulation. The student activity is analysed through dimensions of complex causality. The…
Descriptors: Computer Simulation, Junior High School Students, Probability, Statistical Distributions
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Lem, Stephanie; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim – Educational Psychology, 2013
Recent studies have shown that the interpretation of graphs is not always easy for students. In order to reason properly about distributions of data, however, one needs to be able to interpret graphical representations of these distributions correctly. In this study, we used Tversky's principles for the design of graphs to explain how 125…
Descriptors: Graphs, Data Interpretation, College Freshmen, Design
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Simsek, Omer; Yazar, Taha – Eurasian Journal of Educational Research, 2016
Problem Statement: The educational technology standards for teachers set by the International Society for Technology in Education (the ISTE Standards-T) represent an important framework for using technology effectively in teaching and learning processes. These standards are widely used by universities, educational institutions, and schools. The…
Descriptors: Educational Technology, Self Efficacy, Validity, Reliability
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Lee, Hollylynne S.; Kersaint, Gladis; Harper, Suzanne R.; Driskell, Shannon O.; Jones, Dusty L.; Leatham, Keith R.; Angotti, Robin L.; Adu-Gyamfi, Kwaku – Statistics Education Research Journal, 2014
This study examined a random stratified sample (n = 62) of teachers' work across eight institutions on three tasks that utilized dynamic statistical software. We considered how teachers may utilize and develop their statistical knowledge and technological statistical knowledge when investigating a statistical task. We examined how teachers engaged…
Descriptors: Statistics, Knowledge Level, Pedagogical Content Knowledge, Computer Software
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Lem, Stephanie; Onghana, Patrick; Verschaffel, Lieven; Van Dooren, Wim – Statistics Education Research Journal, 2013
Data distributions can be represented using different external representations, such as histograms and boxplots. Although the role of external representations has been extensively studied in mathematics, this is less the case in statistics. This study helps to fill this gap by systematically varying the representation that accompanies a task…
Descriptors: Foreign Countries, Graphs, Statistical Distributions, College Freshmen
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Phelps, James L. – Educational Considerations, 2012
In most school achievement research, the relationships between achievement and explanatory variables follow the Newton and Einstein concept/principle and the viewpoint of the macro-observer: Deterministic measures based on the mean value of a sufficiently large number of schools. What if the relationships between achievement and explanatory…
Descriptors: Academic Achievement, Computation, Probability, Statistics
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