Investigating Student Understanding of Histograms

Kaplan, Jennifer J.; Gabrosek, John; Curtiss, Phyllis; Malone, Chris

doi:10.1080/10691898.2014.11889701

Cited by 34 publications

(56 citation statements)

References 8 publications

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“…Our materials were developed as extensions of the Statway curriculum that was itself designed to help community college remedial math students.We selected this area given the enormous audience for remedial math, the rising workplace demand for statistics, and the documented challenge many students find when learning about histograms [4]. Our activities and assessments were designed to address some of the core learning objectives about histograms that are also often commonly misunderstood [4].…”

Section: Approach and Preliminary Resultsmentioning

confidence: 98%

Learning the Features Used To Decide How to Teach

Lee

Runde

Jibril

et al. 2015

Proceedings of the Second (2015) ACM Conference on Learning @ Scale

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As a step towards scaling personalized instruction, we seek to automatically identify the key features of the interactive learning process teachers use to select the next activity when teaching a single student. Such features could both inform computational student models designed to facilitate instructional decisions, and help enable automated self-improving teaching systems that leverage this identified feature set. We present preliminary results that a very small set of features is almost as good as a much larger set of features at predicting human tutor decisions when teaching students about histograms.

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Section: Approach and Preliminary Resultsmentioning

confidence: 98%

Learning the Features Used To Decide How to Teach

Lee

Runde

Jibril

et al. 2015

Proceedings of the Second (2015) ACM Conference on Learning @ Scale

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show abstract

“…Care is taken to explain the properties of histograms to the students owing to the difficulty students face understanding histograms. [60][61][62][63][64] Measurement bias inherent to quantum dot properties is relevant to students' decisions. Students may fail to measure a quantum dot which happens to be off for longer than the experiment.…”

Section: Pedagogical Designmentioning

confidence: 99%

“It’s Fundamental”: Quantum Dot Blinking Experiment to Teach Critical Thinking

et al. 2019

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Analysis of stochastic processes can be used to engender critical thinking. Quantum dots have a reversible, stochastic transition between luminescent and non-luminescent states. The luminescence intermittency is known as blinking, and is not evident from ensemble measurements. In order to stimulate critical thinking, students design, perform, and analyze a semiconductor quantum dot blinking laboratory experiment. The design of the experiment and stochastic nature of the data collected require students to make judgements throughout the course of the single-particle measurement and analysis. Some of the decisions do not have uniquely correct answers, challenging the students to engage in critical thinking. We propose that students' self-examined decision making develops a constructivist view of science. The experiment is visually striking, interdisciplinary, and develops higher order thinking.

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“…Previous studies have indicated that students struggle to determine which of two or more datasets represented by similar scaled histograms has the greatest or least variability (Cooper 2002;delMas et al 2007;Cooper and Shore 2008;Chaphalkar and Leary 2014;Kaplan et al 2014). One commonly reported misconception is that the variability of data in a histogram is judged by variability of bar heights; such that the greater the variability of the bar heights, the greater the variability of the data (Cooper and Shore 2008;Lem et al 2013;Chaphalkar and Leary 2014) and that levelness of bars indicates little variability (delMas et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…qualitative), meaning of axes, and whether or not the graph illustrates the distribution of values of a variable. Several studies have reported that confusion exists in distinguishing the features of the various types of graphs that use bars (e.g., relevance of ordering values or cases along the horizontal axis and meaning associated with the axes) and that this confusion can lead to student difficulty in interpreting data in graphs using bars (Bright and Friel 1998;delMas et al 2005;Garfield and Ben-Zvi 2008;Kaplan et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Assessing Students' Understanding of Variability in Graphical Representations that Share the Common Attribute of Bars

Cooper

2018

Journal of Statistics Education

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Everyday encounters with graphical representations include a variety of graphs that superficially appear similar due to their use of bars. This article examines students' conceptions and misconceptions regarding the interpretation of variability in histograms, bar graphs, and value bar charts. A multiple choice assessment with brief written justifications was administered to students in an introductory level college statistics course. Assessment items included comparisons of variability among same-type graphs and questions regarding students' ability to identify key features of the graphs. The study revealed that students struggle to make sense of the underlying structures of the various types of graphs and often disregard graph type as they transfer correct conceptions of variability for one type of graph erroneously to other types of graphs.

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Investigating Student Understanding of Histograms

Cited by 34 publications

References 8 publications

Learning the Features Used To Decide How to Teach

Learning the Features Used To Decide How to Teach

“It’s Fundamental”: Quantum Dot Blinking Experiment to Teach Critical Thinking

Assessing Students' Understanding of Variability in Graphical Representations that Share the Common Attribute of Bars

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