Statistical Reasoning Ability, Self-Efficacy, and Value Beliefs in a Reform Based University Statistics Course

Olani, Aboma; Hoekstra, Rink; Harskamp, Evert; Werf, van der Margaretha

doi:10.25115/ejrep.v9i23.1427

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…Central tendency Students have misconceptions about measures of central tendency: mode (Huck, 2009), median (Cooper & Shore, 2008), and mean (Olani, Hoekstra, Harskamp, & Van der Werf, 2010). When asked to identify the three measures of central tendency from a histogram, where frequency information is displayed on the y-axis, students make some common errors that stem from misinterpreting the information portrayed by the axes (reading the y-axis as data values and not frequency) and by the bars.…”

Section: Misconceptions About Summary Statisticsmentioning

confidence: 99%

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Cui

Liu

2021

Atten Percept Psychophys

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In the age of big data, we are constantly inventing new data visualizations to consolidate massive amounts of numerical information into smaller and more digestible visual formats. These data visualizations use various visual features to convey quantitative information, such as spatial position in scatter plots, color saturation in heat maps, and area in dot maps. These data visualizations are typically composed of ensembles, or groups of related objects, that together convey information about a data set. Ensemble perception, or one's ability to perceive summary statistics from an ensemble, such as the mean, has been used as a foundation for understanding and explaining the effectiveness of certain data visualizations. However, research in data visualization has revealed some perceptual biases and conceptual difficulties people face when trying to utilize the information in these graphs. In this tutorial review, we will provide a broad overview of research conducted in ensemble perception, discuss how principles of ensemble encoding have been applied to the research in data visualization, and showcase the barriers graphs can pose to learning statistical concepts, using histograms as a specific example. The goal of this tutorial review is to highlight possible connections between three areas of research-ensemble perception, data visualization, and statistics education-and to encourage research in the practical applications of ensemble perception in solving real-world problems in statistics education.

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Section: Misconceptions About Summary Statisticsmentioning

confidence: 99%

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Cui

Liu

2021

Atten Percept Psychophys

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“…Nevertheless, high school students appear to have poor statistical reasoning ability [2], and they often harbor numerous misconceptions about statistical reasoning, as found in our preliminary and earlier studies. For instance, in descriptive statistics, students frequently make mistakes in answering questions regarding measures of central tendency [3,4] and variability [5–7]. Statistical reasoning is a neglected area, particularly compared to the areas of statistical literacy and statistical thinking [8].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Assessing High School Students' Statistical Reasoning

2016

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Based on a synthesis of literature, earlier studies, analyses and observations on high school students, this study developed an initial framework for assessing students’ statistical reasoning about descriptive statistics. Framework descriptors were established across five levels of statistical reasoning and four key constructs. The former consisted of idiosyncratic reasoning, verbal reasoning, transitional reasoning, procedural reasoning, and integrated process reasoning. The latter include describing data, organizing and reducing data, representing data, and analyzing and interpreting data. In contrast to earlier studies, this initial framework formulated a complete and coherent statistical reasoning framework. A statistical reasoning assessment tool was then constructed from this initial framework. The tool was administered to 10 tenth-grade students in a task-based interview. The initial framework was refined, and the statistical reasoning assessment tool was revised. The ten students then participated in the second task-based interview, and the data obtained were used to validate the framework. The findings showed that the students’ statistical reasoning levels were consistent across the four constructs, and this result confirmed the framework’s cohesion. Developed to contribute to statistics education, this newly developed statistical reasoning framework provides a guide for planning learning goals and designing instruction and assessments.

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“…Such research, however, is considerably lacking in the Caribbean region. Given the many questions that remain on the potential impact of prior performance in mathematics on the statistical performance of university students (Macher et al, 2011;Olani et al, 2011), the general anxieties related to statistics (Macher et al, 2013), the need for comparative data on other populations of students within the field of statistics education, and the empirical abyss that exists within the Caribbean, the study asked the following questions:…”

Section: Caribbean Contextmentioning

confidence: 99%

“…Yet another group of scholars has explored mathematical anxiety, defined in many cases as the extent to which students are worried about working with and manipulating numbers or their problem-solving skills (Ashcraft & Krause, 2007;Onwuegbuzie, 1997;Paechter et al, 2017;Zeidner, 1991). This line of investigation on previous mathematics performance, has spurred related studies on the relationship between PMP, SA, and EFP (Olani et al, 2011;Paechter et al, 2017). Given the many questions around the effects of previous mathematics performance on statistical performance Paechter et al (2017) contended that PMP remains necessary, but not sufficient to explain reported variability in students' EFP in statistics courses.…”

Section: Previous Mathematics Performance (Pmp)mentioning

confidence: 99%

Prior Mathematics Performance, Statistics Anxiety, Self-Efficacy and Expectations for Performance in Statistics: A Survey of Social Sciences Students in a Caribbean Institution of Higher Education

Esnard¹,

Alladin²,

Samlal³

2021

SERJ

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The objectives of this study were to examine (i) the expectation for performance (EFP) in statistics of first year university students, (ii) the relative effect of previous mathematics performance (PMP), perceived statistical self-efficacy (SSE), and statistics anxiety (SA) for understanding these expectations, and (iii) whether students’ EFP scores differ based on sex and academic discipline. Findings point to average to high EFP in statistics, with no differences in these levels based on sex or academic discipline. PMP had little effect on students’ EFP, but, moderate effects on their levels of SA. While SSE positively affected both students’ SA, this measure produced a negative effect on their EFP in statistics. Both SSE and SA negatively affected students’ EFP in statistics, but with minimally higher levels for the latter.

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Statistical Reasoning Ability, Self-Efficacy, and Value Beliefs in a Reform Based University Statistics Course

Abstract: Introduction. The study investigated the degree to which students" statistical reasoning abili-

Cited by 12 publications

References 19 publications

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

A Framework for Assessing High School Students' Statistical Reasoning

Prior Mathematics Performance, Statistics Anxiety, Self-Efficacy and Expectations for Performance in Statistics: A Survey of Social Sciences Students in a Caribbean Institution of Higher Education

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