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Full-Text Articles in Higher Education

Real Data Is Messy... And Manageable, Beverly Wood, Carl Clark Jan 2017

Real Data Is Messy... And Manageable, Beverly Wood, Carl Clark

Publications

Using real data in an introductory statistics course is a delicate balance between reality and manageability. The internet is awash with data that is useful for students to answer questions of interest to them but it is not always formatted as neatly as textbook data. The ASA's recently endorsed GAISE College Report 2016 points to the plausibility of considering multivariable thinking even if only at a rudimentary level. With both messy and multivariable data in mind, we present some activities/projects and sources for data to give introductory students the opportunity to engage with real data.


Effects Of Growth Mindset Training On Undergraduate Statistics Students, Valorie L. Zonnefeld Jul 2016

Effects Of Growth Mindset Training On Undergraduate Statistics Students, Valorie L. Zonnefeld

Faculty Work Comprehensive List

Undergraduate introductory statistics courses have experienced numerous changes in the past century, for instance, increased enrollment and diversification of students required to take the courses. Promising research has been conducted on mathematical mindsets, however, no research is available for introductory statistics courses. This presentation addresses the effect of growth mindset training on students in mathematics.


Gaiseing Into The New Guidelines, Robert Carver, Megan Mocko, Jeffrey Witmer, Beverly Wood May 2016

Gaiseing Into The New Guidelines, Robert Carver, Megan Mocko, Jeffrey Witmer, Beverly Wood

Publications

The first GAISE College Report came out in 2005. Over the past ten years our discipline has changed in many ways, including but not limited to what type of data is easily available, the technology that we use, as well as how we teach students. In this presentation we will briefly start with how the new GAISE 2016 guidelines and goals have changed, including the two new emphases of statistical thinking: giving students experience with multivariable thinking and with the investigative process. So how do you start to implement these new ideas? In this presentation, we will demonstrate an activity …


Multivariate Thinking In An Intro Stats Course – Is It Possible?, Beverly Wood May 2016

Multivariate Thinking In An Intro Stats Course – Is It Possible?, Beverly Wood

Publications

Many of our students have an intuitive sense that there is more to the story than univariate or bivariate data can tell us. We can acknowledge and encourage that habit of digging deeper by demonstrating some ways to look at additional variables. Simpson’s paradox and side-by-side scatter plots are ways to provide a glimpse of more complex analysis that are accessible to students in an introductory course with or without strong quantitative skills.


Mindsets, Attitudes, And Achievement In Undergraduate Statistics Courses, Valorie L. Zonnefeld May 2015

Mindsets, Attitudes, And Achievement In Undergraduate Statistics Courses, Valorie L. Zonnefeld

Faculty Work Comprehensive List

The purpose of this study was to determine the effects of theories of intelligence and an intervention of incremental mindset training on students’ attitudes toward statistics and their mastery of content in an introductory statistics college course. The sample was 547 undergraduate students at a small, faith-based, liberal arts college in the Midwest.

A pretest-posttest design was used for the three instruments implemented. The Comprehensive Assessment of Outcomes in a first Statistics course (CAOS) assessed students’ statistical literacy. The Student Attitudes Towards Statistics – 36© (SATS©) assessed six components of students’ attitudes toward statistics including affect, cognitive competence, difficulty, effort, …


Statistical Models Of Self-Efficacy In Stem Students, Sarah Painter Aug 2014

Statistical Models Of Self-Efficacy In Stem Students, Sarah Painter

Journal of Undergraduate Research at Minnesota State University, Mankato

Persistence through undergraduate education may be explained by self-efficacy. It is the belief in one’s self to persevere through challenges. Bandura stated four areas that are thought to influence self-efficacy: mastery experience, social persuasion, vicarious experience, and physiological state. In this study, we focused on general and academic self-efficacy in STEM students, in the hopes of learning more about the relationships between Bandura’s categories, demographics, and self-efficacy. Data was taken from two institutions: one, a large research focused university, and the other, a smaller teaching focused university. In the first phase, surveys on general self-efficacy were taken at both institutions …