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Full-Text Articles in Science and Mathematics Education
Real Data Is Messy... And Manageable, Beverly Wood, Carl Clark
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.
Gaiseing Into The New Guidelines, Robert Carver, Megan Mocko, Jeffrey Witmer, Beverly Wood
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
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.
The Levels Of Conceptual Understanding In Statistics (Locus) Project: Results Of The Pilot Study, Douglas Whitaker, Steven Foti, Tim Jacobbe
The Levels Of Conceptual Understanding In Statistics (Locus) Project: Results Of The Pilot Study, Douglas Whitaker, Steven Foti, Tim Jacobbe
Numeracy
The Levels of Conceptual Understanding in Statistics (LOCUS) project (NSF DRL-111868) has created assessments that measure conceptual (rather than procedural) understanding of statistics as outlined in GAISE Framework (Franklin et al., 2007, Guidelines for Assessment and Instruction in Statistics Education, American Statistical Association). Here we provide a brief overview of the LOCUS project and present results from multiple-choice items on the pilot administration of the assessments with data collected from over 3400 students in grades 6-12 across six states. These results help illustrate students’ understanding of statistical topics prior to the implementation of the Common Core State Standards. Using the …
Community Service-Learning In Statistics: Course Design And Assessment, Debra L. Hydorn
Community Service-Learning In Statistics: Course Design And Assessment, Debra L. Hydorn
Mathematics
Service-learning projects are a useful method for students to learn both the practice and value of statistical methods. Effective service learning, however, depends on several factors and can be implemented according to a variety of models. In this article, different models for incorporating service-learning in statistics courses are presented along with example statistics courses. Principles for good service-learning practice will also be presented as a means for assessing the quality of a service-learning course component.