An International Journal on the Teaching and Learning of
JSE Volume 22, Number
In this paper, we describe an in-class experiment that is easy to implement with large groups of students. The experiment takes approximately 15-20 minutes to run and involves each student completing one of four types of Sudoku puzzles and recording the time it takes to completion. The resulting data set can be used as a teaching tool at an introductory level right through to an advanced level of statistics. Basic methods for describing and displaying data as well as the intricacies that arise with real data may be discussed in an introductory course. The range of more sophisticated analyses that can be taught with the data set include chi-squared tests for independence, ANOVA, t- and F-tests, logistic regression and survival analysis. We describe and provide the tools to implement the experiment and illustrate several potential teaching topics using a collected data set.
Key Words: Descriptive statistics; Contingency tables; Logistic regression; ANOVA; Survival analysis; Active learning.
This meta-analytic study focused on the quantitative integration and synthesis of the accumulated pedagogical research in undergraduate statistics education literature. These accumulated research studies compared the academic achievement of students who had been instructed using one of the various forms of small-group learning methods to those who had been instructed using lecture-based instruction. The meta-analytic results showed that cooperative, collaborative, and inquiry-based learning methods were used in college-level statistics courses. The results also showed that cooperative and collaborative learning methods supported the effectiveness of the small-group learning methods in improving students’ academic achievement with an overall average effect-size of 0.60. In contrast, the effectiveness of inquiry-based learning was close to zero. This significant positive average effect-size indicated that using small-group learning methods in statistics classrooms could increase the achievement of college students, increasing the scores on a statistics exam from the 50th to the 73rd percentile. In addition, the multilevel analysis revealed that the effect sizes were influenced significantly by the publication-year of the studies, with the most recently published studies having lower effect sizes. The major implication of this study is that evidence-based research supports the effectiveness of active small-group learning methods in promoting students’ achievement in statistics.
Key Words: Statistics Education; Active Learning; Cooperative Learning; Collaborative Learning; Meta-Analysis; Hierarchical Linear Modeling.
Eiki Stake and Amy Vashlishan Murray
Although Bayesian methodology has become a powerful approach for describing uncertainty, it has largely been avoided in undergraduate statistics education. Here we demonstrate that one can present Bayes' Rule in the classroom through a hypothetical, yet realistic, legal scenario designed to spur the interests of students in introductory- and intermediate-level statistics classes. The teaching scenario described in this paper not only illustrates the practical application of Bayes' Rule to legal decision-making, but also emphasizes the cumulative nature of the Bayesian method in measuring the strength of the evidence. This highlights the Bayesian method as an alternative to the traditional inferential methods, such as p value and hypothesis tests. Within the context of the legal scenario, we also introduce DNA analysis, implement a modified version of Bayes' Rule, and utilize Bayes’ Factor in the computation process to further promote students' intellectual curiosities and incite lively discussion pertaining to the jury decision-making process about the defendant's status of guilt.
Key Words: Bayes’ Rule; Bayes’ Factor; Forensic evidence; DNA fingerprint analysis; Legal decision-making.
The purpose of the current research was to investigate the relationship between preference for numerical information (PNI), math self-concept, and six types of statistics anxiety in an attempt to establish support for the nomological validity of the PNI. Correlations indicate that four types of statistics anxiety were strongly related to PNI, and two were not related. Math self-concept was also strongly related to PNI. Results suggest that higher PNI is associated with higher math self-concept and lower statistics anxiety in graduate students, and indicate support for the nomological validity of the PNI within the context of graduate statistics classes.
Key Words: Academic anxiety; Statistics education; Graduate students; Nomological validity.
with Statistics Educators
Ron Wasserstein is Executive Director of the American Statistical Association. He previously served as Vice-President for Academic Affairs and Professor of Statistics at Washburn University. This interview took place via email on January 21 – February 24, 2014.
I located 16 articles and one book that have been published from November 2013 through February 2014 that pertained to statistics education. In this column, I highlight a few of these articles that represent a variety of different journals that include statistics education in their focus. I also provide information about the journals and book and a link to their websites so that abstracts of additional articles may be accessed and viewed.
The aim of this short column is to provide an overview of resources and events from CAUSEweb (www.causeweb.org) and MERLOT (www.merlot.org) to help you stay connected with the Statistical Education community.
Data Sets and Stories
In this article, we present a data set and case study exercise that can be used by educators to teach a range of statistical concepts including Simpson’s paradox. The data set and case study are based on a real-life scenario where there was a claim of discrimination based on ethnicity. The exercise highlights the importance of performing rigorous statistical analysis and how data interpretations can accurately inform or misguide decision makers.
Key Words: Univariate analysis; Bivariate analysis; Specific variation; Outliers; Weighted average; Simpson’s paradox.