An International Journal on the Teaching and Learning of
JSE Volume 21, Number
Researchers and statistics educators consistently suggest that students will learn statistics more effectively by conducting projects through which they actively engage in a broad spectrum of tasks integral to statistical inquiry, in the authentic context of a real-world application. In keeping with these findings, we share an implementation of discovery projects for students in elementary statistics classes. We delineate the purpose and scope of two types of projects— one covering linear regression analysis and the other covering comparisons with basic t-tests (matched pairs or two independent samples). We describe a set of curriculum materials developed to help instructors facilitate such projects and share access to these materials. We give examples of how the curriculum materials guide each stage of project implementation. We detail the requirements and student activities during each phase of the student-directed projects: Students select their own research topic, define their own variables, and devise and carry out their own data collection plan before analyzing and interpreting their data. Students then articulate their results, both in a written report and in a brief formal presentation delivered to the class. We give examples of specific projects that students have conducted. Finally, we discuss the potential benefits of such projects, including possible factors mediating those benefits.
Key Words: Student-centered; Experiential learning; t-test; Linear regression; Research into practice.
We developed an introductory statistics course for pre-service elementary teachers. In this paper, we describe the goals and structure of the course, as well as the assessments we implemented. Additionally, we use example course work to demonstrate pre-service teachers’ progress both in learning statistics and as novice teachers. Overall, the course aims to help pre-service teachers recognize the importance of statistics in the elementary curriculum, as well as the integral role they, as teachers, can play in a student’s entire statistical education. Our course serves as a model/resource for others interested in pre-service teacher development.
Key Words: Course development; Teacher preparation; Elementary education; Assessment.
Aisling M. Leavy, Ailish Hannigan, and Olivia Fitzmaurice
Most research into prospective secondary mathematics teachers’ attitudes towards statistics indicates generally positive attitudes but a perception that statistics is difficult to learn. These perceptions of statistics as a difficult subject to learn may impact the approaches of prospective teachers to teaching statistics and in turn their students’ perceptions of statistics. This study is the qualitative component of a larger quantitative study. The quantitative study (Hannigan, Gill and Leavy 2013) investigated the conceptual knowledge of and attitudes towards statistics of a larger group of prospective secondary mathematics teachers (n=134). For the purposes of the present study, nine prospective secondary teachers, eight of whom were part of the larger study, were interviewed regarding their perceptions of learning and teaching statistics. This study extends our understandings garnered from the quantitative study by exploring the factors that contribute to the perception of statistics as being difficult to learn. The analysis makes explicit the tensions in learning statistics by highlighting the nature of thinking and reasoning unique to statistics and the somewhat ambiguous influence of language and context on perceptions of difficulty. It also provides insights into prospective teachers’ experiences and perceptions of teaching statistics and reveals that prospective teachers who perceive statistics as difficult to learn avoided teaching statistics as part of their teaching practice field placement.
Key Words: Statistics; Teacher education; Perceptions of difficulty; Attitudes, Beliefs.
with Statistics Educators
Chris Franklin is Senior Lecturer, Undergraduate Coordinator, and Lothar Tresp Honoratus Professor of Statistics at the University of Georgia. She is a Fellow of the American Statistical Association and received the USCOTS Lifetime Achievement Award in 2013. This interview took place via email on August 16, 2013 – October 9, 2013.
I located 19 articles that have been published from August through October 2013 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 journal and a link to the journal's website 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
Recent approval of HPV vaccines and their widespread provision to young women provide an interesting context to gain experience with the application of statistical methods in current research. We demonstrate how we have used data extracted from a meta-analysis examining the efﬁcacy of HPV vaccines in clinical trials with students in applied statistics courses at both introductory and intermediate university levels. The data are suitable for various techniques in categorical data analysis including comparison of proportions, analysis of contingency tables, logistic regression and log-linear models. These data are relevant to all young people and, because of their health science context, can be used in courses in biostatistics or the health sciences as they allow for further discussion of meta-analyses and randomized controlled trials. We also discuss how we have used these data to promote discussion of statistical issues such as statistical versus practical signiﬁcance, independence, and a common misconception involving the interpretation of p-values.
Key Words: Biostatistics; Categorical data analysis; Chi-square test of homogeneity; Experiments; HPV; Inference for proportions; Log-linear models; Logistic regression; Meta-analysis; Randomized controlled trials; Relative risk.
Ananda B. W. Manage and Stephen M. Scariano
Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In particular, the first principal component is seen to explain a substantial portion of the variation in a linear combination of some commonly used measures of cricket prowess. This application provides an excellent, elementary introduction to the topic of principal component analysis.
Key Words: Multivariate statistics; Sports data; Principal components; Cricket.