Samantha C. Bates Prins
Student-Centered Instruction In A Theoretical Statistics Course

This paper provides an example of how student-centered instruction can be used in a theoretical statistics class. The author taught a two-semester undergraduate probability and mathematical statistics sequence using primarily teacher-centered instruction in the first semester and primarily student-centered instruction in the second semester. A subset of the students in the teacher-centered course also took the student-centered course. Student feedback suggests that the student-centered approach, while more difficult for both student and instructor, is beneficial when compared to the teacher-centered approach. The specific method of implementation will need to vary with class size and level of student preparation but the authorÕs example presents a starting point for those interested in moving away from a traditional teaching approach in theoretical statistics classes.

Key Words: Learner-centered instruction; Statistics education.

Dimitris Ghinis, Konstantinos Korres, and Sotiris Bersimis
Difficulties Greek Senior High School Students Identify in Learning and the Teaching of Statistics: The Case of Experimental and Private High Schools

The present paper examines the difficulties Greek senior high school students identify in learning Statistics and how these difficulties are related to the courseÕs level of difficulty. Also it examines the difficulties students identify that teachers face while teaching Statistics, their suggestions for changes and how these difficulties and suggestions are related to the level of the studentsÕ satisfaction by the method of teaching. In the paper a caseÐstudy is presented, that was designed and realized at the Department of Statistics and Insurance Sciences of the University of Piraeus. In the study 163 students from Experimental and Private High Schools participated, all attending the 3rd grade of Greek senior high school.

Key Words: Learning and Teaching of Statistics; StatisticsÕ Syllabus; Difficulties in Leaning and Teaching of Statistics.

Jeffrey J. Green, Courtenay C. Stone, Abera Zegeye, and Thomas A. Charles
How Much Math Do Students Need to Succeed in Business and Economics Statistics? An Ordered Probit Analysis

Because statistical analysis requires the ability to use mathematics, students typically are required to take one or more prerequisite math courses prior to enrolling in the business statistics course. Despite these math prerequisites, however, many students find it difficult to learn business statistics. In this study, we use an ordered probit model to analyze the impact of alternative prerequisite math course sequences on the grade performance of 1,684 business and economics statistics students at a large Midwestern university. In addition, we show how imposing a minimum grade requirement of C- for the math prerequisite course would influence student success in the business statistics course. Although several studies have examined the impact of different math skills, our study is the first to provide a detailed analysis of the impact of different prerequisite math course sequences on student performance in business statistics. We demonstrate that, other things the same, taking more math credit hours, taking math courses that emphasize calculus, and imposing a minimum grade of C- on the prerequisite math course have significant positive impacts on student grade performance in the business and economics statistics course.

Key Words: Introductory business statistics; Math prerequisites; Math topics; Student performance; Minimum prerequisite math grade requirement.

Melinda Miller Holt and Stephen M. Scariano
Mean, Median and Mode from a Decision Perspective

The classroom activity described here allows mathematically mature students to explore the role of mean, median and mode in a decision-making environment. While students discover the importance of choosing a measure of central tendency, their understanding of probability distributions, maximization, and prediction is reinforced through active learning. The lesson incorporates the GAISE recommendations by actively engaging students in the process of statistical problem-solving in a realistic situation.

Key Words: Probability distribution; Return function; Prediction.

Thomas Jaki
Recording Lectures as a Service in a Service Course

Courses for non-statistics majors (service courses) play an integral role in teaching statistics and pose some unique challenges. In these courses, students are often undermotivated on the one hand while on the other hand the syllabus frequently is overly crowded. In this manuscript we target the issues arising from the latter problem by making use of technology. The use of screen capture, a fast and easy way of recording lectures, is discussed through an example of an introductory statistics course for first year biology students at Lancaster University. Student feedback on the use of these recordings is discussed.

Key Words: CamStudio; e-learning; Learning support; Recording lectures; Screen capture; Service course.

Jennifer J. Kaplan, Diane G. Fisher, and Neal T. Rogness
Lexical Ambiguity in Statistics: What Do Students Know about the Words Association, Average, Confidence, Random and Spread?

Language plays a crucial role in the classroom. The use of specialized language in a domain can cause a subject to seem more difficult to students than it actually is. When words that are part of everyday English are used differently in a domain, these words are said to have lexical ambiguity. Studies in other fields, such as mathematics and chemistry education suggest that in order to help students learn vocabulary instructors should exploit the lexical ambiguity of the words. The study presented here is a pilot study that is the first in a sequence of studies designed to understand the effects of and develop techniques for exploiting lexical ambiguities in the statistic classroom. In particular, this paper describes the meanings most commonly used by students entering an undergraduate statistics course of five statistical terms.

Key Words: Statistics Education, Lexical Ambiguity, Language, Word Usage.

Thomas J. Pfaff and Aaron Weinberg
Do Hands-On Activities Increase Student Understanding?: A Case Study

This article describes the design, implementation, and assessment of four hands-on activities in a introductory college statistics course. In the activities, students investigated the ideas of the central limit theorem, confidence intervals, and hypothesis testing. Five assessments were administered to the students, one at the beginning and end of the course, and three in between the activities. We found that, despite our attempts to engage our students in active reflection, their performance on the assessments generally did not improve. These results raise important issues about the design of pedagogical tools and activities as well as the need to gather data to assess their effectiveness.

Key Words: Hands-On Demonstration; Active Learning; Central Limit Theorem; Confidence Interval; Hypothesis Testing.

Eleanor M. Pullenayegum and Lehana Thabane
Teaching Bayesian Statistics in a Health Research Methodology Program

Despite the appeal of Bayesian methods in health research, they are not widely used. This is partly due to a lack of courses in Bayesian methods at an appropriate level for non-statisticians in health research. Teaching such a course can be challenging because most statisticians have been taught Bayesian methods using a mathematical approach, and this must be adapted in order to communicate with non-statisticians. We describe some of the examples we used whilst teaching a course in Bayesian methods to a group of health research methodologists.

Key Words:

Ning-Zhong Shi, Xuming He, and Jian Tao
Understanding Statistics and Statistics Education: A Chinese Perspective

n recent years, statistics education in China has made great strides. However, there still exists a fairly large gap with the advanced levels of statistics education in more developed countries. In this paper, we identify some existing problems in statistics education in Chinese schools and make some proposals as to how they may be overcome. We hope that our study can benefit the development of statistics education in China, and encourage statistics educators and researchers in other countries to help address these important issues in China and possibly in other developing countries.

Key Words: Activity-based statistics; Quality of teaching; Thinking mode.

Jeffrey C. Sklar and Rebecca Zwick
Multimedia Presentations in Educational Measurement and Statistics: Design Considerations and Instructional Approaches

Proper interpretation of standardized test scores is a crucial skill for K-12 teachers and school personnel; however, many do not have sufficient knowledge of measurement concepts to appropriately interpret and communicate test results. In a recent four-year project funded by the National Science Foundation, three web-based instructional presentations in educational measurement and statistics were developed and evaluated (Zwick et al., 2008). These modules were found to be particularly effective for pre-service K-12 teachers. The primary challenge of the project was to deliver the material in three short 25-minute web-based presentations. In this paper, we discuss the design principles, technical considerations, and specific instructional approaches implemented in the modules, invoking principles from cognitive psychology research. Based on evidence gathered from our project and previous research in teacher education and multimedia learning, we offer suggestions for presenting educational measurement and statistics concepts in a multimedia learning environment.

Key Words: Test scores; Assessment; Pedagogy; Web-based.

Audbjorg Bjornsdottir and Joan Garfield
Teaching Bits: Statistics Education Articles from 2009

We located 61 articles that have been published from January till November 2009 that pertain to statistics education. In this column, we highlight a few of these articles that represent a variety of different journals that include statistics education in their focus. We also provide information about the journal and a link to their website so that abstracts of additional articles may be accessed and viewed.

Deborah J. Rumsey
Random Thoughts on Teaching: Let's Just Eliminate the Variance

The sample variance, s2, is a common staple in the traditional introductory statistics course and textbook when presenting options to measure the amount of ÔspreadÕ or ÔvariabilityÕ within a data set. Once the formula, calculations, and examples involving the sample variance have been presented, one then moves on to the sample standard deviation, s, by taking the square root of the variance. And we never look back. From there we only use the standard deviation when calculating, measuring, interpreting, and comparing the amount of variability in one or more data sets. But what do we leave behind for the students to sort out as a result of discussing sample variance? My answer is confusion.