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 author’s 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.
The present paper examines the difficulties Greek senior high school students identify in learning
Statistics and how these difficulties are related to the course’s 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
students’ satisfaction by the method of teaching. In the paper a case–study 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; Statistics’ Syllabus;
Difficulties in Leaning and Teaching of Statistics.
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.
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.
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.
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.
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
Key Words: Hands-On Demonstration; Active Learning; Central Limit Theorem;
Confidence Interval; Hypothesis Testing.
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.
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.
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
Key Words: Test scores; Assessment; Pedagogy; Web-based.
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.
The sample variance, s2, is a common staple in the traditional introductory statistics course and
textbook when presenting options to measure the amount of ‘spread’ or ‘variability’ 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.