Journal Article

  • This study aimed to quantify the influence of student attributes, coursework resources, and
    online assessments on student learning in business statistics. Surveys were administered to
    students at the completion of both online and on-ground classes, covering student perception and
    utilization of internal and external academic resources, as well as intrinsic motivating factors for
    success in the course. Student performance as defined by quality points, various assignment
    points, and time spent on assignments, was not significantly different between on-ground and
    online students. However, use of resources and tools to complete homework and learn new topics
    differed. As a whole, students predominantly utilized homework as the first tool to learn new
    topics and complete homework, suggesting a paradigm shift in the way instructors should cater
    to student’s learning habits.

  • Statistics courses that focus on data analysis in isolation, discounting the scientific inquiry
    process, may not motivate students to learn the subject. By involving students in other steps of
    the inquiry process, such as generating hypotheses and data, students may become more
    interested and vested in the analysis step. Additionally, such an approach might better prepare
    students to tackle real research questions outside of the statistics classroom. Presented here is a
    classroom activity utilizing the popular Hasbro board game Operation, which requires student
    involvement in the entire research process. Highlighted are ways this activity uncovers a number
    of research issues. A number of categorical and continuous variables are collected, making the
    activity amenable to a variety of statistical investigations and thus easy to imbed into any
    curriculum. Designed to mimic a real-world research scenario, this fun activity provides a guided
    yet flexible research experience from start to finish.

  • Web-augmented traditional lecture, fully online, and flipped sections, all taught by the same
    instructor with the same course schedule, assignments, and exams in the same semester, were
    compared with regards to student attitudes; statistical reasoning; performance on common
    exams, homework, and projects; and perceptions of the course and instructor. The Survey of
    Attitudes Toward Statistics-36 (SATS-36) instrument and eight questions from the Statistical
    Reasoning Assessment (SRA) were given both at the beginning and end of the semester to
    measure change. The students selected their own sections, but the students in the sections were
    similar demographically, with similar pre-course college grade point averages. The SATS-36
    showed increases in affect, cognitive competence, and perceived easiness and decreases in value, interest, and effort from beginning to end of the semester for all sections. Only affect and perceived easiness showed any differences for section, with traditional higher than online on
    average for both. Results from the SRA questions showed an increase in correct statistical
    reasoning skills and decrease in misconceptions for all sections over the semester. Traditional
    students scored higher on average on all three exams, but there were no significant differences
    between sections on homework, the project, or on university evaluations of the course or
    instructor. Results are contextualized with prior educational research on course modalities, and
    proposals for future research are provided.

  • In this paper, we compare an introductory statistics course taught using a flipped classroom
    approach to the same course taught using a traditional lecture based approach. In the lecture
    course, students listened to lecture, took notes, and completed homework assignments. In the
    flipped course, students read relatively simple chapters and answered reading quiz questions
    prior to class and completed workbook activities in class. The workbook activities consisted of
    questions (multiple choice, short answer, computation) designed to help students understand
    more complex material. Over one year after taking the course (median = 20 months), students
    took a standardized test of their knowledge of statistics as well as nine other content areas in
    psychology. Students in the flipped course outperformed the students in the lecture course on the
    statistics portion of the test (d =.43), but not on non-statistics portions of the test.

  • Although the use of simulation to teach the sampling distribution of the mean is meant to provide
    students with sound conceptual understanding, it may lead them astray. We discuss a
    misunderstanding that can be introduced or reinforced when students who intuitively understand
    that “bigger samples are better” conduct a simulation to explore the effect of sample size on the
    properties of the sampling distribution of the mean. From observing the patterns in a typical
    series of simulated sampling distributions constructed with increasing sample sizes, students
    reasonably—but incorrectly—conclude that, as the sample size, n, increases, the mean of the
    (exact) sampling distribution tends to get closer to the population mean and its variance tends to
    get closer to ????2/n, where ????2 is the population variance. We show that the patterns students
    observe are a consequence of the fact that both the variability in the mean and the variability in
    the variance of simulated sampling distributions constructed from the means of N random
    samples are inversely related, not only to N, but also to the size of each sample, n. Further,
    asking students to increase the number of repetitions, N, in the simulation does not change the

  • Abstract
    The statistical preparation of in-service teachers, particularly middle school teachers, has been an
    area of concern for several years. This paper discusses the creation and delivery of an
    introductory statistics course as part of a master’s degree program for in-service mathematics
    teachers. The initial course development took place before the advent of the Common Core State
    Standards for Mathematics (CCSSM) and the Mathematics Education of Teachers (MET II)
    Reports, and even before the GAISE Pre-K-12 Report. Since then, even with the
    recommendations of MET II and the widespread implementation of the CCSSM, the guidance
    available to faculty wishing to develop a statistics course for professional development of in service
    teachers remains scarce. We give an overview of the master’s degree program and discuss aspects of the course, including the goals for the course, course planning and development, the instructional team, course delivery and modifications, and lessons learned
    through five offerings. With this paper, we share our experiences developing such a course, the
    evolution of the course over multiple iterations, and what we have learned about its value to the
    middle-level teachers who have participated. As more and more universities are being asked to
    develop courses specifically for in-service teachers, we wrote this article in the hopes of
    providing guidance to others, and to share our lessons learned.

  • This paper explores the use of a lesser-known dynamic model for the median, a foundational
    topic that starts in the middle school curriculum and is associated with student misconceptions
    and knowledge gaps. This model appears to offer a rich vehicle to explore the median
    interactively in greater conceptual depth that includes some of its more subtle associated ideas.
    An exploratory study to assess performance of this model in a class for pre-service middle school
    teachers yielded evidence that students who completed the dataset sequence associated with the
    model gained further insight about the median, especially concerning how the mean and median
    are affected differently by outliers. Analyses of open ended questions as well as empirical results
    of multiple-choice questions are used to assess the overall learning outcomes gained by students.
    A one-minute video is offered to illustrate key points of the model.

  • Many adults who need an understanding of statistical concepts have limited mathematical skills.
    They need a teaching approach that includes as little mathematical context as possible. Iterative
    participatory qualitative research (action research) was used to develop a statistical literacy
    course for adult learners informed by teaching in traditional first year university courses,
    workplace based training, teacher workshops and Masters of Public Policy courses. The latter
    learners in particular regularly come across confidence intervals and statistical significance in
    their everyday reading. The goal is to give them a conceptual rather than theoretical
    understanding of inferential concepts by developing inferential statistics logic through the
    introduction of exact probabilities in simple non-parametric tests (two-tailed coin tossing) and
    then contingency tables and parametric situations. The final course developed for the New
    Zealand Certificate of Official Statistics uses “hands-on” examples to reinforce concepts before
    proceeding to computer simulations. It emphasizes evaluation of the strength of statistical
    significance and its relationship to the possible cost of making an incorrect decision. Case studies
    that have influenced government policy reinforce inferential concepts and demonstrate the
    importance of statistics in complex real problems.

  • Undergraduate students who have just completed an introductory statistics course often lack deep
    understanding of variability and enthusiasm for the field of statistics. This paper argues that by
    introducing the commonly underemphasized concept of measurement error, students will have a
    better chance of attaining both. We further present lecture materials and activities that introduce
    metrology, the science of measurement, which were developed and tested in a pilot study at Iowa
    State University. These materials explain how to characterize sources of variability in a dataset,
    in a way that is natural and accessible because the sources of variability are observable.
    Everyday examples of measurements, such as the amount of gasoline pumped into a car, are
    presented, and the consequences of variability within those measurements are discussed. To
    gauge the success of the material, students’ initial and subsequent understanding of variability
    and their attitude toward the usefulness of statistics were analyzed in a comparative study.
    Questions from the CAOS and ARTIST assessments that pertain to using variability to make comparisons, understanding the standard deviation, and using graphical representations of
    variability were included in the assessment. The results of the comparative study indicate that
    most students who were exposed to the material improved their understanding of variability and
    had a greater appreciation of the value of statistics.

  • Bayesian methodology continues to be widely used in statistical applications. As a result, it is
    increasingly important to introduce students to Bayesian thinking at early stages in their
    mathematics and statistics education. While many students in upper level probability courses can
    recite the differences in the Frequentist and Bayesian inferential paradigms, these students often
    struggle using Bayesian methods when conducting data analysis. Specifically, students tend to
    struggle translating subjective belief to the specification of a prior distribution and the
    incorporation of uncertainty in the Bayesian inferential approach. The purpose of this paper is to
    present a hands-on activity involving the Beta-Binomial model to facilitate an intuitive
    understanding of the Bayesian approach through subjective problem formulation which lies at
    the heart of Bayesian statistics.