Journal Article

  • Finding ways to enhance introductory students' understanding of probability ideas and theory is a goal of many first-year probability courses. In this article, we explore the potential of a prototype tool for Markov processes using dynamic visualizations to develop in students a deeper understanding of the equilibrium and hitting times distributions. From the literature and interviews with practitioners, we identified core probability concepts, problematic areas, and possible solutions from which we developed design principles for the tool and accompanying tasks. The tool and tasks were piloted on six introductory probability students using a two-person protocol. The main findings highlight that our tool and tasks seemed to assist students to engage with probability ideas, to develop some intuition for Markov processes, to enhance their distributional ideas, to work between representations, and to see structure within the mathematics representations. The implications for teaching and learning are discussed.

  • “Flipping” the classroom refers to a pedagogical approach in which students are first exposed to didactic content outside the classroom and then actively use class time to apply their newly attained knowledge. The idea of the flipped classroom is not new, but has grown in popularity in recent years as the necessary technology has improved in terms of quality, cost, and availability. Many biostatistics instructors are adopting this format, but some remain unsure whether such a change would benefit their students. One potential barrier to adopting a flipped classroom is the common misconception that only a single approach is available. Having adopted the flipped approach in their own courses, the authors participated in an invited panel at the 2014 Joint Statistical Meetings held in Boston, Massachusetts entitled “Flipping the Biostatistics Classroom.” A theme emerged from the panel's discussions: rather than being a one-size-fits-all approach, the flipped biostatistics classroom offers a high degree of flexibility, and this flipped approach can—and should—be tailored to instructors' specific target audience: their students. Several of these varied approaches to the flipped classroom and practical lessons learned are described.

  • There has been a recent emergence of scholarship on the use of fun in the college statistics classroom, with at least 20 modalities identified. While there have been randomized experiments that suggest that fun can enhance student achievement or attitudes in statistics, these studies have generally been limited to one particular fun modality or have not been limited to the discipline of statistics. To address the efficacy of fun items in teaching statistics, a student-randomized experiment was designed to assess how specific items of fun may cause changes in statistical anxiety and learning statistics content. This experiment was conducted at two institutions of higher education with different and diverse student populations. Findings include a significant increase in correct responses to questions among students who were assigned online content with a song insert compared with those assigned content alone.

  • As an extension to an activity introducing Year 5 students to the practice of statistics, the software TinkerPlots made it possible to collect repeated random samples from a finite population to informally explore students’ capacity to begin reasoning with a distribution of sample statistics. This article provides background for the sampling process and reports on the success of students in making predictions for the population from the collection of simulated samples and in explaining their strategies. The activity provided an application of the numeracy skill of using percentages, the numerical summary of the data, rather than graphing data in the analysis of samples to make decisions on a statistical question. About 70% of students made what were considered at least moderately good predictions of the population percentages for five yes–no questions, and the correlation between predictions and explanations was 0.78.

  • Much has been made of the flipped classroom as an approach to teaching, and its effect on student learning. The volume of material showing that the flipped classroom technique helps students better learn and better retain material is increasing at a rapid pace. Coupled with this technique is active learning in the classroom. There are many ways of “flipping the classroom.” The particular realization of the flipped classroom that we discuss in this article is based on a method called “Just-in-Time Teaching (JiTT).” However, JiTT, in particular, and the flipped classroom, in general, is not just about watching videos before class, or doing activities during class time. JiTT includes assigning short, web-based questions to students based on previously viewed material. Typically, Internet-based questions are constructed to elicit common misunderstandings from the students, so that the instructor can correct such misunderstandings immediately in the next class period, hence the name, “Just-in-Time Teaching.” The addition of these pre-class questions is what separates JiTT from a general flipped classroom model. Even as the research on the superiority of JiTT as a learner-centered pedagogical method mounts, aids for the instructor have not, at least not as quickly. This article is focused on the instructor—with aids to help the instructor begin using the JiTT flipped classroom model in statistics courses.

  • Missing data mechanisms, methods of handling missing data, and the potential impact of missing data on study results are usually not taught until graduate school. However, the appropriate handling of missing data is fundamental to biomedical research and should be introduced earlier on in a student's education. The Summer Institute for Training in Biostatistics (SIBS) provides practical experience to motivate trainees to pursue graduate training and biomedical research. Since 2010, SIBS Pittsburgh has demonstrated the feasibility of introducing missing data concepts to trainees in a small-group project-based setting that involves both simulation and data analysis. After learning about missing data mechanisms and statistical techniques, trainees apply what they have learned to a NIDDK/NIH-funded Hepatitis C treatment study, to examine how various hypothesized missing data patterns can affect results. A simulation is also used to examine the bias and precision of these methods under each missing data pattern. Our experience shows that under such project-based training, advanced topics, such as missing data, can be presented to trainees with limited statistical preparation, and ultimately, can further their statistical literacy and reasoning.

  • Flipped classrooms have become an interesting alternative to traditional lecture-based courses throughout the undergraduate curriculum. In this article, we compare a flipped classroom approach to the traditional lecture-based approach to teaching introductory biostatistics to first-year graduate students in public health. The traditional course was redesigned to include video lectures and online quizzes which the students were expected to complete before coming to class, followed by a short in-class lecture and time working on applied statistics problems in class. We compared the opinions of the biostatistics field and confidence applying biostatistics methods of 46 students who took the flipped course to 52 students who took the traditional, lectured-based course offering. We found similar end-of-semester opinions and levels of confidence between students in the flipped classes and those in the traditional, lecture-focused classes, though students in the flipped course reported very high satisfaction with the model.

  • Recently Watkins, Bargagliotti, and Franklin (2014) discovered that simulations of the sampling
    distribution of the mean can mislead students into concluding that the mean of the sampling
    distribution of the mean depends on sample size. This potential error arises from the fact that the
    mean of a simulated sampling distribution will tend to be closer to the population mean with
    large sample sizes than it will with small sample sizes. Although this pattern does not change as
    a function of the number of samples, the size of the difference between simulated sampling
    distribution means does and can be made invisible to observers by using a very large number of
    samples. It is now practical for simulations to use these very large numbers of samples since the
    speed of computers and even mobile devices is sufficient to simulate a sampling distribution
    based on 1,000,000 samples in just a few seconds. Research on the effectiveness of sampling
    distribution simulations is briefly reviewed and it is concluded that they are effective as long as
    they are used in a pedagogically sound manner.

  • Traditional lecture-centered classrooms are being challenged by active learning hybrid curricula.
    In small graduate programs with limited resources and primarily non-traditional students,
    exploring how to use online technology to optimize the role of the professor in the
    classroom is imperative. However, very little research exists in this area. In this study, the
    use of short statistical computing video tutorials was explored using a pilot study in a small
    Public Health Program at the University of New Mexico. The videos were implemented in
    two Master’s-level biostatistics courses and student perception of the videos was assessed
    using quantitative surveys and qualitative focus groups. The results from 16 survey respondents
    and 12 focus group participants are presented across the two courses. Viewing rates
    for the videos were high, with 15 out of 16 respondents reporting usually or always viewing
    the videos. Overall perception of the videos as a learning tool was positive, with 14
    out of 16 respondents agreeing that the videos offer advantages to them. Two prominent
    themes emerged in our analysis: (1) the usability and convenience of the videos and (2) the
    deeper learning facilitated by having the videos available. We conclude that the short video
    tutorials were a useful learning tool in our study population.

  • At Wake Forest University, a student who is blind enrolled in a second course in statistics.
    The course covered simple and multiple regression, model diagnostics, model selection,
    data visualization, and elementary logistic regression. These topics required that the student
    both interpret and produce three sets of materials: mathematical writing, computer
    programming, and visual displays of data. While we did find scattered resources for blind
    students taking mathematics courses or introductory statistics courses, we found no complete
    account of teaching statistical modeling to students who are blind. We also discovered
    some challenges in stitching together multiple partial solutions. This paper outlines
    our specific approach. We relied heavily on integrating the use of multiple existing technologies.
    Specifically, this paper will detail the extensive use of screen readers, LATEX, a
    modified use of R and the Braille R package, a desktop Braille embosser, and a modified
    classroom approach.