Undergraduate research experiences (UREs), whether within the context of a mentor-mentee experience or a classroom framework, represent an excellent opportunity to expose students to the independent scholarship model. The high impact of undergraduate research has received recent attention in the context of STEM disciplines. Reflecting a 2017 survey of statistics faculty, this article examines the perceived benefits of UREs, as well as barriers to the incorporation of UREs, specifically within the field of statistics. Viewpoints of students, faculty mentors, and institutions are investigated. Further, the article offers several strategies for leveraging characteristics unique to the field of statistics to overcome barriers and thereby provide greater opportunity for undergraduate statistics students to gain research experience.
One of the first simulation-based introductory statistics curricula to be developed was the NSF-funded Change Agents for Teaching and Learning Statistics curriculum. True to its name, this curriculum is constantly undergoing change. This article describes the story of the curriculum as it has evolved at the University of Minnesota and offers insight into promising new future avenues for the curriculum to continue to affect radical, substantive change in the teaching and learning of statistics. Supplementary materials for this article are available online.
Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higher-level courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students “correlation does not imply causation” or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.
While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking. Due to this legacy, Bayesian ideas are not required for undergraduate degrees and have largely been taught at the graduate level; however, with recent advances in software and emphasis on computational thinking, Bayesian ideas are more accessible. Statistics curricula need to continue to evolve and students at all levels should be taught Bayesian thinking. This article advocates for adding Bayesian ideas for three groups of students: intro-statistics students, undergraduate statistics majors, and graduate student scientists; and furthermore, provides guidance and materials for creating Bayesian-themed courses for these audiences. Supplementary files for this article are available on line.
One of the main goals of statistics is to use data to provide evidence in support of an argument. This article will discuss some popular forms of writing assessments currently in use, to demonstrate the differences between the methods for structuring the students' learning to support their arguments with evidence. We share a model, which was originally created to assess students in introductory statistics and has been adapted for the second course in statistics, which takes a unique approach toward assessing the students' understanding of statistical concepts through writing. In this model, students are expected to answer prompts that required them to (1) take a stance on an argument, (2) defend their position with facts given in the prompt, (3) discern the implications that those facts implied, and (4) give a proper conclusion to their argument. We provide examples of a few of the writing assignment prompts used in the course, their intended assessment purpose, and common answers that students gave to these assignments. Supplementary materials for this article are available online.
This article is the first to thoroughly investigate the state of undergraduate statistics education in the Arab world. It discusses evidence with respect to the quality of education in general and statistics education in particular. Based on a survey of statistics programs in Arab universities, several issues pertaining to curriculum structure, pedagogical practices, and matching learning outcomes with labor market needs are discussed. The survey results reveal a gap between the undergraduate statistics programs in Arab universities and the international curriculum guidelines. This gap signals the urgent need for reforming and enhancing statistics education to address the needs of the labor market in this era of information. Recommendations and strategic reforms based on best international practices are outlined.
In the first years of their economics degree programs, students will face many problems successfully dealing with a range of subjects with quantitative content. Specifically, in the field of statistics, difficulties to reach some basic academic achievements have been observed. Hence, a continuing challenge for statistics teachers is how to make this subject more appealing for students through the design and implementation of new teaching methodologies. The latter tend to follow two main approaches. On the one hand, it is useful for the learning process to propose practical activities that can connect theoretical concepts with real applications in the economic context. On the other hand, we should design multidisciplinary activities that link concepts from different subjects. With this goal in mind, in this article we propose a complete activity for first year students in business administration and economics degree programs, aimed to reinforce some basic statistical and economic concepts, while other basic transversal skills are also practiced, all within the subject of statistics.
Increasingly students, particularly those in the social sciences, work with survey data collected through a more complex sampling method than a simple random sample. Failing to understand how to properly approach survey data can lead to inaccurate results. In this article, we describe a series of online data visualization applications and corresponding student lab activities designed to help students and teachers of statistics better understand survey design and analysis. The introductory and advanced materials presented are designed to focus on a conceptual understanding of survey data and provide an awareness of the challenges and potential misuse of survey data. Suggestions and examples of how to incorporate these materials are also included. Supplementary materials for this article are available online.
To incorporate active learning and cooperative teamwork in statistics classroom, this article introduces a creative three-dimensional educational tool and an in-class activity designed for introducing the topic of agglomerative hierarchical clustering. The educational tool consists of a simple bulletin board and color pushpins (it can also be realized with a less expensive alternative) based on which students work collaboratively in small groups of 3-5 to complete the task of agglomerative hierarchical clustering: they start withnsingleton clusters, each corresponding to a pushpin of a unique color on the board, and work step by step to merge all pushpins into one single cluster using the single linkage, complete linkage, or group average linkage criteria. We present a detailed lesson plan that accompanies the designed activity and also provide a real data example in the.for this article are available online.
We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students' Bayesian thinking with Bayesian methods applied to real data problems. We leverage modern Bayesian computing techniques not only for implementing Bayesian methods, but also to deepen students' understanding of the methods. Collaborative case studies further enrich students' learning and provide experience to solve open-ended applied problems. The course has an emphasis on undergraduate research, where accessible academic journal articles are read, discussed, and critiqued in class. With increased confidence and familiarity, students take the challenge of reading, implementing, and sometimes extending methods in journal articles for their course projects.
It is imperative to foster data acumen in our university student population in order to respond to an increased attention to statistics in society and in the workforce, as well as to contribute to improved career preparation for students. This article discusses 13 learning outcomes that represent achievement of undergraduate data acumen for university level students across different disciplines.
We performed an empirical study of the perceived quality of scientific graphics produced by beginning R users in two plotting systems: the base graphics package ("base R") and the ggplot2 add-on package. In our experiment, students taking a data science course on the Coursera platform were randomized to complete identical plotting exercises using either base R or ggplot2. This exercise involved creating two plots: one bivariate scatterplot and one plot of a multivariate relationship that necessitated using color or panels. Students evaluated their peers on visual characteristics key to clear scientific communication, including plot clarity and sufficient labeling. We observed that graphics created with the two systems rated similarly on many characteristics. However, ggplot2 graphics were generally perceived by students to be slightly more clear overall with respect to presentation of a scientific relationship. This increase was more pronounced for the multivariate relationship. Through expert analysis of submissions, we also find that certain concrete plot features (e.g., trend lines, axis labels, legends, panels, and color) tend to be used more commonly in one system than the other. These observations may help educators emphasize the use of certain plot features targeted to correct common student mistakes. Supplementary materials for this article are available online.
The use of online student response systems (OSRSs) is increasing within tertiary education providers, however, research investigating their potential to enhance student engagement is limited. The aim of the current study was to examine the impact of an OSRS using an experimental crossover design. Quantitative data measuring student engagement was compared from pre- to post-intervention. A qualitative analysis was used to further investigate student perceptions of the OSRS. The results from this study suggest that OSRSs may be appropriate tools to increase student engagement in undergraduate statistics classes. Despite no significant change in engagement scores observed when students were exposed to the OSRS than when they were not, students appreciated the novelty of the OSRS and perceived it to have had a positive impact on their learning experience. Suggestions for how to exploit the advantages of OSRSs and directions for further research are discussed.
Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra "Correlation does not imply Causation." To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.
We designed a sequence of courses for the DataCamp online learning platform that approximates the content of a typical introductory statistics course. We discuss the design and implementation of these courses and illustrate how they can be successfully integrated into a brick-and-mortar class. We reflect on the process of creating content for online consumers, ruminate on the pedagogical considerations we faced, and describe an R package for statistical inference that became a by-product of this development process. We discuss the pros and cons of creating the course sequence and express our view that some aspects were particularly problematic. The issues raised should be relevant to nearly all statistics instructors. Supplementary materials for this article are available online.
Due to the developments in Bayesian applications and associated computational resources in the past 30 years, there are many Bayesian texts currently available. Some of the texts, such as Gelman et...
Sample survey design is a topic usually taught to students undertaking a minor or major in statistics in the latter part of their bachelor's degree. This article describes an assessment project that fosters active learning and helps to develop a set of essential skills for statistical practice. The project is completed in pairs and submitted in two parts. This allows feedback from the first part to be acted upon for the second part. Ideally, students would gain experience sampling from an actual population. However, the time involved in obtaining approval from the university's ethics committee may not be feasible for a short course. An alternative is to use an online virtual population such as theIslands, which provides students with an experience in setting up a sampling frame, requesting consent from potential participants, and collecting data. Proficiency in written communication and teamwork are highly valued by employers of statistics graduates. This project encourages collaborative learning in the design of the sample survey, statistical analysis of data collected, and the development of a final written report. It can easily be adapted for first year students and also be extended to suit Honors or Masters level students.
Informally testing the fit of a probability distribution model is educationally a desirable precursor to formal methods for senior secondary school students. Limited research on how to teach such an informal approach, lack of statistically sound criteria to enable drawing of conclusions, as well as New Zealand assessment requirements led to this study. Focusing on the Poisson distribution, the criteria used by ten Grade 12 teachers for informally testing the fit of a probability distribution model was investigated using an online task-based interview procedure. It was found that criteria currently used by the teachers were unreliable as they could not correctly assess model fit, in particular, sample size was not taken into account. The teachers then used an interactive goodness of fit simulation-based visual inference tool (GFVIT) developed by the first author to determine if the teachers developed any new understandings about goodness of fit. After using GFVIT teachers reported a deeper understanding of model fit and that the tool had allowed them to take into account sample size when testing the fit of the probability distribution model through the visualization of expected distributional shape variation. Hence, a new informal test for the fit of a probability distribution is proposed.
Allan Rossman (AR): As part of the special cluster of articles about teaching Bayesian statistics, JSE editor Jeff Witmer asked me to organize and moderate a panel discussion with a goal of providi...
Many statistics departments in institutions throughout the world hire graduate students to teach and assist with the teaching of undergraduate and graduate-level statistics courses. As many of these graduate student instructors and graduate teaching assistants (GTAs) have little or no previous experience teaching statistics, statistics departments are faced with the challenge of preparing their graduate students for teaching roles. Articles have been written sharing various departments' strategies for GTA training and development programs, however, articles are often not supported by empirical research. This article provides a review of empirical research regarding graduate students' preparation for teaching—first focusing on graduate students in statistics, specifically, and second offering what can be learned from studies of graduate students in other disciplines. We conclude with ten research-based recommendations for preparing graduate students to teach statistics, along with practical ideas for how to implement them.
