Webinars

In an effort to respond to the growing need to support active engagement with the entire data analysis pipeline at the introductory level, Carnegie Mellon Statistics & Data Science is building ISLE (Integrated Statistics Learning Environment), an interactive, e-learning platform that removes the computing cognitive load and lets students explore Statistics & Data Science concepts in structured and unstructured ways. Usable both inside and outside of the classroom, the browser-based platform also supports student-driven inquiry and case studies. The platform is flexible enough to allow adaptation, providing different modes of data analysis instruction, active learning opportunities, group work, and exercises for different subsets of the population. Students are also able to build their own case studies with little restriction or faculty intervention. In an effort to characterize different student approaches to data analysis, we track and model every click, word used, and decision made throughout the data analysis pipeline from loading the data to the final written report. These metrics can be displayed to instructors, some in real-time and some in report format. In this demonstration, we will give an overview of ISLE’s capabilities and show some insightful examples of modeling student behavior (changing over time) with a particular focus on how students write about data. Webinar participants will be able to interact with an ISLE Data Explorer during the talk.

Course Based Undergraduate Research Experiences (CUREs) are rapidly becoming a model for undergraduate science education in which interdisciplinary students enroll in a course that is focused on a research question and students themselves generate hypotheses, develop protocols, generate the data, analyze and present the outcome. Hence, the experimental design and statistical analysis of the student developed research questions is critical. This presentation will include the experimental design process activities and handouts that guide the students from a variety of backgrounds through all phases of the experiment: Pre-planning, experimental design and analysis. A discussion of the implementation of multiple CUREs will also be discussed.

At Cleveland State University, with funding from NSF, we have adopted a supplemental instruction model for all precalculus courses and select sections of calculus. In this approach, the supplemental instruction is mandatory and led by upperclassman that we call SPTs (STEM Peer Teachers). In this webinar I will discuss the model, the result of higher pass rates in these classes and how we have begun adapting this approach in statistics I & II classes.

The Islands is a virtual world, created by Dr. Michael Bulmer from the University of Queensland, that can be used as a vehicle for student-led data collection. The Islands allows students to encounter “real-world” issues like obtaining consent, respondents who don’t tell the truth, measurement variability, and more in a safe environment. We have begun the early stages of investigating how student enjoyment, confidence, and learning differ between projects that use The Islands versus those that have students collect their own “real-world” data. In this webinar, we will introduce several features of The Islands, explain how we have used it in our introductory statistics classes, and share initial results from our research comparing these two types of projects.

As our economy, society, and daily life become increasingly dependent on data, work across nearly all fields is becoming more data driven, affecting both the jobs that are available and the skills that are required. At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine were asked to set forth a vision for the emerging discipline of data science at the undergraduate level. The study committeem considered the core principles and skills undergraduates should learn and discussed the pedagogical issues that must be addressed to build effective data science education programs. The report underscores the importance of preparing undergraduates for a data-enabled world and recommends that academic institutions and other stakeholders take steps to meet the evolving data science needs of students. In this webinar, implications, opportunities, and challenges for statistics educators will be discussed along with the study findings.   Resources: http://nas.edu/EnvisioningDS

FiveThirtyEight.com is a data journalism website founded by Nate Silver that makes many of the datasets used for their articles openly available on GitHub.com. The fivethirtyeight R package acts as an intermediary to make all this data, its documentation, and links to the original articles easily accessible to R users. Furthermore, the package "tames" the data: the data is pre-processed enough so that the biggest barriers to data exploration faced by novice R users are eliminated, but not so much that the true nature of the data as it exists "in the wild" is completely betrayed. In this webinar, I will present the corresponding set of "tame" data principles, discuss the pedagogical thinking behind them, and present illustrative examples involving datasets from articles on FiveThirtyEight.com.

The learnr R package provides a new multimedia approach for teaching statistics and programming with R. Building on R Markdown, this package allows teachers to create interactive tutorials containing narrative, figures, illustrations, and equations, code exercises (R code chunks that users can edit and execute directly), multiple choice quiz questions, videos, and interactive Shiny components. Tutorials built with this tool can be used for checking and reinforcing students' understanding and have the benefit of being self-paced and provide instant feedback. In this webinar we will demonstrate how to use the learnr package to build interactive R tutorials and discuss best practices for using them.

Online courses and 'flipped' classrooms are becoming more commonly found in statistics/biostatistics. A gap exists in the literature in regard to a systematic study of instructors' of these types of (bio)statistics courses. We conducted a survey to elicit these instructor's responses in terms of implementation, ratings, recommendations, and opinions, and we report on n=46 such instructors. In this webinar, we describe characteristics of these respondents' courses, as well as summarizing their responses on various aspects. Results are given both overall, as well as for different subgroups of interest. Our findings should be useful to inform statistics educators who might be considering adopting these formats.

Healthcare professionals and faculty depend on the health and medical literature to keep current with clinical information and best evidence-based practices. Yet, little is known about their knowledge of, and comfort level with, statistics. We conducted a research study on health sciences faculty to assess their knowledge about statistics. A probability sample of schools of dentistry, nursing, medicine, pharmacy, and public health were selected, and faculty were invited to complete a brief online survey that included 9 demographic-related questions and a 10-question statistics knowledge instrument. In this webinar we will present study results, including aggregated findings for the 708 respondents, as well as interesting discipline-specific findings. Implications for statistics educators will be discussed, and time will be allotted for questions from the audience.

Over the last few years, there has been a consensus that data science students should be involved in all stages of the data analysis process, from data preparation and wrangling, to presentation and visualization. But data science courses have varied widely in their implementation. Some courses go into great depth about statistical models and machine learning, while others focus on tools like XML, SQL, and web scraping. While there is no question that a budding data scientist must acquire these skills eventually, what should be covered in a course on data science? I suggest that data science courses be organized around three core concepts: paradigms for representing data, paradigms for manipulating data, and paradigms for visualization. These are topics of genuine intellectual merit that are underrepresented elsewhere in the statistics and computer science curriculum. The tools are secondary, and I suggest how such a course could be taught using R examples using the tidyverse or using Python examples.