Updated Guidelines, Updated Curriculum: TheGAISE College Reportand Introductory Statistics for the Modern Student

Medical graduate views on statistical learning needs for clinical practice: a comprehensive survey

“…Collaborative learning among students may be employed as a useful means of addressing the challenges of providing unique datasets and timely comprehensive feedback to a large cohort of students. As noted in the current GAISE guidelines [19, 29], collaborative learning can enhance student skills in communicating statistics, which is also of relevance to clinical practice (Additional file 4).…”

Teaching Creative and Practical Data Science at Scale

“…We are instructors involved in designing and teaching new, large courses on data science that aim to teach hands-on data analysis through computational work. Building on the lessons learned from having run these courses for several thousand undergraduate students and on the work of other data science educators (Baumer 2015;Carver et al 2016;Çetinkaya-Rundel and Ellison 2020;Hicks and Irizarry 2018;Loy et al 2019; National Academies of Sciences, Engineering and Medicine 2018; Wood et al 2018;Yan and Davis 2019), in this article, we discuss our approach to, and goals for, teaching data science. In these goals, we find much alignment with the vision of Nolan and Temple Lang, including their positioning of statistics (or data analysis more broadly) as flexible problem solving, and in their call for pedagogy of data analytical skills to be grounded in practical work with real datasets, to which we also add a goal of scaling data education to meet demand.…”

“…There has been a massive shift in the impact that students with solid statistical and computational training will have on the world. The resulting profound and long-lasting effects data scientists will have on society necessitates that data science education incorporate impactful, real-world data into all data science courses (Donoho 2017;Neumann et al 2013), highlight and teach the importance of context surrounding the data used in analysis (Carver et al 2016;National Academies of Sciences 2018;Wood et al 2018), and focus on the impacts and ethical implications of working with data in d y' data-centric landscape, explicitly discussing and teaching ethical practice and frameworks throughout (Baumer et al 2020;Saltz et al 2018). These topics can be introduced to students through specific examples throughout the course as well as by sharing relevant and accessible literature (Angwin et al 2016;Eubanks 2018;Hicks 2017;Noble 2018;O'N 2016).…”

Math anxiety: the impact on traditionally underserved and marginalized adult female undergraduate students in elementary statistics

“…Recommendations from the 2016 GAISE college report specify that instructors should aim to promote statistical literacy by utilizing data sets in tandem with a student's course of study to improve the understanding of statistical outcomes (Wood, Mocko, Everson, Horton, & Velleman, et al, 2017). Some academic organizations, in an effort to remove the generality of instruction, have placed elementary statistics within the offerings of a student's major course of study.…”

“…This report recommends that prior to teaching an elementary statistic course, instructors must have experience facilitating at least two statistical methods courses that include a higher level of data analysis (Wood, et al 2017). This recommendation accepts the preceding criteria, but adds that the instructor must have proven to be successful through the use pf a teaching strategy assessed by administrators and other faculty, based on course observations and student feedback.…”