Student Gains in Conceptual Understanding in Introductory Statistics with and without a Curriculum Focused on Simulation-Based Inference

Using “simulation-based inference” (SBI) such as randomization tests as the primary vehicle for introducing students to the logic and scope of statistical inference has been advocated with the potential of improving student understanding of statistical inference, as well as the statistical investigative process as a whole. Moving beyond the individual class activity, entirely revised introductory statistics curricula centering on these ideas have been developed and tested. In this presentation we will discuss three years of cross-institutional tertiary-level data in the United States comparing SBI-focused curricula and non-SBI curricula (roughly 15,000 students). We examine several pre/post measures of conceptual understanding in the introductory algebra-based course, using hierarchical modelling to incorporate student-level, instructor-level, and institutional-level covariates

Student Performance in Curricula Centered on Simulation-Based Inference: A Preliminary Report

Simulation-based inference (e.g., bootstrapping and randomization tests) has been advocated recently with the goal of improving student understanding of statistical inference, as well as the statistical investigative process as a whole. Preliminary assessment data have been largely positive. This article describes the analysis of the first year of data from a multi-institution assessment effort by instructors using such an approach in a college-level introductory statistics course, some for the first time. We examine several pre-/post-measures of student attitudes and conceptual understanding of several topics in the introductory course. We highlight some patterns in the data, focusing on student level and instructor level variables and the application of hierarchical modeling to these data. One observation of interest is that the newer instructors see very similar gains to more experienced instructors, but we also look to how the data collection and analysis can be improved for future years, especially the need for more data on nonusers

Finding Meaning in a Multivariable World: A Conceptual Approach to an Algebra-Based Second Course in Statistics

Although the teaching of the first course in statistics has improved dramatically in recent years, there has been less focus on a similarly conceptual-based second course aimed at non-majors. We present a curriculum for the second course, designed to expand statistical literacy across disciplines, which focuses on conceptual understanding of multivariable relationships through data visualization, study design, the role of confounding variables, reduction of unexplained variation, and simulation-based inference, rather than the mathematically-based discourse often used in the second course. Our curriculum uses a student-centered pedagogical approach, utilizing guided discovery activities based on real-world case studies, facilitated by student-focused technology tools. Highlights of the curriculum and student assessment will be shared

Challenging the State of the Art in Post-Introductory Statistics: Preparation, Concepts, and Pedagogy

The demands for a statistically literate society are increasing, and the introductory statistics course ( Stat 101 ) remains the primary venue for learning statistics for the majority of high school and undergraduate students. After three decades of very fruitful activity in the areas of pedagogy and assessment, but with comparatively little pressure for rethinking the content of this course, the statistics education community has recently turned its attention to use of randomization-based methods to illustrate core concepts of statistical inference. This new focus not only presents an opportunity to address documented shortcomings in the standard Stat 101 course (for example, improving students’ reasoning about inference), but provides an impetus for re-thinking the timing of the introduction of multivariable statistical methods (for example, multiple regression and general linear models). Multivariable methods dominate modern statistical practice but are rarely seen in the introductory course. Instead these methods have been, traditionally, relegated to second courses in statistics for students with a background in calculus and linear algebra. Recently, curricula have been developed to bring multivariable content to students who have only taken a Stat 101 course. However, these courses tend to focus on models and model-building as an end in itself. We have developed a preliminary version of an integrated one to two semester curriculum which introduces students to the core-logic of statistical inference through randomization-methods, and then introduces students to approaches for protecting against confounding and variability through multivariable statistical design and analysis techniques. The course has been developed by putting primary emphasis on the development of students’ conceptual understanding in an intuitive, cyclical, active-learning pedagogy, while continuing to emphasize the overall process of statistical investigations, from asking questions and collecting data through making inferences and drawing conclusions. The curriculum successfully introduces introductory statistics students to multivariable techniques in their first or second course

Broadening the Impact and Effectiveness of Simulation-Based Curricula for Introductory Statistics

The demands for a statistically literate society are increasing, and the introductory statistics course “Stat 101” remains the primary venue for learning statistics for the majority of high school and undergraduate students. After three decades of very fruitful activity in the areas of pedagogy and assessment, but with comparatively little pressure for rethinking the content of this course, the statistics education community has recently turned its attention to focusing on simulation-based methods, including bootstrapping and permutation tests, to illustrate core concepts of statistical inference within the context of the overall statistical investigative process. This new focus presents an opportunity to address documented shortcomings in the standard Stat 101 course (e.g., seeing the big picture; improving statistical thinking over mere knowledge of procedures). Our group has developed and implemented one of the first cohesive curricula that (a) emphasizes the core logic of inference using simulation-based methods in an intuitive, cyclical, active-learning pedagogy, and (b) emphasizes the overall process of statistical investigations, from asking questions and collecting data through making inferences and drawing conclusions. Improved conceptual understanding and retention of inference and study design that had been observed when using early versions of the curriculum at a single institution, are now being evaluated at dozens of institutions across the country with thousands of students using the fully integrated, stand-alone version of the curriculum. Encouraging preliminary results continue to be observed. We are now leveraging the tremendous national momentum and excitement about the approach to greatly expand implementations of simulation-based curricula by offering workshops around the country to diverse sets of faculty, offering numerous online support structures including: a blog, freely available applets, free instructor materials, earning objective-based instructional videos, free instructor-focused training videos, a listserv, and peer-reviewed publications covering both rationale and assessment results. Many hundreds of instructors have been directly impacted by our workshops and hundreds more through access to the free online materials. We are also in the midst of valuating widespread transferability of the approach across diverse institutions, students, and learning environments and deepening our understanding of how students’ attitudes and conceptual understanding develop using this approach through an assessment project involving concept and attitude inventories with over 10,000 students across 200 different instructors

Interviews with Charley King, Barbara Hall Maricle, Vern Kear, Sherry Smith, Elizabeth Stoskopt, Martha Margheim, Verna Schneider, Edith M. Hill, Master John Sackett, Rose Arnold, Olga Elizabeth Luschen Dennis, and Clarence Loredstsch

Interviews with Charley King, Barbara Hall Maricle, Vern Kear, Sherry Smith, Elizabeth Stoskopt, Martha Margheim, Verna Schneider, Edith M. Hill, Master John Sackett, Rose Arnold, Olga Elizabeth Luschen Dennis, and Clarence Loredstsch The first tape is missing. Content begins with the partial interview of Olga Elizabeth Luschen Dennis. 00:00:00 - Family in Russell County (partial) 00:01:02 - Meeting her husband for the first time 00:02:32 - Girlhood experiences and reminiscences 00:03:50 - Pre-recorded History of Fort Larned 00:07:04 - Unknown speaker, Kit Carson\u27s killing of a mule he mistook for an Indigenous American 00:10:46 - Buffalo Bill Cody 00:13:03 - Stories about the Kiowa 00:16:06 - Woodland Tribe in Pawnee County, KS 00:18:24 - Quivira Tribe in Pawnee County, KS 00:21:46 - Exhuming Indigenous remains in 1958 00:23:16 - Woodland Tribe pottery 00:27:32 - Dog skeleton 00:33:57 - Woodland Tribe weapons 00:35:42 - Woodland Tribe ornamentation 00:39:05 - Introduction to interview with Clarence Loredstsch by Louise Maxwell. This portion of the recording is muffled and difficult to understand. 00:39:43 - Fort Fletcher 00:45:30 - Graves and landmarkshttps://scholars.fhsu.edu/sackett/1054/thumbnail.jp

Quantitative Evidence for the Use of Simulation and Randomization in the Introductory Statistics Course

The use of simulation and randomization in the introductory statistics course is gaining popularity, but what evidence is there that these approaches are improving students’ conceptual understanding and attitudes as we hope? In this talk I will discuss evidence from early full-length versions of such a curriculum, covering issues such as (a) items and scales showing improved conceptual performance compared to traditional curriculum, (b) transferability of findings to different institutions, (c) retention of conceptual understanding post-course and (d) student attitudes. Along the way I will discuss a few areas in which students in both simulation/randomization courses and the traditional course still perform poorly on standardized assessments

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure