Webinars

  • Mentoring Undergraduate Statistics Students for USPROC and Beyond

    Xizhen Cai (Williams College), Yue Jiang (Duke University), Claire Kelling (Carleton College), Victoria Woodard (University of Notre Dame)
    Monday, December 16, 2024 - 4:00pm ET
    This CAUSE webinar will feature a panel of experienced mentors who have successfully guided students in the Undergraduate Statistics Project Competition (USPROC). The panelists will share insights from their experiences mentoring award-winning student projects and discuss strategies for fostering student success in both classroom statistics projects and independent research. Attendees will receive practical advice on how to effectively mentor undergraduate students in research, including project development, data analysis, and communication of results, while inspiring a passion for statistics and research excellence.
  • Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data

    Anna Fergusson (University of Auckland | Waipapa Taumata Rau)
    Tuesday, December 10, 2024 - 4:00pm ET
    In this December edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data. Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can support the teaching of data science and computational thinking. Research is needed about the design of tasks that support high school statistics teachers to learn new statistical and computational approaches such as digital image analysis and classification models. Using their design framework, the authors describe construction of a task that introduces classification modeling using grayscale digital images. The task was implemented within a teaching experiment involving six high school statistics teachers. Their findings from this exploratory study indicated that the task design seemed to support statistical and computational thinking practices related to classification modeling and digital image data. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2351570
  • Simulation-Based Inference: Random Sampling vs. Random Assignment? What Instructors Should Know

    Beth Chance (California Polytechnic State University, San Luis Obispo), Karen McGaughey (California Polytechnic State University, San Luis Obispo), Soma Roy (California Polytechnic State University, San Luis Obispo)
    Tuesday, November 12, 2024 - 4:00pm ET
    In this November edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Simulation-Based Inference: Random Sampling vs. Random Assignment? What Instructors Should Know. “Simulation-based inference” is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this webinar, the presenters highlight for teachers some implications of different simulation strategies when analyzing two variables. In particular, does it matter whether the simulation models random sampling or random assignment? They present examples from comparing two means and simple linear regression, highlighting the impact on the standard deviation of the null distribution. They also highlight some possible extensions that simulation-based inference easily allows. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2333736  
  • TSHS Fall 2024 Webinar: “Clarifying practical relevance of p-values and confidence intervals. Clear choice of words and images, that foster conceptual understanding and steer away from misconceptions.”

    Hilary Watt, Imperial College London
    Tuesday, October 29, 2024 - 3:00pm ET
    TSHS Fall 2024 Webinar: “Clarifying practical relevance of p-values and confidence intervals. Clear choice of words and images, that foster conceptual understanding and steer away from misconceptions.” The ASA Section on Teaching of Statistics in the Health Sciences (TSHS) is excited to present our Fall 2024 webinar. Our speaker will be Hilary Watt of the Imperial College London. She will discuss the practical relevance of p-values and confidence intervals. The webinar is FREE and open to all. Details and registration information are below. TITLE: Clarifying practical relevance of p-values and confidence intervals. Clear choice of words and images, that foster conceptual understanding and steer away from misconceptions. PRESENTER: Hilary Watt, Imperial College London DATE/TIME: Tuesday, October 29, 2024, 3:00pm Eastern Time (US & Canada) VENUE: Online webinar hosted using the Zoom platform ABSTRACT: Informal p-value and CI interpretations are often used in health science teaching yet may be too crude to help much in developing understanding. Not all students deduce CIs and p-values practical relevance from their definitions. This talk suggests transparent explanatory interpretations, that promote understanding of their practical relevance. They are compact enough for routine repetition whilst teaching. Referring to the sample (not blood sample!) as participants provides a clear starting point. Repeatedly clarifying the relationship of participants to the population, including the random sampling assumption, addresses the issue that ‘population’ is poorly understood. Implications of random sampling being impractical can be discussed. Images of the distribution of sample statistics are useful for understanding calculation methods yet can readily be misinterpreted. Strategies are suggested to offset the risk of common misinterpretations. This includes use of images designed to foster conceptual understanding, for both CIs and for p-values (for p-values, illustrating their continuous nature). The intention is to make statistics easier and more intuitive to understand. The inspiration is to steer towards higher standards of data interpretation within health sciences. REGISTRATION: To register for the webinar, please complete this form: https://uniofqueensland.syd1.qualtrics.com/jfe/form/SV_4I9TBkB3xKNS7AO We will send the Zoom link for the webinar to your email address the day before the webinar.
  • Philosophy within Data Science Ethics Courses

    Sara Colando (Carnegie Mellon University) and Johanna Hardin (Pomona College)
    Tuesday, October 8, 2024 - 4:00pm ET
    In this October edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Philosophy within Data Science Ethics Courses. There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy. Here, the speakers present a framework for bringing together key data science practices with ethics topics. The ethics topics were collated from sixteen data science ethics courses with public-facing syllabi and reading lists. The speakers encourage individuals who are teaching data science ethics to engage with the philosophical literature and its connection to current data science practices, which are rife with potentially morally charged decision points. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2394542   
  • What They Remember May Not Be What They Understand: A Study of Mnemonic Recall and Performance by Introductory Statistics Students

    Megan Mocko (University of Florida), Amy Wagler (University of Texas El Paso), Lawrence Lesser (University of Texas El Paso)
    Tuesday, September 17, 2024 - 4:00pm ET
    In this September edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article What They Remember May Not Be What They Understand: A Study of Mnemonic Recall and Performance by Introductory Statistics Students. The speakers will discuss a large-scale (n = 1323) survey of mnemonic recall, self-reported familiarity, cued explanation, and application by introductory statistics students that was conducted at a large research university in the southeastern United States. The students were presented 14 mnemonics during the fall 2017 term. Different nonoverlapping cohorts of students were asked at different time points to complete a survey about mnemonic use. At each time point, the students were asked to recall any mnemonic that they remembered, explain the mnemonic when cued, self-report their degree of familiarity, and apply the mnemonic. Of the 14 mnemonics, acronym-type mnemonics were recalled more frequently, but longer phrase-type mnemonics were explained and applied more often. These findings suggest that instructors should provide scaffolding to move a student from recalling a mnemonic to using a mnemonic toward successful completion of the statistics problem at hand. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2334905 
  • Guidelines and Best Practices to Share Deidentified Data and Code

    Nicholas Horton (Amherst College) and Sara Stoudt (Bucknell University)
    Monday, August 26, 2024 - 4:00pm ET
    In 2022, the Journal of Statistics and Data Science Education (JSDSE) instituted augmented requirements for authors to post deidentified data and code underlying their papers. These changes were prompted by an increased focus on reproducibility and open science, and a recent review of data availability practices noted that "such policies help increase the reproducibility of the published literature, as well as make a larger body of data available for reuse and re-analysis" (PLOS ONE, 2024). In this talk, Nicholas Horton and Sara Stoudt present their recent editorial for JSDSE, discussing the motivation and process for sharing deidentified data and code. Because institution, environment, and students differ across readers of the journal, it is especially important to facilitate the transfer of a journal article's findings to new contexts. This process may require digging into more of the details, including the deidentified data and code. The presenters will present a review of why the requirements for code and data sharing were instituted, summarize ongoing trends and developments in open science, discuss options for data and code sharing, and share advice for authors. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2364737
  • The Teaching of Introductory Statistics: Results of a National Survey

    Chelsey Legacy (University of Minnesota) and Pablo Vivas Corrales (University of Minnesota)
    Tuesday, June 18, 2024 - 4:00pm ET
    In this June edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article The Teaching of Introductory Statistics: Results of a National Survey. The authors describe their use of the updated Statistics Teaching Inventory (STI) to examine the current state of the curricular and instructional practices being used by U.S.-based, college-level introductory statistics instructors. They explore the extent to which instructors report that their introductory statistics courses are aligned with recommended practices as outlined by the 2016 GAISE College Report. Data were collected from a sample of college-level U.S.-based, college-level introductory statistics instructors. Results based on 228 usable responses indicated that instructors, by-and-large, tended to be following the GAISE recommendations, especially related to content. However, courses may not yet be aligned with newer content recommendations (e.g., provide students opportunities to work with multivariate data), and there is still a large percentage of instructors that are not embracing student-oriented pedagogies and assessment methods.
  • Assessment and Rubric Development: Strategies for consistent scoring.

    Laura Ziegler, Iowa State University
    Friday, May 24, 2024 - 12:00am ET
    This webinar shares experiences and guidance for development of assessment tasks and scoring rubrics associated with open-ended, free response tasks.  The session also draws comparisons between implications when the assessment is used in a classroom vs research context.  Acknowledgment: this webinar was prepared and delivered with the support of NSF Project CLASSIFIES (DUE: IUSE #2236150).  Disclaimer: AP® is a trademark registered by College Board, which is not affiliated with and does not endorse this presentation or Iowa State University’s events, programs, etc
  • Culturally Relevant Data in Teaching Statistics and Data Science Courses

    Travis Weiland (University of Houston) and Immanuel Williams (California Polytechnic State University, San Luis Obispo)
    Tuesday, May 21, 2024 - 4:00pm ET
    In this May edition of the JSDSE/CAUSE webinar series, we highlight the 2023 article: Culturally Relevant Data in Teaching Statistics and Data Science Courses. The authors will discuss how to make data more meaningful to students through the choice of data and the activities they use to draw upon students' lived experiences. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics education they develop the idea of culturally relevant data, which they use to implement culturally relevant pedagogy in teaching data-intensive courses, leveraging the centrality of context through data in both statistics and data science to engage students particularly from historically marginalized groups in STEM. They provide suggestions as to ways of finding or creating culturally relevant data and using them, and they also present findings from pilot work on implementing these data in statistics courses.   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2249969   Please join us! Ciaran Evans

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