The IDS (Intro to Data Science) project at UCLA is looking for a post-doc interested in conducting fundamental research in teaching and/or learning of data science at the high school level. The IDS project can make available classrooms teaching the IDS curriculum (Introduction to Data Science) and will provide administrative support for IRB approvals for classroom investigations. We are interested in projects that address basic questions such as: what are students learning in the IDS classroom? what hinders/helps this learning, what is the role of programming/coding in learning? how does what we know about statistics education help us study data science education? Is there a data science "framework" (learning trajectory/progression) that can be developed for HS and what role would a single class have? what do students learn and think about data structures? what should they learn? does DS education foster equity? how can this be improved? what features do or don't? does DS learning enhance mathematical learning/practice beyond the DS curriculum? how does the IDS classroom learning speak to the ASA GAISE document? to the California Math Framework? to other states' standards?
While research is the primary activity, the post doc will also teach one class per year for UCLA students. This can be an undergraduate or graduate level course addressing research in statistics/data science education, equity issues in mathematics/statistics/data science education, or a class for future teachers. We are open to suggestions.
Ideally the position will start in Fall 2024. It is a two-year position, and so the ending date would be Spring of 2026. Benefits for post-docs are described here: https://www.postdoc.ucla.edu/resources/appointment-information/postdoctoral…https://www.postdoc.ucla.edu/wp-content/uploads/ucpsbpbooklet.pdf
Interested parties should email Rob Gould at rgould(a)stat.ucla.edu<mailto:firstname.lastname@example.org>
The Department of Statistics at Grand Valley State University invites applications for a full-time, tenure-track position of Assistant Professor to begin August 6, 2024.
This is an open field search, and candidates from Statistics, Biostatistics, Data Science or a related field with a commitment to quality teaching and strong data analysis and computing skills are encouraged to apply. We are especially interested in candidates from underrepresented groups and individuals who have experience working with diverse student and community populations.
Duties include teaching, appropriate with your expertise, throughout the statistics curriculum including introductory service courses and graduate courses; participating in professional scholarship, which is broadly defined in our department and includes theoretical and pedagogical research and consulting activities; and department and university service. The teaching load is typically 9 credit hours per semester.
Applications are accepted online at https://jobs.gvsu.edu/.
For information regarding application materials, see our complete position description at www.gvsu.edu/stat<http://www.gvsu.edu/stat>. Grand Valley State University is an affirmative action, equal opportunity institution located in West Michigan. Review of candidate materials will begin on January 8, 2024 with applications accepted until the position is filled.
Happy Monday & we hope you had a wonderful thanksgiving break last week!
CAUSE Research Reading Group meetings continue! Our next meeting is
scheduled on *Friday, December 1st*, at *3:00-4:00PM ET* (2-3pm CT, 1-2pm
MT, 12-1pm PT). Please find the link for registration and the link to the
Tunstall, S. L. (2018). Investigating College Students' Reasoning With
Messages of Risk and Causation. Journal of Statistics Education, 26(2),
Zoom: Register in advance for this meeting:
Our host for this session will be *Federica Zoe Ricci* (thank you so much,
Federica!). After registering, you will receive a confirmation email
containing information about joining the meeting. Don’t worry if you
haven’t participated in our previous meetings yet. All meetings are
independent from each other, so please join us if you are interested. All
As always, please don’t hesitate to reach out to Shu-Min or Megan if you
have any questions or suggestions.
*About Year 2024*: This is going to be our last session in 2023 and there
will be no meeting in the third week of December. Megan and I are
considering continuing this research reading group & welcome your feedback,
suggestions, and comments. Please share your thoughts with us on December 1
st, or via email. We are particularly interested in knowing what “themes”
or “topics” you would like to read and discuss with the others. We will
send out more information about this reading group in mid-December.
Look forward to having you join us on Friday,
Shu-Min & Megan
We are actively seeking volunteers to participate as judges for the upcoming round of the Undergraduate Statistics Project Competition (USPROC)<https://www.google.com/url?q=https://www.google.com/url?q%3Dhttps://www.cau…>. Twice a year, students have the opportunity to submit written projects from statistics/data science courses or capstone projects. The judging process would begin in January, following the December 20th submission deadline.
Would you be willing to serve as a judge for this round of submissions? If so, please sign up using the following link:
Based on the typical number of submissions, judging should not amount to more than 6 hours of work. It is done independently, with some e-mail discussion (when needed) to decide on prizes. To provide you with a timeline overview, we would send you projects to score in January 2024 and allow about a month to complete the judging. Each project is scored by multiple judges. If you are able to help, we will send further instructions closer to the submission deadline. Thank you in advance for your consideration!
If you have colleagues who teach undergraduate statistics courses or who advise undergraduate statistics research, please share the information in this email with them. Your assistance in spreading the word about the USPROC competition and the opportunity to serve as a judge is highly appreciated!
Best, the USPROC & eUSR Co-Chairs,
Jennifer Ward (Clark College)
Juanjuan Fan (San Diego State University)
Ciaran Evans (Wake Forest University)
Monika Hu (Vassar College)
Shaoyang Ning (Williams College)
Title: Implementation of Alternative Grading Methods in a Mathematical Statistics Course
Tuesday, November 21, 2023 4:00 - 4:30 pm EST
Presented by: Brenna Curley, Moravian University and Jillian Downey, Gustavus-Adolphus College
Abstract: In this November edition of the JSDSE/CAUSE webinar series, we highlight the 2023 article: Implementation of Alternative Grading Methods in a Mathematical Statistics Course. The authors will discuss how alternative grading methods, such as standards-based grading, provide students multiple opportunities to demonstrate their understanding of the learning outcomes in a course. These grading methods allow for more flexibility and help promote a growth mindset by embracing constructive failure for students. Implementation of these alternative grading methods requires developing specific, transparent, and assessable standards. The authors will also discuss that moving away from traditional methods requires a mindset shift for how both students and instructors approach assessment. While providing multiple opportunities is important for learning in any course, these methods are particularly relevant to an upper-level mathematical statistics course where topics covered often provide an additional challenge for students as they lie at the intersection of both theory and application. By providing multiple opportunities, students have the space for constructive failure as they tackle learning both a conceptual understanding of statistics and the supporting mathematical theory. In this webinar the authors will share their experiences—including both challenges and benefits for students and instructors—in implementing standards-based grading in the first semester of a mathematical statistics course (i.e., focus primarily on probability).
Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2249956
The webinar is free but pre-registration is required. Please sign up at: https://www.causeweb.org/cause/webinar/jsdse/2023-11
Please join us!
Happy Friday! CAUSE Research Reading Group meetings continue! Our next meeting is scheduled for Friday, November 17th, at 3 pm ET (2 pm CT, 1 pm MT, noon PT). Please find the link for registration and the link to the article below.
ZAPATA-CARDONA, L. U. C. I. A. (2023). THE ROLE OF CONTEXTS IN SUPPORTING EARLY STATISTICAL REASONING IN DATA MODELING. STATISTICS EDUCATION RESEARCH JOURNAL, 22(2), 5-5. https://iase-web.org/ojs/SERJ/article/view/448/485
Zoom: Register in advance for this meeting: https://ufl.zoom.us/meeting/register/tJIof-CrpzwsEtyIDIHIgZrxb0bfhP7hGRkM
After registering, you will receive a confirmation email containing information about joining the meeting.
Our host for this article will be Megan Mocko.
Don't worry if you haven't participated in our previous meetings yet. All meetings are independent of each other, so please join us if you are interested. All are welcome!
Look forward to having you join us next Friday,
Megan & Shu-Min
Information Systems and Operations Management
WARRINGTON COLLEGE OF BUSINESS
UNIVERSITY OF FLORIDA
Stuzin Hall 351B
PO Box 117169, Gainesville, FL 32611
The Consortium for the Advancement of Undergraduate Statistics Education is happy to announce our 90th Cartoon Caption Contest! Each month a cartoon, drawn by British cartoonist John Landers, is posted for you and your students to suggest statistical captions (cartoons are posted at the beginning of the month and submissions are due at the end of the month). The caption contest is offered as a fun way to get your students thinking independently about statistical concepts.
The next cartoon and the entry rules for the contest ending November 30 (intended as a great activity leading into the Thanksgiving break) are at
The best submission will be posted on CAUSEweb and the winner(s) will receive their choice of a coffee mug or t-shirt imprinted with the final cartoon or free registration to eCOTS2024.
The October caption contest cartoon is shown above. The judges found the winning caption to be “When the climate plays dice, even the skiers can't predict the variation in snowfall!” submitted by Ian Bang, a student at the Friends Seminary in New York City. Ian’s caption is intended to emphasize the theme that whether we're looking at climate data or any other field, recognizing and managing variation is key to drawing meaningful insights from data and making reliable forecasts. An honorable mention this month goes to Larry Lesser from The University of Texas at El Paso for his caption: “Fall predictions of late spring ski conditions are dicey!,” that can be used to remind students of the increasing uncertainty when making predictions farther into the future.
Thanks to everyone who submitted a caption and congratulations to our winners!