Date	Matin	Après-midi
Lundi 16 juin		Coffee-Discussion about Neurodiversity in higher education 🕒 16h-17h 🏫 Espace Alchimie 📍 MRV- Campus Université de Toulouse
Mardi 17 juin	Workshop-Assessment and Grading 🕒 10h30-12h00 - 🏫 Salle 3-R0-4 📍 INP-ENSIACET
Mercredi 18 juin		Workshop-Generative AI 🕒 14h-16h 🏫 Salle 3-R0-6 📍 INP-ENSIACET
Jeudi 19 juin		Coffee-Discussion about Climate Change education 🕒 14h-15h 🏫 Salle 3-R0-4 📍 INP-ENSIACET
Vendredi 20 juin	Workshop -Midsemester Feedback 🕒 10h30-12h00 🏫 Salle 3-R0-6 📍 INP-ENSIACET

Les ateliers sont limités à 26 participants.

Inscrivez-vous via ce lien : https://evento.renater.fr/survey/venue-de-janet-rankin-inscription-aux-differents-ateliers-et-temps-d-echanges-cafe-w4es6669

(Merci de vous désinscrire en cas d'empêchement ou de nous contacter à siup@univ-toulouse.fr)

 

Description des ateliers

Assessment and Grading

What is the purpose of grades in higher education? Are grades accurate measures of how much students have learned? Do they motivate or impede student learning? What are the affordances and drawbacks of normative grading (grading on a curve) - and why is it so prevalent in higher education in the US? Participants in this workshop will discuss these and other grading-related questions with the goal of being able to select grading systems and strategies that best support their course material, philosophies, student demographics and that are consistent with potential departmental and/or institutional constraints.

Pre-workshop assignment

• Read: Schinske and Tanner (2014) Teaching More by Grading Less (or Differently) [Provides context for grading in the US]
• Read: 20 small starts for alternative grading, Robert Talbert
• Consider: times as a learner when grades were particularly motivating and/or de-motivating. Be prepared to discuss in class.

Optional reading

• Anything in Robert Talbert’s & David Clark’s terrific blog: Grading for Growth, In particular:
• Traditional grades are (still) not mathematically valid, David Clark
• When alternate grading goes (almost) wrong Guilia Toti
• Case study in Grading for Growth (Talbert & Clark): Kay C Dee's specifications-graded biomedical engineering course Rose-Holman Inst. of Tech.
• C.H. Arnaud (2021) How an alternative grading system is improving student learning

 

Mid-semester feedback to improve teaching and learning

The collection of mid-semester formative feedback from students is an extremely effective way to gain targeted and specific information from students about what factors are supporting or hindering their learning.

• Mid-semester feedback is intended solely for the instructor to improve student learning.
• Instructors can make decisions about potential changes to the subject in response to the student feedback.
• Providing feedback to instructors prompts students to reflect on their learning in the subject and consider how their behaviors in the class are impacting the learning process.  

In this workshop we will discuss ways to collect targeted and actionable feedback in a variety of teaching contexts. (large and small classes, labs, introductory and advanced classes)

 

Generative AI- Come with your computer

Generative AI is here - and is likely to play a larger and larger role in higher education.

In this workshop we will use the Backward Design framework to (re)design and deliver courses that effectively leverage the affordances while mitigating the threats of GenAI. In particular, you will:

• Engage with GenAI tools
• Refine your goals for learning
• Rethink your assignments & assessments
• Consider alternate subject formats and learning experiences
• Build in equity, accessibility and student privacy
• Articulate your expectations wrt the acceptable use of GAI

 

Pre-workshop Readings/Assignment

• Watch: Getting Started with AI-Enhanced Teaching: A Practical Guide for Instructors (~10 min. video)
• Read:
  • AI didn't make homework ineffective, by Josh Brake
  • If you will be teaching writing-intensive courses - please browse this doc for resources related to GAI and writing.  IMO - Marc Watkins & Jane Rosenzweig are writing some great stuff about GAI and writing

 

• Optional Reading/Listening:
  • If you are completely new to generative AI (GAI) and large language models - I suggest this video (optional):
  • GAI Basics: a Technical Primer - MT Sloan  Prof. Rama Ramakrishnan, MIT Sloan (Video); part of Sloan Technology Services AI hub 
  • AND: If you want a laugh or a cry - listen to the FA2024 KTCP Syllabus as a Gemini NotebookLM-generated Podcast
  • Please browse these articles on bias & the environmental impact of GAI (the first & second articles are good places to start.  In addition, there are many other resources and sections in this doc that may be of interest to you.

 

Complete the following activities:

• Consider a prompt for a student assignment in a course that you are likely to teach in the near future. 
  • Articulate:
    • Why does this assignment make sense for this course?
    • What are specific learning outcomes associated with this assignment?
• Create or Access an GAI account:
• Enter the prompt from the assignment or question you investigating, and consider the AI’s response: 
  • How did GAI do?  
  • What aspects of GAI’s response are accurate, reasonable?
  • What aspects are incorrect or incomplete?
  • Are students likely to be able to evaluate the quality of the response?
  • How might AI undercut the goals of this assignment?
  • Do you need to change the assignment? Do you need to change your ILOs?  You may wish to use the Backward Design framework to guide your thinking.

Please come to the workshop ready to discuss your pre class assignment.

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