CanCOTS 2025

The first Canadian Conference on Teaching Statistics (CanCOTS 2025) will be hosted by HEC Montréal from Wednesday June 11th to Thursday June 12th, 2025. CanCOTS will be a participant-driven working meeting in the style of a curated “unconference”. After an initial roundtable aimed at engaging participants with state-of-the-art literature, the participants will split in small groups, each working on a priority area of interest. These priority areas will be short-listed before the meeting, so participants can arrive already having an idea of what they may want to collaboratively contribute to with their time, and can bring potential resources to share with the group.

Each small group will define and, at the end of the meeting, leave with concrete deliverables, either in progress or completed. Depending on the priority area, these deliverables could be in the form of conference proceeding papers or less traditional formats (e.g., banks of exercises, guidelines for curriculum, sample learning activities and assessment items, etc.)

Opening remarks

Format

Each small working group will be led by a team leader who will facilitate discussion and encourage progress toward the agreed upon deliverable(s). This team leader will also be responsible for giving a short introduction to the priority area to help launch the first day, and to motivate the working sessions that follow. Each group will present to all participants at the beginning of the second day to give a quick recap of the progress they made, and a final presentation at the end of the conference. The team leaders will be selected from volunteers from the community of statistics educators attending the conference, and will oversee the deliverables. CanCOTS will also be used to discuss the current state and needs of the statistical education community in Canada.

Financial support

We are reserving $3K of the budget for travel support thanks to funding from CANSSI, which will be awarded based on need. The priority will be for graduate students and early career researchers (less than five years from latest degree) without support from Tri-Council funding or equivalent grants. Priority will be given in order received until funds are exhausted.

Funding will be allocated by the scientific committee after the conference and adjudicated based on need. Participants must send their CV and a cover letter to the scientific committee to ask for support, explaining how they’d use the travel funds, whether not receiving funding would limit their ability to attend, and whether they have other sources of (partial) funding; the scientific committee will adjudicate the funding based on participants needs.

Priority areas

Participants must choose a working group upon registration (it will be possible to modify the latter by changing your answer in the form). Each working group will define and, at the end of the meeting, leave with concrete deliverables, either in progress or completed. The group leaders will contact participants at the beginning of June to exchange material and decide on outcomes.

The list of working groups and team leaders is

  1. Designing a modern master program in statistics or data science (Wesley Burr, Lisa Lix) - A.547 Laurent-Ferreira
  2. Interactive R tutorials for learning and assessments (Russell Steele, David Riegert and Léo Belzile) - A.536 MNP
  3. Evidence-based teaching for learning in statistics education (Asal Asleman) - A.545 Denis-Girouard
  4. Teaching advanced analytical topics in biostatistics courses (Kuan Liu) - A.563 Chongqing
  5. Incorporating artificial intelligence and ethics in statistics curriculum (Anne-Sophie Charest) - C.530 Telesystem
  6. Incorporating causal inference in statistics courses (G. Alexi Rodríguez-Arelis) - A.579 CTI Capital

Access to the Google Drive: bit.ly/cancots

Designing a modern master program in statistics or data science

Deliverables:

  • Define potential learning objectives for a new Master’s program
  • Enumerate current challenges and opportunities at Canadian universities in constructing new Master’s programs in Statistics/Biostatistics,
  • Describe the role of AI in developing the curriculum and facilitating change in existing curriculum
  • Discuss experiential learning opportunities.

Interactive R tutorials for learning and assessments

Thanks to software development, it is possible to use R for randomization of assignments and assessments, including in WebWork and via rexams. There are also numerous R packages (learnr, webR) that allow for interactive coding with automated feedback, a feature which is useful in large classrooms. This working group will explore research questions revolving around use of these tools in the classroom for asynchronous, online of flipped classrooms.

Objectives

  • Determine under which circumstances interactive tutorials are most useful
  • Provide guidelines and strategies for efficient design of questions and exercises to assess learning
  • Explore different methods and integration with learning management systems
  • Determine how use of large language models (LLM) can help with question generation, and how to make questions LLM-proofed in the context of assessment.

Deliverables:

  • Peer-reviewed publication or a white paper reviewing the objectives
  • Online tutorial (book format) with examples of questions and details about implementation
  • Database with sample questions and examples for different platforms

Teaching advanced analytical topics in biostatistics courses

There is an increasing need to design biostatistics courses covering advanced and newer modelling techniques that better support graduate students in health sciences. I have observed two distinct features of this student cohort

  • students are eager to learn and implement new modelling techniques they see in their field of literature (e.g., causal random forest, target trial etc), and
  • they often lack the foundational knowledge and programming skills needed to fully understand these approaches.

Many of these students went on to work as clinician scientists, and methodologists at public health agencies and pharma. Traditional teaching models/curriculums for teaching undergraduate health or life sciences students do not effectively translate to this audience, motivating new approaches that align with their unique needs.

Objectives: This working group can help discuss best practices for teaching advanced biostatistics to graduate health sciences students, modify curriculum design that balances theory with application, and create assessments that foster strategies to enhance interdisciplinary collaboration between health scientists and biostatisticians.

Deliverables: The proposed deliverables can potentially include a set of recommendations for course design, strategies for integrating statistical training into health sciences curricula, and a framework for strengthening interdisciplinary connections to bridge knowledge and research silos.

Incorporating artificial intelligence and ethics in statistics curriculum

Objectives: defining clear learning outcomes for the students. In my experience, establishing well-defined learning objectives ensures that instructional activities are purposeful and that assessments align with intended goals.

For generative AI, for instance, the key question should not be whether to allow its use in courses but rather how it should reshape the learning outcomes we prioritize. Are there traditional skills that should be de-emphasized? What new competencies should be developed? Should we just allow students to work with generative AI, as we do with text editors and such, or should we actually teach how to use them, and evaluate their outputs, more like we do with statistical software. I propose starting by drafting specific learning objectives, followed by discussions on how to teach and assess them effectively, including concrete evaluation methods and grading rubrics.

Evidence-based learning

Objectives:

  • Discuss evidence-based approaches to assessment and how they can be aligned with course learning objectives to improve both feedback and learning outcomes.
  • Explore pedagogical designs that support student-centered learning, focusing on strategies that promote active engagement, critical thinking, and deeper conceptual understanding in statistics.
  • Examine research on simulation-based inference and how this innovative approach, especially through visualization, supports students’ intuitive grasp of statistical inference and complex concepts.
  • Engage participants in constructing concept and/or mind maps to visualize connections between evidence-based teaching strategies and core components of statistics education, such as assessment design, conceptual understanding, and student-centered learning.

Deliverables:

  • Summarize published interventions, focusing on their goals, methods, and outcomes.
  • Reflect on the feasibility and transferability of these strategies across diverse institutional contexts.
  • Share group-generated concept and mind maps that integrate assessment, pedagogy, and instructional tools.
  • Synthesize key insights into a working white paper that outlines future directions for pedagogy, research, and faculty development.

Incorporating causal inference in statistics courses

Causal inference is not often covered in core statistics courses despite it’s prevalence in biostatistics and clinical trials. Most introductory books are too technical for users. Some core concepts are covered as part of experimental designs courses, but these often don’t touch upon modern topics such as A/B testing for online experiments or quasi-experiments. Coverage of methods for observational data is often treated separately altogether, if at all.

Deliverables: the proposed deliverables for a working group of this class is an overall course curriculum on causal inference (including lesson plan, learning objectives, and an overall structure of course content) with two fundamental pillars: experimentation and quasi-experimentation. A stretch goal of this working group would be incorporating data science-flavored topics such as A/B testing.

Venue

The conference will take place on the 5th floor of the Hélène Desmarais building.

501, rue de la Gauchetière Ouest Montréal, QC H2Z 1Z5

The main room will be Salle de cours MNP (A.536) (pdf map) and adjacent study rooms (A.545 Denis-Girouard, A.547 Laurent-Ferreira, A.563 Chongqing, A.565 Colombo, A.579 CTI Capital).

The downtown campus is easily reached by public transport, either via metro (Square-Victoria-OACI station, orange line), or from the Gare Centrale REM station.

Some employees of STM, the Montreal Public Transport system, will be on strike during CanCOTS. Unless there is an agreement before next week, service will be affected on June 10th and 11th (buses and metro will only run during rush hours) and on June 12th (regular service during rush hours, 50% service outside of that window). Please plan accordingly; it is easy to get around via Bixi rental bikes or by foot downtown.

Schedule

Wednesday June 11th

  • 09:00-09:10 - Introduction from the organizers
  • 09:10-10:00 - 5 to 10 minutes introduction from each group lead, including question period
  • 10:00-10:15 - Coffee break
  • 10:15-12:15 - Initial breakout
  • 12:15-13:45 - Lunch break
  • 13:45-16:00 - Second breakout
  • 16:00-16:25 - Coffee break
  • 16:25-17:30 - Summaries of the first day, brief presentations, wrap-up
  • 18:30-20:30 - Diner Le 409

Thursday, June 12th

  • 09:00-09:10 - Welcome back
  • 09:10-12:00 - Third breakout
  • 12:00-13:30 - Lunch break (room D.726)
  • 13:30-14:15 - Summary presentations of accomplishments, framework for remaining work, timelines, discussion of final objective deliverables
  • 14:15-14:30 - Coffee break
  • 14:30-17:00 - Fourth and final breakout, chance to frame out remaining work and set up workflows for July/August

Accommodation

Please note that the conference ends the day prior to Formula 1 Montreal start, so we encourage you to book your accommodation well in advance. Hotels nearby include the following

Satellite workshop

A satellite workshop for college instructors on simulation-based inference will take place on Tuesday, June 10th. The focus is on the Probability and Statistics course offered in the pre-university degree in Natural sciences, Sciences and arts, and Science, Computer Science and Mathematics, among others. The day will include a two hours session in the morning. Participants will then collaboratively work on a project of their choice in small group to bring something home. The four working groups are

  • incorporation of large thematic datasets
  • adding programming languages for data science for calculations
  • simulations and apps to illustrate probability and statistical concepts
  • construction of exercises databases (WeBWork, learnR), etc.

There will be a session in English and one in French; please specify your language of preference when registering.

Registration is 35$ and covers lunch and coffee breaks. The event will take place in Hélène-Desmarais building on the fifth floor.

Schedule:

  • Registration and welcome (9:00), room A.536 (MNP)
  • Mini-course (9:30-10:30, 11:00-12:00), room A.549 (Port Moresby)
  • Lunch break (12:00-13:00)
  • Group work (13:00-16:30), rooms A.549 Port Moresby, A.536 MNP, study rooms A.545 and A.547
  • Wrap-up and short presentations (16:30-17:00), room A.549 Port Moresby

Background

“Conferences On Teaching Statistics” have a long and rich history, starting from the earliest such international conferences (International Conference on Teaching Statistics – ICOTS) in 1982 (Sheffield, UK) and 1986 (Victoria, Canada). A total of eleven ICOTS have been held, every 4 years since 1982, and a number of regional versions, including

have also been held.

Organizers

Scientific committee

Local organizers

Special thanks to Antonietta Florio and Jennifer Caron for administrative support.

Sponsors

We gratefully acknowledge financial support from