Teaching and learning the science of data: statistics education at U of T during the data revolution

Teaching and Learning the Science of Data: Statistics Education at the University of Toronto during the Data Revolution

“[It] was an ideal time to re-imagine our undergraduate programs of study, with the goal of creating sustainable, scalable, and rich learning experiences that will prepare our graduates for a lifetime of learning and innovation.”

Description

This project was a complete redesign of undergraduate programs of study in Statistics at the University of Toronto. For Statistics, the recent emergence of Data Science and new sources of data has meant that jobs and research opportunities that were not imagined just a few years ago are now in dire need of statisticians — people who are able to gain insights from data and to contribute to the development of new approaches and tools to deal with novel, unstructured, and larger datasets.

Coupled with this external context, the Department of Statistical Sciences has seen astounding growth in enrolment in undergraduate programs of study in Statistics. Thus, it was an ideal time to re-imagine our undergraduate programs of study, with the goal of creating sustainable, scalable, and rich learning experiences that will prepare our graduates for a lifetime of learning and innovation.

Division(s)

Department of Statistical Sciences, Faculty of Arts & Science.

Project Lead(s)

Alison Gibbs (Primary Investigator)
Nathan Taback, Bethany White (Co Investigators).

Year LEAF Granted

2015-2016

Funding Stream

Impact

LEAF Priority Area(s)

Experiential Learning

Curriculum Design

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Impact of the Project on Students

The following courses were most directly impacted by the project. These include new courses, a revived course (STA365H1, with modernized context), and a course that was completely transformed in its learning outcomes and grew in enrolment from about 100 students per year to approximately 1000 students per year (STA130H1).

  • STA130H1: 480 students in Winter 2018, 480 students in Fall 2018, 480 students in Winter 2019, 530 students in Fall 2019 and 480 students in Winter 2020.
  • STA237H1: 469 students in Fall 2019 and 198 students in Summer 2020.
  • STA238H1: 542 students in Winter 2020 and 200 students in Summer 2020.
  • STA314H1: 213 students in Fall 2018 and 260 students in Fall 2019.
  • STA365H1: 127 students in Winter 2020.

Impact of the Project on Faculty

There have been several conversations with University of Toronto colleagues interested in the curriculum renewal process and/or the outcomes of the project. These include:

  • Undergraduate programs in Physiology, Economics, and Math at St George;
  • Undergraduate programs in Statistics at both Scarborough and Mississauga campuses;
  • The Department of Mathematics and Computational and Sciences at UTM.

Resources Developed from the Project

This project resulted in the creation of a repository that is accessible via SharePoint to all faculty in the Department of Statistical Sciences. It includes:

  1. Program Learning Outcomes and Curriculum Maps
  2. Student and alumni survey instruments
  3. Analyses and summaries of surveys and student achievement
  4. Course materials developed for STA130H1, STA238H1, STA305H1 (assessments and rubrics, lecture materials, tutorial materials, project materials, course notes and computational supplements, etc.)
  5. Learning activities to illustrate the develop of computational, Bayesian, and predictive methods in existing 2nd and 3rd year courses
  6. Remedial R tutorials

Future Plans

STA313H1, STA492H1 were introduced to the program in 2020-21.

Partnership(s) Development

There is an ongoing collaboration with Sotirios Damouras of UTSC on the development of adaptive expertise in undergraduate Statistics, which arose from discussions about this project and the adoption of some related ideas at UTSC.