The primary research objective of the Data Literacy Research Institute is to examine data literacy and how it is integral for tackling the big scientific, social, political, and cultural questions. Experts come together to create interdisciplinary research programs focused on

1. Identifying the current effects of weak data literacy for the layperson to the expert in areas such as misinformation and decision-making;
2. Understanding existing barriers to training and access; and
3. Developing interventions, recommendations, best practices, and guidance to minimize the growing divide brought on by disparities in individual competencies with data.

Data Literacy Education: A New Approach to Quantitative Methods Instruction (SSHRC Insight Grant, 2023-2028)

This project aims to develop and test a modern data literacy curriculum designed to replace traditional quantitative methods courses for non-major undergraduates. Utilizing the systematic ADDIE instructional design framework, the project equips students with essential critical thinking and data interpretation skills while actively working to reduce student data anxiety.

Grounded in Self-Determination Theory and Integrated Knowledge Translation, the initiative followed a multi-phase approach:

• Phase 1 (Fall 2025): Partnered with 13 student design co-creators using structured consultations and the ADDIE model to build the curriculum.

• Phase 2 (Winter 2026): Piloted the finalized 12-module course, Talk Data to Me: An Introduction to Everyday Data, with a cohort of 90 undergraduate students.

Developed in partnership with Teaching and Learning Support Services (TLSS) to meet evidence-based instructional standards, the course bridges the gap between complex statistics and real-world application. Building on this success, the research team will expand the framework in Fall 2026 by applying the Phase 1 co-creation model to two new complementary courses, one serving as a prerequisite and one as a follow-up to Talk Data to Me.

Understanding and Improving Data Literacy in Canadian News Media (SSHRC Insight Development Grant, 2024-2026)

This initiative investigates how numerical data are presented in Canadian news media and how audiences interpret quantitative information. Structured across three sequential phases, the research utilizes automated web scraping and Nvivo content analysis on CBC News stories to categorize data types, employs Tobii eye-tracking technology in the Triple-I Lab to monitor real-time audience reading comprehension and gaze behavior, and uses a general population experimental design to evaluate whether optimized presentation formats improve data understanding and counter the spread of misinformation.

Algorithms and Misinformation Research Project

Aimed at studying the impact of algorithms on misinformation and the risk of algorithmic bias in automated decision-making, this initiative involves researching social media habits and biases. Its primary deliverables include an educational toolbox for students aged 15 to 20 featuring classroom-ready course sequences, and interactive workshops. Extending these critical thinking initiatives beyond the university level, the project incorporates a research partnership with Lycée Claudel scheduled to launch in the Fall 2026 term to analyze digital claim verification behaviours and build early cognitive resilience before students transition into higher education.

Publications will be posted here.