A Narrative Approach to Building Computational Capacity in Professional Masters Students

Wednesday 4:30pm-5:45pm TSU - Humphries: 118
Poster Session Part of Wednesday


Karen Smith, University of Toronto Scarborough
Conor Anderson, University of Toronto
The goal of a professional Masters program is to help students rapidly transition from undergraduate learners to work-ready professionals. This presents a unique challenge for instructors to balance conceptual content with continually-evolving and often technical real-world application.

The applied nature of professional Masters programs is often what attracts students to these programs, yet students sometimes struggle with the accelerated pace of technical skills development required in many of the courses. This is particularly true of the data-intensive discipline of climate change impact assessment (CCIA). Consequently, past approaches to teaching CCIA have opted to circumvent the technical aspects of the field, leading to lack of methodological transparency and leaving students ill-equipped to enter the work-force.

To facilitate improved transparency and technical skill-building in CCIA, we have developed a new series of step-by-step, coherently narrated, open-source python labs aimed at building students' computational capacity and confidence, while providing foundational knowledge in CCIA and the opportunity to interact with state-of-the-art methods and data.

Our in-progress research focuses on assessing the effectiveness and limitations of our approach, with an emphasis on quantifying the extent to which the labs have improved (1) students' confidence with learning and applying new technologies and (2) their self-assessment of their work-readiness.