Université Laval
AC-1: Academic Courses

Prior to 2022, to identify SD-courses at Université Laval, we asked teachers to self-assess this aspect using a questionnaire. Roughly 16% of all our courses were self-assessed by teachers this way, limiting our possibility to reach the AC1 objective of 20% of courses in SD. Also, Université Laval chose recently to bonify this assessment with an SDG-mapping of our teaching and research activities. So, in 2022, our original methodology based on Key competencies in sustainability (Wiek et al, 2011) was bonified by a new approach. To answer to issues highlighted above (participation rate / representativity of this indicator in an institution-wide approach + implementation of SDG), we are now partly relying on artificial intelligence. This automatic learning is developed in complementarity with the self-assessment approach : filling the questionnaire is still mandatory to obtain the “intern SD-label”, and the automatic approach (detailed below) helps extend the SD/SDG-mapping to all courses at Université Laval for accreditations and key performance indicators calculations (more accurately, A.I. helps extend the mapping to all non-excluded courses at Université Laval – exclusions as per STARS | AASHE indications for that criteria). SDG-mapping project: During Summer 2022, Université Laval has undergone a vast study of syllabus contents regarding 23 parameters (17 SDGs, SD-course, social engagement-course, sustainable health-course, responsible entrepreneurship-course, entrepreneurship course, experiential learning-course). Students were hired, trained and directed to read and analyze 4879 syllabi from all cycles. 994 courses were identified as either in SD, in social engagement, sustainable health or responsible management (a single course counts only once even if it’s been identified by multiple labels). The excel spreadsheets show the rough data along with the calculations. Since then, these analyses were used as “ingredients to feed” automatic learning and then tested the replicability of the results. The analysis could be replicated adequately 79,2% of the time. And we’re counter-checking some of the analysis to improve this replicability ratio, though it’s already a good ratio for the sector, or so it is the point of view of our intern A.I. specialists who commented the results. It’s already planned to promote the questionnaire to teachers to improve the automatic learning process as a measure of continuous improvement. Also, in order to validate the analyzing capacity of the hired students, 15 courses were thoroughly analyzed by their responsible teachers. Results showed that teachers are systematically (14 times out of 15) more generous about their courses contribution towards SDGs than students. The results we’ve obtained so far can therefore be qualified as conservative. The A.I. still in development will permit to identify automatically the lists of courses in SD and its contribution to SDGs for years to come. Continuous improvements and retroactions are planned to improve the strength of the analysis. This work has been pursued to thesis and memoires summaries, for SDG-mapping of research, but data isn’t available yet. In parallel of those operational activities, a research project was funded for SDG-mapping, under the direction of teacher Stéphane Roche at Université Laval. Partners from various sectors have supported the project (NGOs, municipality, SDSN-Canada…) We’re planning to add a paper to support submitted data for the next STARS AASHE accreditation process. As mentioned, all the SDG-Mapping has been developed as an add-on to the already well documented approach to label a course&programs in SD at Université Laval. Description of previous methodology being updated: Based on Arnim Wiek, Withycombe and Redman’s “Key competencies in sustainability: a reference framework for academic program development” (2011), a mandated work group at Université Laval produced an evaluation grid for courses and programs. These grids have been submitted to teachers responsible for specific courses and heads of programs in order to make an inventory of courses and programs related to sustainability. Three different levels of integration of SD in courses were proposed to teachers (sensitization, introduction, in-depth), in-depth courses were considered sensibility courses for STARS purposes. All these courses in SD are regrouped and promoted under one label (SD-courses) : https://www.ulaval.ca/les-etudes/cours/repertoire.html?attributs=DEDU&page=0 The list of courses in sustainability is the result of auto-evaluations by responsible teachers, using a well-anchored theoretical questionnaire that addresses the development of key competencies in sustainability (KCS) as inscribed in their syllabus. This means there’s a learning outcome in SD related to those courses, written in the course’s syllabus and as evaluated by the responsible teacher. Sustainability courses at Université Laval respects “sampling and data standards” and “standards and Terms” in AC-01 p. 4-6 of the technical manual, as these courses provide "skills to address sustainability-related problems". For more details around the methodology we used, please write to daniel.forget@vraidd.ulaval.ca. As well, several papers about our methodology have been published, amongst them : « Implementing Sustainability in the Classroom at Université Laval » (Richard, Forget et Gonzalez, 2017).

The total number of academic departments (or the equivalent) at Université Laval is 81, including direction departments. However, for this credit, we excluded the Faculty of Graduate and Postdoctoral Studies (Faculté des études supérieures et postdoctorales) and 17 "direction or institutional" departments (Engineering&science direction, social science direction...) as these academic departments does exclusively offer courses that are excluded from the label "courses in sustainability". Courses in these departments are linked to thesis, research activities, internships, clinical...