Analysing the DCS curriculum with analytics (Fall 2025)

This posting closed on the date above on the department’s list of instruction-initiated projects for CSC494H1. William Yao and Connor Sicheri were selected for the project.

Project description #

There are many paths students may take in their studies in the Department of Computer Science. These paths are not only a function of a student’s interests, but also of structural properties of the department’s curriculum, such as the program requirements, courses offered, and their prerequisite and corequisite requirements. Prior art has shown that curricular structure may have an impact on student success (including, for example, graduation time and GPA) [1, 2, 3]. One framework for analysis of curricular structure is to representing the curriculum as a graph, leading to quantitative metrics that represent its complexity [4]. These metrics provide insights into the efficiency of the curriculum and enable comparisons of program curricula offered across different universities.

In this project, you will extend and and use an existing tool for curricular analytics. This tool create graph-based representations of curricula, uses these representations to compute various quantitative metrics, and display visualizations of curricula and these metrics in ways that are accessible to students and university staff. Extensions to the tool may include changing the definition of graph-related metrics, creating new metrics, and/or enhancing/modifying/creating different visualizations. Use of this tool will involve analysing our, and potentially other, computer science curriculum, with the end goal of producing a publication.

This project is ideal for students interested in the intersection of computer science theory, software engineering, and computer science education research.

Cited above:

[1] Wigdahl, Jeffrey, et al. “Curricular efficiency: What role does it play in student success?.” 2014 asee annual conference & exposition. 2014.

[2] Slim, Ahmad, et al. “The impact of course enrollment sequences on student success.” 2016 ieee 30th international conference on advanced information networking and applications (aina). IEEE, 2016.

[3] Molontay, Roland, et al. “Characterizing curriculum prerequisite networks by a student flow approach.” IEEE Transactions on Learning Technologies 13.3 (2020): 491-501.

[4] Heileman, Gregory L., et al. “Curricular analytics: A framework for quantifying the impact of curricular reforms and pedagogical innovations.” arXiv preprint arXiv:1811.09676 (2018).

Other citations:

[5] Hansen, John, Amanda Nemeth, and John Stewart. “Extending curricular analytics to analyze undergraduate physics programs.” Physical Review Physics Education Research 20.2 (2024): 020143.

[6] Li, X. V., M. B. Rosson, and B. Hellar. “A synthetic literature review on analytics to support curriculum improvement in higher education.” EDULEARN23 Proceedings (2023): 2130-2143.

[7] Atalla, Shadi, et al. “An intelligent recommendation system for automating academic advising based on curriculum analysis and performance modeling.” Mathematics 11.5 (2023): 1098.

Skills #

Strong software engineering skills and experience with front-end development is required (the tool is written with Python and Django). These skills may be demonstrated through course work (e.g., CSC301, CSC309) and/or relevant internships and projects. Strong proficiency in concepts covered in first-year computer science is required.

Experience creating data visualizations (e.g., by experience with CSC316) is an asset. Interest in computer science education is an asset.

Notes #

This project is supervised by Mario Badr and David Liu.