Analysing the DCS curriculum with analytics

This was posted at the date above on the department’s list of instruction-initiated projects for CSC494H1. Marco Marchesano was 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 design, implement, and use a tool for curricular analytics. This tool will create graph-based representations of curricula, use 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. While this tool will use the Department of Computer Science curriculum as a starting point, it may be used to compare our curriculum to similar curricula at other universities. Finally, this tool may be extended beyond what is described in prior art to consider other relevant factors (e.g., program learning outcomes).

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

[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).

Skills #

Strong software engineering skills and experience with front-end development is required. 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.