VLSI-Inspired Methods for Student Learning Community Creation and Refinement

TL;DR

This paper introduces a VLSI-inspired framework for creating and refining student learning communities in online and blended education, optimizing for internal connectivity and minimal external links to enhance learning and reduce disease spread.

Contribution

It adapts VLSI clustering algorithms and refinement techniques to model and optimize student learning communities from enrollment networks.

Findings

Best Choice algorithm outperforms others in creating optimal communities.

Simulated Annealing significantly improves cluster quality.

Framework effectively handles both small and large enrollment datasets.

Abstract

COVID-19 significantly disrupted how educational contents are delivered in academic institutions, rapidly accelerating the adoption of online and blended learning. This thesis explores the creation and refinement of optimized student learning communities as a mean to support online and blended learning in the pandemic and post-pandemic setting. Students enrolled in university courses can be modeled as an enrollment network akin to a circuit netlist. Learning communities are created by clustering students into groups, optimized for maximum internal connection to support student learning, and minimum external connection to reduce disease transmission. Three VLSI-based clustering algorithms: Hyperedge Coarsening, Modified Hyperedge Coarsening, and Best Choice, are modified to cluster student enrollment networks. The learning communities created by the clustering algorithms are further refined by the Simulated Annealing algorithm using the same optimization criteria. The Learning Community Creation and Refinement Framework combines all three stages of network modeling, learning community creation, and learning community refinement. The proposed framework is tested on both the 3rd year Electrical Engineering Fall 2020 enrollment dataset and a very large Fall 2020 and Winter 2021 enrollment dataset. Best Choice performed the best among the clustering algorithms, capable of creating learning communities for the optimization criteria for a given maximum cluster size. Simulated Annealing can refine the clustering results by significantly increase cluster quality. The framework is capable of creating and refining learning communities for both the small and the large enrollment networks, but it is better suited for creating tailored learning communities at a program level. Future work, including creating student learning communities based on other optimization criteria, should be explored.