VLSI-Inspired Methods for Student Learning Community Creation and Refinement
| dc.contributor.advisor | Behjat, Laleh | |
| dc.contributor.advisor | Gibbs Van Brunschot, Erin | |
| dc.contributor.author | Cao, Sheng Lun | |
| dc.contributor.committeemember | Moshirpour, Mohammad | |
| dc.contributor.committeemember | Dimitrov, Vassil | |
| dc.date | 2021-11 | |
| dc.date.accessioned | 2021-08-12T21:16:53Z | |
| dc.date.available | 2021-08-12T21:16:53Z | |
| dc.date.issued | 2021-08-06 | |
| dc.description.abstract | The unprecedented global pandemic 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 courses at a university can be modelled as an enrolment network similar to a circuit netlist. Learning communities are created by clustering students into groups, optimizing 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 enrolment networks. Further experimentations are conducted for Best Choice to fine-tune its clustering parameters. The learning communities created by the clustering algorithms are further refined by the Simulated Annealing algorithm using the same optimization criteria. Experiments are performed to fine-tune the algorithmic parameters of Simulated Annealing. The Learning Community Creation and Refinement Framework combines all three stages of network modelling, learning community creation, and learning community refinement. The proposed framework is tested on both the 3rd year Electrical Engineering Fall 2020 enrolment dataset and a very large Fall 2020 and Winter 2021 enrolment 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 is able to 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 enrolment 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. | en_US |
| dc.identifier.citation | Cao, S. L. (2021). VLSI-Inspired Methods for Student Learning Community Creation and Refinement (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/39090 | |
| dc.identifier.uri | http://hdl.handle.net/1880/113728 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | VLSI | en_US |
| dc.subject | Learning Community | en_US |
| dc.subject | Clustering | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Engineering Education | en_US |
| dc.subject.classification | Education | en_US |
| dc.subject.classification | Engineering | en_US |
| dc.title | VLSI-Inspired Methods for Student Learning Community Creation and Refinement | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Electrical & Computer | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |