Level UP- Effect of COVID-19 pandemic on education Phase 1

To conduct a province-wide study of school divisions’ experiences with teacher retention before and after the COVID-19 pandemic. This will be achieved by distributing a survey inquiring about how staff employing, retention, and dismissal have changed since the pandemic. The survey will also identify hiring practices and division properties such as rural/urban. Once complete, this survey will be submitted to the College of Alberta School of Superintendents (CASS), Alberta’s educational leaders that oversee the success of school systems in Alberta. Survey data will be used with data analysis techniques; unsupervised learning techniques such as clustering will identify key factors associated with higher or lower staff hiring, retention, and other quantifiers. Patterns associated with hiring techniques will be analyzed, and conclusions about population geographical factors’ relation to the survey results will be drawn.

Level UP- Effect of COVID-19 pandemic on education Phase 2

To conduct a province-wide study of school divisions’ experiences with teacher retention before and after the COVID-19 pandemic. This will be achieved by distributing a survey inquiring about how staff employing, retention, and dismissal have changed since the pandemic. The survey will also identify hiring practices and division properties such as rural/urban. Once complete, this survey will be submitted to the College of Alberta School of Superintendents (CASS), Alberta’s educational leaders that oversee the success of school systems in Alberta. Survey data will be used with data analysis techniques; unsupervised learning techniques such as clustering will identify key factors associated with higher or lower staff hiring, retention, and other quantifiers. Patterns associated with hiring techniques will be analyzed, and conclusions about population geographical factors’ relation to the survey results will be drawn.

Level UP- Effect of COVID-19 pandemic on education Phase 3

This project aims to determine the impact that COVID-19 has had on Alberta teacher retention. Over the last two Phases, a survey was distributed to every public and Catholic school division across Alberta, querying the state of school staff hiring, retention, resignation, and retirement before and after the COVID-19 pandemic. This Phase will conclude the survey collection process, focusing on analyzing the data collected. Many data mining analysis methods will be employed to determine common factors across these teacher retention results, including clustering, decision tree analysis, correlation tables, and more. The R coding language will be used for the analysis, which will be run in the RStudio development environment. Following this analysis, a research paper will be composed, including related works, background information, and an in-depth explanation of the study and the results.

Developing CF for video data fit as multivariate functional data. Phase 1

positions available: 2 This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects). To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.