NorQuest College knows that the world is constantly evolving, and that new, innovative ideas and products are constantly being created. Currently, some of the most promising emergent technologies involve artificial intelligence and virtual reality. Emergent technologies provide the opportunity to incorporate into existing products and services or develop novel applications that create significant social impact.
NorQuest has the experience and ability to identify emergent technologies and uncover opportunities to implement them that others don’t see.
Below are two emergent technology research projects underway at NorQuest College. The first project shows how technology can be applied to enhance the outcome of soft skills development training for the benefit of job seekers, employers, communities and society, while the second demonstrates the role of emergent technology in developing business solutions.
Virtual Reality Soft Skills
This initiative was supported by the Government of Alberta Workforce Strategies Grant.
- Project lead: Jens Kilden, MBA
- Project coordinator: Niru Raghavan
- Funding dates: 2022-2025
- Project partners:
- Government of Alberta
- Elixir Simulations
- EmployAbilities
- Action for Healthy Communities
- Red Deer Polytechnic
- Lakeland Collage
- KOVR
- Red Iron Labs
- Vizworx
About the project
As the population of Alberta grows and new Albertans start looking for employment opportunities, there is an abundance of opportunities to help job seekers and employers with strategies to find and retain new employees. The Virtual Reality Soft Skills project created a series of virtual reality training scenarios that help employers and job seekers develop strategies for finding and retaining employment. The project consists of three job seeker and three employer scenarios where participants take part in a series of virtual reality learning activities focused on interview and job retention-related strategies.
Working with partners across the province to tailor the scenarios to client-specific needs, organizations have successfully incorporated the VR Soft Skill scenarios into their training sessions. To date, over 300 participants have taken part in the project, and feedback has been extremely positive, with most participants agreeing that the training helped them in their job search.
Data Decisions: Visualizations and ML Modeling of Rental Property Data
This applied research initiative was supported by the Government of Canada through a Statistics Canada grant.
- Project lead: Uchenna Mgbaja, PMP®
- Key team members: Md Mahbub Mishu, PhD (Co-Instructor), Maryam Zamani, Sumithra Balamurugan, and Aya Heba
- Funding dates: January-April 2023
- Project partner: Community Data Program
About the project
The Community Data Project was an initiative aimed at addressing the knowledge gap in Canada’s rental housing market by collecting, analyzing, and visualizing rental listings data. The project used data science and machine learning techniques—including exploratory data analysis (EDA), machine learning modelling, and interactive visualizations—to uncover rental market trends across both metropolitan and small community markets.
Project impact
- Data-driven decision-making: The project provided valuable insights into housing availability, price distributions, and key factors influencing rental costs in Ontario, empowering community stakeholders, policymakers, and real estate professionals.
- Machine learning predictions: Advanced predictive models were developed to forecast rental prices and classify property types based on historical data, offering actionable intelligence for renters and investors.
- Interactive application: A user-friendly web application was deployed to enable stakeholders to explore rental trends across Ontario, providing transparency and accessibility to previously fragmented housing data.
- Supporting underserved communities: A major focus of this initiative was on small and underrepresented communities, helping local governments and organizations make informed housing policy decisions.
Future developments
Building on this, the future goal for the project is to expand its dataset to include additional provinces. Planned enhancements include real-time data integration and additional features to better support rural housing analysis.
Access the full report here: Data to Decisions: Visualizations and ML Modeling of Rental Property Data