The use of Data Visualization

The objective of this initiative is to increase stakeholder engagement with research results and analysis through the adaptation and development of data visualizations.

Project Category:
Exploiting available research more effectively
Topic Area:
Equity and Inclusion, Knowledge Mobilization, Science Education
Ministry Priority
(2011-2013):
Equity
Project Lead(s):

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Brief Background:
The objective of this initiative is to increase stakeholder engagement with research results and analysis through the adaptation and development of data visualizations. It is proposed that the focused development and dissemination of data visualization will improve both the analysis of large data sets and subsequent knowledge mobilization of equity issues related to students’ post-secondary transitions.
Knowledge Mobilization Activities:

Through a series of meetings, stakeholder groups will be presented with traditional research reports as well as data visualizations . Knowledge will then be mobilized in two ways through: (1) a workshop for education researchers; (2) publications and conferences and (3) dissemination of project activities through the Ontario Metropolis Centre (see http://www.ceris.metropolis.net/?p=5728 ).

The research team will approach both AERO and the HLM-LRC to coordinate a Special Interest Group Session (SIGS) or workshop for education researchers . This presentation will provide participants with an overview of the project, a summary of results to date, and a hands-on workshop that will build capacity for immediate implementation and adaptation in their organizations. With interest from other Ontario researchers, it may be feasible to establish a provincial equivalent to the Durham District School Board’s Data Visualization team.

Outcomes:
This approach to knowledge mobilization is expected to have a greater influence on problem solving strategies through a differentiated approach to reporting. Often the backgrounds and perspectives of stakeholders are different and knowledge transfer can be optimized through the use of appropriate visualizations. Although the focus of this project is specific to TDSB student data sets, the techniques that are adapted and developed will be applicable to any data set.