In my last post, I explored how Cormier’s work enacting change at an educational organization in PEI involved social cognition and momentum theory. This week’s post asks us to discuss the impact of data analytics to support change and moderate associated risks of big data.
Switching gears to my line of work of developing cycling coaches, data is an area of weakness for our organization. Cycling Canada’s new 2030 strategic plan called for a significant restructuring of goals and initiatives, including grassroots programs, inspirational events, gracious champions, all guided by a community focus. This initiative also called for hiring a data specialist to analyze the effectiveness of coach development programs, first involvement events and activities (Sport for Life LTD 3.0, 2019, p. 15), and club, provincial, and national programs.
Regarding coach development specifically, there is an opportunity to understand better the factors driving participation in coach development programs across cycling and similar sports. At the moment, data is collected by various parties, including the Coaches Association of Canada (CAC), each of the 65 national sport organizations, like Cycling Canada (CC), and provincial organizations, Cycling BC (CBC). Since coach development responsibilities are split into general multi-sport modules (CAC), sport-specific theory modules (CC), and sport-specific practical modules (CBC), participation records, satisfaction surveys, and customer interactions are diversified across the numerous layers of bureaucracy. As a result, opportunities to streamline the experience, consolidate user data and satisfaction surveys, and better inform policy and educational design updates are available.
Creating opportunities to share data between these three organizational levels requires a common set of guiding values and principles (Open University (nd). We also need to decide what information helps clarify a situation and what information helps enact change (Marsh, Pane & Hamilton, 2006). Indeed making “making moral decisions when resources are limited” (Joynt & Gomersall, 2005, as cited by Prinsloo & Slade, 2014, p. 321) is very challenging. Still, data is not sufficiently used to guide policy. Instead of critically evaluating the status of sport and our responsibilities to our members, we are enacting the concepts of educational triage to fix gaps in the short run and help the ‘loudest’ user groups. In an ideal world, de-identified user data could even help us understand participation and completion factors across numerous sports and countries, similar to the JISC model (Schlater, Peasgood, & Mullan, 2016).
By adopting an empathic and comprehensive review of the impacts of data, we can begin to explore its application in the sports development context. And with great appreciation for the privacy of our members, their inclusion in the process, and the guiding values of non-malevolence (do no harm) and benevolence (provide support), perhaps we can use data more deliberately and create spaces that encourage compliance and engagement among our sports community’s most influential leaders, our coaches.
References
Cycling Canada. (2021). Strategic Plan 2020-2030. Retrieved from https://www.cyclingcanada.ca/wp-content/uploads/2021/02/CC_-Strategic_Plan_2020-2030-FINAL.pdf
Sport for Life. (2019). Long Term Development 3.0. Retrieved from https://sportforlife.ca/wp-content/uploads/2019/06/Long-Term-Development-in-Sport-and-Physical-Activity-3.0.pdf
Marsh, J., Pane, J., & Hamilton, L., (2006). Making Sense of Data-Driven Decision Making in Education: Evidence from Recent RAND Research. Santa Monica, CA: RAND Corporation.
Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research In Open And Distributed Learning, 15(4), 306-331.
Schlater, N., Peasgood, A, & Mullan, J. (2016). Learning analytics in higher education: A review of UK and international practice. Jisc.