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Project Information

Name: Junior hockey competence analytics
Abstract     Analytics is about awareness of the states of knowledge of users. Users can become aware of their owns states of knowledge at different levels. Analytics measures such levels of each user, and engages them in taking initiatives to hop from one knowledge state to the next. The hops happen mostly gradually, depending on the capacity of the user, punctuated by dramatic jumps. Analytics identifies such scenarios where dramatic jumps are necessary and offers the information needed to enact such jumps. Learning Analytics, in the context of Ice Hockey, is the study of detection, analysis, and generation of moments of progress awareness about skater, goaltender, and team experiences. By employing recent advances in statistics, machine learning algorithms and sensor technologies, this research aims to build a big data learning analytics solution that provides progress awareness scenarios and means to improve junior hockey player competency and self-reflection. Using sensors, the proposed solution will observes player characteristics both on and off ice, and will develop computational models of competency growth for players. The solution then offers personalized feedback, informing player selection and highlighting training opportunities, which both measure and improve player and team effectiveness.
Start Date 2017-04-01
End Date 2019-03-31
Research Areas
Artificial Intelligencecognitive modellingdata mining
e-learningLearning Analyticsmachine learning
Funding Source Name Amount
Mitacs, Canada110,000
Collaboration Type Collaborator
IndustryEighty Seven Inc., Canada
Faculty Researcher(s) Role
Vivek KumarPrincipal investigator

Updated March 16 2017 by FST Technical Staff

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