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|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 |
|Research Areas |
|Artificial Intelligence||cognitive modelling||data mining|
|e-learning||Learning Analytics||machine learning|
|Funding Source Name ||Amount |
|Collaboration Type ||Collaborator |
|Industry||Eighty Seven Inc., Canada|
|Faculty Researcher(s) ||Role |
|Vivek Kumar||Principal investigator|
Updated March 16 2017 by FST Technical Staff