Faculty Advisor

Agu, Emmanuel O.

Faculty Advisor

Claypool, Mark L.

Abstract

This project examines exergame enjoyment in order to find clusters of exergames that produce similar enjoyment. First, the team developed a classification system to group exergames together. Following that, data was gathered through experimentation, where participants played exergames and had values of their enjoyment recorded for each exergame played. The strongest relationship between classifications, according to a resulting relationship network, was between Control-Adventure and Control-Action, and so both classifications produced similar measured enjoyment. Finally, the gathered data was put to use in a functioning recommender system that recommended users exergames that they have not played. This developed recommender system returned accurate recommendations 83% of the time.

Publisher

Worcester Polytechnic Institute

Date Accepted

March 2018

Project Type

Interactive Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

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