Intelligent Robot Learning Laboratory (IRL Lab) Data Mining the CougarCard for Student Fitness

By: Yunshu Du and Matthew E. Taylor

WSU’s Recreation Center (the Rec) is among the most frequently visited campus facilities. However, students may prefer to avoid the Rec when it is most crowded. Our work aims to solve this problem by predicting how crowded the Rec will be at different times by leveraging the university’s CougCard system.

CougCard, the WSU official identification card, is used by all students when entering the Rec. This work used anonymized CougCard data from the Rec and applied data-driven techniques to analyze student exercise trends. A predictive decision tree model was successfully built to predict the peak hours at the Rec. A web-based application for the model is currently under construction with the goal of suggesting when the Rec will be more or less busy.

Our long term goal is to make students more (quantitatively) satisfied with their experience at the Rec and/or (quantitatively) increase the number of times they visit the Rec to exercise. Additionally, our system can assist Rec managers with shift scheduling and fitness event planning. Future work includes building personal fitness recommendations into the application and increasing the number of areas the application monitors and predicts crowdedness (e.g., the CUB’s food court). [1]

[1] [pdf] Yunshu Du and Matthew E. Taylor. Work In-progress: Mining the Student Data for Fitness . In Proceedings of the 12th International Workshop on Agents and Data Mining Interaction (ADMI) (at AAMAS), Singapore, May 2016.
author={Yunshu Du and Matthew E. Taylor},
title={{Work In-progress: Mining the Student Data for Fitness }},
booktitle={{Proceedings of the 12th International Workshop on Agents and Data Mining Interaction ({ADMI}) (at {AAMAS})}},
abstract = {Data mining-driven agents are often used in applications such as waiting times estimation or traffic flow prediction. Such approaches often require large amounts of data from multiple sources, which may be difficult to obtain and lead to incomplete or noisy datasets. University ID card data, in contrast, is easy to access with very low noise. However, little attention has been paid to the availability of these datasets and few applications have been developed to improve student services on campus. This work uses data from CougCard, the Washington State University official ID card, used daily by most students. Our goal is to build an intelligent agent to improve student service quality by predicting the crowdedness at different campus facilities. This work in-progress focuses on the University Recreation Center, one of the most popular facilities on campus, to optimize students’ workout experiences.}

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