Wi-Fi Feature Engineering for Detection of Campus Social Dynamics and Academic Performance Prediction
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Social interaction amongst students can often affect their university experience drastically. The manner in which students collaborate and socialize with their peers can also be used as a helpful metric to predict their academic performance. Traditional methods of quantifying social interaction like surveys suffer from reliability issues stemming from factors such as social desirability bias. In this research, our team investigates a method to predict academic performance that leverages Wi-Fi logs. The logs span a time period of 14 weeks and in their raw form can be used to estimate a student’s location, albeit with low spatial resolution. Analysis of these logs however can lead to fairly accurate inferences of collocations amongst students. We then found that a 0.75 rate of correlation exists between student performance predicted from these collocations and actual performance. These findings are significant in that they could demonstrate the utility of Wi-Fi data in applications such as well-being and mental health.