Knowledge Exchange - Campus Research Month: Using Azure AutoML to Analyze the Effect of Attendance and Seat Choice on University Student Grades
 

Using Azure AutoML to Analyze the Effect of Attendance and Seat Choice on University Student Grades

Presentation Type

Oral Presentation

Mentor/Supervising Professor Name

Alférez, Harvey

Abstract (Description of Research)

Teachers often claim that class attendance and sitting at the front of a classroom improves student grades. This study employs machine learning on a private University's attendance data to analyze this claim. We perform a correlation analysis in Azure by training regression models. No correlation is found. Next we use the K-means clustering algorithm in Azure. At k=2 clusters, a cluster with perfect attendance shows a higher average grade than a cluster with a late attendance average. Seat choice within the classroom does not prove important to the clustering models.

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Apr 20th, 8:00 AM Apr 20th, 10:00 AM

Using Azure AutoML to Analyze the Effect of Attendance and Seat Choice on University Student Grades

On Campus

Teachers often claim that class attendance and sitting at the front of a classroom improves student grades. This study employs machine learning on a private University's attendance data to analyze this claim. We perform a correlation analysis in Azure by training regression models. No correlation is found. Next we use the K-means clustering algorithm in Azure. At k=2 clusters, a cluster with perfect attendance shows a higher average grade than a cluster with a late attendance average. Seat choice within the classroom does not prove important to the clustering models.