Title

Preventing student drop out at CWU

Presenter Information

Suvarna Arakh

Document Type

Oral Presentation

Location

SURC 140

Start Date

16-5-2013

End Date

16-5-2013

Abstract

This data-mining project aims to assess the risk of CWU undergraduate students’ attrition. We choose a moment at the end of a student’s first year of study to predict the risk of evasion and decide if counseling should recommended. Once the at-risk students are detected, many actions may be taken (such as psychological and educational support, registering orientation, shift change, etc.) to reduce drop-out risk. As input data, we use the student's profile, extracted from the university’s database, including the student's academic results. We use the Google Prediction API, which is Google's collection of cloud-based machine learning tools for prediction.

Faculty Mentor(s)

Razvan Andonie

Additional Mentoring Department

Computer Science

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May 16th, 8:40 AM May 16th, 9:00 AM

Preventing student drop out at CWU

SURC 140

This data-mining project aims to assess the risk of CWU undergraduate students’ attrition. We choose a moment at the end of a student’s first year of study to predict the risk of evasion and decide if counseling should recommended. Once the at-risk students are detected, many actions may be taken (such as psychological and educational support, registering orientation, shift change, etc.) to reduce drop-out risk. As input data, we use the student's profile, extracted from the university’s database, including the student's academic results. We use the Google Prediction API, which is Google's collection of cloud-based machine learning tools for prediction.