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Predicting Undergraduate Student Dropout Using Artificial Intelligence, Big Data and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting Undergraduate Student Dropout Using Artificial Intelligence, Big Data and Machine Learning./
作者:	Kunchala, Vikas.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	65 p.
附註:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28547303
ISBN:	9798544226918

Predicting Undergraduate Student Dropout Using Artificial Intelligence, Big Data and Machine Learning.
Kunchala, Vikas.

Predicting Undergraduate Student Dropout Using Artificial Intelligence, Big Data and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 65 p.

Source: Masters Abstracts International, Volume: 83-03.

Thesis (M.S.)--University of Georgia, 2021.

This item must not be sold to any third party vendors.

The aim of this research work is predicting undergraduate student dropout in a public post-secondary education institution in the Southeast United States. The main sources of data are college database storage and National Student Clearinghouse. Datasets DS-57, DS-11 and DS-101 are created from those sources. All datasets are trained using suitable classification machine learning models. Agile practices are followed to perform experiments. From the results, it is observed that important features predictive of dropouts are related to academic performance and financial aid. Models are evaluated on percent accuracy and F-measure. Random Forest performed with 0.86 F-measure and 87.04 percent classification accuracy. Further training with ensemble machine learning techniques improved F-measure to 0.903 and classification accuracy to 90.8 percent.

ISBN: 9798544226918Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

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