UB ScholarWorks

Utilizing Big Data Analytics to Improve Education

Show simple item record

dc.contributor.author Manohar, Annapoorna
dc.contributor.author Gupta, Pooja
dc.contributor.author Priyanka, Veena
dc.contributor.author Uddin, Muhammad Fahim
dc.date.accessioned 2016-05-23T13:38:11Z
dc.date.available 2016-05-23T13:38:11Z
dc.date.issued 2016-04-28
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1616
dc.description.abstract Analytics can be defined as the process of determining, assessing, and interpreting meaning from volumes of data. It has been categorized in three different categories - descriptive, predictive and prescriptive. Predictive analysis can serve many segments of society as it can reveal hidden relationship which may not be apparent with descriptive modeling. Analytics advancement plays an important role in higher education planning. It answers several questions such as -which students will enroll in particular course, what courses are on trending or obsolete, what is the level of student satisfaction in the current education system, effectiveness of online study environment, how to design a better curriculum, likelihood of students transfer, drop out or failure to complete the course. Not only, data analytics helps in analyzing above points but also can be helpful in predictive modeling for faculty, administrative and students groups who are looking out for genuine results about the university rankings, based on which they make their decisions. Using the dataset “Academic Ranking of World Universities, 2003-2014”, we studied and analyzed to forecast how university’s management and faculty could adapt to changes to improve their education and thereby the ranking of their universities in the upcoming years. Microsoft SQL Server Data Mining Add-ins Excel 2008 was employed as a software mining tool for predicting the trending university ranking. This research paper concentrates upon predictive analysis of university ranking using forecasting based on data mining technique. en_US
dc.language.iso en_US en_US
dc.publisher ASEE
dc.subject Analytics en_US
dc.subject Big data en_US
dc.subject Data mining en_US
dc.subject Education data en_US
dc.title Utilizing Big Data Analytics to Improve Education en_US
dc.type Presentation en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Kingston, RI en_US
dc.event.name 2016 ASEE Northeast Section Conference en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ScholarWorks


Advanced Search

Browse

My Account