Abstract

Educational Data Mining (EDM) is a research area that analyzes educational data and extracts interesting and unique information to address education issues. EDM implements computational methods to explore data for the purpose of studying questions related to educational achievements. A common task in an educational environment is the grouping of students and the identification of communities that have common features. Then, these communities of students may be studied by a course developer to build a personalized learning system, promote effective group learning, provide adaptive contents, etc. The objective of this thesis is to find an approach to detect student communities and analyze students who do well academically with particular sequences of classes in each community. Then, we compute one or more sequences of courses that a student in a community may pursue to higher their chances of obtaining good academic performance.

Notes

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Graduation Date

2019

Semester

Spring

Advisor

Jha, Sumit Kumar

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0007529

URL

http://purl.fcla.edu/fcla/etd/CFE0007529

Language

English

Release Date

May 2019

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Restricted to the UCF community until May 2019; it will then be open access.

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