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.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu
Jha, Sumit Kumar
Master of Science (M.S.)
College of Engineering and Computer Science
Length of Campus-only Access
Masters Thesis (Open Access)
Shao, Yuan, "Student Community Detection and Recommendation of Customized Paths to Reinforce Academic Success" (2019). Electronic Theses and Dissertations. 6304.