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Student Community Detection and Recommendation of Customized Paths to Reinforce Academic Success
| Title: | Student Community Detection and Recommendation of Customized Paths to Reinforce Academic Success. |
 47 views 20 downloads |
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| Name(s): |
Shao, Yuan, Author Jha, Sumit Kumar, Committee Chair Zhang, Wei, Committee Member Zhang, Shaojie, Committee Member University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2019 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | 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. | |
| Identifier: | CFE0007529 (IID), ucf:52623 (fedora) | |
| Note(s): |
2019-05-01 M.S. Engineering and Computer Science, Computer Science Masters This record was generated from author submitted information. |
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| Subject(s): | educational data mining -- community detection -- course recommendation -- student -- academical success | |
| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0007529 | |
| Restrictions on Access: | public 2019-05-15 | |
| Host Institution: | UCF |
