Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/321252
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dc.date.accessioned2021-04-20T18:36:06Z-
dc.date.available2021-04-20T18:36:06Z-
dc.identifier.urihttp://hdl.handle.net/10603/321252-
dc.description.abstractStudent s education facilitates their career and growth.Educational data mining has emerged as a new research field. To begin with, a survey has been conducted to understand the need of a system which could assist the students in enhancing their learning. The positivity of responses leads to the exhaustive review of existing models focusing on present study to analyze learning behaviour of students and to predict their grades. It then studied about various constructs affecting intellectual performance of students. The research considers psychological constructs of students related to their academic performance. To identify the causal constructs, the study undergoes exploratory factor analysis followed by confirmatory factor analysis. Based on the identified constructs, primary data is collected with the help of structured questionnaire targeting final year undergraduate students.To examine the interrelationships between the factors and to study the impact of non-intellectual constructs on student s academic performance structured equation modelling has been implemented. Different models have been constructed and the models fit values showed that students academic performance can be enhanced by revising their psychometric parameters. To test the significance of the constructs, chi square test (goodness of fit measure) is performed and meta-analysis is done using random-effects model. It is implemented to identify the average weighted correlations between academic score of a student and each nonintellectual construct. It also measures heterogeneity in effect sizes, residual deviation and standard deviation measure of effect sizes. Classifying the students, according to their academic scores, as high, average and low will help in identifying the differences in their learning behaviour. In the end the recommendation rules have been generated using decision tree (ID3) algorithm suggesting on how to improve learning behaviour of students in order to enhance their intellectual performance.
dc.format.extent
dc.languageEnglish
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dc.rightsuniversity
dc.titleRecommender System for Student Learning Behaviour Based on Psychometric Data Analytics
dc.title.alternative
dc.creator.researcherBurman, Iti
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Theory and Methods
dc.subject.keywordData mining
dc.subject.keywordEducational tests and measurements
dc.subject.keywordEngineering and Technology
dc.subject.keywordLearning curve (Psychometrics)
dc.subject.keywordPsychological tests
dc.subject.keywordPsychology--Mathematical models
dc.subject.keywordPsychometrics
dc.description.note
dc.contributor.guideSom, Subhranil and Hossain, Syed Akhtar
dc.publisher.placeNoida
dc.publisher.universityAmity University, Noida
dc.publisher.institutionAmity Institute of Information Technology
dc.date.registered
dc.date.completed2020
dc.date.awarded
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Amity Institute of Information Technology

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01_title.pdfAttached File25.57 kBAdobe PDFView/Open
02_certificate.pdf128.26 kBAdobe PDFView/Open
03_preliminary pages.pdf191.59 kBAdobe PDFView/Open
04_chapter 1.pdf156.3 kBAdobe PDFView/Open
05_chapter 2.pdf209.86 kBAdobe PDFView/Open
06_chapter 3.pdf283.29 kBAdobe PDFView/Open
07_chapter 4.pdf1.2 MBAdobe PDFView/Open
08_chapter 5.pdf418.32 kBAdobe PDFView/Open
09_chapter 6.pdf454.78 kBAdobe PDFView/Open
10_chapter 7.pdf189.99 kBAdobe PDFView/Open
11_appendix.pdf132.78 kBAdobe PDFView/Open
12_references.pdf266.02 kBAdobe PDFView/Open
80_recommendation.pdf203.36 kBAdobe PDFView/Open


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