Development And Application Of Predictive Models For Students Academic Performance In Higher Education Institutions

dc.contributor.guideSridharan, R and K, Ratna Kumar
dc.coverage.spatial
dc.creator.researcherK K, Eldose
dc.date.accessioned2023-01-02T04:55:55Z
dc.date.available2023-01-02T04:55:55Z
dc.date.awarded2021
dc.date.completed2021
dc.date.registered2012
dc.description.abstractHigher education institutions stimulate and encourage the interconnection of learning, newlineresearch and innovation. As higher education is a significant contributor to the country s newlineability to compete in the global marketplace, it is critical to our economic strength, social newlinewell-being, and position as a world leader. Students academic performance is the source newlineof great concern and interest of researchers, parents, institutions, and government. newlineAccording to the National Board of Accreditation (NBA) in India, academic performance newlineof students forms an important criterion in the assessment of the quality of engineering newlineprogrammes. The present research focuses on determining the factors that influence the newlineacademic performance of students in engineering institutions. Predictive models are newlinedeveloped that can aid students and educators to take preventive measures for achieving newlinebetter performance. newlineThe first study deals with the development and analysis of a Markov model to evaluate newlinethe academic performance of students in the undergraduate engineering degree newlineprogramme in an institution located in South India. Initially, the transition probability newlinematrix is developed for the academic progress of students using the frequency matrix newlineobtained from historical data. The probability of movement of students between newlinesuccessive years of the academic programme is computed using the transition newlineprobabilities. Applying the Markovian process, the expected number of years spent in a newlinespecified transient state before absorption and the probabilities of absorption are newlinedetermined. The analysis of results reveals that remedial classes and mentoring activities newlinehave played a significant role in increasing the graduation probability as well as newlinedecreasing the withdrawal probability. newlineThe second study focuses on identification and prioritization of students requirements newlineand NBA parameters with specific reference to the postgraduate programmes offered by newlinean institute of national importance in South India newline
dc.description.note
dc.format.accompanyingmaterialDVD
dc.format.dimensions
dc.format.extent
dc.identifier.urihttp://hdl.handle.net/10603/434585
dc.languageEnglish
dc.publisher.institutionDepartment of Mechanical Engineering
dc.publisher.placeCalicut
dc.publisher.universityNational Institute of Technology Calicut
dc.relation
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering
dc.subject.keywordEngineering Mechanical
dc.subject.keywordMachine learning
dc.subject.keywordData mining
dc.titleDevelopment And Application Of Predictive Models For Students Academic Performance In Higher Education Institutions
dc.title.alternative
dc.type.degreePh.D.

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