Predicting Academic Performance of Students in Higher Institutions with k-NN Classifier

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dc.contributor.authorOmisore, O.M
dc.contributor.authorAzeez, N.A
dc.date.accessioned2019-02-11T11:36:16Z
dc.date.available2019-02-11T11:36:16Z
dc.date.issued2016
dc.description.abstractEducational Information is the pre-processed data of profile and characters in any institution of learning. The volume of such data depends on the level of learning in the institution. As a result, in several institutions of higher learning, the massive information hosted bulges out of their database and thereby making it very difficult to establish presence of common consistent interesting patterns needed for decision making. In this paper, k-Nearest Neighborhood (k-NN) classifier is adopted for predicting academic performance of students in higher institution. Case study of returning students in department of computer science, University of Lagos, Nigeria is observed. Experimental result shows the classifier can predict performance of students who can be distinctive, hapless or intermediate in their studies.en_US
dc.identifier.citationOmisore, O. M., Azeez, N. A (2016) “Predicting academic performance of students in higher institutions with k-nn classifier” Journal of the Nigerian Association of Mathematical Physics, Vol. 34 (March 2016), pp 249-262en_US
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/3689
dc.language.isoenen_US
dc.publisherJournal of the Nigerian Association of Mathematical Physicsen_US
dc.subjectEducation, Data Mining, Data Classification, Predictive Model, Nearest Neighborhood, Performance Predictionen_US
dc.titlePredicting Academic Performance of Students in Higher Institutions with k-NN Classifieren_US
dc.typeArticleen_US
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