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Autor/inn/enSood, Sakshi; Saini, Munish
TitelHybridization of Cluster-Based LDA and ANN for Student Performance Prediction and Comments Evaluation
QuelleIn: Education and Information Technologies, 26 (2021) 3, S.2863-2878 (16 Seiten)Infoseite zur Zeitschrift
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ZusatzinformationORCID (Sood, Sakshi)
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1360-2357
DOI10.1007/s10639-020-10381-3
SchlagwörterMultivariate Analysis; Artificial Intelligence; Academic Achievement; Prediction; Course Selection (Students); Computer Uses in Education; Dropout Prevention
AbstractFor a productive life, education plays a critical role to fill individual life with value and excellence. Education is compulsory to provide things that individuals partake in to compete in the modern world. Predicting the academic performance of the student is the most successive research in this era. A different set of approaches and methods are incorporated to increase student performance. However, this is a challenging task due to the wrong course selection. In the proposed study, we have used the hybrid approach consisting of Cluster-based Linear Discriminant Analysis (CLDA) and Artificial Neural Network (ANN) to provide the prospective students with the motivational comments and the video recommendations by which students can choose the right subject and the comments will facilitate the students with the insight reasons of dropout opted by other students for this course. The outcomes of this study will help in the reduction of the number of dropouts. The students will be able to choose an appropriate course for performance enhancement and carrier excel. (As Provided).
AnmerkungenSpringer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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