文章基本信息

标题：Prediction Model on Student Performance based on Internal Assessment using Deep Learning
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作者：Sadiq Hussain ; Zahraa Fadhil Muhsion ; Yass Khudheir Salal 等
期刊名称：International Journal of Emerging Technologies in Learning (iJET)
印刷版ISSN：1863-0383
出版年度：2019
卷号：14
期号：08
页码：4-22
DOI：10.3991/ijet.v14i08.10001
出版社：Kassel University Press
摘要：Educational Data Mining plays a crucial role in identifying academically weak students of an institute and helps to develop different recommendation system for them. Students from three colleges of Assam, India were considered in our research which their records were run on deep learning using sequential neural model and adam optimization method. The paper compared other classification methods such as Artificial Immune Recognition System v2.0 and Adaboost, to find out the prediction of the results of the students. The highest classification rate was 95.34% produced by the deep learning techniques. The Precision, Recall, F-Score, Accuracy, and Kappa Statistics Performance were calculated as a statistics decisions to find the best classification methods. The dataset used in this paper was 10140 student records. Directing the student for their future plan comes from discovering the hidden patterns by using Data Mining techniques.
关键词：Educational Data Mining;Deep Learning;Classification;academically weak students