文章基本信息

标题：Implementation of Latest Machine Learning Approaches for Students Grade Prediction
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作者：D.Sreenivasulu ; J Sirisha Devi ; P.Arulprakash 等
期刊名称：International Journal of Early Childhood Special Education
电子版ISSN：1308-5581
出版年度：2022
卷号：14
期号：3
页码：9887-9894
DOI：10.9756/INT-JECSE/V14I3.1141
语种：English
出版社：International Journal of Early Childhood Special Education
摘要：Education is a necessary for a fruitful and happy life, and it enhances the quality of a person's existence in both worth and excellence. Self-confidence and the ability to participate in today's world are two reasons why education is so important. Education has had to deal with several difficulties over the years. In order to improve the quality of learning, several teaching and learning approaches are proposed. Computers and mobile devices are used in every aspect of daily life, and numerous materials may be found online at any time. When it comes to teaching and learning, Artificial Intelligence (AI) has had a remarkable rise in many areas including education. To improve learning and teaching, higher education institutions are using technologies into their traditional methods. Education systems are faced with difficult issue of predicting students' performance semester after semester. Education systems contain a ton of information that can be used by teachers to develop effective teaching practises. The primary goal of this research is to demonstrate that it is possible to train and model a dataset, and that a reliable prediction model can be built. Among other things, this research intends to discover the most significant factors that influence student achievement and to identify the most appropriate machine-learning method to forecast their performance. Data from student registries and web - based learning platforms are the most commonly used datasets in academic research. The importance of ML approaches in predicting at-risk pupils and dropout rates has been demonstrated, and this has led to an increase in student achievement.
关键词：machine learning;Student’s Performance;Prediction;Learning Analytics;Visualization