Research Article | Open Access
Implementation of Latest Machine Learning Approaches for Students Grade Prediction
Mr. D.Sreenivasulu, Dr J Sirisha Devi, Dr.P.Arulprakash, Dr S. Venkataramana, Dr Kutubuddin Sayyadliyakat Kazi
Pages: 9887-9894
Abstract
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.
Keywords
machine learning, Student’s Performance, Prediction, Learning Analytics, Visualization.