Abstract
Increase in computer usage for different purposes in different fields has made the computer important to learn things. Machine learning made systems to learn things and work accordingly on their own. Among the different fields that use machine learning, the education field is one. In the education field, machine learning has led to the advent of a digital-enabled classroom, speech recognition, adaptive learning techniques, and development of artificial instructor. Along with this, the prediction has its importance. In the education field, the main problem is students drop out. The machine learning predictive modeling approach can be used to identify the students who are at-risk and inform the instructor and students before reducing the dropouts. The main intention of this paper is to model a system that could be a solution to reduce the drop-outs and increase the education standards in students by early predicting their risk in a course.
Highlights
The world and the society around us say the importance of education through the inventions of new things each second
This paper focuses mainly to predict different factors that affect student education
It is a task in which based on the example input-output pair called labelled dataset, the learning function maps input to an output
Summary
The world and the society around us say the importance of education through the inventions of new things each second. The problem here is since everyone is not intelligent, based on their understanding level, listening skills, attending the classes, interest in the course, there will be an effect in percentages or marks of the students. This results in dropouts in most schools and colleges. In the year 1959, Arthur Samuel coined the name Machine Learning which explains the study and the construction of algorithms These algorithms are designed in such a way that, they can learn and improve themselves when exposed to the new data.
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