A Review on Machine Learning approaches in the education sector with Real-Time Data

Abstract

The mounting role of Machine Learning (ML) has transformed the scope and paradigm of education amongst its different branches. Proper implementation of the machine learning techniques in the education sector facilitates the students to perform their actions in a better way. Machine learning techniques train the machine to complete the task automatically by using a learning process. Mathematical models using machine learning algorithms are used to analyze, predict and curate decisions. These decisions are taken without relying on the explicitly defining system, models, and parameters. These days' smart services like E-learning, Internet of things (IoT), and cloud-based models are used frequently in the education sector. There are many factors affecting education. In this review, various educational frameworks have been discussed to see the role of machine learning in it. These frameworks demonstrate that during the learning process parameters like students' mental health, and teaching effectiveness makes difference to the students. Parameters affecting student mental health have been discussed based on the existing studies. Teaching effectiveness on the other hand is a very crucial parameter that needs to be considered during the class. So, to see the effect of these parameters various educational frameworks have been analyzed. After analyzing the educational framework and the above parameters, future research problems based on framework analysis have been discussed. These research problems will give a new direction to the educational sector including the role of machine learning in it.