Social Network Analysis in Education: A Study

Abstract

These days social media sites have been gaining huge attention. Millions of people are accessing social media like Facebook, Instagram, Twitter, etc. Because of very affordable Internet, people are spending hours on it. People are taking interest on social network for information, user’s opinion on diverse subject matters. A wide range of techniques of data mining allowed us to detect useful data from massive database by some of the algorithms where we can find the patterns of users’ thinking and trend on social network. It also reduces difficulties by the time of discovering contents. Not just that, a huge data also comes from the educational system. This data is used to realize the knowledge in decision-making. Educational data mining methods are designed to understand and measure the performance of students and also helpful to study students’ behaviour. Data mining technique is also important to survey the history and application in traditional educational system, intelligent teaching system, e-learning, and web-based educational system. Over a time period, education in the rural areas has improved and available. Still there are lot of countries cannot stand uniquely. So the system of education has to bear a major alteration by redesigning its framework. To solve that problem, different sectors have evolved in educational environment changes for the further development in urban areas. In the educational environment, different attributes are associated between each other like the location and type of the college, groups, courses, etc. By the mining technique, the data will find the unknown rules, and it will analyse which is suitable and can be built for academic planning in higher learning. It is helpful for a proper understanding of the educational environment different sides to the course construction and other improvement for student’s performance theoretical areas. In this chapter, there are used data mining technique and strong rules in education environment which identify and understand the pattern of students’ success in different areas. Analysing and prediction from data are covered in this chapter.