International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
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A Survey on Student’s Academic Experiences Using Social Media Data Sushmita Kumari 1, Sonal Rai 2, Dr.Shiv Kumar3 M.Tech Scholar, Department of CSE, Lakshmi Narain College of Technology & Excellence, Bhopal (M.P), India1 Assistant Professor, Department of CSE, Lakshmi Narain College of Technology & Excellence, Bhopal (M.P), India2 Professor & Head, Department of CSE, Lakshmi Narain College of Technology & Excellence, Bhopal (M.P), India3 -----------------------------------------------------------------------***-------------------------------------------------------------------------Abstract: Student’s social media behaviour reveals about their day-to-day life. Students post their experiences on social networking sites be it personal or academic. Analysis of these posts, however, is not an easy task. Pure manual analysis is not fruitful as data increases at a rapid rate. There is a workflow developed by the survey topic that assimilates the qualitative analysis and data mining techniques. The focus is primarily on engineering students’ posts so their problems can be analyzed. This uses the approach of multi-label classification, which is due to building of categories among students’ problems-heavy study load, sleep deprivation, lack of social engagement, etc. This enables social media to shed light on students’ academic experiences.
used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration Data mining can be viewed as a result of the natural evolution of information technology. Data mining is iterative process.
Keyword: Education, computers and education, web-text analysis, Social Networking.
Data transformation: In this step data will be transformed into form that is appropriate for mining.
1. Introduction
Data mining: In this step some intelligent methods are applied for extracting data patterns.
The online social life of people defines the complete picture of their life experiences. There is a lot that can be analyzed and explored beneath this social media data of the people. Student’s learning experiences can be studied from their online posts. But, the problem arises when these posts are counted on and on and it becomes a tedious job to study them. Hence, pure manual analysis cannot alone result out into productivity. It needs use of data mining techniques for the analysis. Students’ online conversations reveal aspects of their experiences that are not easily observed in formal classroom settings. Traversal of engineering students’ informal conversations to trace their root problems hence is essential[1]. 1.1 Overview of Data Mining and Social Media
Data cleaning: It is a process of removing noise and inconsistent data. Data integration: In this step data from multiple sources are combined. Data selection: In this step data relevant for mining task is selected.
Pattern evaluation: In this step truly interesting patterns representing knowledge based on some interestingness measure are identified. Knowledge presentation: In this step visualization and knowledge representation techniques are used to present the mined knowledge to the user. A social network can be defined as a set of people, organization or other social entities connected by set of social relationship such as friendship, co-working or information exchange. Social network analysis focuses on the analysis of the pattern of relationships among people, organization, states and such social entities. In this paper a survey of work done in the field of social network analysis is done and this paper also concentrates on the future trends in research on social network analysis.[9]
Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to availability of large amount of data and imminent need for turning such data into useful information and knowledge. Data mining is the process of digging through data and looking meaningful trends and patterns. The information and knowledge gained can be
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