Elicitation of Student Learning Experiences from Twitter using Data Mining Techniques
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2017 by IJCTT Journal | ||
| Volume-47 Number-3 |
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| Year of Publication : 2017 | ||
| Authors : Pratiba D, Samrudh J, Dadapeer, Srikanth J | ||
| DOI : 10.14445/22312803/IJCTT-V47P124 |
Pratiba D, Samrudh J, Dadapeer, Srikanth J "Elicitation of Student Learning Experiences from Twitter using Data Mining Techniques". International Journal of Computer Trends and Technology (IJCTT) V47(3):165-169, May 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Big Data analytics facilitates entities to analyze a combination of data which may be structured, semi structured or unstructured. It acts as an added value to business related information and also provides an insight about various aspects of the respective business. Big data Analytics needs excellent technology to resourcefully process huge amount of data within acceptable elapsed times. The informal conversations carried out by students on various Social Media platforms such as Facebook, Twitter etc give a lot of information on their experience of education along with a major highlight on their opinion and concerns about the system of learning. Data from these kinds of platform and environment give an unbiased view and helps in improving the experience of student learning. But, the real challenge lies in analyzing and drawing conclusion from this kind of data. Here, human interpretation of these student experiences becomes a requirement. But, the ever increasing data stresses on the need to have automatic techniques for analysis of the data. The proposed system develops a workflow to put together both, data mining techniques at a large scale and an adequate amount of qualitative analysis. This paper puts the spotlight on posts by engineering students on Twitter and its analysis to comprehend problems in their educational experiences.
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Keywords
Big Data, Social Media, Data Analysis, Student Learning Experiences.