Elicitation of Student Learning Experiences from Twitter using Data Mining Techniques

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-47 Number-3
Year of Publication : 2017
Authors : Pratiba D, Samrudh J, Dadapeer, Srikanth J
DOI :  10.14445/22312803/IJCTT-V47P124

MLA

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.

References
[1] G. Siemens and P. Long, “Penetrating the fog: Analytics in learning and education,” Educause Review, vol. 46, no. 5, pp. 30–32, 2011.
[2] Cheng mingzhi, Xin Yang, bao Jingbing, Wang Cong, Yang Yixian,: ”A Random Walk Method for Sentiment Classification”, proceedings of Second International Conference on Future Information technology and management Engineering,2013 IEEE conference, Sanya, Dec 13-14,2013,pp.327-330
[3] David Ediger, Karl Jiang,Jason Riedly, David A Bader, Courtney Corley Rob Farber William N Reynolds,”Masisve Social Network Analysis: “Mining Twitter for Social Good”, IEEE 39th International Conference on Parrel Processing, San Dego CA, Sep 13-16,2013,pp.583-593
[4] M. Rost, L. Barkhuus, H. Cramer, and B. Brown, “Representation and communication: challenges in interpreting large social media datasets,” in Proceedings of the 2013 conference on Computer supported cooperative work, 2013, pp. 357–362.
[5] M. Clark, S. Sheppard, C. Atman, L. Fleming, R. Miller, R. Stevens, R. Streveler and K. Smith, “Academic pathways study: Processes and realities,” in Proceedings of the American Society for Engineering Education Annual Conference and Exposition, 2008.
[6] C. J. Atman, S. D. Sheppard, J. Turns, R. S. Adams, L. Fleming, R. Stevens, R. A. Streveler, K. Smith, R. Miller, L. Leifer, K. Ya suhara, and D. Lund, “Enabling engineering student success: The final report for the Center for the Advancement of Engineering Education,” Morgan & Claypool Publishers, Center for the Advancement of Engineering Education, 2010.
[7] Loo Hanley, Timothy Ong Chee Aik, Raymond Wee Keat Kheng & Lim See Yew, “Mining Twitter Data to understand student behavior” IEEE 63rd Annual Conference International Council for Educational Media, Myanmar, c 5-8.2011,125-223
[8] R. Ferguson, “The state of learning analytics in 2012: A review and future challenges,” Knowledge Media Institute, Technical ReportKMI-2012-01, 2012.
[9] Pallavi K., Pagare Department of Computer Engineering, MET?s Institute of Engineering, Nashik, Savitribai Phule Pune University, Maharashtra, India, “Analyzing Social Media Data for Understanding Student’s Problem International Journal of Computer Applications”(0975 –8887) Innovations and Trends in Computer and Communication Engineering (ITCCE-2014)
[10] Huang Sui, You Jianpinh, Zhang Hongxian, Zhou Wei, ”Sentiment Analysis of Chinese Micro-blog using Semantic Sentiment Space Model”, Proceeding of 2nd International Conference on Computer Science and network technology Guangzhou, China, Jul 12-26.2012, Vol. 1,pp.512-614
[11] Seyed-Alii, Bahrainian, Andreas Dengael, ” sentiment Analysis using Sentiment features”, Proceeding of International Conference on Web Intelligence(WI) and Intelligent Agent Technology(IAT), Germany, Aug 18-25.2012,pp.1040-1050
[12] Rabia Batool Asad Masood Khattak, Jahanzeb maqbool and Sungyoung Lee, kung Hee,” Precise Tweet Classification and Sentimantal Analysis”, Procedding of International Joint Conference on computer Science, South Korea, Dec 12-15.2012,pp.1204-1236
[13] Beiming Sun, Vincent TY, ”AnalysingSentimental Influence of Posts on Social Netwroks”,Proceedings of IEEE 18th International Conference on Computer supported Cooperative Work, Cairo, Egypt, Sept 29-30,2010.pp,23-24
[14] Ana Mihanovic,Hrvoje Gabelica, Zagreb, Croatia, ”Big Data and Sentiment Analysis using KNIME: Online Reviews vs Social Media ” ,Proceedingsof International Conference, Croatia, Mar 30-31,2010.pp.345-360
[15] F. Santos-Sanchez, Member, IEEE and A. Mendez-Vazquez .Members, IEEE, “Sentimental Analysis for e-services” IIAI 3rd International Conference on Advanced Applied Informatics , Kigali, Uganda, Jan 12-13,2010.pp.1120-1134
[16] R. Baker and K. Yacef, “The state of educational data mining in 2009: A review and future visions,” Journal of Educational Data Mining, vol. 1, no. 1, pp. 3–17, 2009.

Keywords
Big Data, Social Media, Data Analysis, Student Learning Experiences.