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Reseach Article

Analyzing Social Media Data to Explore Students’ Academic Experiences

by Priya Lande, Vipul Dalal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 4
Year of Publication: 2016
Authors: Priya Lande, Vipul Dalal
10.5120/ijca2016908258

Priya Lande, Vipul Dalal . Analyzing Social Media Data to Explore Students’ Academic Experiences. International Journal of Computer Applications. 135, 4 ( February 2016), 13-16. DOI=10.5120/ijca2016908258

@article{ 10.5120/ijca2016908258,
author = { Priya Lande, Vipul Dalal },
title = { Analyzing Social Media Data to Explore Students’ Academic Experiences },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 4 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume135/number4/24036-2016908258/ },
doi = { 10.5120/ijca2016908258 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:34:49.159626+05:30
%A Priya Lande
%A Vipul Dalal
%T Analyzing Social Media Data to Explore Students’ Academic Experiences
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 4
%P 13-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The casual conversational style used by the students on any front stage environment can educate extensively about their learning process. The collection of data from such an open environment can bring out many important and unknown factors about students’ behaviour, their opinions, their feelings their concerns pertaining to their educational system. The inspection of such data can be said to be very provocative. The reflection of students’ feelings over the social network, however, has to undergo the human eye to get properly interpreted, which is possible but upto a certain extent, as a result of ever-growing data. In this paper, problems of engineering students have been considered. This has been worked upon by analysing engineering students’ tweets from the hashtag #enggproblems on Twitter. Analysis was carried out over 15,000 tweets. These problems were related to heavy study load, negative emotions, sleep problems, lack of social engagement, diversity issues etc. A multi-label classifier was executed to classify and categorize tweets. This technique can dig up into the casual conversations of students and educate about the factors that affect the learning process of students.

References
  1. 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 co-operative work, 2013, pp. 357-362
  2. M.Clark, S.Sheppard, C.Atman, L. Fleming, R. Miller, R. Stevens, R. Streveler, K. Smith, “Academic pathway study: Processes and Realities”, in Proceedings of American Society for Engineering Education and Annual Conference and Exposition, 2008.
  3. 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. Yasuhara and D. Lund, “Enabling Engineering Students’ Success: the final report for the Center for the Advancement of Engineering Education,” Morgan and Claypool Publishers, Center for the Advancement of Engineering Education, 2008.
  4. E. Goffman, The Presentation of Self in Everyday Life. Lightning Source Inc, 1959.
  5. E. Pearson, “All The World Wide Web’s a stage: the performance of identity in online social networks,” First Monday, vol. 14, no. 3, pp. 1-7, 2009.
  6. R. Fergusson, the state of learning analytics in 2012: “A review and future challenges,” Knowledge Media institutes, Technical report, KMI-2012-01, 2012.
  7. Xin chen, Krishna Madhvan, Mihaela Vorvoreanu: Mining Social Media Data for Understanding Students’ Learning Experiences in IEEE Transactions on Learning Technologies, 2014.
  8. D. Davidov, O. Tsur and A. Rappoport, “Enhanced Sentiment learning using Twitter Hashtags and smileys,” in Proceedings of 23rd International Conference on Computational Linguistics: Posters, 2010, pp. 241-249.
  9. A. Go, R. Bhayani and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Stanford, pp.1-12, 2009.
  10. B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu and M. Demirbas, “Short text classification in twitter to improve information filtering,” in Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, New York, NY, USA, 2010, pp. 241-842.
Index Terms

Computer Science
Information Sciences

Keywords

Naïve bayes multi-label classifier twitter analysis education.