CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Analyzing Social Media Data for Understanding Student�s Problem

Published on December 2014 by Pallavi K. Pagare
Innovations and Trends in Computer and Communication Engineering
Foundation of Computer Science USA
ITCCE - Number 3
December 2014
Authors: Pallavi K. Pagare
1be6680f-265d-4188-9d5f-17fd2afa8fb7

Pallavi K. Pagare . Analyzing Social Media Data for Understanding Student�s Problem. Innovations and Trends in Computer and Communication Engineering. ITCCE, 3 (December 2014), 17-22.

@article{
author = { Pallavi K. Pagare },
title = { Analyzing Social Media Data for Understanding Student�s Problem },
journal = { Innovations and Trends in Computer and Communication Engineering },
issue_date = { December 2014 },
volume = { ITCCE },
number = { 3 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 17-22 },
numpages = 6,
url = { /proceedings/itcce/number3/19056-2022/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Innovations and Trends in Computer and Communication Engineering
%A Pallavi K. Pagare
%T Analyzing Social Media Data for Understanding Student�s Problem
%J Innovations and Trends in Computer and Communication Engineering
%@ 0975-8887
%V ITCCE
%N 3
%P 17-22
%D 2014
%I International Journal of Computer Applications
Abstract

Social media allows the creation and interactions of user created content. Social medium places include Facebook, Twitter etc. Student's casual discussion on social media focused into their educational experience, mind-set, and worry about the learning procedure. Information from such uninstrumented environments can present valuable data to report student problem. Examining data from such a social meadiacan be challenging task. The problem of student's experiences reveal from social media sited need human analysis or Interaction. It pays attention on engineering student's Twitter posts to know problem and troubles in their educational practices. This paper proposes a workflow to put together both qualitative investigation and large-scale data mining scheme. First a sample is taken from student and then qualitative analysis conducted on that sample which is associated to engineering student's educational life. So only tweets related to engineering student is collected. It is found that engineering students encounter problems such as heavy learning load, lack of social meeting, and sleep deficiency. Based on this outcome, a multi-label classification algorithm that is Naive Bayes Multi-label Classifier algorithm is applied to categorize tweets presenting student's problems. Then decision tee algorithm is applied to make more accurate result it will perform filtering. The algorithm prepares a detector of student problems. This study presents a tactic and outcome that demonstrate how casual social media data can present insight into student's incident.

References
  1. Xin Chen, MihaelaVorvoreanu, and Krishna Madhavan. " Mning Social Media Data for Understanding Students' Learning Experiences" IEEE tarnsactions on learning Technologies, ID, DOI 10. 1109/TLT. 2013. 2296520
  2. G. Siemens and P. Long, "Penetrating the fog: Analytics in learning and education," Educause Review, vol. 46, no. 5, pp. 30–32, 2011
  3. 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.
  4. 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.
  5. R. Ferguson, "The state of learning analytics in 2012: AReview and future challenges," Knowledge Media Institute, Technical Report KMI-2012-01, 2012.
  6. 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.
  7. G. Tsoumakas, I. Katakis, and I. Vlahavas, "Mining Multi-label data,"Data mining and knowledge discovery handbook, pp. 667-685, 2010
  8. Dipak V Patil and R S Bichkar. Article: Issues in Optimization of Decision Tree Learning: A Survey. International Journal of Applied Information Systems 3(5):13-29, July 2012. Published by Foundation of Computer Science, New York, USA
Index Terms

Computer Science
Information Sciences

Keywords

Social Networking Web-text Analysis Education Social Network Analysis Computer And Education Data Mining