Volume 6, Issue 4 (3-2020)
Human Information Interaction 2020, 6(4): 42-49 |
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Babaee M, Rastegarpour H. Opinion Mining, Social Networks, Higher Education. Human Information Interaction 2020; 6 (4)
URL: http://hii.khu.ac.ir/article-1-2871-en.html
URL: http://hii.khu.ac.ir/article-1-2871-en.html
Kharazmi University
Abstract: (2528 Views)
Background and Aim: With the advent of technology and the use of social networks such as Instagram, Facebook, blogs, forums, and many other platforms, interactions of learners with one another and their lecturers have become progressively relaxed. This has led to the accumulation of large quantities of data and information about students' attitudes, learning experiences, opinions, and feelings about the teaching-learning process. Opinion mining is one of the growing applications of data mining knowledge which by discovering patterns and models in users' opinions could help higher education to well plan, make well-versed policies, and to have fruitful management. Therefore, the purpose is to describe the applications of opinion mining to advance the excellence of higher education in Iran.
Methodology: Research method is an applied qualitative one. Population comprises of all the research and books associated with opinion mining that were available in reputable databases of IEEE, SSCI, Elsevier, CIVILICA, and Science Direct during the research data collection period in the spring of 2019. Using the convenience sampling method, 35 articles were selected with the aim of reviewing and describing educational opinion mining and analyzing its application in higher education.
Results: Based on the studies, it was found that opinion mining can be used as an effective tool in three parts: 1. Improving student performance; 2. Designing better online courses; and 3. Evaluating the efficiency of the educational activities of universities, professors, and various programs. Therefore it can also help to recognize the existing shortcomings, strengths, and weaknesses.
Conclusion: Higher education can scrutinize the sentiments, opinions, and ideas generated by students through opinion mining. Exploring this valuable information enables educational institutions, principals, and educators to make more appropriate decisions in education and improve the quality of educational services which leads to the improvement of academic performance and better career choices for individuals.
Methodology: Research method is an applied qualitative one. Population comprises of all the research and books associated with opinion mining that were available in reputable databases of IEEE, SSCI, Elsevier, CIVILICA, and Science Direct during the research data collection period in the spring of 2019. Using the convenience sampling method, 35 articles were selected with the aim of reviewing and describing educational opinion mining and analyzing its application in higher education.
Results: Based on the studies, it was found that opinion mining can be used as an effective tool in three parts: 1. Improving student performance; 2. Designing better online courses; and 3. Evaluating the efficiency of the educational activities of universities, professors, and various programs. Therefore it can also help to recognize the existing shortcomings, strengths, and weaknesses.
Conclusion: Higher education can scrutinize the sentiments, opinions, and ideas generated by students through opinion mining. Exploring this valuable information enables educational institutions, principals, and educators to make more appropriate decisions in education and improve the quality of educational services which leads to the improvement of academic performance and better career choices for individuals.
References
1. Abadeh S, M., Mahmoudi, S., TaherParvar, M. (2012). Applied Data Mining, Tehran:Niaz Danesh Publications.
2. Abdelrazeq, A., Janßen, D., Tummel, C., Jeschke, S., & Richert, A. (2016). Sentiment Analysis of Social Media for Evaluating Universities. In Automation, Communication and Cybernetics in Science and Engineering 2015/2016 (pp. 233-251). Springer, Cham. [DOI:10.1007/978-3-319-42620-4_19]
3. Abdous, M., He, W., & Yen, C.-J. (2012). Using data mining for predicting relationships between online question theme and final grade. Educational Technology & Society, 15(3),77-88.
4. Adamopoulos, P. (2013). What makes a great MOOC? An interdisciplinary analysis of student retention in online courses.
5. Alban, M., & Mauricio, D. (2019). Predicting university dropout through data mining: A Systematic Literature. Indian Journal of Science and Technology, 12(4), 1-12. [DOI:10.17485/ijst/2019/v12i4/139729]
6. Balahadia, F. F., Fernando, M. C. G., & Juanatas, I. C. (2016, May). Teacher's performance evaluation tool using opinion mining with sentiment analysis. In 2016 IEEE Region 10 Symposium (TENSYMP) (pp. 95-98). IEEE. [DOI:10.1109/TENCONSpring.2016.7519384]
7. Barracosa, J., & Antunes, C. (2011). Anticipating teachers' performance. Proc. of Int. W. on Knowl. Discovery on Educational Data (KDDinED@ KDD). ACM.
8. Baruah, T. D. (2012). Effectiveness of Social Media as a tool of communication and its potential for technology enabled connections: A micro-level study. International Journal of Scientific and Research Publications, 2(5), 1-10.
9. Chen, X., Vorvoreanu, M., & Madhavan, K. (2014). Mining social media data for understanding students' learning experiences. IEEE Transactions on Learning Technologies, 7(3), 246-259. [DOI:10.1109/TLT.2013.2296520]
10. Federkeil, G. (2013). Internationale hochschulrankings-eine kritische bestandsaufnahme. Beiträge zur Hochschulforschung, 35(2), 34-48.
11. Garcia-Saiz, D., Palazuelos, C., & Zorrilla, M. (2014). Data mining and social network analysis in the educational field: An application for non-expert users. In Educational Data Mining (pp. 411-439). Springer, Cham. [DOI:10.1007/978-3-319-02738-8_15]
12. Grljević, O., Bošnjak, Z., & Kovačević, A. (2020). Opinion mining in higher education: a corpus-based approach. Enterprise Information Systems, 1-26. [DOI:10.1080/17517575.2020.1773542]
13. Haddi, E., Liu, X., & Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26-32. [DOI:10.1016/j.procs.2013.05.005]
14. Hisserich, J., & Primsch, J. (2010). Wissensmanagement in 140 Zeichen: Twitter in der Hochschullehre. Community of Knowledge (Hg.), Nächste Generation Wissensmanagement. Wie sich der Umgang mit Wissen und Kommunikation wandelt, 11(2010), 23-35. [In Germany]
15. Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133-145 [DOI:10.1016/j.compedu.2012.08.015]
16. Immaculate Mary, C., & Pushpavalli, R. (2017, November). Automation of Feedback Analysis for Educational Enhancement. In Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017-Dec 15th-16th 2017) organized by Sona College of Technology, Salem, Tamilnadu, India. [DOI:10.2139/ssrn.3126655]
17. Jothi, A. J., Santiago, M. S., & Arockiam, L. (2016). A Methodological Framework to Identify the Students' Opinion using Aspect based Sentiment Analysis. Int. J. Eng. Res, 5. [DOI:10.17577/IJERTV5IS020528]
18. Karami M. (2008). Application of data-mining and text-mining analyzer tools in agility on healthcare organizations. Jha, 10 (30):15-20. [In Perian]
19. Kechaou, Z., Ammar, M. B., & Alimi, A. M. (2011, April). Improving e-learning with sentiment analysis of users' opinions. In 2011 IEEE Global Engineering Education Conference (EDUCON) (pp. 1032-1038). IEEE. [DOI:10.1109/EDUCON.2011.5773275]
20. Maragoudakis, M., Loukis, E., & Charalabidis, Y. (2011, August). A review of opinion mining methods for analyzing citizens' contributions in public policy debate. In International Conference on Electronic Participation (pp. 298-313). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-23333-3_26]
21. Mathapati, S., & Manjula, S. H. (2017). Sentiment analysis and opinion mining from social media: A review. Global Journal of Computer Science and Technology.
22. Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in human behavior, 31, 527-541. [DOI:10.1016/j.chb.2013.05.024]
23. Ramesh, A., Goldwasser, D., Huang, B., Daumé III, H., & Getoor, L. (2013, December). Modeling learner engagement in MOOCs using probabilistic soft logic. In NIPS Workshop on Data Driven Education (Vol. 21, p. 62).
24. Ray, S., & Saeed, M. (2018). Applications of educational data mining and learning analytics tools in handling big data in higher education. In Applications of Big Data Analytics (pp. 135-160). Springer, Cham. [DOI:10.1007/978-3-319-76472-6_7]
25. Saa, A. A. (2016). Educational Data Mining & Students' Performance Prediction. International Journal of Advanced Computer Science & Applications, 1, 212-220.
26. Shelke, N. M., Deshpande, S., & Thakre, V. (2012). Survey of techniques for opinion mining. International Journal of Computer Applications, 57(13), 0975-8887.
27. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
28. Song, D., Lin, H., & Yang, Z. (2007, September). Opinion mining in e-learning system. In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007) (pp. 788-792). IEEE. [DOI:10.1109/NPC.2007.51]
29. Smeureanu, I., & Bucur, C. (2012). Applying supervised opinion mining techniques on online user reviews. Informatica Economică, 16(2), 81-91.
30. Wen, M., Yang, D., & Rose, C. (2014, July). Sentiment Analysis in MOOC Discussion Forums: What does it tell us?. In Educational data mining 2014.
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