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Mining CMS Data to Understand Students' Learning Issues

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Prakhar Gautam, Santosh K. Vishwakarma
10.5120/ijca2017914519

Prakhar Gautam and Santosh K Vishwakarma. Mining CMS Data to Understand Students' Learning Issues. International Journal of Computer Applications 168(10):38-44, June 2017. BibTeX

@article{10.5120/ijca2017914519,
	author = {Prakhar Gautam and Santosh K. Vishwakarma},
	title = {Mining CMS Data to Understand Students' Learning Issues},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {10},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {38-44},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume168/number10/27914-2017914519},
	doi = {10.5120/ijca2017914519},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Students face a lot of problems in their college/engineering life. CMS (Content Management System) is a platform for students to post their problems and let the authorities know what exactly their issues are. The data collected from students is huge. It’s important to extract some useful ‘knowledge’ from this data. Data Mining, which is a process of extracting useful information from a huge dataset, is applied to the CMS data to understand students' learning issues. This way, they can have a better future and a good academic career. Traditionally, educational researchers have been using methods such as surveys, interviews, to collect data, which is very time consuming and inefficient. Also, these methods have not given much insight into students' problems. Researchers have also used social media data, but the social media data is unreliable, unauthentic and mostly anonymous. In this dissertation work, the focus is on mining CMS data, which is authentic and real, as it doesn't allow users to go anonymous. CMS data is much more reliable as compared to other platforms.

In this dissertation work, data mining technique known as Classification (where the Engineering students' problems are classified into certain classes) is used to implement a model where students' problems can be analysed which they face in their day to day college life, and also suggest the solutions for the same.

The knowledge extracted after applying Data Mining algorithms will be very useful for policy makers and educators in making informed decisions. The data generated by engineering students in future can also be mined and solutions can be provided instantly.

References

  1. 80% of engineers in India unemployable http://www.thehindubusinessline.com/economy/over-80-engineering-graduates-in-indiaunemployablestudy/article8147656.ece
  2. M. V. K. M. Xin Chen, "Mining Social Media Data for Understanding Students’ Learning Experiences," IEEE Transactions on Learning Technologies, vol. 7, no. 3, pp. 246 - 259, 06 January 2014.
  3. C. Yuan, "Data mining techniques with its application to the dataset of mental health of college students," in Advanced Research and Technology in Industry Applications (WARTIA), 2014 IEEE , Ottawa, ON, Canada, 29-30 Sept. 2014.
  4. A. P. A. Banumathi, "A novel approach for upgrading Indian education by using data mining techniques," in Technology Enhanced Education (ICTEE), 2012 IEEE International Conference, Kerala, India, 01 June 2012.
  5. R. Z. X. S. Y. Bo Guo, "Predicting Students Performance in Educational Data Mining," in Educational Technology (ISET), 2015 International Symposium , Wuhan, China, 27-29 July 2015.
  6. N. M. Nyalleng Moorosi, "Privacy in mining crime data from social Media: A South African perspective," in Information Security and Cyber Forensics (InfoSec), 2015 Second International Conference , Cape Town, South Africa, 15-17 Nov. 2015.
  7. Data Mining Concepts. Classification: https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/classify.htm#DMCON004
  8. Naive Bayes Archives - Analytics Vidhya https://www.analyticsvidhya.com/blog/2015/09/naivebayes-explained
  9. RapidMiner www.rapidminer.com
  10. Content Management System (CMS) http://searchcontentmanagement.techtarget.com/definition/content-management-system-CMS

Keywords

Students’ problems, Engineering Students’, Data Mining, RapidMiner, Text Mining, CMS, Classification, Naive Bayes Classifier.