Advertisement

Mining CMS Log Data for Students’ Feedback Analysis

  • Ashok VermaEmail author
  • Sumangla RathoreEmail author
  • Santosh K. VishwakarmaEmail author
  • Shubham GoswamiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)

Abstract

In the current scenario of educational system, data storage and retrieval have been an important issue. Many universities have huge amount of databases which require proper mining to generate patterns and knowledge. Nowadays, several learning platforms like Moodle have implemented to achieve the need of educators, administrators, and learner. These platforms have been great assets for educators; still mining of the large data is required to uncover various interesting patterns and facts for decision-making process for the benefits of the students. This research paper examines various text classification algorithms to analyze various students’ problems. After extracting useful patterns from the database, it will be very useful for the concerned authorities and institute management in making better and informed decisions for providing solutions to all those students’ problems. The results obtained in our experiments are very useful to classify students’ problems as well as they are used to detect other interesting patterns about the Moodle CMS data.

Keywords

Educational data mining CMS Moodle Feedback system RapidMiner Classification Naive Bayes classifier 

References

  1. 1.
    Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. ElsevierGoogle Scholar
  2. 2.
    Dutt A, Ismail MA, Herawan T (2017) A systematic review on educational data mining. IEEE AccessGoogle Scholar
  3. 3.
    Dutt A, Ismail MA, Herawan T (2017) A systematic review on educational data mining. IEEE AccessGoogle Scholar
  4. 4.
  5. 5.
    Jain PS (2016) Mining social media data for understanding students learning experiences. Int J 1(2)Google Scholar
  6. 6.
    Yuan C (2014) Data mining techniques with its application to the dataset of mental health of college students. In: 2014 IEEE Workshop on advanced research and technology in industry applications (WARTIA). IEEEGoogle Scholar
  7. 7.
    Banumathi A, Pethalakshmi A (2012) A novel approach for upgrading Indian education by using data mining techniques. In: 2012 IEEE International Conference on Technology enhanced education (ICTEE), Kerala, IndiaGoogle Scholar
  8. 8.
    Xing W et al (2015) Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Comput Human Behav 47: 168–181CrossRefGoogle Scholar
  9. 9.
    Moorosi N, Marivate V (2015) Privacy in mining crime data from social media: a South African perspective. In: 2015 Second International Conference on Information Security and Cyber Forensics (InfoSec), Cape Town, South Africa, 15–17 Nov 2015Google Scholar
  10. 10.
  11. 11.
    Conijn R et al (2017) Predicting student performance from LMS data: a comparison of 17 blended courses using Moodle LMS. IEEE Trans Learn Technol 10(1):17–29CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Sir Padampat Singhania UniversityUdaipurIndia
  2. 2.Gyan Ganga Institute of Technology and SciencesJabalpurIndia

Personalised recommendations