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)


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.


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


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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

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