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.
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Verma, A., Rathore, S., Vishwakarma, S.K., Goswami, S. (2019). Mining CMS Log Data for Students’ Feedback Analysis. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Springer, Singapore. https://doi.org/10.1007/978-981-13-1165-9_39
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DOI: https://doi.org/10.1007/978-981-13-1165-9_39
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