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Multilevel Analysis of Student’s Feedback using Moodle Logs in Virtual Cloud Environment


Affiliations
1 Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India
2 Department of Computer Science & Engineering, Gyan Ganga Institute of Technology & Sciences, Jabalpur, India
 

In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of 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.

In this research work, different data mining classification models are applied to analyse and predict students’ feedback based on their Moodle usage data. The models described in this paper surely assist the educators, decision maker, mentors to early engage with the issues as address by students. In this research, real data from a semester has been experimented and evaluated. To achieve the better classification models, discretization and weight adjustment techniques have also been applied as part of the pre – processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our experiments also shows that by using weight adjustment techniques like information gain and support vector machines improves the performance of classification models.


Keywords

Educational Data, Educational Data Mining, LMS, Moodle, Feedback System, Weight Adjustment Techniques.
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  • Multilevel Analysis of Student’s Feedback using Moodle Logs in Virtual Cloud Environment

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Authors

Ashok Verma
Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India
Sumangla Rathore
Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India
Santosh Vishwakarma
Department of Computer Science & Engineering, Gyan Ganga Institute of Technology & Sciences, Jabalpur, India
Shubham Goswami
Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India

Abstract


In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of 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.

In this research work, different data mining classification models are applied to analyse and predict students’ feedback based on their Moodle usage data. The models described in this paper surely assist the educators, decision maker, mentors to early engage with the issues as address by students. In this research, real data from a semester has been experimented and evaluated. To achieve the better classification models, discretization and weight adjustment techniques have also been applied as part of the pre – processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our experiments also shows that by using weight adjustment techniques like information gain and support vector machines improves the performance of classification models.


Keywords


Educational Data, Educational Data Mining, LMS, Moodle, Feedback System, Weight Adjustment Techniques.

References