Multilevel Analysis of Student's Feedback Using Moodle Logs in Virtual Cloud Environment

摘要

In the current digital era, education system has witness tremendous growth in data storage and efficientretrieval. Many Institutes have very huge databases which may be of terabytes of knowledge andinformation. The complexity of the data is an important issue as educational data consists of structural aswell 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 oflearning platform like Moodle have implemented to integrate the requirement of educators, administratorsand learner. Although this type of platforms are indeed a great support of educators, still mining of thelarge data is required to uncover various interesting patterns and facts for decision making process for thebenefits of the students.In this research work, different data mining classification models are applied to analyse and predictstudents’ feedback based on their Moodle usage data. The models described in this paper surely assist theeducators, 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 classificationmodels, 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 theclassifier model must be appropriate in terms of accuracy and other important evaluation measures. Ourexperiments also shows that by using weight adjustment techniques like information gain and supportvector machines improves the performance of classification models.