Improvements for Existing Computer Adaptive Systems with Efficient Question Classification Techniques

Traditional learning methods such as reading books, pdf and course materials are being out of date and smart learning and adaptive learning is emerging with the growing technologies. The ultimate goal of this paper is to present some improvements to existing adaptive testing systems in terms of Moodle Adaptive Quiz Plugin. The major improvement is to integrate question classification with the adaptive quiz plugin. Question classification is done based on subject areas and difficulty levels of the questions. For that data mining techniques have to be taken into consideration which includes the details of data preprocessing and comparison between different classification techniques. This paper presents the detailed implementation process, pros and cons and the comparison between Decision Tree, Naive Bayes, SVM (Support Vector Machines), Random Forest and Artificial Neural Network in terms of question classification based on subject areas. And also the data preprocessing step for acquiring the train data set and accuracy scores of the above classifiers have also being descriptively presented. The classification of the questions from the difficulty levels also can be stated as another phase of this paper. A pre-answered question scheme was separated regarding difficulty levels using their accuracy scores. These questions and difficulty level classification also are done using previously mentioned classifiers and their results also included in this paper. The results of this classification could be integrated with the adaptive quiz plugin as future work. Moreover, some improvements for the existing Adaptive Quiz Plugin has also suggested in the context of this paper.