Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naive Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Lately, personalized learning approach attracted the attention of so many researchers due to its capacity to improve the quality of educational system. This approach provides opportunities to maximize the potential of all students based on their profile. This indicates the necessity of grouping learners! profiles appropriately in order to optimize contribution of personalized learning approach in achieving learning objective. The problems occurred when classifying learners! profile especially when dealing with large number of learners, restricted time to classify, and requirement of authentic data. To solve the problem, we proposed the implementation of Bloom's taxonomy-based serious game as an assessment tool replacing paper-based tool for the gameplay data collection. Three different methods namely: BN, NB, and J48 were implemented to obtain the highest accuracy of classification. Our study finds that the NB classifier gives the highest percentage accuracy that is 92.31%. This classifier has the similar accuracy with BN but with lower error rate. In view of the strength of agreement, the result is categorized Very Good (kappa = 0.85).