Design of Personalization Exam Classification Model Based on Imbalanced Class

Abstract

Currently, personalized learning has become a necessity in the learning process. Research and implementation of personalization in the planning phases and implementation phases of learning have been extensively studied. However, that research has not yet reached the application stage in the learning assessment phase. Providing homogeneous examination material to each student has not considered the characteristics of learners. Even though the achievements from the assessment phase will provide a measure of the quality of the learning process as a whole. This research has analyzed the individual characteristics model, which is derived as a benchmark for identification of the information and characteristics of the test material, which is then formulated into a classification model based on supervised learning. This study identified text dataset questions and labeled unbalanced multi-classes. This presents a challenge to carry out experiments to find the most optimal data training strategy, the results provide optimal strategy combination results Logic: ENS, Verbal: ENS, Visual: CW+RES+ENS, Natural: CW+RES+ENS. Accuracy measurement results Logic (SVM): 0.85, Verbal (LR) 0.87, Visual (LR) : 0.93, Natural (NN) 0.93.