A Novel Hybrid Learning Achievement Prediction Model: A Case Study in Gamification Education Applications (APPs)

Abstract

Adaptive Neuro Fuzzy Inference System (ANFIS) used to be applied to finance, engineering, material design, and decision-making management in past research, but seldom to predict educational learning performance. In recent research, gamification learning material design is often applied to reinforce learning performance, while the prediction of gamification learning performance is seldom discussed. This study therefore applies Rough set theory to extract Core Set and generating rule, ANFIS for learning achievement predication. In order to evaluate the performance of proposed model, the VCCSEGLS dataset are collected as experimental dataset and compared with other models. The results show that the proposed method outperforms the listing models in accuracy. The three key factors are extract, (G7) Time spent on game-based learning, (L1) Examination, normal drugs and treatment, and (L2) Integration ability (time scoring, stability scoring, strain capacity, completeness scoring).The proposed model also can offer accurate predictions and provide some simple decision rules, which can be accurately used by decision-makers and game designers.