A TMFV-Based Adaptive E-Learning System for Generating and Allocating Questions

In this study, an adaptive e-learning system called the Adaptive Learning in Multi-form Vocabulary Teaching (AL-TMFV) system is developed. The system is designed to automatically set questions, including setting the description of the question by Gaussian Naive Bayes algorithm and setting the options of the question by a pre-training model of Text-to-Text Transfer Transformer(T5). In addition, the system dynamically monitors changes in students' knowledge proficiency over time by an allocation algorithm of Highest Response Ratio Next (HRRN). Overall, the framework can be used to create effective adaptive learning systems that reduce the burden on teachers to efficiently improve student academic performance.