Design and research of educational mode in context of teaching gamification

Abstract

With the development of science and technology, the education model is also constantly evolving. Teaching games is an increasingly concerned educational strategy, which can improve students’ interest and participation in learning through gamification. In order to further optimize the effect of teaching gamification, this paper uses a clustering algorithm to analyse students’ learning situation and determine the main factors affecting students’ participation, including learning motivation, learning tasks, social interaction and game experience. Based on the results of learning situation analysis, the naive Bayes algorithm is used to analyse students learning behaviour. The model can predict students’ future learning needs and interests according to their learning history and behaviour, so as to recommend the most suitable gamified learning resources for them. The design and implementation of this model can help to improve the effect of teaching gamification and provide students with a more personalized learning experience. At the same time, the model also helps teachers to better understand students’ learning needs and interests, so as to better guide their learning.