A study on the design of a deep learning model for classroom based on user participation and game chemistry processes

被引:0
|
作者
Wang, Qing [1 ]
机构
[1] Hang Zhou Polytech, Sch Early Childhood Educ, Hangzhou 311402, Peoples R China
关键词
User participation; Online assessment system; Gamification; Deep learning pattern; Learning ability; Classroom learning;
D O I
10.1016/j.entcom.2024.100727
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In gamified learning, user engagement has always been an important concern in the field of education. For mass learning, the integration of classroom learning and gamification can make users well -stimulated and help them to improve and cultivate their comprehensive abilities. With the characteristics of efficiency, fairness, and objectivity, online assessment systems (OJ systems) have become an important tool for learning. However, the following limitations still exist in current OJ systems: First, most OJ systems cannot perceive and quantify the user's learning level at the knowledge point level. This can lead to a mismatch between the difficulty of study questions adapted by the system to the user and the user's learning ability; second, most OJ systems lack research and design of motivation methods. These limitations can negatively affect the enhancement of students' motivation and the consolidation and improvement of their learning ability. Given this, the article designs and implements a gamified online assessment system (Game_OJ) based on user participation and the game chemistry process. The online assessment system uses a modified Bayesian knowledge tracking model (CC-BKT). The application of the Bayesian knowledge tracking model can accurately perceive and quantify students' learning of each knowledge point. The online assessment system introduces the gamification idea to design the motivation methods in the OJ system, including the design of gamification elements and the design of the challenge process of gamified learning, aiming to enhance students' motivation to learn. It promotes students' deep learning in the classroom and outside the classroom. The CC-KPT model proposed in this paper has an average increase of 2.15 % in AUG value and an average increase of 3.25 % in accuracy compared to the same type of model, and the speed of response and the median speed of processing demand of this system is within 1 s when 500 users use the system at the same time, which meets the needs of on -campus teaching scenarios.
引用
收藏
页数:9
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