Design of online teaching interaction mode for vocational education based on gamified-learning

• Published in: Entertainment computing Vol. 50; p. 100647
• Main Authors: Ma, Zhongbao, Li, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2024
• Subjects: Game design; Gamified-learning; Genetic-based algorithms; Traveler-type problems; Game design; Traveler-type problems; Gamified-learning; Genetic-based algorithms
• ISSN: 1875-9521; 1875-953X
• DOI: 10.1016/j.entcom.2024.100647

•China has been actively building its modern vocational education system, indicating progress in this area.•Gamified-learning is emerging as a new approach to learning, combining computer games and online learning to make learning more enjoyable and accessible.•The developed gamified-learning software focuses on traveler-type problems, allowing students to engage with the game while gaining knowledge and practicing problem-solving skills.•The paper conducts in-depth research on the game's help system and optimizes its algorithm, resulting in improved response speed, increased fun, enhanced playability, and better teaching effectiveness. Along with the process of building China's modern vocational education system, China's higher vocational education has made great progress. With the development of computer and Internet technology, gamified learning, as a new way of learning, combines the advantages of computer games and online learning, which not only meets the needs of people to learn anytime and anywhere, but also increases the fun of learning activities. In this paper, we developed a gamified learning software with traveler-type problems as the research content, through the interaction with the game, so that students can think in the game and learn knowledge through the game. Through the questionnaire for research and analysis, this game is good game fun and can stimulate learning interest well. In addition, this paper carries out an in-depth study of the game's help system, optimizes the algorithm for the help system, and proposes an improved genetic algorithm. The reverse learning method is adopted to improve the accuracy and convergence speed of the optimal solution; then the Metropolis criterion is used to improve the crossover and mutation operators to enhance the local search ability of the algorithm; finally, the concept of realistic elite learning is introduced to further enhance the local search ability of the algorithm. The simulation results show that the algorithm is effectively improved in convergence performance and solution accuracy, which can significantly improve the response speed of the help system, effectively improve the game's fun, and improve the game's playability.