Empirical Study of Adaptive Serious Games in Enhancing Learning Outcome

Abstract

Use of serious games to teach concepts of various important topics including Cybersecurity is growing. A figure of merit for the serious games could be learning outcome and user experience(UX). With enhanced learning outcome and user experience, the player is likely to favourably rate a game. The organisation supporting such games would also benefit from such efficient training process. We report an empirical comparison of two cybersecurity games namely ; Use of Firewalls for network protection and concepts of Structured Query Language (SQL) injections to get unauthorised access to online databases. We have designed these games in two versions. The version without using adaptive features provide a baseline to compare efficacy of the machine learning based adaptive game while comparing the learning outcomes and user experience (UX). The efficacy of the Machine Learning (ML) agent in providing the adaptability to the game play is based on classification of player to two categories viz. Beginner and Expert using historical player data on three relevant attributes. The game dynamics is modulated based on the player classification to ensure that game challenge is optimally suited to player type and the player continues to experience playful flow in different stages of the game.

Keywords

• Adaptive Serious games
• Machine Learning
• Cybersecurity
• Learning Outcome
• User Experience (UX)
• LM-GM framework