A multi-agent system for an intelligent driving instruction application

Abstract

Scenario-based learning uses interactive scenarios to present the user with situations that need user input to be resolved in order to teach the user the correct behaviour. Traditionally these scenarios would be presented in a rigid order that is linearly increasing in difficulty. Every student however has a different learning rate. Studies have shown that students can lose motivation when challenged too much or too little. This project aims to improve learning efficiency and user satisfaction by adapting the scenario content to the user's skill and knowledge level.
A driving instructor application was developed where users are presented with short interactive scenarios in a 3D environment where they take control of the vehicle using a steering wheel and pedals and learn to make correct decisions when driving. For the research experiment the sessions were divided into two different modes, linear or adaptive. In the linear mode there was a strict gradual increase in difficulty. In the adaptive mode, the user's performance in the previous scenarios determined which scenario is presented next. The collected on user performance and self-efficacy showed no significant difference in user performance. There was however a significant increasement in self-efficacy when users played the adaptive session. This validates the positive value that personalization could bring to training applications.