Data underlying the research of: “A multi-agent system for an intelligent driving instruction application”

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.<br>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. The sessions were divided into two different modes, linear or adaptive. In the adaptive mode, the user's performance in the previous scenarios determines which scenario is presented next. Data is collected on user performance, self-efficacy and user-satisfaction.

基于场景的学习（Scenario-based learning）通过交互式场景向学习者呈现需通过用户输入方可解决的情境，以此传授正确行为范式。传统模式下，此类场景通常以固定线性顺序呈现，难度随学习进度逐步提升。但每位学习者的学习节奏各不相同。已有研究表明，当学习挑战过高或过低时，学习者易丧失学习动力。本项目旨在根据学习者的技能与知识水平动态调整场景内容，以此提升学习效率与用户满意度。
本项目开发了一款驾驶教练应用，用户可在3D环境中体验简短交互式场景，通过方向盘与踏板操控车辆，学习驾驶过程中的正确决策方法。该训练课程分为两种模式：线性模式与自适应模式。在自适应模式中，系统会依据用户在前序场景中的表现，为其匹配后续的学习场景。本次研究将收集用户的学习表现、自我效能感（self-efficacy）与用户满意度三类数据。