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

DOI:10.4121/bad8ac56-dc64-478e-8281-b126583eb4aa.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/bad8ac56-dc64-478e-8281-b126583eb4aa

Datacite citation style

Doorn, Miriam (2023): Data underlying the research of: “A multi-agent system for an intelligent driving instruction application”. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/bad8ac56-dc64-478e-8281-b126583eb4aa.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

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. 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.

History

  • 2023-08-31 first online, published, posted

Publisher

4TU.ResearchData

Format

R code, CSV data files, PDF, txt

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Computer Science

DATA

Files (16)