VR Learning and Behavior Dataset

DOI

This package contains datasets derived from experimental data from two studies. Both studies employed a mixed-methods approach with university participants using an industrial VR application for training in electrical maintenance tasks. The first dataset corresponds to a study that used an experimental design with 60 participants divided into two groups: the interactive VR group (labeled as 'VR') and the passive monitor viewing group (labeled as 'Monitor'). This data was used to perform various analytical methods to examine learning outcomes and self-efficacy. The second dataset comes from a study that increased the number of participants in the VR group by 27, bringing the total to 57 participants. This study used a quantitative research design and the data was used to implement a Structural Equation Modelling (SEM) approach. This analysis was conducted to investigate the different factors affecting learning in VR.

The experimental design and data management plan received approval from the Tilburg University ethics committee (REDC # 20201035).

The data files are also present in their preferred file format. Preferred formats are file formats of which DANS – based on international agreements – is confident that they will offer the best long-term guarantees in terms of usability, accessibility and sustainability. For more information on preferred file formats, see https://dans.knaw.nl/en/file-formats/.

Identifier
DOI https://doi.org/10.34894/T1VAKP
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/T1VAKP
Provenance
Creator Mousavi, Mohammad Ali ORCID logo; Powell, Wendy ORCID logo; Louwerse, Max M. ORCID logo; Hendrickson, Andrew T. ORCID logo
Publisher DataverseNL
Contributor Mousavi, Mohammad Ali; DataverseNL
Publication Year 2023
Rights CC-BY-4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Mousavi, Mohammad Ali (Tilburg University, Department of Cognitive Science, and Artificial Intelligence)
Representation
Resource Type Experimental data; Dataset
Format text/plain; application/pdf; text/csv; application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Size 4895; 163301; 4571; 13377; 9794; 19947
Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Construction Engineering and Architecture; Engineering; Engineering Sciences; Humanities; Life Sciences; Medicine; Social Sciences; Social and Behavioural Sciences; Soil Sciences