Data underlying the BSc thesis: Dynamically Transparent Ghost Instructors and Their Effect on Learning and Skill Retention in Virtual Reality Environments

DOI:10.4121/abb0629c-9f42-4a0f-917f-d5d666554738.v2
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/abb0629c-9f42-4a0f-917f-d5d666554738

Datacite citation style

Visser, Cassandra (2024): Data underlying the BSc thesis: Dynamically Transparent Ghost Instructors and Their Effect on Learning and Skill Retention in Virtual Reality Environments. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/abb0629c-9f42-4a0f-917f-d5d666554738.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Version 2 - 2024-07-01 (latest)
Version 1 - 2024-06-26

The bachelor thesis aimed to explore the effect that dynamically transparent ghost instructors can have on the learning and retention of skills in Virtual Reality environments. Ghost instructors are shown to the student as a semi-transparent ghost avatar superimposed on the student's avatar so that students may see and imitate the intended movement from a first-person perspective. The research contrasts the effect of statically transparent (constant transparency) ghost instructors with that of dynamically transparent (transparency depends on student performance) ghost instructors through a user study in which students were taught to trace a 3D curve in a virtual environment. The data collected was logged based on information about this virtual environment, such as student and ghost locations, the error between them, and the resulting ghost transparency. The data files consist of two parts: a partial-CSV header providing trial information, followed by CSV-formatted logs.

History

  • 2024-06-26 first online
  • 2024-07-01 published, posted

Publisher

4TU.ResearchData

Format

Text files starting with a non-CSV information header, followed by logged data in CSV format.

Organizations

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

DATA

Files (157)