Data underlying the BSc thesis: The influence of assessment type on students' learning gain in k-means clustering

DOI:10.4121/48e59b4b-de29-4007-9963-019d72c2a00e.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: 10.4121/48e59b4b-de29-4007-9963-019d72c2a00e
Datacite citation style:
El Aissati, Madeline (2024): Data underlying the BSc thesis: The influence of assessment type on students' learning gain in k-means clustering. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/48e59b4b-de29-4007-9963-019d72c2a00e.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Delft University of Technology logo

Usage statistics

36
views
17
downloads

Time coverage

01/05/2024-10/06/2024

Licence

CC0

This study aimed to investigate the influence of assessment types on students' learning gain in k-means clustering. The learning gain was calculated using the difference between the pre- and post-test scores, where 7 was the highest possible score on the tests (normalised gain, adapted from Hake's formula). Data was collected from 20 participants, the complete dataset includes the individual learning gain of participants.

History

  • 2024-06-25 first online, published, posted

Publisher

4TU.ResearchData

Format

*.xlsx

Organizations

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

DATA

Files (2)