Published May 23, 2022
| Version v1
Dataset
Open
Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training
Description
This repository contains supplementary materials for the following journal paper:
Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.
Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.
In Springer Education and Information Technologies. 2022.
https://doi.org/10.1007/s10639-022-11093-6
Preprint available at: https://arxiv.org/abs/2307.08582
How to cite
If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).
@article{Svabensky2022applications,
author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia},
title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}},
journal = {Education and Information Technologies},
publisher = {Springer},
volume = {27},
year = {2022},
issn = {1360-2357},
url = {https://doi.org/10.1007/s10639-022-11093-6},
doi = {10.1007/s10639-022-11093-6},
}
Attached content
The files included in the ZIP archive are:
- `All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search.
- `Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator.
- `Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator.
- `Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review.
- `Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers.
- `Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.
Notes
Files
2022-EaIT-Applications-materials.zip
Files
(2.4 MB)
| Name | Size | Download all |
|---|---|---|
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md5:e8a682c59466c6febf8120bd23e15c04
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2.4 MB | Preview Download |
Additional details
Related works
- Is supplement to
- Journal article: 10.1007/s10639-022-11093-6 (DOI)