Pythia - 1st Evaluation
<p>Results of a survey in the higher education area. Participants are German students and the survey is about evaluating the learning management system Pythia after Röhrl et al. (S. Röhrl, S. Staufer, V.K. Nadimpalli, F. Bugert, F. Hauser, L. Grabinger, D. Bittner, T. Ezer, J. Mottok (2024) PYTHIA - AI SUGGESTED INDIVIDUAL LEARNING PATHS FOR EVERY STUDENT, INTED2024 Proceedings, pp. 2871-2880.)</p> <p> </p> <p>Learning path algorithms:</p> <ul> <li>Tyche (https://doi.org/10.21125/inted.2024.1080 & https://zenodo.org/doi/10.5281/zenodo.10461677) is a Markov model</li> <li>Nestor (https://doi.org/10.21125/iceri.2023.1144) is a Bayesian network</li> </ul> <p> </p> <p>Learning element categories:</p> <ul> <li>Data: https://doi.org/10.5281/zenodo.10022143 & https://doi.org/10.5281/zenodo.10476168</li> <li>Publications: https://doi.org/10.21125/iceri.2023.0815 & https://doi.org/10.21125/inted.2024.1087</li> </ul> <p> </p> <p>The following aspects are evaluated:</p> <ul> <li>General aspects</li> <li>Graphical user interface</li> <li>Usability</li> <li>Learning elements</li> <li>Learning paths</li> <li>Other aspects</li> </ul> <p> </p> <p>The survey itself is structured as follows:</p> <ol> <li>Demographic data</li> <li>Usage behavior</li> <li>Usability</li> <li>GUI</li> <li>Learning elements</li> <li>Learning paths</li> </ol> <p> </p> <p>Our software engineering course has the following structure:</p> <ol> <li>Introduction</li> <li>Development processes</li> <li>Requirements engineering</li> <li>UML</li> <li>Design Pattern</li> <li>Safety and Security</li> <li>Software testing</li> </ol> <p> </p> <p>Further notes:</p> <p>The ranking of the learning elements range from 1 to 9. 1 is the best leaning element, while 9 is the worst.</p> <p> </p> <p> </p> <p>The corresponding scientific paper can be found via ORCID (https://orcid.org/0009-0009-4346-8678) as of September 2024.</p> <p> </p> <p>The presented work is supported by the ‘German Federal Ministry of Research, Technology and Space’ (BMFTR) through the granting of the funding project HASKI (FKZ: 16DHBKI035).</p>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0