Abstract
In recent years, computer science education has increasingly focused on the development and application of automated code assessment methods. Test-based assessments of student submissions generate large quantities of data that could be used for learning analytics. Existing literature highlights a predominant use of unit tests for grading rather than for extracting insights into students’ challenges in learning programming concepts. Moreover, competence models, which systematically outline required learning outcomes, are increasingly important during curriculum design. We introduce a novel approach for systematically developing programming assignments aligned with competence models. This method also aids educators in creating unit tests that support competence-based learning analytics. As our method maps competences to individual test cases and thereby quantifies a student’s proficiency in an assignment, this helps educators to evaluate, whether students have the ability to understand and master the required competences. Experimental application of our method demonstrated enhanced clarity in understanding student assignments and a considerable improvement in the quality of learning analytics data.
| Originalsprache | Englisch |
|---|---|
| Titel | Proceedings of the 36th Conference on Software Engineering Education and Training (CSEE&T 2024), July 29 - August 1, 2024, Würzburg, Germany. |
| Herausgeber*innen | Andreas Bollin, Ivana Bosnic, Jennifer Brings, Marian Daun, Meenakshi Manjunath |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 9798350378979 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - Aug. 2024 |
Publikationsreihe
| Name | Software Engineering Education Conference, Proceedings |
|---|---|
| ISSN (Print) | 1093-0175 |
Wissenschaftszweige
- 102006 Computer Supported Cooperative Work (CSCW)
- 102015 Informationssysteme
- 102016 IT-Sicherheit
- 102020 Medizinische Informatik
- 102022 Softwareentwicklung
- 102027 Web Engineering
- 102034 Cyber-Physical Systems
- 509026 Digitalisierungsforschung
- 102040 Quantencomputing
- 502032 Qualitätsmanagement
- 502050 Wirtschaftsinformatik
- 503015 Fachdidaktik Technische Wissenschaften
JKU-Schwerpunkte
- Digital Transformation
Projekte
- 1 Abgeschlossen
-
CodeAbility Austria
Groher, I. (Projektleiter*in), Plösch, R. (Projektleiter*in) & Wimmer, M. (Projektleiter*in)
01.01.2020 → 31.12.2024
Projekt: Geförderte Forschung › Bund / Land / Gemeinden
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