Bibliometric Analysis of Automated Assessment in Programming Education: A Deeper Insight into Feedback

Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task....

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Veröffentlicht in:Electronics (Basel) Jg. 12; H. 10; S. 2254
Hauptverfasser: Paiva, José Carlos, Figueira, Álvaro, Leal, José Paulo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 15.05.2023
Schlagworte:
Automation
Bibliographic coupling
Bibliometrics
Citations
Collaboration
Computer programming
Computer science
Concept mapping
Datasets
Education
Feedback
Mechanization
Programming
Rating of
Scientometrics
Software
Students
Study and teaching
Trends
Portugal
ISSN:2079-9292, 2079-9292
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Zusammenfassung:Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12102254