1,721,105 research outputs found
Oups, I did it again! Long-term effects of direct feedback in programming exercises
No full textThis paper examines the long-term impact of direct feedback in programming assignments, using an automatic grading system. Data from 69,701 submissions from 849 students in seven semesters were analyzed, with a focus on two specific code quality defects reported by Checkstyle: EX1 (Redundant Boolean evaluation) and CB1 (Redundant if statement that could be replaced with return). The analysis involved pre-processing the dataset to identify these defects and track their correction cycles. The results show that approximately 70 % of the students successfully corrected the defects after the initial feedback and did not include the same defect again in subsequent submissions. However, around 30 % of the students included the same defect again after a first correction cycle, suggesting a less efficient feedback processing. The findings highlight the importance of feedback formats in fostering deeper understanding and reflection, and demonstrate that other strategies are necessary to support students who repeatedly struggle with the same difficulties
On the Influence of Task Size and Template Provision on Solution Similarity
No full textIn most cases of programming education, there is not a single correct answer to a given task. Instead, the same problem can be solved by two or more pieces of program code that look very different. At the same time, two or more pieces of program code that look very similar may actually solve very different problems. It is thus not easy to foresee which degree of similarity one can expect for all or at least the correct submissions to a given programming task. Since several applications may benefit from some kind of prediction of the similarity, this paper presents first, preliminary results from research on that topic. In particular, it presents results from an empirical study on the influence of exercise size and template provision. Results indicate that both factors are not suitable as simple predictors and that other factors have to be taken into account as well. Nevertheless, the results help to generate hypothesis for more detailed subsequent studies
Proceedings Seventh Workshop Automatische Bewertung von Programmieraufgaben (ABP 2025) – Complete Volume
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Wieviel Ähnlichkeit ist in Programmierprüfungen normal?
No full textWerkzeuge zur Identifikation von Plagiaten in Programmieraufgabe messen häufig die Ähnlichkeit zwischen Abgaben. Die Interpretation dieser Werte zur Identifikation von Verdachts¬momenten ist jedoch schwierig, da eine gewisse Ähnlichkeit von Abgaben unvermeidlich ist. Es ist insbesondere unklar, welche Ähnlichkeitsverteilung innerhalb einer Kohorte ohne Plagiate als normal angesehen werden kann und welche Abweichungen davon als Verdachtsmoment genutzt werden können. Der vorliegende Beitrag vergleicht dazu unter Nutzung zweier Werkzeuge zur Plagiatserkennung unter Aufsicht erstellte Abgaben zu Prüfungsaufgaben mit solchen, die ohne Aufsicht entstanden sind. Die Ergebnisse zeigen, dass die Ermittlung einer „Baseline“ für die erwartbare Ähnlichkeit schwierig ist, da auch schon kleine Änderungen der Aufgabenstellung starke Auswirkungen auf die Ähnlichkeit der Lösungen haben können
Piloteinsatz einer E-Assessment-Plattform für die grafische Modellierung
No full textDie KEA-Mod-Plattform ermöglicht es, Modellierungsaufgaben mit verschiedenen Modellierungssprachen wie z.B. UML, Petri-Netzen, EPK oder BPMN durch Dozierende zu erstellen und von Studierenden bearbeiten zu lassen. Die Plattform kam in einer großen Lehrveranstaltung mit ca. 250 Studierenden zum Piloteinsatz. Die Studierenden konnten mit Hilfe der Plattform und des integrierten Modellierungswerkzeugs eine Aufgabenreihe mit Modellierungsaufgaben zu Petri-Netzen bearbeiten und einreichen. Anschließend erhielten die Studierenden automatisiert generiertes Feedback. Das Poster beschreibt die Evaluation dieses Piloteinsatzes aus der Perspektive der Studierenden und bietet erste Ergebnisse in Bezug auf die Plattform-Usability und zur wahrgenommenen Lernförderlichkeit des Feedbacks
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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