P356 - DELFI 2024 - Die 22. Fachtagung Bildungstechnologien

Permanent URI for this collectionhttps://dl.gi.de/handle/20.500.12116/44489

Authors with most Documents  

Schroeder, Ulrik
6
Strickroth, Sven
4
Görzen, Sergej
3
Harrer, Andreas
3
Schiffner, Daniel
3

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  • Conference demo
    Adaptive Learning Systems in Programming Education: A Prototype for Enhanced Formative Feedback
    (Gesellschaft für Informatik e.V., 2024) Lohr, Dominic; Berges, Marc; Chugh, Abhishek; Striewe, Michael; Schulz, Sandra; Kiesler, Natalie
    Formative feedback is crucial in programming education, yet many learning systems fall short, concentrating mostly on pinpointing errors rather than guiding learners on how to resolve them. This is particularly unhelpful for novices who often lack advanced skills like debugging. Feedback is considered more valuable when it addresses error causes rather than just symptoms. However, this is challenging using only conventional methods like unit testing. Identifying error causes requires detailed information about both the error and the learner. Our proposed prototype introduces a new approach to integrating programming exercises into adaptive learning systems. It directly categorizes student code into so-called answer classes using a combination of static and dynamic code analysis. When integrated with data derived from a learner model, this approach enables tailored feedback that lowers the barrier to learning programming while keeping motivation high.
  • Conference paper
    Term Extraction for Domain Modeling
    (Gesellschaft für Informatik e.V., 2024) Kruse, Theresa; Lohr, Dominic; Berges, Marc; Kohlhase, Michael; Moghbeli, Halimeh; Schütz, Marcel; Schulz, Sandra; Kiesler, Natalie
    Adaptive learning systems need to use domain and learner models to provide meaningful support for learners. Building fine-grained domain models by hand is very time-consuming, so the demand for partial automation is high. This paper investigates how term extraction tools can support constructing a domain model. Therefore, we study if different automatic term extraction tools give comparable results to a human annotator. Our results show that the current extraction tools support the process, but their results are not directly usable and still need human adjustments.
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