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Auflistung nach Autor:in "Dannath, Jesper"

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  • Konferenzbeitrag
    Case study: Using LLMs to assist with solving programming homework assignments
    (Proceedings of DELFI Workshops 2024, 2024) Deriyeva, Alina; Dannath, Jesper; Paaßen, Benjamin
    Nowadays, students have the option of using LLMs for assistance in solving homework assignments. Moreover, most LLMs, like ChatGPT, are also trained on large sets of source code and thus can be used to assist in programming exercises. In this paper, we present a case study based on data collected over the course of 1.5 semesters, where students of three programming-related courses were explicitly permitted to use such models while solving homework assignments. In a qualitative evaluation, we observe that there might be a difference between targeted requests for an answer to specific questions and requests for a complete solution from an LLM. Particularly, targeted requests might be pedagogical feasible and enhance the learning experience. Additionally, we discuss the potential of LLM applications in programming education, with a focus on the intermediate level and beyond.
  • Conference paper
    Evaluating Task-Level Struggle Detection Methods in Intelligent Tutoring Systems for Programming
    (Proceedings of DELFI 2024, 2024) Dannath, Jesper; Deriyeva, Alina; Paaßen, Benjamin
    Intelligent Tutoring Systems require student modeling in order to make pedagogical decisions, such as individualized feedback or task selection. Typically, student modeling is based on the eventual correctness of tasks. However, for multi-step or iterative learning tasks, like in programming, the intermediate states towards a correct solution also carry crucial information about learner skill. We investigate how to detect learners who struggle on their path towards a correct solution of a task. Prior work addressed struggle detection in programming environments on different granularity levels, but has mostly focused on preventing course dropout. We conducted a pilot study of our programming learning environment and evaluated different approaches for struggle detection at the task level. For the evaluation of measures, we use downstream Item Response Theory competency models. We find that detecting struggle based on large language model text embeddings outperforms chosen baselines with regard to correlation with a programming competency proxy.
  • Conference poster
    Relation between struggle and learning personality in programming exercises
    (Proceedings of DELFI 2024, 2024) Deriyeva, Alina; Dannath, Jesper; Paaßen, Benjamin
    Personality-related characteristics can have an impact on learning experiences and learning outcomes. Moreover, understanding learning approaches of students can help to make personalized pedagogical decisions. Particularly, this has a potential to improve learning outcomes and mitigate user attrition in digital learning environments (DLEs). We hypothesize that persistent individual characteristics may influence a learners’ tendency to struggle during programming exercises. In a study within a digital learning environment for Python programming (N=55), we evaluated the relation between learners’ self-reported personality characteristics and their tendency to struggle during the exercises. We find that, in our sample, some problem solving approaches and all learning preferences are related to struggle indicators. These results indicate that it may be helpful to include personality-related features, like problem solving strategies, as context information for pedagogical modeling in DLEs.