Beutling, Nils

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Nils
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Nils Beutling

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2023 - 20242

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Gerade angezeigt 1 - 2 von 2
  • Kein Vorschaubild vorhanden
    Publikation
    Personalised course recommender: Linking learning objectives and career goals through competencies
    (Stanford University, 2024) Beutling, Nils; Spahic, Maja; Petrick, Ron; Geib, Christopher
    This paper presents a Knowledge-Based Recommender System (KBRS) that aims to align course recommendations with students' career goals in the field of information systems. The developed KBRS uses the European Skills, Competences, qualifications, and Occupations (ESCO) ontology, course descriptions, and a Large Language Model (LLM) such as ChatGPT 3.5 to bridge course content with the skills required for specific careers in information systems. In this context, no reference is made to the previous behavior of students. The system links course content to the skills required for different careers, adapts to students' changing interests, and provides clear reasoning for the courses proposed. An LLM is used to extract learning objectives from course descriptions and to map the promoted competency. The system evaluates the degree of relevance of courses based on the number of job-related skills supported by the learning objectives. This recommendation is supported by information that facilitates decision-making. The paper describes the system's development, methodology and evaluation and highlights its flexibility, user orientation and adaptability. It also discusses the challenges that arose during the development and evaluation of the system.
    04B - Beitrag Konferenzschrift
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    Publikation
    Comparison of general-purpose and domain-specific modeling languages in the IoT domain: A case study from the OMiLAB community
    (Sun SITE, Informatik V, RWTH Aachen, 2023) Fedeli, Arianna; Beutling, Nils; Laurenzi, Emanuele; Polini, Andrea; Morichetta, Andrea; Buchmann, Robert Andrei; Sandkuhl, Kurt; Seigerroth, Ulf; Kirikova, Marite; Møller, Charles; Forbrig, Peter; Gutschmidt, Anne; Ghiran, Ana-Maria; Marcelletti, Alessandro; Härer, Felix; Re, Barbara; Johansson, Björn
    The Internet of Things (IoT) is a revolutionary concept that has rapidly transformed how we interact with technology and the world around us. In response to the inherent complexity and heterogeneity of the IoT domain, there has been a surge in the development of modeling languages and supporting platforms for developing IoT applications. Among the many modeling options available, one can distinguish between General-Purpose Modeling Languages (GPML) and Domain-Specific Modeling Languages (DSML). Each language has unique characteristics, offering distinct levels of abstraction and expressiveness crucial for effective IoT solution modeling. The challenge of selecting the most suitable language remains, with developers needing to weigh the benefits and drawbacks of each option carefully. This paper compares GPML and DSML regarding their characteristics, benefits, and drawbacks. By identifying key factors to consider when choosing a modeling language for IoT solutions, this research aims to provide valuable insights for a decision-making framework to help practitioners with this choice. To validate the findings and practical implications, a practical workshop was conducted. After creating a smart room scenario using the X-IoT DSML, the participants confirmed the advantages of DSML regarding user-friendliness, higher abstraction, improved communication, faster development, and the ability for non-experts to participate in the IoT application development process.
    04B - Beitrag Konferenzschrift