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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Author: search.filters.author.Schroeder, UlrikHas files: Yes

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1 - 6 of 6
  • Conference paper
    Informiertes Zustimmungsmanagement für Learning Analytics in Lernmanagementsystemen
    (Gesellschaft für Informatik e.V., 2024) Judel, Sven; Zeise, Christopher; Schroeder, Ulrik; Schulz, Sandra; Kiesler, Natalie
    Dieser Beitrag präsentiert ein Konzept um Lernende angemessen über Learning Analytics zu informieren und ihnen das Management von Zustimmungen zu Datensammlungen und -verarbeitungen einfach aber vollständig zu ermöglichen. Drei Komponenten übernehmen die Aufklärung, die Zustimmungsabfrage beim ersten Aufruf des Lernmanagementsystems sowie jedes Lernraums und die Anpassung der Zustimmungen zu jeder Zeit. Die Komponenten zum Management wurden beispielhaft für Moodle umgesetzt und mit Studierenden evaluiert. Die Ergebnisse deuten auf eine gute und sichere Nutzbarkeit hin, welche den Lernenden mehr Kontrolle über ihre Daten gibt.
  • Conference demo
    Neugestaltung eines Mixed Reality Prototypen für das SchülerlARbor Chemie
    (Gesellschaft für Informatik e.V., 2024) Meinhardt, Felix; Görzen, Sergej; Werkes, Richard; Pelzer, Philipp; Krause, Daniel; Schroeder, Michael; Schroeder, Ulrik; Schulz, Sandra; Kiesler, Natalie
    Augmented Reality (AR) stellt großes, noch unausgeschöpftes Potenzial für Lernanwen- dungen dar. Im Chemieunterricht bietet sich etwa das Visualisieren von mikroskopischen Prozessen oder Modellen mithilfe von AR an. Diese Demo stellt einen quelloffenen AR Prototypen für die Microsoft HoloLens vor, welcher Begleitmaterialien für verschiedene Module im Schülerlabor für Chemie der RWTH Aachen bietet. Die Themen der aktuellen Version behandeln Piezokristalle, den Aufbau und die Funktionsweise von Lithium-Ionen-Akkus sowie virtuelle Experimente zu den Konfigurationsmöglichkeiten dieser Akkus. Zusätzlich ist eine Einführung in HoloLens-Interaktionen enthalten, um die Einstiegsbarriere für diese moderne Technologie zu mindern. Evaluiert wurde zunächst die Bedienbarkeit der Applikation.
  • Conference demo
    Web-based prototype of a visual and interactive deep learning simulation
    (Gesellschaft für Informatik e.V., 2024) Koch, Christian; Salmen, Frederic; Schroeder, Ulrik; Schulz, Sandra; Kiesler, Natalie
    Due to its rapid development and growing relevance in society, the research field of artificial intelligence (AI) is finding its way into more and more schools. One subsection of artificial intelligence, deep learning, is especially relevant since most of the rapid development of the study field in recent years can be attributed to it. Because of its complex nature, deep learning is very suitable to be taught in a visual and interactive way, which can benefit learning outcomes. Existing solutions can convey basic knowledge about deep learning, but none of them are well suited to customization or facilitating deep understanding. We introduce a visual and interactive deep learning simulation with rich possibilities for personalization by teachers and students, usable in any web context.
  • Conference demo
    BuddyAnalytics: A dashboard and reporting tool for study program analysis and student cohort monitoring
    (Gesellschaft für Informatik e.V., 2024) Görzen, Sergej; Röpke, René; Schroeder, Ulrik; Schulz, Sandra; Kiesler, Natalie
    With students leaving digital traces in Campus Management Systems when registering and completing courses and exams, there is a growing interest in data-driven study program analysis and student cohort monitoring. To support study program designers with tasks, such as planning courses and exams, creating (re-)accreditation reports, and improving curricula and study plans, insights into the students’ behavior throughout their studies present valuable input and may allow for evidence-based curriculum development. This demo presents BuddyAnalytics, a web-based tool providing dashboards and analysis reports for study program analysis and cohort monitoring. It enables study program designers to review various metrics and indicators relevant to understanding students’ behavior and potential issues of study programs. Developed using user-centered design methodology, the tool is closely tailored to users’ needs and requirements. Future evaluation with the target group will assess its suitability and provide valuable feedback for improving the tool over time.
  • Conference paper
    Correlation of Error Metrics in Python CS1 Courses
    (Gesellschaft für Informatik e.V., 2024) Brocker, Annabell; Schroeder, Ulrik; Schulz, Sandra; Kiesler, Natalie
    Timely and effective feedback is essential for novice programmers. Despite significant advancements in Large Language Models enabling the generation of understandable feedback in CS1 courses, determining appropriate timing for delivering feedback automatically remains a persistent challenge. Compiler messages serve as a fundamental communication channel between programmers and computers, signaling syntactic and runtime errors. Various metrics associated with these messages could potentially signify the need for intervention. Hence, it is imperative to explore the correlations among these error metrics. This study conducts a comprehensive analysis of multiple public Python datasets to evaluate error metrics and explore their correlations. The findings offer insights into a potential indicator for determining appropriate timing of feedback in programming education.
  • Conference paper
    FAIR Learning Technologies with Web Components and Packages
    (Gesellschaft für Informatik e.V., 2024) Salmen, Frederic; Breuer, Martin; Görzen, Sergej; Persike, Malte; Schroeder, Ulrik; Schulz, Sandra; Kiesler, Natalie
    Making the diverse software artifacts of the learning technologies community findable, accessible, interoperable, and reusable (FAIR) can be a technical challenge. We introduce a concept informed by our research involving packages and components to achieve FAIRness for web-based artifacts. This result is presented as a guideline to make FAIR technology choices when creating web-based learning technologies. The guideline compares classic choices with new paths afforded by technological innovation of the web platform. Supported by practical examples (learning analytics dashboards, e-assessment, and explorables) we discuss practical applications of our result.
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