EduBot Unleashed - Elevating Digital Competence in Online Collaborative Learning
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
This study introduces a data-driven approach to measure and enhance digital competencies in Online Collaborative Learning (OCL) environments. Recognizing the critical role of digital competence for student engagement and collaborative success, this research leverages Learning Analytics (LA) and Large Language Models (LLMs) within a Conversational Agent (CA), “EduBot.” EduBot analyzes user activity data and provides tailored feedback to triger self-reflection for competence development. Experts identified 57 behavioral patterns of digital competence, of which five were operationalized based on alignment with available data types and measurability. Integrated into an OCL framework, EduBot collected and analyzed activity data from student interactions, providing feedback on communication, ethical practices, and collaborative tool use. The evaluation involved 20 students, revealing mixed responses: some participants found EduBot beneficial for self-reflection, while others raised concerns about feedback accuracy. Key findings highlight EduBot's potential in fostering digital competencies, though challenges in data precision and CA personalization remain. This research contributes a foundational model for competence-based feedback in digital learning and emphasizes the need for refined data collection to fully capture complex digital behaviors.
Details
Originalsprache | Englisch |
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Titel | 2024 21st International Conference on Information Technology Based Higher Education and Training, ITHET 2024 |
Erscheinungsort | Paris |
Herausgeber (Verlag) | IEEE |
Seitenumfang | 9 |
ISBN (elektronisch) | 979-8-3315-1663-5 |
ISBN (Print) | 979-8-3315-1664-2 |
Publikationsstatus | Veröffentlicht - 16 Jan. 2025 |
Peer-Review-Status | Ja |
Externe IDs
unpaywall | 10.1109/ithet61869.2024.10837628 |
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Mendeley | f60cde3c-49b9-3a86-9ae8-fccd8a4ebbc0 |
Scopus | 85218139674 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Conversational Agent, Digital Competence, Large Language Models, Learning Analytics, Online Collaborative Learning