Böck, Felix; Landes, Dieter; Sedelmaier, Yvonne (2024)
(2), 187-196.
DOI: 10.5220/0012556100003693
Learners in higher education tend to become an increasingly heterogeneous group. Paying proper attention to individual differences is a challenge that may be leveraged by individualized automated recommendations of learning elements. This presupposes some knowledge of the learners’ profiles which can be captured in so-called learner models. Yet, so far, there is no comprehensive overview of existing standards and their contribution related to learner models. This paper presents the results of a systematic literature research devoted to norms and standards in the area of learner models. As it turns out, 16 norms or standards have some relationship to learner models, 3 of them present their versions of a learner model. None of the standards and norms offers a comprehensive learner model, but in their entirety these models provide hints on reasonable contents and structure of learner models.
Kohls, Niko (2024)
Gesundeitstag 2024 der Stadt Coburg.
Albrecht, Matthias; Streuber, Stephan; Assländer, Lorenz; Streuber, Stephan (2024)
Springer Virtual Reality 28 (28).
DOI: 10.1007/s10055-024-01006-y
Zagel, Christian (2024)
IHK Abschlussfeier.
Schaub, Michael (2024)
Campus Design Open 2024 – Tag der Bauingenieure.
Ziegler, Jörg; Koppen, Gemma (2024)
Interview für die Marburger Bund Zeitung 2024/76 (5), 12.
Stöver, Heino; Hößelbarth, Susann (2024)
Sammelband. 6., vollständig überarbeitete Auflage. Idstein: Fachhochschulverlag 6.
Kaemmerer, Harald; Kohls, Niko; Freilinger, Sebastian (2024)
Poster Presentation 57th Annual Meeting of the Association for European Paediatric and Congenital Cardiology to be held in Porto, Portugal, between 8-11 May 2024..
Stöver, Heino; Hößelbarth, Susann (2024)
Stöver, Heino & Hößelbarth, Susann (Hrsg.) (2024). Drogenpraxis, Drogenpolitik, Drogenrecht. 6. vollständig überarbeitete Auflage. Idstein: Fachhochschulverlag, 129-138.
Stöver, Heino; Hößelbarth, Susann (2024)
Stöver, Heino & Hößelbarth, Susann (Hrsg.) (2024). Drogenpraxis, Drogenpolitik, Drogenrecht. 6. vollständig überarbeitete Auflage. Idstein: Fachhochschulverlag, 251-261.
Stöver, Heino; Hößelbarth, Susann (2024)
Stöver, Heino & Hößelbarth, Susann (Hrsg.) (2024). Drogenpraxis, Drogenpolitik, Drogenrecht. 6. vollständig überarbeitete Auflage. Idstein: Fachhochschulverlag, 444-464.
Hößelbarth, Susann; Stöver, Heino (2024)
Stöver, Heino & Hößelbarth, Susann (Hrsg.) (2024). Drogenpraxis, Drogenpolitik, Drogenrecht. 6. vollständig überarbeitete Auflage. Idstein: Fachhochschulverlag, 285-302.
Hößelbarth, Susann; Stöver, Heino (2024)
Stöver, Heino & Hößelbarth, Susann (Hrsg.) (2024). Drogenpraxis, Drogenpolitik, Drogenrecht. 6. vollständig überarbeitete Auflage. Idstein: Fachhochschulverlag, 315-327.
Hößelbarth, Susann; Stöver, Heino (2024)
Stöver, Heino & Hößelbarth, Susann (Hrsg.) (2024). Drogenpraxis, Drogenpolitik, Drogenrecht. 6. vollständig überarbeitete Auflage. Idstein: Fachhochschulverlag, 513-526.
Kohls, Niko; Heinrich, Michael (2024)
Podcast Hochschule Coburg.
Lehmann, Alexander; Landes, Dieter (2024)
Proc. 15th IEEE Global Engineering Education Conference (EDUCON 2024), Kos, Griechenland 2024.
In modern education, individualized learning environments play a vital role by allowing learners to tailor their learning paths based on personal needs, interests, and abilities. Achieving effective individualization relies on dynamic adaptation of the learning path, typically facilitated by recommender systems. These systems offer personalized suggestions, commonly employing content-based or collaborative filtering approaches. However, traditional recommender systems often lack consideration of the semantics of learning elements. To address this limitation, ontology-based recommender systems integrate semantic modeling, establishing additional connections within a domain to enhance precision and context in recommendations. Notably, these systems mitigate the cold start problem and are particularly advantageous in learning environments with limited data. While videos are prevalent in learning platforms, their unstructured nature poses challenges for processing. This paper introduces an innovative approach, leveraging Large Language Models, specifically GPT, to extract metadata from learning videos. The proposed method intelligently augments videos and links them to a domain ontology, enabling the integration of videos into ontology-based recommender systems. The application of this approach is demonstrated through a case study in software engineering education, showcasing its potential to enhance individualized learning experiences in specific domains. The presented method offers an automated alternative to manual video processing, aligning with the evolving landscape of education technology.
Landes, Dieter; Sedelmaier, Yvonne; Böck, Felix; Lehmann, Alexander; Fraas, Melanie; Janusch, Sebastian (2024)
Landes, Dieter; Sedelmaier, Yvonne; Böck, Felix; Lehmann, Alexander; Fraas, Melanie...
Proc. 15th IEEE Global Engineering Education Conference (EDUCON 2024), Kos, Griechenland 2024.
Students in higher education tend to become increasingly heterogeneous groups of learners. This is due to different levels of prior knowledge or competences, diverse
learning styles, differing affinity to (digital) media, and other factors. Learner-centred education needs to cope with that heterogeneity in order to make specific learning offers to the
individual learner. This is difficult in physical classes where the coaching effort cannot be increased without limitation. This paper presents an individualized digital learning environment, iLE, that is intended to be used as an additional learning aid that supplements physical classes. iLE provides recommendations of learning material such as learning videos targeted to the specific needs of individual learners. The paper presents the technical approach behind iLE, in particular a combination of data- and knowledge-driven artificial intelligence techniques, as well as the didactical underpinning of iLE.
Dehghani, Ali; Patiño Studencki, Lucila (2024)
10th. International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS) 2024.
Schaub, Michael (2024)
HLH 75 (05), 22-25.
DOI: 10.37544/1436-5103-2024-05-22
Kohls, Niko (2024)
Organisation.