Verbundprojekt: Von Lernenden Lernen: Ganzheitliche daten- und wissensunterstützte Hochschulbildung und deren Gestaltung - VoLL-KI, Teilvorhaben: Individualisierte Lernnavigation und Lernsituation

Von Lernenden Lernen (VoLL-KI)

„Von Lernenden Lernen“, kurz „VoLL-KI“ ist ein Gemeinschaftsprojekt der Hochschule Coburg zusammen mit der Friedrich-Alexander-Universität Erlangen-Nürnberg und der Otto-Friedrich-Universität Bamberg, in dem anstrebt wird, Studierenden selbstgesteuertes Lernen in einer digitalen adaptiven Lernumgebung zu ermöglichen. Zu dieser Lernumgebung zählt ein Empfehlungssystem, das bedarfsgerecht digitale Lernelemente wie Lernvideos, Übunsgaufgaben, Quizzes u.ä. empfiehlt, die auf den Kompetenzstand des Lernenden und weitere Profilelemente abgestimmt sind, ein KI-Chatbot und mit Hilfe virtueller Realität unterstützte Lernszenarien.

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Second Workshop on AI for AI Education (AI4AILearning)

Kohlhase, Michael; Leidner, Jochen L.; Schmid, Ute; Wolter, Diedrich (2024)

held at KI 2024: 47. Deutsche Jahrestagung für Künstliche Intelligenz, Würzburg, 23.09. - 27.09.2024.

Peer Reviewed

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Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part I

Nowaczyk, Slawomir; Biecek, Przemyslaw; Chung, Neo Christopher; Vallati, Mauro...

Communications in Computer and Information Science (CCIS) 1947.

Peer Reviewed

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Language-Model Assisted Learning How to Program?

Leidner, Jochen L.; Reiche, Michael (2023)

Workshop on AI for AI Learning Held at ECAI 2023, Kakow, Poland, September 30, 2023.

Peer Reviewed

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Topic Segmentation of Educational Video Lectures Using Audio and Text

Dimitsas, Markos; Leidner, Jochen L. (2023)

Workshop on AI for AI Learning Held at ECAI 2023, Kakow, Poland, September 30, 2023.

Peer Reviewed

Bridging the Programming Skill Gap with ChatGPT: A Machine Learning Project with Business Students

Reiche, Michael; Leidner, Jochen L. (2023)

Workshop on AI for AI Learning Held at ECAI 2023, Kakow, Poland, September 30, 2023.

Peer Reviewed

Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part II

Nowaczyk, Slawomir; Biecek, Przemyslaw; Chung, Neo Christopher; Vallati, Mauro...

Communications in Computer and Information Science (CCIS) 1948.
DOI: 10.1007/978-3-031-56066-8_22

Peer Reviewed

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Tackling Learning Obstacles in Learning Videos by Thematic Ad-hoc Recom-mendations

Lehmann, Alexander; Landes, Dieter (2023)

Proc. 26th International Conference on Interactive Collaborative Learning / 52nd Int. Conf. on Engineering Pedagogy (ICL2023), S. 1499-1506.

Peer Reviewed

ABSTRACT

Learning videos enjoy great popularity in a digitalized world, especially since their use is usually possible regardless of time and location. Learners use this advantage mainly in self-study. Supervision, as for example in classroom teaching, is rather difficult and learners are usually left to their own devices when learning obstacles arise. However, not treating learning obstacles can have serious consequences, ranging from a gradual loss of the learners’ motivation to the termination of the learning project. Consequently, learning obstacles must be identified and treated in order to support an efficient learning process. Fortunately, a digital learning environment opens up many opportunities to support learners automatically. This paper explains an approach to identify potential learning obstacles in video learning based on indirect feedback. The first part of the approach to removing learning obstacles in learning videos is based on an analysis of learners' click interaction within a video to identify potential problem areas. Building on this, the second part provides first thematically relevant ad hoc video recommendations for the potentially identified learning obstacle. In order to verify whether there is actually a learning obstacle, the third part explicitly induces learners to give indirect or direct feedback on whether the recommendations have helped them and, consequently, whether they have removed an actual learning obstacle.

Improving Learning Motivation for Out-of-Favour Subjects

Böck, Felix; Landes, Dieter; Sedelmaier, Yvonne (2023)

15th International Conference on Computer Supported Education, CSEDU 2023; Prague; Czech Republic; 21 April 2023 through 23 April 2023; Code 188800 1, S. 190-200.
DOI: 10.5220/0011841400003470

Peer Reviewed

ABSTRACT

Many curricula encompass subjects that are deemed less interesting or not important by a large share of students since they cannot perceive their true significance. It is an open question how students can be compelled to get involved with these subjects after all. This paper presents a novel concept how this can be accomplished. In particular, the paper argues that four important requirements must be met, namely that learning can also be accomplished in a less formal environment than regular lectures, learning may happen independent of physical presence at the university and whenever students see themselves fit, learning is based on small units, and students enjoy getting involved in the matter. As a proof-of-concept, this approach has been used in programming education for students of electrical engineering, based on sending short summaries via WhatsApp and adding playful elements. such as quizzes. An evaluation of the proof-of-concept over two terms provides indication of the viabi lity and usefulness of the approach, but also highlights several opportunities for extensions and refinements.

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