Abstract
The digital transformation of industry environments creates new demands but also opportunities for vocational education and training (VET). On the one hand, the introduction of new digital learning tools involves the risk of creating a digital parallel world. On the other hand, such tools have the potential to provide intelligent and contextualized access to information sources and learning materials. In this work, we explore approaches to provide such intelligent learning resource recommendations based on a specific learning context. Our approach aims at automatically analyzing learning videos in order to extract keywords, which in turn can be used to discover and recommend new learning materials relevant to the video. We have implemented this approach and investigated the user-perceived quality of the results in a real-world VET setting. The results indicate that the extracted keywords are in line with user-generated keywords and summarize the content of videos quite well. Also, the ensuing recommendations are perceived as relevant and useful.
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Notes
- 1.
Evonik Industries AG (2020). https://corporate.evonik.com/en. Retrieved: 2020-02-26.
- 2.
Speech Recognition Library, Anthony Zhang (2017). https://pypi.org/project/SpeechRecognition/. Retrieved: 2020-02-25.
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Schulten, C., Manske, S., Langner-Thiele, A., Hoppe, H.U. (2020). Digital Value-Adding Chains in Vocational Education: Automatic Keyword Extraction from Learning Videos to Provide Learning Resource Recommendations. In: Alario-Hoyos, C., Rodríguez-Triana, M.J., Scheffel, M., Arnedillo-Sánchez, I., Dennerlein, S.M. (eds) Addressing Global Challenges and Quality Education. EC-TEL 2020. Lecture Notes in Computer Science(), vol 12315. Springer, Cham. https://doi.org/10.1007/978-3-030-57717-9_2
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