Abstract
Recently, multiple applications of machine learning have been introduced. They include various possibilities arising when image analysis methods are applied to, broadly understood, video streams. In this context, a novel tool, developed for academic educators to enhance the teaching process by automating, summarizing, and offering prompt feedback on conducting lectures, has been developed. The implemented prototype utilizes machine learning-based techniques to recognise selected didactic and behavioural teachers’ features within lecture video recordings. Specifically, users (teachers) can upload their lecture videos, which are preprocessed and analysed using machine learning models. Next, users can view summaries of recognized didactic features through interactive charts and tables. Additionally, stored ML-based prediction results support comparisons between lectures based on their didactic content. In the developed application text-based models trained on lecture transcriptions, with enhancements to the transcription quality, by adopting an automatic speech recognition solution are applied. Furthermore, the system offers flexibility for (future) integration of new/additional machine-learning models and software modules for image and video analysis.
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Notes
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We have an ethical acceptance from the NTU to use this dataset in the reported experiments.
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Acknowledgements
This work is done in cooperation with Nanyang Technological University, in the frame of OMINO (Overcoming Multilevel Information Overload) grant (no 101086321) funded by the European Union under the Horizon Europe and by the Polish Ministry of Education and Science within (International Projects Co-Financed program). (However, the views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the European Research Executive Agency can be held responsible for them.)
This research was also carried out with the support of the Faculty of Mathematics and Information Science at Warsaw University of Technology, its Laboratory of Bioinformatics and Computational Genomics, and the High-Performance Computing Center.
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Wróblewska, A. et al. (2024). Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries. In: Herodotou, C., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 14th International Conference. MIS4TEL 2024. Lecture Notes in Networks and Systems, vol 1171. Springer, Cham. https://doi.org/10.1007/978-3-031-73538-7_12
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