Using deep learning to understand the level of attention and engagement of students. Ensuring privacy, having real-time feedback on the delivery of coursework will help lecturers/presenters, make improvements vs. waiting once or twice a year for this information.
This deep dive is all about neural networks - training them using best practices, debugging them and maximizing their performance using cutting edge research.
Students in the future will be able to personalise their learning while teachers can monitor their engagement and behaviour, according to ed-tech experts. Opening the EdTechX conference in London today, Benjamin Vedrenne-Cloquet said the future of education lies with artificial intelligence and deep learning, citing the movement towards data and "deep tech" in new ed-tech companies, away from the "lighter tech" of digitisation of content seen at the beginning of the decade.
note footnote at the bottom: "http://www.sciencemag.org/content/313/5786/504.abstract, http://www.cs.toronto.edu/~amnih/cifar/talks/salakhut_talk.pdf. In a strict sense, this work was obsoleted by a slew of papers from 2011 which showed that you can achieve similar results to this 2006 result with “simple” algorithms, but it’s still true that current deep learning methods are better than the best “simple” feature learning schemes, and this paper was the first example that came to mind. [return]"
Z. Long, Y. Lu, und B. Dong. (2018)cite arxiv:1812.04426Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:1710.09668.
A. Dulny, A. Hotho, und A. Krause. KI 2022: Advances in Artificial Intelligence - 45th German Conference on AI, Trier, Germany, September 19-23, 2022, Proceedings, Volume 13404 von Lecture Notes in Computer Science, Seite 75--89. Springer, (2022)