Personalized learning efficiency data analysis based on multi-scale convolution architecture and hybrid loss-Reference-Cited by-同舟云学术

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Personalized learning efficiency data analysis based on multi-scale convolution architecture and hybrid loss

Published:2023-10-19 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Jin Dan,Wen Xiaolan,Wen Yiming

Funder

Zhejiang Research Institute of Education Sciences

Teaching Reform Project in Zhejiang Institute of Communications

Annual Research Project funded by Zhejiang Institute of Communications

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-09099-3.pdf

Reference37 articles.

1. Raj NS, Renumol VG (2022) A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. J Comput Educ 9(1):113–148

2. Bhutoria A (2022) Personalized education and artificial intelligence in United States, China, and India: a systematic review using a human-in-the-loop model. Comput Educ: Artif Intell 1:100068

3. Watters A (2023) Teaching machines: the history of personalized learning. MIT Press

4. Alamri HA, Watson S, Watson W (2021) Learning technology models that support personalization within blended learning environments in higher education. TechTrends 65:62–78

5. Alam A (2022) Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses: Reforming education in the age of artificial intelligence. In: advanced computing and intelligent technologies: proceedings of ICACIT 2022. Singapore: Springer Nature Singapore, pp 395–406

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