Research on the Construction of Incorporating Multimodal Large Models into the BOPPPS Teaching Model
- DOI
- 10.2991/978-94-6463-600-0_11How to use a DOI?
- Keywords
- Multimodal; large model; BOPPPS teaching model
- Abstract
Multimodal large models are increasingly becoming a hot topic in the field of artificial intelligence, with significant advancements in general domains, yet they are still in their infancy in education. The BOPPPS teaching model, which is student-centered and oriented around educational objectives, has greatly enhanced teaching effectiveness and is widely applied. This paper first combines multimodal large models with the BOPPPS teaching model to explore the advantages of applying large models in the implementation of BOPPPS, establishing a BOPPPS teaching model supported by large models to promote students’ active knowledge construction and the cultivation of higher-order thinking skills. Secondly, it compares the BOPPPS teaching model supported by multimodal large models with the traditional BOPPPS model from multiple dimensions. Finally, it analyzes the challenges that large models face in course teaching.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Danyang Liu AU - Weiwei Lian AU - Xianglian Jin PY - 2024 DA - 2024/12/13 TI - Research on the Construction of Incorporating Multimodal Large Models into the BOPPPS Teaching Model BT - Proceedings of the 4th International Conference on New Media Development and Modernized Education (NMDME 2024) PB - Atlantis Press SP - 82 EP - 91 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-600-0_11 DO - 10.2991/978-94-6463-600-0_11 ID - Liu2024 ER -