Design and Practice of a Classroom Feedback Teaching Model with Skewness-Peakiness Test Method
- DOI
- 10.2991/978-94-6463-172-2_100How to use a DOI?
- Keywords
- skewness-peakiness test; teaching model; all-time; new-field; learning metaverse
- Abstract
Entering the metaverse era, immersive learning, as a unique and chic form of learning, has gradually become a normalized way of learning. Based on the flow theory, this research constructs a all-time, new-field learning metaverse teaching model to empower traditional classrooms, and analyzes the classroom effects of this teaching model using data generated from teaching practices as samples using SPSS24.0 software. The results show that the overall effect of the all-time, new-field learning metaverse model in the classroom is significant, and there is a positive relationship between learners’ engagement in the all-time, new-field learning metaverse classroom and their immersion experience. There is a positive effect on the exercise of learners’ thinking and the cultivation of their creative abilities.
- Copyright
- © 2023 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 - Yi Ding AU - Ruogu Zhang AU - Ningning Zhang AU - Zhuzhu Zhang AU - Qi Cheng AU - Shu Zhang PY - 2023 DA - 2023/06/30 TI - Design and Practice of a Classroom Feedback Teaching Model with Skewness-Peakiness Test Method BT - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023) PB - Atlantis Press SP - 950 EP - 959 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-172-2_100 DO - 10.2991/978-94-6463-172-2_100 ID - Ding2023 ER -