Mental imagery scaffolding: The effects of detail richness and text load on geography learning

Yun Zhou, Fanqi Yi, Bingyu Dong,Guangli Zhang,Yi Zhang,Tao Xu

Education and Information Technologies(2024)

引用 0|浏览0
暂无评分
摘要
The growing importance of 3D animations in current teaching approaches becomes increasingly apparent, offering an effective way to visualize complex spatial concepts and processes in geography learning through outstanding visual representation and details. However, the effects of detail richness and text load of 3D animation on learning about processes remain unclear. Addressing this research gap, the present study adopts a quasi-experimental design involving four classes ( n = 106) in the context of a geography lesson and evaluates four conditions in a 2 × 2 between-subjects design consisting of detail richness (high vs. low) and text load (high vs. low). The lessons on the rotation and revolution of the Earth were delivered by the same instructor across all conditions. Knowledge acquisition, cognitive load, learning experience, and emotions of students were measured. The results revealed that students were significantly better able to acquire knowledge immediately when exposed to the high detailed visuals but low text load condition. Low detail richness and high text load independently resulted in increased cognitive load. We also observed a significant effect of detail richness on the dimensions of pleasure and arousal, with higher levels of details associated with larger values in these dimensions. This research suggests that when the learning objective necessitates the engagement of mental imagery, incorporating detailed visuals can facilitate learning. The findings contribute to our understanding of how detailed imagery is linked to learning objectives about processes and expand our knowledge regarding the design of detail richness and text of 3D animation in the context of geography learning.
更多
查看译文
关键词
3D animation,Detail richness,Visual representation,Realism,On-screen text,Geography learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要