功能可见性
协作学习
计算机科学
感知
建设性的
工作区
计算机支持的协作学习
人机交互
过程(计算)
心理学
知识管理
人工智能
神经科学
机器人
操作系统
作者
Chen‐Chung Liu,I-Chen Hsieh,Wen Chen,Ming Chang,Shih Hsun Fan Chiang,Meng‐Jung Tsai,Chau‐Lyan Chang,Fu‐Kwun Hwang
标识
DOI:10.1016/j.compedu.2020.104029
摘要
Recent advancements in Web and HTML5 techniques have made collaborative simulations able to support a shared workspace and more synchronous collaboration in a single simulation. However, previous studies show divergent findings regarding the effects of the new features of collaborative simulations. To better understand the affordances and limitations of collaborative simulations, this study analyzed how 64 students learned in two different collaborative learning settings: individual-based simulation and collaborative simulation. To better reveal how students attended to different parts of the simulation, the researchers collected eye movement data and analyzed students’ discourse, perceptions of collaboration and learning performance. The findings revealed that the two groups demonstrated similar levels of learning gains. However, the students who used the collaborative simulation displayed lower levels and less frequency of individual attention to the simulation than those using the individual-based simulation. However, the joint attention and discourse analysis suggests that students learning with the collaborative simulation are more likely to engage in effective collaborative learning while demonstrating more constructive discourse threads than those using the individual-based simulation. The findings support that the collaborative simulations transformed the learning experience into a cohesive collaborative learning process. This study also found that the relationship between the joint attention and the discourse features as well as the perceived collaborative experience are not decisive but are influenced by the features of the collaborative learning environment. This article discusses the implications of the use of collaborative simulations and eye movement for supporting collaborative learning, and addresses the direction for future studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI