Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China.
School of Information Technology in Education, South China Normal University, Guangzhou 510631, China.
Int J Environ Res Public Health. 2022 Jun 7;19(12):6984. doi: 10.3390/ijerph19126984.
Research on online collaborative learning has explored various methods of collaborative improvement. Recently, learning analytics have been increasingly adopted for ascertaining learners' states and promoting collaborative performance. However, little effort has been made to investigate the transformation of collaborative states or to consider cognitive load as an essential factor for collaborative intervention. By bridging collaborative cognitive load theory and system dynamics modeling methods, this paper revealed the transformation of online learners' collaborative states through data analysis, and then proposed an optimized mechanism to ameliorate online collaboration. A quasi-experiment was conducted with 91 college students to examine the potential of the optimized mechanism in collaborative state transformation, awareness of collaboration, learning achievement, and cognitive load. The promising results demonstrated that students learning with the optimized mechanism performed significantly differently in collaboration and knowledge acquisition, and no additional burden in cognitive load was noted.
在线协作学习的研究已经探索了各种协作改进的方法。最近，学习分析越来越多地被用于确定学习者的状态和促进协作表现。然而，很少有研究致力于探讨协作状态的转变，或者将认知负荷作为协作干预的一个重要因素来考虑。本研究通过将协作认知负荷理论和系统动力学建模方法相结合，通过数据分析揭示了在线学习者协作状态的转变，然后提出了一种优化机制来改善在线协作。通过一项有 91 名大学生参与的准实验，检验了优化机制在协作状态转变、协作意识、学习成绩和认知负荷方面的潜力。结果表明，使用优化机制学习的学生在协作和知识获取方面的表现有显著差异，并且认知负荷没有增加。
