数据驱动的协作学习活动设计与优化策略研究

The Study on Designing and Optimizing Collaborative Learning Based on the Data-driven Approach

协作学习活动的设计和优化无论在理论还是实践层面都是一大难题。目前,主要存在重结果轻过程、重经验轻数据、数据来源单一、不重视设计、优化体系不完善等问题。事实上,基于多源数据的设计与优化才能使得协作学习达到理想的效果。该文提出基于数据驱动的协作学习活动设计和优化策略,并通过六次协作学习活动探索如何科学设计并优化协作学习活动。采用的数据来源包括协作学习设计方案的数据、设计与实施的一致性、协作学习实施过程中信息流的属性以及协作学习结果四大类11个指标。优化策略包括优化任务设计、提供多元媒体类型、设计不同难度的目标和任务、设计认知和元认知脚手架、搭建协作共享环境、明确交互规则等。研究结果表明,优化后的协作学习活动较优化前在方案质量、设计与实施的一致性、协作学习的信息流属性以及协作学习结果均有显著提升。

The design and optimization of collaborative learning activities have been a major concern.So far,there are many problems such as stressing results over processes,stressing experiences over data,paying no attention to design,imperfect optimization and so on.This study proposed a data-driven approach to designing and optimizing six collaborative learning activities.The data sources included collaborative learning design plans,the consistency between design and implementation,attributes of information flows during collaborative learning,and collaborative learning results.Totally,there are eleven indicators among these four categories.The proposed optimization strategies included optimizing task design,providing multiple media types,designing different levels objectives and tasks,providing cognitive and meta-cognitive scaffolding,building shared collaborative learning environments,and making appropriate interaction rules.The results indicated that the design quality of collaborative learning,the consistency between design and implementation,attributes of information flows,and collaborative learning results significantly improved after optimization.