Collaborative Research: Big Data from Small Groups: Learning Analytics and Adaptive Support in Game-based Collaborative Learning

这是一个支持计算机支持的协作学习(CSCL)的新模式的研究项目，该模式结合了基于游戏的学习和基于问题的学习的优点。好的基于游戏的学习环境将丰富的场景和引人入胜的活动结合在一起，意外地为学生提供学习。这些学习环境也为玩家提供了合作实现游戏目标的机会。良好的以问题为基础的学习环境为解决复杂和结构不良的问题提供了支持。这两种类型的学习环境的结合有望提供基于游戏的学习的参与度和丰富性，以及让学生参与真实科学的支持环境。这两个环境都是基于计算机的，因此学生和教师的行为和互动都会被捕捉下来进行分析。对采集到的数据进行学习分析，为教师提供有关学生学习的信息，为学生反思和改进学习提供学习信息，为系统设计人员提高新的CSCL环境的有效性提供信息。科学问题域是中学生的环境科学。CSCL环境是一个基于游戏的学习环境，包含了基于问题的学习。CSCL环境和学生之间的交互通过收集基于认知、情感和元认知状态的学生数据来增强，这些状态是使用人工智能技术推断的。使用特定的策略来帮助学生构建解释，有效地推理，并成为自我导向的学习者。该项目的主要成果包括基于游戏的学习的协作脚手架模型，该模型可在课堂上使用，以帮助学生学习STEM内容，以及旨在支持教师扮演引导者和合作者角色的学习分析。该项目的一个目标是广泛传播CSCL系统。

This is a research project supporting a new model of Computer Supported Collaborative Learning (CSCL) that combines the advantages of game based learning with problem based learning. Good game based learning environments combine rich scenarios with engaging activities to serendipitously provide student learning. These learning environments also provide an opportunity for players to collaborate in reaching their game goals. Good problem based learning environments provide support for the solution of complex and ill-structured problems. The combination of these two types of learning environments promises to provide the engagement and richness of game based learning with the support environment to engage students in authentic science. Both of these environments are computer based so the actions and interactions of the students and teachers are captured for analysis. Applying learning analytics to the captured data provides information on student learning for the teacher, provides learning information to the student for self-reflection and improved learning, and provides information for the system designer to improve the effectiveness of the new CSCL environment.The scientific problem domain is environmental science for middle school students. The CSCL environment is a game based learning environment that incorporates problem based learning. The interaction between the CSCL environment and the student is enhanced by the collection of data on the student based on cognitive, affective, and metacognitive states that are inferred using artificial intelligence technologies. Specific strategies are employed to help students construct explanagions, reason effectively, and become self-directed learners. Key outcomes of the project include a model of collaborative scaffolding for game based learning that is usable in classrooms to help students learn STEM content and learning analytics designed to support the teacher in the roles of guide and collaborator. A goal of the project is wide dissemination of the CSCL system.