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

协作研究：来自小组的大数据：基于游戏的协作学习中的学习分析和自适应支持

• 批准号: 1561655
• 负责人: James Lester
• 金额: $ 124.96万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561655&HistoricalAwards=false
• 关键词: Collaborative; Research; Big; Data; Small

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 promise 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.

这是一个支持计算机支持协作学习（CSCL）新模型的研究项目，该模型结合了基于游戏的学习和基于问题的学习的优点。良好的基于​​游戏的学习环境将丰富的场景与引人入胜的活动结合起来，为学生提供偶然的学习机会。这些学习环境还为玩家提供了协作实现游戏目标的机会。良好的基于​​问题的学习环境为解决复杂和结构不良的问题提供支持。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支持环境，让学生参与真正的科学。这两种环境都是基于计算机的，因此学生和教师的行为和交互都会被捕获以进行分析。将学习分析应用于捕获的数据，为教师提供有关学生学习的信息，为学生提供学习信息以进行自我反思和改进学习，并为系统设计者提供信息以提高新的 CSCL 环境的有效性。科学问题领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，其中包含基于问题的学习。基于使用人工智能技术推断的认知、情感和元认知状态收集学生的数据，从而增强了 CSCL 环境与学生之间的互动。采用特定的策略来帮助学生构建解释、有效推理并成为自主学习者。该项目的主要成果包括基于游戏的学习的协作支架模型，该模型可在课堂上使用，帮助学生学习 STEM 内容，以及旨在支持教师扮演指导者和协作者角色的学习分析。该项目的目标是广泛传播 CSCL 系统。

项目成果

Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network

• DOI: 10.18653/v1/2022.sigdial-1.47
• 发表时间: 2022
• 作者: Kyungjin Park;Hyunwoo Sohn;Wookhee Min;Bradford W. Mott;Krista D. Glazewski;C. Hmelo‐Silver;James Lester

A Real-time Teacher Dashboard for a Game-based Collaborative Inquiry Learning Environment

基于游戏的协作探究学习环境的实时教师仪表板

• 发表时间: 2022
• 期刊: In Proceedings of the International Conference of the Learning Sciences
• 作者: Chen, Y.;Bae, H.;Saleh, A.;Uttamchandani, S.;Hmelo-Silver, C. E.;Glazewski, K.;Mott, B. W.;Lester, J.
• 通讯作者: Lester, J.