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.

这是一个研究项目，支持一种新的计算机支持协作学习模型（CSCL），该模型将基于游戏学习的优势与基于问题的学习相结合。良好的基于​​游戏的学习环境将丰富的场景与参与活动相结合，以偶然地提供学生学习。这些学习环境还为玩家提供了一个合作实现其游戏目标的机会。良好的基于​​问题的学习环境为解决复杂和结构性不良问题的解决方案提供了支持。这两种类型的学习环境的结合承诺将提供基于游戏的学习的参与和丰富性与支持环境，使学生参与真实科学。这两个环境都是基于计算机的，因此捕获了学生和老师的动作和互动以进行分析。将学习分析应用于捕获的数据提供有关教师学生学习的信息，向学生提供学习信息以进行自我反思和改善学习，并为系统设计师提供信息，以提高新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，结合了基于问题的学习。 CSCL环境与学生之间的相互作用通过使用人工智能技术推断出的认知，情感和元认知状态来增强学生的数据。采用特定策略来帮助学生构建解释，有效理性并成为自我指导的学习者。该项目的关键结果包括用于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内容和学习分析，旨在为教师提供指南和合作者的角色。该项目的一个目标是对CSCL系统的广泛传播。

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.