Collaborative Research: Adaptable Game-based, Interactive Learning Environments for STEM Education (AGILE STEM)

所有年龄段的学习者都应该准备好与新兴和技术驱动的工作环境进行互动。此外，人们对在线学习的日益依赖，以及新冠肺炎疫情导致的在线学习前所未有的加速，预计将永远改变教育格局。因此，有必要促进数字教学平台的发展。机器学习和高保真模拟环境等新兴技术有可能创造定制和适应性强的学习环境，以支持STEM学习成果。该项目通过推进有关设计和创建基于游戏的适应性互动STEM学习环境的知识，服务于国家利益。在这些学习环境的设计阶段纳入代表性不足的少数民族和女性学习者，其可移植性以及这些环境的定制和适应性，有可能增强教育平等、参与和学习成果，并将其可用性扩大到多个STEM领域。此外，叙述和模拟模型的灵感来自现实世界的系统。因此，学习环境有望提高学习者对传统教学方法难以理解的复杂系统概念的理解，并有助于为美国未来的STEM劳动力培养急需的技能。拟议的新兴技术不一定需要使用专门的设备，这消除了可扩展性和边界实施和使用的障碍。该项目的主要目标是自动定制和适应三维（3D）模拟游戏学习环境，以提高参与度，并更深入地了解它们的设计、开发和部署，对学习和自我调节学习（SRL）技能的影响，以及从学习环境到现实应用的知识可转移性。该项目解决了在以下重点领域缺乏科学证据和/或工作的问题：1)使用新兴和先进的机器学习方法减少基于3D模拟游戏的学习环境生成内容障碍的潜力；2)创建定制的内容和自适应的3D模拟游戏为基础的学习环境，以提高和保持学习者的动机和参与，通过教学辅助内容脚手架加强学习，并增加知识从游戏到现实应用的可转移性；3)评估在线和住宿环境中所有学习者群体的学习环境的有效性；4)探索基于模拟游戏的学习环境中的学习者决策和行为数据，以及眼动、面部表情、生物信号和使用数据如何增强对决策/使用与SRL技能发展之间关系的认识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Learners of all ages are expected to be prepared to interact with emerging and technology-driven work environments. In addition, the growing reliance on online learning and its unprecedented and unexpected acceleration due to the COVID-19 pandemic are expected to change the education landscape forever. Thus, there is a need to grow the development of digital platforms for teaching and learning. Emerging technologies such as machine learning and high fidelity simulated environments have the potential to create customized and adaptable learning environments to support STEM learning outcomes. This project serves the national interest by advancing the knowledge about designing and creating adaptable game-based, interactive learning environments for STEM. The inclusion of underrepresented minority and female learners in the design stages of these learning environments, their portability, as well as the capability of these environments to be customized and adaptive have the potential to enhance education equality, engagement, and learning outcomes, and broaden their usability to several STEM domains. Moreover, the narratives and simulation models are inspired by real-world systems. Therefore, the learning environments are expected to enhance the learner’s understanding of complex system concepts that are challenging to understand using traditional teaching approaches and will help build the much-needed skills for the U.S. future STEM workforce. The proposed emerging technologies do not necessarily need access to specialized equipment, which eliminates barriers to scalability and border implementation and use. The primary goals of this project are to automatically customize and adapt three-dimensional (3D) simulated game-based learning environments to improve engagement, and provide a deeper understanding of their design, development, and deployment, impact on learning and self-regulated learning (SRL) skills, and knowledge transferability from the learning environments to real-life applications. The project addresses the lack of scientific evidence and/or work in the following thrust areas: 1) the potential of reducing the barriers to content generation of 3D simulated game-based learning environments using emerging and advanced machine-learning methods; 2) creating customized content and adaptive 3D simulated game-based learning environments that improve and maintain learners motivation and engagement, enhance learning via instructional assistive content scaffolding, and increase knowledge transferability from game to real-life applications; 3) assessing the effectiveness of the learning environments for all learner groups in online and residential settings; and 4) exploring how learner decision-making and behavior data in the simulated game-based learning environments, and eye-tracking, facial expressions, bio-signals, and usage data, enhance knowledge about the relationships between decision-making/usage and SRL skills development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.