Investigating the Impact of Co-Learning Systems in Providing Customized, Real-Time Student Feedback

目前STEM教育的一个前沿是开发个性化的学习环境，帮助学生学习具有挑战性的主题。在主动学习课程和实验室活动期间，这种需求尤其迫切。 该项目将开发共同学习系统，以提高学生在STEM实验室活动中的表现。共同学习系统由配备传感器的计算机系统组成。这些系统可以向人类学习，并与人类互动，为他们提供定制的实时性能反馈。 从该项目中吸取的经验教训也可以应用于其他领域的STEM教育，如K-12环境，在那里共同学习机器人实验室导师可以帮助减轻对科学教师的时间和注意力的一些需求。学习系统能够通过为学生提供定制的实时表现反馈来提高学生在本科STEM实验室活动中的表现。该项目将利用现有的商用现成技术开发这些系统。 传感器输入将包括使用多模态传感设备收集的音频、视频、深度、骨架和3D网格数据。共同学习系统将被集成到实验室环境中，以捕获和翻译与STEM课堂环境和学生在实验室活动中的互动有关的数据。数据的类型将包括与学生口头查询有关的音频数据、与学生手势模式有关的骨架数据以及学生作业的内容。 该项目将研究适合在STEM实验室活动中发现与学生学习有关的知识的机器学习算法。 工作将评估学生对共同学习系统的看法，并评估共同学习系统在实验室期间提高性能的能力。研究人员将比较学生学习成果之间的可比学生群体受到不同数量的互动与共同学习系统。这些结果将提供有关共同学习系统的潜力的信息，以增强传统教学，并提供一个有效的，自动化的，个人的STEM学习环境。

A current frontier in STEM education is the development of individualized learning environments that can help students learn challenging topics. The need is especially acute during active learning sessions and laboratory activities. This project will develop co-learning systems to enhance student performance in STEM laboratory activities. Co-learning systems consist of computer systems equipped with sensors. These systems can learn from humans and in turn interact with humans providing them with customized, real-time performance feedback. The lessons learned from the project can also be applied in other areas of STEM education such as the K-12 environment where co-learning robotic laboratory tutors could help alleviate some of the demands on the time and attention of science teachers.The specific research objective of this Improving Undergraduate STEM Education (IUSE) project is to test the hypothesis that co-learning systems are able to enhance student performance in undergraduate STEM laboratory activities by providing them with customized, real-time performance feedback. The project will utilize existing commercial, off-the-shelf technologies in development of the systems. Sensor input will consist of audio, video, depth, skeletal, and 3D mesh data collected using a multimodal sensing device. The co-learning systems will be integrated into the laboratory environments to capture and translate data pertaining to STEM classroom environments and student interactions during laboratory activities. The type of data will include audio data pertaining to student verbal queries, skeletal data pertaining to student gesture patterns, and the content of student work. The project will investigate machine learning algorithms suitable for discovering knowledge pertaining to student learning during STEM laboratory activities. Work will assess the students' perception of co-learning systems and evaluate the ability of co-learning systems to improve performance during laboratories. The investigators will compare student learning outcomes between comparable student groups subject to different amounts of interaction with the co-learning systems. These results will provide information about the potential of co-learning systems to augment traditional teaching and provide an effective, automated, personal STEM learning environment.