i-learn: empowering engineering learners using visualizations in mixed reality and machine learning ecosystems

i-learn：在混合现实和机器学习生态系统中使用可视化为工程学习者提供支持

• 批准号: 2040422
• 负责人: Ivan Mutis
• 金额: $ 55.14万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040422&HistoricalAwards=false
• 关键词: learn; empowering; engineering; learners; using

In the field of engineering education, significant research on learning with technologies has focused on either the learner’s interaction with the technology, on technology-enabled learning material, or on final learning outcomes. Research on learning with technologies involving the influence of individual cognitive function (perceptual, attentional, and cognitive skills) is relatively unexplored. There remains a gap in knowledge on how or why learners arrive at different results in the learning process. The i-learn research project bridges that divide by considering the learner’s individual characteristics as they execute problem-solving tasks while interacting with the advanced technology-enabled environments – a machine-learning (ML) and mixed-reality (MX) technology. The i-learn research project’s theoretical and empirical methods will provide fundamental knowledge about how learners’ cognitive function can be augmented through the advancements of these technologies, closing the gap in understanding the diversity of individuals’ perceptual and cognitive abilities in engineering education. The perspective of tailoring MX with ML technologies to predict individual problem-solving harnesses opportunities to research whether learners’ interactions with these technologies leads to improved task performance. i-learn creates MX-based environments with features informed by ML models not merely as interactive features but as a medium of communication for perceptive-action. By focusing on components of learning cognition (e.g., working memory and sustained attention) as well as mental simulation and situational awareness, this project (1) uses ML predictive modelling to explore the effects of individual differences on learners’ performance in problem-solving tasks modelling, (2) studies learners’ moments of impasse in problem-solving tasks that demand spatial and cognitive ability, and (3) develops and assesses the effectiveness of an interactive and adaptive MX platform for learning when used by students with different cognitive and attentional abilities. This project will improve the fundamental understanding of how technologies should be designed to enhance technology-enabled learning. It promotes embodied cognition, pioneers individualized learning using ML, and advances insights on the effect of motor activity on operations linked to visuo-spatial abilities. Research outcomes will inform the design of adaptive learning technologies and the customization of instruction in engineering education.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.

在工程教育领域，关于用技术学习的重要研究集中在学习者与技术的互动、技术支持的学习材料或最终的学习结果上。涉及个体认知功能(知觉、注意力和认知技能)影响的技术学习的研究相对较少。对于学习者如何或为什么在学习过程中得出不同的结果，仍然存在知识差距。I-Learning研究项目通过考虑学习者在执行问题解决任务时的个人特征来弥合这一鸿沟，同时与先进的技术支持环境-机器学习(ML)和混合现实(MX)技术-交互。I-Learning研究项目的理论和经验方法将提供关于如何通过这些技术的进步来增强学习者认知功能的基础知识，缩小在理解工程教育中个人感知和认知能力多样性方面的差距。用ML技术定制MX来预测个体问题解决的观点利用机会来研究学习者与这些技术的互动是否会导致任务绩效的提高。I-Learning创建了基于MX的环境，其功能由ML模型提供信息，不仅作为交互功能，而且作为感知行动的沟通媒介。通过关注学习认知的组成部分(如工作记忆和持续注意)以及心理模拟和情境意识，本项目(1)使用ML预测模型来探索个体差异对学习者在问题解决任务建模中的表现的影响；(2)研究学习者在需要空间和认知能力的问题解决任务中的僵局时刻；(3)开发和评估交互的、适应性的MX学习平台在不同认知和注意能力的学生使用时的有效性。该项目将提高对如何设计技术以加强技术支持学习的基本理解。它促进了体验式认知，开创了使用ML的个性化学习，并对运动活动对与视觉空间能力有关的操作的影响提出了见解。研究成果将为工程教育中适应性学习技术的设计和教学的定制提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。