Cognitive load and representational competence: The development of an adaptive learning system to assist students with structure creation

认知负荷和表征能力：开发自适应学习系统以帮助学生进行结构创建

• 批准号: 1610084
• 负责人: Nathaniel Grove
• 金额: $ 29.42万
• 依托单位: University of North Carolina at Wilmington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1610084&HistoricalAwards=false
• 关键词: Cognitive; load; representational; competence; development

Many science, technology, engineering, and mathematics (STEM) students are required to take chemistry courses, learn to construct representations to help them visualize the structures of molecules that make up matter, and use these representations to understand the properties of matter. This structure-creation process is often difficult for students and may be hindered by issues associated with "cognitive load", which is the global amount of mental effort connected to a task. In instances where the overall cognitive load is high, students may struggle with completing the task and learning may be adversely impacted. The goals of this project are to study fundamental issues surrounding the role that cognitive load plays in undergraduate chemistry students' ability to construct representations, to use the results of these studies to develop an adaptive learning system that will help students become more proficient at creating chemical representations, and to assess the impact this system has on students' construction abilities and learning of chemistry. This work has the potential to enhance students' proficiency with chemical representations, and therefore, help them gain a more thorough understanding of key chemistry concepts that promote success in STEM.This NSF Improving Undergraduate STEM Education (IUSE: EHR) project seeks to better understand the interplay between cognitive load and students' abilities to construct representations of chemical structure. It constitutes one of the first formal research studies to use physiological metrics such as heart rate and electroencephalography to measure cognitive load as undergraduate chemistry students work to solve problems. Unlike other methods of measuring load, these techniques can be gathered in real time and have been shown, in research conducted in other disciplines, to be responsive to small changes in load. Results of these studies will then be used to design and develop an adaptive learning system that will focus on helping chemistry students construct Lewis and skeletal structures of molecules. The system will make use of an initial diagnostic system that is informed by the research results to ascertain the students' construction ability and provide Socratic-style feedback as they seek help in creating their representations. Although the system developed for this project will be specifically designed for the chemistry classroom, the general methodological approaches developed will likely have broad applicability to other STEM disciplines and serve as models for similar work in those fields.

许多科学、技术、工程和数学 (STEM) 学生需要学习化学课程，学习构建表示以帮助他们可视化构成物质的分子结构，并使用这些表示来理解物质的属性。这种结构创建过程通常对学生来说很困难，并且可能会受到与“认知负荷”相关的问题的阻碍，“认知负荷”是与任务相关的整体脑力劳动量。在整体认知负荷较高的情况下，学生可能难以完成任务，学习可能会受到不利影响。该项目的目标是研究认知负荷在本科化学学生构建表征的能力中所起的作用的基本问题，利用这些研究结果开发一个自适应学习系统，帮助学生更加熟练地创建化学表征，并评估该系统对学生的构建能力和化学学习的影响。这项工作有可能提高学生对化学表示的熟练程度，从而帮助他们更全面地了解促进 STEM 成功的关键化学概念。这个 NSF 改善本科生 STEM 教育（IUSE：EHR）项目旨在更好地了解认知负荷与学生构建化学结构表示的能力之间的相互作用。它是首批使用心率和脑电图等生理指标来测量化学本科生解决问题时的认知负荷的正式研究之一。与其他测量负载的方法不同，这些技术可以实时收集，并且在其他学科进行的研究中已被证明可以对负载的微小变化做出响应。这些研究的结果将用于设计和开发一个自适应学习系统，该系统将重点帮助化学学生构建分子的路易斯结构和骨架结构。该系统将利用基于研究结果的初始诊断系统来确定学生的构建能力，并在学生寻求帮助以创建表征时提供苏格拉底式反馈。尽管为本项目开发的系统将专门为化学课堂设计，但开发的通用方法可能对其他 STEM 学科具有广泛的适用性，并可作为这些领域类似工作的模型。