BPEC: Collaborative Research: Creating Personalized Learning Pathways by Managing Cognitive Load

BPEC：协作研究：通过管理认知负荷创建个性化学习路径

• 批准号: 1440864
• 负责人: Paul Freiling
• 金额: $ 5.35万
• 依托单位: St Louis Science Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440864&HistoricalAwards=false
• 关键词: BPEC; Collaborative; Research; Creating; Personalized

Washington University, in collaboration with the St. Louis Science Center will develop and evaluate a computer programing environment that uses personalized learning pathways to better engage and support young learners. In both formal and informal education settings, examples of solved problems are an important learning resource, enabling self-teaching, and supporting the inherent heterogeneity found in classrooms. Recent studies of programmers at varying experience levels revealed that selecting and adapting example code shared on the web is often used to support just in time learning and to access infrequently used techniques. However, while examples can be powerful learning tools, they must be well-matched to the learner?s experience level and learning preferences. The project staff will develop computer algorithms that predict what future problems and examples will maximize learning for a specific learner.Research on learning via examples has demonstrated that by controlling the cognitive load for a given learner through the selection and presentation of examples and their related practice problems, it is possible to improve learner?s success on near and far transfer tasks. This proposal hypothesizes that by carefully controlling the cognitive load, it will be possible to construct personalized learning pathways that help a learner to efficiently move from a novice understanding to mastery of a concept. This project aims to answer the following questions:1) Is it possible to predict the perceived cognitive load for a future problem, given a learner?s history and use this to select an appropriate next problem for that learner? 2) What are the factors of a learner?s history that are most predictive of the perceived cognitive load for a future problem?3) Is it possible to effectively use the predicted cognitive load to construct personalized learning pathways for an individual learner?

华盛顿大学与圣路易斯科学中心合作将开发和评估计算机编程环境，该环境使用个性化学习途径更好地吸引和支持年轻学习者。在正规和非正式的教育环境中，解决问题的例子是一个重要的学习资源，实现了自我教学，并支持教室中固有的异质性。对各种经验水平的程序员的最新研究表明，在网络上共享和适应示例代码通常用于在及时学习中支持并访问不经常使用的技术。但是，尽管示例可以是强大的学习工具，但必须与学习者的经验水平和学习偏好相匹配。 项目人员将开发计算机算法，以预测哪些未来的问题和示例将最大化特定学习者的学习。通过示例进行学习的搜索表明，通过选择和呈现示例及其相关实践问题的选择和呈现，通过控制给定学习者的认知负担及其相关的实践问题，可以在近距离转移任务上提高学习者的成功。该建议假设，通过仔细控制认知负担，可以构建个性化的学习途径，以帮助学习者有效地从新手理解到掌握概念。该项目旨在回答以下问题：1）考虑到学习者的历史并使用它来为该学习者选择适当的下一个问题，是否有可能预测未来问题的认知负荷？ 2）学习者的历史是什么因素最能预测未来问题的认知负荷？3）是否有可能有效利用预测的认知负担来为个别学习者构建个性化的学习途径？