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: 1440996
• 负责人: Caitlin Kelleher
• 金额: $ 42.2万
• 依托单位: Washington University
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440996&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?

华盛顿大学将与圣路易斯科学中心合作，开发和评估一种使用个性化学习途径的计算机编程环境，以更好地吸引和支持年轻学习者。在正规和非正规教育环境中，已解决问题的例子都是一种重要的学习资源，能够实现自学，并支持课堂上固有的异质性。最近对不同经验水平的程序员进行的研究表明，选择和调整在网络上分享的示例代码通常用于支持及时学习和获取不常用的技术。然而，尽管例子可以是强大的学习工具，但它们必须与学习者的经验水平和学习偏好很好地匹配。项目工作人员将开发计算机算法，以预测未来哪些问题和示例将使特定学习者的学习最大化。通过示例学习的研究表明，通过选择和呈现示例及其相关练习问题来控制给定学习者的认知负荷，可以提高学习者在近迁移和远迁移任务上的成功率。这一建议假设，通过仔细控制认知负荷，将有可能构建个性化的学习路径，帮助学习者有效地从新手理解到掌握一个概念。本项目旨在回答以下问题：1)在给定学习者的历史的情况下，是否有可能预测未来问题的认知负荷，并据此为该学习者选择合适的下一个问题？2)学习者历史中哪些因素最能预测未来问题的认知负荷？3)是否有可能有效地利用预测的认知负荷为个体学习者构建个性化的学习路径？