Adaptive Sequencing and Perceptual Learning Technologies in Mathematics and Science

数学和科学中的自适应排序和感知学习技术

• 批准号: 1109228
• 负责人: Philip Kellman
• 金额: $ 126.33万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1109228&HistoricalAwards=false
• 关键词: Adaptive; Sequencing; Perceptual; Learning; Technologies

The purpose of this project is to increase our understanding of how to optimize the features of learning technology systems, which have the potential to be applied widely across domains, settings, and age groups. The team of researchers from the University of California, Los Angeles, and the University of Pennsylvania focuses on learning technology that integrates (1) principles of perceptual learning that accelerate learners' abilities to recognize key structures and relations in science and math domains, and (2) adaptive learning algorithms that use a constant stream of performance data to adapt the learning process to each individual. The collaborating partners include two K-12 schools serving diverse populations and two community colleges.The researchers will investigate the role of response time data as a novel input into both spacing and the setting of learning criteria in adaptive and perceptual learning systems. Specific hypotheses will be tested in a series of randomized controlled experiments. Students will complete pretests, posttests, and delayed posttests to evaluate gains and long-term durability of learning. The researchers aim (1) to test adaptive sequencing that utilizes learner response times (along with accuracy) to guide spacing in learning; (2) to improve learning systems based on understanding the role of response time in setting learning criteria; (3) to develop integrative adaptive and perceptual learning systems that incorporate best practices for the use of combined speed and accuracy data; and (4) to demonstrate the feasibility and effectiveness of such systems across STEM learning domains and age groups by testing learning modules for elementary mathematics and high school and college chemistry.This project is a major step in the merging of principles of cognitive science with learning technology in service of STEM learning.

该项目的目的是加深我们对如何优化学习技术系统功能的理解，这些系统有可能广泛应用于跨领域、环境和年龄组。来自加州大学洛杉矶分校和宾夕法尼亚大学的研究人员团队专注于学习技术，该技术集成了（1）感知学习原理，可加速学习者识别科学和数学领域关键结构和关系的能力，以及（2）自适应学习算法，使用恒定的性能数据流来适应每个人的学习过程。合作伙伴包括两所服务于不同人群的 K-12 学校和两所社区大学。研究人员将研究响应时间数据作为自适应和感知学习系统中的间隔和学习标准设置的新颖输入的作用。具体假设将通过一系列随机对照实验进行检验。学生将完成预测试、后测试和延迟后测试，以评估学习的收获和长期持久性。研究人员的目标是（1）测试自适应排序，利用学习者的反应时间（以及准确性）来指导学习间隔； (2) 在了解响应时间在制定学习标准中的作用的基础上改进学习系统； (3) 开发综合适应性和感知学习系统，其中纳入使用速度和准确性组合数据的最佳实践； (4) 通过测试小学数学、高中和大学化学的学习模块，展示此类系统跨 STEM 学习领域和年龄组的可行性和有效性。该项目是将认知科学原理与学习技术融合以服务于 STEM 学习的重要一步。