Statistical Methods for Response Process Data

响应过程数据的统计方法

• 批准号: 2310664
• 负责人: Xueying Tang
• 金额: $ 16.17万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310664&HistoricalAwards=false
• 关键词: Statistical; Methods; Response; Process; Data

The development of technology allows for the collection of diverse data but also poses challenges in statistical analysis. This research project aims to develop methods for analyzing response process data generated from recent computer-based educational assessments. Such data provide detailed information on test-takers behaviors that traditional item response data cannot capture. However, the complex format of the data and the diversity of human behaviors make it challenging to utilize the information systematically and efficiently. This project will develop innovative methods to understand and identify individual differences in learning and problem-solving. The resulting information will be valuable for designing individualized instruction or intervention strategies to support student success and advocate inclusiveness and equity in education. Additionally, user-friendly software will be developed for practitioners' use, and this project will provide research training opportunities for graduate and undergraduate students.Response process data are an emerging type of data that tracks a respondent's interaction with computer-based items. This project aims to provide innovative, scalable, and interpretable statistical methods for utilizing rich information in response process data. Specifically, this project will focus on developing 1) a data-driven method for extracting features from process data, 2) a latent variable model for understanding how response process dynamics are driven by respondents' latent traits, and 3) a scalar-on-process regression model for describing statistical relationships between response process and other observed variables. Novel computational algorithms will be designed for statistical inference. The strong interpretability of the models will open the black box created by previous machine-learning-based approaches for process data, making it easier to validate the results and gain a deeper understanding of students' problem-solving behaviors. The outcomes of this project will enable educators to better evaluate students and design effective educational strategies.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.

技术的发展使得能够收集各种数据，但也给统计分析带来了挑战。本研究项目旨在开发用于分析最近基于计算机的教育评估产生的反应过程数据的方法。这些数据提供了传统项目反应数据无法捕捉的考生行为的详细信息。然而，数据的复杂格式和人类行为的多样性使得系统和有效地利用信息具有挑战性。该项目将开发创新的方法来理解和识别学习和解决问题的个体差异。由此产生的信息将有助于设计个性化教学或干预战略，以支持学生取得成功，并倡导教育的包容性和公平。此外，还将开发便于使用的软件供从业人员使用，该项目将为研究生和本科生提供研究培训机会。该项目旨在提供创新的，可扩展的和可解释的统计方法，以利用响应过程数据中的丰富信息。具体而言，本项目将重点开发1）用于从过程数据中提取特征的数据驱动方法，2）用于理解响应过程动态如何由受访者的潜在特质驱动的潜在变量模型，以及3）用于描述响应过程和其他观测变量之间的统计关系的标量过程回归模型。新的计算算法将被设计用于统计推断。模型的强大可解释性将打开先前基于机器学习的方法为过程数据创建的黑盒，使其更容易验证结果并更深入地了解学生的问题解决行为。该项目的成果将使教育工作者能够更好地评估学生和设计有效的教育策略。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。