CIF21 DIBBs: PD: Enhancing and Personalizing Educational Resources through Tools for Experimentation

CIF21 DIBB：PD：通过实验工具增强和个性化教育资源

• 批准号: 1724889
• 负责人: Neil Heffernan
• 金额: $ 49.46万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724889&HistoricalAwards=false
• 关键词: CIF21; DIBBs; PD; Enhancing; Personalizing

This project would automate the creation and data analysis of randomized controlled experiments (RCEs). RCEs are question sets designed and delivered to teachers and students in a classroom setting, and can be used to compare alternative educational strategies. This effort builds on an existing educational platform (ASSISTments) developed by the Principal Investigator, and uses a template-based approach to increase the efficiency and reliability of conducting educational research. The goal is to lower the barriers to creating and learning from randomized controlled experiments. The project has the potential to facilitate large-scale learning in education research, reaching hundreds of schools and thousands of students.The project builds upon two prior developments by this team. - ASSISTments is an online learning platform originally designed to provide students with assistance and teachers with assessments (establishing the moniker).  The system is used primarily as an online tutoring system for middle or secondary education, supporting the delivery, collection, and grading of classwork and homework, providing immediate feedback for students and explicit reporting for teachers. To date, 24 randomized controlled experiments comparing educational strategies have been published using this platform. - In addition, AssistmentsTestBed.org is a testbed developed by the PI and his group under a separate NSF grant (#1440753), to identify best practices in education and allow other researchers to propose and run their own studies leveraging ASSISTments as a shared scientific instrument through this testbed. Beneficiaries include education researchers, teachers, and students, with the existing tool being used in over 500 schools and in the education of over 50,000 students. The current project improves two components of the infrastructure that have been resource-intensive bottlenecks in prior research. One task automates the process of study creation and data analysis, through development of a Template Tool that enables studies within ASSISTments. A second task automates statistical analyses and improves usability of the existing data reporting tool (Assessment of Learning Infrastructure, or ALI). The improvements will be achieved, in part, by applying educational data mining algorithms (i.e., deep knowledge tracing) on student data collected before, during, and after experimentation. These analytics will provide researchers with covariates that will significantly improve the agenda of personalizing education. The resulting capability will assist researchers as they design and deliver question sets to teachers and students in a classroom setting, increase the efficiency and reliability of conducting educational research at scale, and streamline the research processes.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the NSF Directorate for Education and Human Resources, Division of Research on Learning in Formal and Informal Settings.

该项目将自动创建随机对照实验 (RCE) 并进行数据分析，这些问题集是在课堂环境中设计并交付给教师和学生的，可用于比较替代教育策略。这项工作建立在现有的教育策略的基础上。平台（ASSISTments）由首席研究员开发，并使用基于模板的方法来提高进行教育研究的效率和可靠性。该项目有潜力降低创建和学习随机对照实验的障碍。促进教育研究中的大规模学习，数百所学校和数千名学生。该项目建立在该团队之前的两项开发之上 - ASSISTments 是一个在线学习平台，最初旨在为学生提供帮助并为教师提供评估（该系统主要用作评估）。用于初中或中等教育的在线辅导系统，支持课堂作业和家庭作业的交付、收集和评分，为学生提供即时反馈并为教师提供明确的报告。迄今为止，已使用该平台发布了 24 个比较教育策略的随机对照实验。 -在此外，AssismentsTestBed.org 是由 PI 及其团队在单独的 NSF 拨款 (#1440753) 下开发的一个测试平台，旨在确定教育方面的最佳实践，并允许其他研究人员利用 ASSISTments 作为共享科学工具来提出和开展自己的研究，通过该测试平台的受益者包括教育研究人员、教师和学生，现有工具已在 500 多所学校和超过 50,000 名学生的教育中使用。当前的项目得到了改进。基础设施的两个组成部分在先前的研究中一直是资源密集型瓶颈。一项任务是通过开发模板工具来自动化研究创建和数据分析的过程，第二项任务是自动化统计分析并提高可用性。现有的数据报告工具（学习基础设施评估，或 ALI）将通过对实验之前、期间和之后收集的学生数据应用教育数据挖掘算法（即深度知识追踪）来实现。 。这些分析将为研究人员提供协变量，从而显着改善个性化教育的议程，从而帮助研究人员在课堂环境中设计并向教师和学生提供问题集，提高大规模进行教育研究的效率和可靠性。 ，并简化研究流程。该奖项由先进网络基础设施办公室颁发，并得到美国国家科学基金会教育和人力资源理事会、正式和非正式环境学习研究部的共同支持。

项目成果

Testing the Validity and Reliability of Intrinsic Motivation Inventory Subscales within ASSISTments

测试 ASSISTments 中内在动机量表分量表的有效性和可靠性

• 发表时间: 2018-01
• 期刊: Proceedings of the Nineteenth International Conference on Artificial Intelligence in Education
• 作者: Ostrow, K. S.;Heffernan, N. T.
• 通讯作者: Heffernan, N. T.

How Flexible Is Your Data? A Comparative Analysis of Scoring Methodologies across Learning Platforms in the Context of Group Differentiation

您的数据有多灵活？

• DOI: 10.18608/jla.2017.42.9
• 发表时间: 2017-07
• 期刊: Journal of Learning Analytics
• 影响因子: 3.9
• 作者: Ostrow, Korinn;Wang, Yan;Heffernan, Neil
• 通讯作者: Heffernan, Neil

The automated grading of student open responses in mathematics

学生数学开放式回答的自动评分

• DOI: 10.1145/3375462.3375523
• 发表时间: 2020-03
• 期刊: Tenth International Conference on Learning Analytics & Knowledge
• 作者: Erickson, John A.;Botelho, Anthony F.;McAteer, Steven;Varatharaj, Ashvini;Heffernan, Neil T.
• 通讯作者: Heffernan, Neil T.

ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface.

ASSISTments 纵向数据挖掘竞赛特刊：前言。

• DOI: 10.5281/zenodo.4008048
• 发表时间: 2020-08
• 期刊: Journal of educational data mining
• 作者: Patikorn, T.;Baker, R. S.;& Heffernan, N. T.
• 通讯作者: & Heffernan, N. T.

Effectiveness of Crowd-Sourcing On-Demand Assistance from Teachers in Online Learning Platforms

在线学习平台中教师众包按需协助的有效性

• DOI: 10.1145/3386527.3405912
• 发表时间: 2020-08
• 期刊: Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S
• 作者: Patikorn, Thanaporn;Heffernan, Neil T.
• 通讯作者: Heffernan, Neil T.