Collaborative Research: Frameworks: Cyber Infrastructure for Shared Algorithmic and Experimental Research in Online Learning

协作研究：框架：在线学习中共享算法和实验研究的网络基础设施

• 批准号: 1931523
• 负责人: Neil Heffernan
• 金额: $ 189.16万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931523&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Cyber; Infrastructure

This project, RAILKaM, will create new technology that will enable twenty researchers during the grant period to run large-scale field experiments where they study basic principles in education and educational psychology in the context of both K-12 mathematics learning and university Massive Online Open Courses (MOOCs). The experiments will be delivered through adaptive learning technology embedded in learning systems already being used by over 100,000 K-12 students and hundreds of thousands of MOOC learners each year. RAILKaM will also support 75 data scientists in conducting analyses on student data after the fact, using carefully redacted datasets that protect student privacy. In facilitating high-power, replicable experiments with diverse student populations and extensive measurement, this infrastructure increases the efficiency and ease of conducting high-quality educational research in online learning environments, bringing 21st-century research methods to education for the long-term betterment of learner outcomes.This project, RAILKaM, will support researchers in more easily running scaled, highly instrumented studies on education and educational psychology, both in K-12 and university Massive Online Open Courses (MOOCs). RAILKaM will leverage ASSISTments, an online learning platform for middle school mathematics homework and classwork used by more than 100,000 students each year. In addition, RAILKaM will build functionality atop the ASSISTments platform so that educational experiments involving scaffolded problem-solving can be easily built into MOOC courses. ASSISTments will use open source APIs to integrate with MOOCs offered by the University of Pennsylvania, branching capacity for investigation to higher education while enabling richer student interactions and data collection than is typically feasible in MOOC courses. These capacities will enable researchers to run online field experiments to test interventions designed to increase student learning and engagement with a focus on how adaptive learning experiences can be optimized. These experiments will be augmented by rich data collection on learners, extending MOOC log data and ASSISTments data with several indicators of learning and engagement not previously available for research at scale. This project will develop the software infrastructure necessary to conduct experiments and collect enriched data, as well as the social infrastructure necessary to select and refine study ideas while maintaining instructor control over the activities that students experience. The combined software and social infrastructure will enable us to engage with researchers who are interested in these issues but who currently lack the infrastructure, technical capacity, or access to learners necessary to conduct high-powered or complex randomized controlled trials. This infrastructure will help these researchers to improve scientific understanding of the principles of human learning, providing a unique shared resource for learning scientists that will have considerable potential for broader impact.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.

该项目 RAILKaM 将创造新技术，使 20 名研究人员能够在资助期内进行大规模现场实验，在 K-12 数学学习和大学大规模在线开放的背景下研究教育和教育心理学的基本原理课程（慕课）。这些实验将通过嵌入学习系统中的自适应学习技术进行，每年已有超过 100,000 名 K-12 学生和数十万 MOOC 学习者在使用该系统。 RAILKaM 还将支持 75 名数据科学家使用经过仔细编辑的数据集来保护学生隐私，对学生数据进行事后分析。通过促进对不同学生群体进行高强度、可复制的实验和广泛的测量，该基础设施提高了在线学习环境中进行高质量教育研究的效率和便利性，将 21 世纪的研究方法引入教育，以长期改善学生的学习状况。 RAILKaM 项目将支持研究人员在 K-12 和大学大规模在线开放课程 (MOOC) 中更轻松地开展规模化、高度仪器化的教育和教育心理学研究。 RAILKaM 将利用 ASSISTments，这是一个用于中学数学作业和课堂作业的在线学习平台，每年有超过 100,000 名学生使用。此外，RAILKaM 将在 ASSISTments 平台上构建功能，以便将涉及支架式问题解决的教育实验轻松构建到 MOOC 课程中。 ASSISTments 将使用开源 API 与宾夕法尼亚大学提供的 MOOC 集成，将调查能力扩展到高等教育，同时实现比 MOOC 课程通常可行的更丰富的学生互动和数据收集。这些能力将使研究人员能够进行在线现场实验，以测试旨在提高学生学习和参与度的干预措施，重点关注如何优化适应性学习体验。这些实验将通过丰富的学习者数据收集得到增强，扩展 MOOC 日志数据和 ASSISTments 数据，并提供一些以前无法用于大规模研究的学习和参与指标。该项目将开发进行实验和收集丰富数据所需的软件基础设施，以及选择和完善学习想法所需的社会基础设施，同时保持教师对学生经历的活动的控制。软件和社交基础设施的结合将使我们能够与对这些问题感兴趣但目前缺乏进行高效或复杂的随机对照试验所需的基础设施、技术能力或接触学习者的研究人员合作。该基础设施将帮助这些研究人员提高对人类学习原理的科学理解，为学习科学家提供独特的共享资源，这些资源将具有相当大的潜力，产生更广泛的影响。该奖项反映了 NSF 的法定使命，并被认为值得通过使用评估来支持基金会的智力价值和更广泛的影响审查标准。

项目成果

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
• 期刊: Journal of educational data mining
• 作者: Patikorn, T.;Baker, R. S.;& Heffernan, N. T.
• 通讯作者: & Heffernan, N. T.

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
• 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.

How to Open Science: Developing and Testing Reproducibility Metrics on the Educational Data Mining Conference

如何开放科学：在教育数据挖掘会议上开发和测试再现性指标

• 发表时间: 2023
• 期刊: Proceedings of the 16th International Conference on Educational Data Mining
• 作者: Haim, A.;Gyurcssan, R.;Baxter, C.;Shaw, S.;Heffernan, N.
• 通讯作者: Heffernan, N.

Precise unbiased estimation in randomized experiments using auxiliary observational data

使用辅助观测数据在随机实验中进行精确无偏估计

• DOI: 10.1515/jci-2022-0011
• 发表时间: 2023
• 期刊: Journal of Causal Inference
• 影响因子: 1.4
• 作者: Gagnon-Bartsch, Johann A.;Sales, Adam C.;Wu, Edward;Botelho, Anthony F.;Erickson, John A.;Miratrix, Luke W.;Heffernan, Neil T.
• 通讯作者: Heffernan, Neil T.

How to Open Science: A Principle and Reproducibility Review of the Learning Analytics and Knowledge Conference

如何开放科学：学习分析和知识会议的原理和可重复性回顾

• DOI: 10.1145/3576050.3576071
• 发表时间: 2023
• 期刊: LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference
• 作者: Haim, Aaron;Shaw, Stacy;Heffernan, Neil
• 通讯作者: Heffernan, Neil