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CAREER: Mapping and enhancing the acquisition of conceptual knowledge using behavior, neural signals, and natural language processing models

职业：使用行为、神经信号和自然语言处理模型来映射和增强概念知识的获取

基本信息

批准号：
2145172
负责人：
Jeremy Manning
金额：
$ 88.16万
依托单位：
Dartmouth College
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2027-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145172&HistoricalAwards=false
关键词：
CAREER Mapping enhancing acquisition conceptual

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The project is also funded from the EHR Core Research (ECR) program with co-funding from the Human Networks and Data Science (HNDS) program in SBE.The goal of this CAREER project at Dartmouth University is to advance our understanding of how students learn STEM concepts through online course videos in order to improve online education. Continued expansion of the internet backbone and improvements in computing hardware have facilitated improvements in video streaming, enabling videos to be more easily downloaded and shared. This, in turn, raises a number of questions of pressing national concern. For example, what makes for an effective course or training program? Which aspects of teaching might be optimized or automated? How and why do learning needs and goals vary across people? How might we lower barriers to achieving a high quality education? The focus of this project is to understand how to provide learners with automatized instruction that is customized to the needs of each individual. It could have a significant impact on the online learning of STEM concepts and how it can be individualized for members of different communities. Moreover, the investigator’s own teaching and mentoring will bring machine learning, cognitive neuroscience, and instructional design together in a way that will provide development opportunities for the next generation of scientists working on frameworks for characterizing and evaluating real-world teaching and learning. He will create summer workshops, a regular tutorial series, and a series of open online courses. The project aims to (1) to build a computational framework for tracking individual students’ conceptual learning and understanding; and (2) to test whether and how brain recordings can be used to estimate ongoing conceptual learning and understanding. The project will collect data of what students learn from real online instructional videos to inform, fit, and test models of real-world conceptual learning in such STEM domains as Astronomy and Computer Science programming. Conceptual knowledge is tested by asking participants to solve applied problems that require them to generalize beyond the specific examples presented during training. Applying text embedding-based models run on these data, the investigators will derive semantic maps both of the concepts taught and of what the students learn. The project will also involve collecting a large dataset from participants who engage with a sequence of course videos while undergoing neuroimaging. The experimental data will be used to construct dynamic estimates of each participant’s moment-by-moment conceptual knowledge and their ability to acquire new knowledge. The project will use natural language processing models to quantify concepts and how they relate. This work will provide a foundation for later research and development of automatic adaptive learning systems that can assess individual conceptual knowledge and edit online instructional material so that it is tailored to that individual.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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。该项目还由EHR核心研究(ECR)计划提供资金，并由SBE的人类网络和数据科学(HNDS)计划共同资助。达特茅斯大学这个职业项目的目标是促进我们对学生如何通过在线课程视频学习STEM概念的理解，以改进在线教育。互联网主干的不断扩大和计算硬件的改进促进了视频流的改进，使视频能够更容易地下载和共享。这进而引发了一些国家迫切关注的问题。例如，什么是有效的课程或培训计划？教学的哪些方面可以优化或自动化？人与人之间的学习需求和目标有何不同？我们如何才能降低实现高质量教育的门槛？本项目的重点是了解如何为学习者提供根据每个人的需求定制的自动化教学。它可能会对STEM概念的在线学习以及如何针对不同社区的成员进行个性化学习产生重大影响。此外，研究人员自己的教学和指导将把机器学习、认知神经科学和教学设计结合在一起，为致力于描述和评估真实世界教学和学习的框架的下一代科学家提供发展机会。他将创建暑期工作坊、定期系列教程和一系列开放的在线课程。该项目旨在(1)建立一个跟踪学生个体概念学习和理解的计算框架；以及(2)测试大脑记录是否以及如何可以用来估计正在进行的概念学习和理解。该项目将收集学生从真实的在线教学视频中学到的数据，以告知、匹配和测试STEM领域(如天文学和计算机科学编程)的真实世界概念学习模型。通过让参与者解决应用问题来测试概念知识，这些问题要求他们在培训期间提供的具体例子之外进行概括。应用基于文本嵌入的模型在这些数据上运行，研究人员将得出教给学生的概念和学生所学的语义地图。该项目还将从参与者那里收集大型数据集，这些参与者在接受神经成像的同时观看一系列课程视频。实验数据将被用来构建每个参与者每一时刻的概念知识及其获取新知识的能力的动态估计。该项目将使用自然语言处理模型来量化概念及其相互关系。这项工作将为以后研究和开发自动适应学习系统奠定基础，该系统可以评估个人概念知识并编辑在线教学材料，以便为该INDIVIDUAL量身定做。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Text embedding models reveal high-resolution insights into conceptual knowledge from short multiple-choice quizzes

文本嵌入模型通过简短的多项选择测验揭示了对概念知识的高分辨率见解

DOI：
发表时间：
2023
期刊：
arXivorg
影响因子：
0
作者：
Fitzpatrick, P. C.;Heusser, A. C.;Manning, J. R.
通讯作者：
Manning, J. R.

Davos: A Python package “smuggler” for constructing lightweight reproducible notebooks

Davos：一个 Python 包“走私者”，用于构建轻量级可复制笔记本

DOI：
10.1016/j.softx.2023.101614
发表时间：
2024
期刊：
SoftwareX
影响因子：
3.4
作者：
Fitzpatrick, Paxton C.;Manning, Jeremy R.
通讯作者：
Manning, Jeremy R.

Feature and order manipulations in a free recall task affect memory for current and future lists

自由回忆任务中的特征和顺序操作会影响当前和未来列表的记忆

DOI：
发表时间：
2023
期刊：
arXivorg
影响因子：
0
作者：
Manning, J. R.;Whitaker, E. C.;Fitzpatrick, P. C.;Lee, M. R.;Frantz, A. M.;Bollinger, B. J.;Romanova, D.;Field, C. E.;Heusser A. C.
通讯作者：
Heusser A. C.

The psychological arrow of time drives temporal asymmetries in inferring unobserved past and future events

心理时间箭头在推断未观察到的过去和未来事件时会导致时间不对称