Collaborative Research: Student Affect Detection and Intervention with Teachers in the Loop

合作研究：学生情绪检测和与教师的干预

基本信息

批准号：
1917713
负责人：
Shiting Lan
金额：
$ 25万
依托单位：
University of Massachusetts Amherst
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：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917713&HistoricalAwards=false
关键词：
Collaborative Research Student Affect Detection

项目摘要

In recent years, there has been increasing effort to integrate modern artificial intelligence technologies into adaptive learning systems to enhance student learning. One key emerging area is in the use of models that can recognize student emotion in context, referred to as affective states. These models typically take the form of machine learning classifiers that recognize affect from the student's interaction with an online learning system. In this project, the investigators will develop adaptive learning systems that actively enlist the help of teachers to develop better student affect detection methods. In return, the system will support the work of teachers by providing them reports on the affective state of each student in real-time. The system will then learn to mimic teachers' choices of intervention methods for disengaged students in order to deliver interventions automatically. Overall, this project is anticipated to lead to i) better understanding of how to leverage and align to teachers' perspectives in detecting and responding to affect, and ii) enhanced intervention by both teachers and automated software that re-engages students and improves learning outcomes.This project will be organized into three phases. First, the investigators will employ active machine learning methods to ask teachers to observe specific students when they have a break in classroom activity; these methods can improve the quality of the affect detectors by providing data on the students whose affective states are most informative to improve the classifier, rather than the standard method of developing these detectors by observing students in round-robin fashion. Second, the investigators will incorporate richer data types (specifically, self-reported confidence ratings of affect labels) into the detectors to improve their quality. These self-reported confidence ratings reflect how uncertain humans are about specific affect judgements, which will be compared to the uncertainty of classifiers, to possibly reveal insights into student affect, such as what the properties are of situations where affect is ambiguous. Third, the investigators will use crowdsourcing to solicit ideas from teachers as to when specific affect interventions will be appropriate for specific students, and will develop automated intervention methods using reinforcement learning. These automated intervention methods are highly scalable since they can enable the system to take the actions the teacher would take to intervene to support different students experiencing negative affect at the same time. This intervention system will be tested in real classrooms as students learn within ASSISTments, a free web-based learning platform used by over 60,000 students a year. If successful, this project will lead to new scientific discoveries on the dynamics of affect and new technology for scalable student affect detection and intervention.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.

近年来，越来越多的努力将现代人工智能技术整合到适应性学习系统中以增强学生学习。一个关键的新兴领域是使用可以在上下文中识别学生情感的模型，称为情感状态。这些模型通常采用机器学习分类器的形式，这些分类器会从学生与在线学习系统的互动中识别影响。在这个项目中，研究人员将开发自适应学习系统，以积极地寻求教师的帮助，以发展更好的学生影响检测方法。作为回报，该系统将通过为每个学生实时提供有关每个学生的情感状态的报告来支持教师的工作。然后，该系统将学会模仿教师为脱离教师的干预方法选择，以便自动提供干预措施。总体而言，预计该项目将导致i）更好地理解如何利用和与教师的观点检测和响应对影响的看法； ii）ii）ii）教师和自动化软件的增强，以重新吸引学生并提高学习范围。这将分为三个阶段。首先，调查人员将采用主动的机器学习方法，要求教师在课堂活动中休息时观察特定的学生。这些方法可以通过提供有关情感状态最有用的数据来改善分类器的学生的数据来提高情感探测器的质量，而不是通过以圆形旋转方式观察学生来开发这些探测器的标准方法。其次，研究人员将将更丰富的数据类型（特别是自我报告的情感标签置信度等级）纳入检测器，以提高其质量。这些自我报告的置信度等级反映了人类对特定影响判断的不确定性，这将与分类器的不确定性相提并论，以揭示对学生情感的见解，例如影响歧义的情况。第三，调查人员将使用众包从教师那里征求有关特定影响干预措施适合特定学生的想法，并将使用增强学习来开发自动干预方法。这些自动干预方法是高度可扩展的，因为它们可以使系统能够采取教师采取的干预行动，以支持同时承受负面影响的不同学生。随着学生在辅助方面学习，该干预系统将在真实的教室中进行测试，这是一个免费的基于Web的学习平台，每年使用60,000多名学生。如果成功的话，该项目将导致有关情感动态和新技术的新科学发现，用于可扩展的学生情感检测和干预。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Process-BERT: A Framework for Representation Learning on Educational Process Data

DOI：
10.48550/arxiv.2204.13607
发表时间：
2022-04
期刊：
ArXiv
影响因子：
0
作者：
Alexander Scarlatos;Christopher G. Brinton;Andrew S. Lan
通讯作者：
Alexander Scarlatos;Christopher G. Brinton;Andrew S. Lan

DiPS: Differentiable Policy for Sketching in Recommender Systems

DOI：
10.1609/aaai.v36i6.20625
发表时间：
2021-12
期刊：
ArXiv
影响因子：
0
作者：
Aritra Ghosh;Saayan Mitra;Andrew S. Lan
通讯作者：
Aritra Ghosh;Saayan Mitra;Andrew S. Lan

Using Past Data to Warm Start Active Machine Learning: Does Context Matter?

使用过去的数据来热启动主动机器学习：上下文重要吗？

DOI：
10.1145/3448139.3448154
发表时间：
2021
期刊：
LAK21: 11th International Learning Analytics and Knowledge Conference
影响因子：
0
作者：
Karumbaiah, Shamya;Lan, Andrew;Nagpal, Sachit;Baker, Ryan S.;Botelho, Anthony;Heffernan, Neil
通讯作者：
Heffernan, Neil

DiFA: Differentiable Feature Acquisition

DOI：
10.1609/aaai.v37i6.25934
发表时间：
2023-06
期刊：
影响因子：
0
作者：
Aritra Ghosh;Andrew S. Lan
通讯作者：
Aritra Ghosh;Andrew S. Lan

Accurate and Interpretable Sensor-free Affect Detectors via Monotonic Neural Networks

通过单调神经网络实现准确且可解释的无传感器情感检测器

DOI：
发表时间：
2020
期刊：
International Conference on Learning Analytics & Knowledge
影响因子：
0
作者：
Lan, Andrew S;Botelho, Anthony;Karumbaiah, Shamya;Baker, Ryan S;Heffernan, Neil
通讯作者：
Heffernan, Neil