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

• 批准号: 1917808
• 负责人: Neil Heffernan
• 金额: $ 24.6万
• 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: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917808&HistoricalAwards=false
• 关键词: Collaborative; Research; Student; Affect; detection

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，人们越来越努力地将现代人工智能技术整合到自适应学习系统中以增强学生的学习。一个关键的新兴领域是使用可以识别情境中学生情绪（称为情感状态）的模型。这些模型通常采用机器学习分类器的形式，识别学生与在线学习系统交互的影响。在这个项目中，研究人员将开发自适应学习系统，积极争取教师的帮助，以开发更好的学生情感检测方法。作为回报，该系统将通过向教师提供每个学生的实时情感状态报告来支持教师的工作。然后，系统将学习模仿教师对注意力不集中的学生选择的干预方法，以便自动进行干预。总体而言，该项目预计将导致 i) 更好地理解如何利用和调整教师的观点来检测和应对情感，以及 ii) 加强教师和自动化软件的干预，重新吸引学生并提高学习成果.该项目将分为三个阶段。首先，调查人员将采用主动机器学习方法，要求教师在课堂活动休息时观察特定学生；这些方法可以通过提供情感状态对改进分类器信息最丰富的学生的数据来提高情感检测器的质量，而不是通过以循环方式观察学生来开发这些检测器的标准方法。其次，研究人员将把更丰富的数据类型（特别是情感标签的自我报告置信度评级）纳入检测器中，以提高其质量。这些自我报告的置信度反映了人类对特定情感判断的不确定性，这将与分类器的不确定性进行比较，以可能揭示对学生情感的洞察，例如情感不明确的情况下的属性是什么。第三，研究人员将利用众包向教师征求关于何时适合特定学生的特定情感干预的想法，并将使用强化学习开发自动干预方法。这些自动干预方法具有高度可扩展性，因为它们可以使系统采取教师采取的干预措施，以支持同时经历负面影响的不同学生。该干预系统将在真实的课堂上进行测试，学生可以在 ASSISTments 中学习，ASSISTments 是一个免费的网络学习平台，每年有超过 60,000 名学生使用。如果成功，该项目将带来关于情感动态的新科学发现以及可扩展的学生情感检测和干预的新技术。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Investigating the Impact of Skill-Related Videos on Online Learning

调查技能相关视频对在线学习的影响

• 发表时间: 2023-07
• 期刊: In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S ’23
• 作者: Prihar; E.
• 通讯作者: E.

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

Process-BERT：教育过程数据表示学习框架

• 发表时间: 2022-07
• 期刊: International Conference on Educational Data Mining (EDM
• 作者: Alexander Scarlatos; Christopher Brinton
• 通讯作者: Christopher Brinton

Automatic Short Math Answer Grading via In-context Meta-learning

通过上下文元学习自动对简短数学答案进行评分

• 发表时间: 2022-07
• 期刊: Proceedings from the 15th International Conference on Educational Data Mining
• 作者: Zhang, Mengxue;Baral, Sami;Heffernan, Neil;Lan, Andrew
• 通讯作者: Lan, Andrew

Automated Scoring of Image-based responses to Open-ended mathematics question.

对开放式数学问题基于图像的回答的自动评分。

• 发表时间: 2023-07
• 期刊: The Proceedings of the 16th International Conference on Educational Data Mining
• 作者: Baral, S;Santhanam, A;Botelho, A;Gurung, A;Heffernan, N
• 通讯作者: Heffernan, N

Effective Evaluation of Online Learning Interventions with Surrogate Measures

使用替代措施有效评估在线学习干预措施

• 发表时间: 2023-07
• 期刊: The Proceedings of the 16th International Conference on Educational Data Mining.
• 作者: Prihar, E;Vanacore, K;Sales, A;Heffernan, N
• 通讯作者: Heffernan, N