Collaborative Research: CSL-MultiAD: Assessing Collaborative STEM Learning through Rich Information Flow based on Multi-Sensor Audio Diarization

协作研究:CSL-MultiAD:通过基于多传感器音频二值化的丰富信息流评估协作 STEM 学习

基本信息

  • 批准号:
    2330366
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-02-01 至 2024-07-31
  • 项目状态:
    已结题

项目摘要

The ability to learn concepts, especially for science and math (STEM) based disciplines, is impacted by educators who inspire, motivate, and create supportive environments and teaching methodologies which lower the entry barrier for students learning STEM subjects. Teaching resources nationwide have historically been constrained as STEM based science content for education expands with increasing student diversity based on prior science exposure in the classroom. A key aspect of student learning is to assess the quality of human communications between student-and-student as well as teacher-and-student. In STEM learning, students who are able to ask the right questions, know what they understand as well as what they need help with, allows educators to structure their teaching methods to help students overcome learning challenges. However, to date, it has been virtually impossible to collect and measure student-to-student or student-to-teacher voice communications in the classroom. Also, current speech technology is not sufficiently effective to overcome multi-speaker and naturalistic communications in classrooms. This project will develop classroom audio collection and measurement tools for students working together to solve problems, as well as teacher involvement with individual/groups of students. The audio collection solution includes both individual recorders on a sub-set of classroom students, as well as central smart speaker microphone collection units within each student group. Computer programs will be developed to analyze who is speaking and when, as well as spot keywords of interest for STEM topics and learning assessment. Privacy is maintained, since audio analysis is focused on high level measures such as individual student word counts, anonymous tagging of each speaker, and connecting conversational turns between students and teachers. A teacher driven keyword set will be used to help measure which students are having problems understanding concepts. These individual communication measured terms will be integrated into a dashboard display, to empower teachers with easy to use feedback on student engagement for STEM learning. The project has the potential to improve the ability to assess learning through classroom communications, and potentially help teachers better direct their time/expertise more efficiently to improve STEM learning for students. This project will develop ways to assess learning in classrooms by measuring the quality of human communication engagement between students-and-peers as well as teachers-and-students. Research has shown that learning is improved if there is dynamic interaction between student-to-student and student-to-teacher in voice communications. The project introduces personal recorders in the classroom to capture voice interactions during the entire day. Next, these multi-microphone recording streams are pooled together, where speech and language processing algorithms will be formulated to perform "audio diarization" - the process of determining "who spoke, what, and when", with potential keywords of interest based on classroom topics identified. The diarization output will drive the formulation of metrics to assess communication engagement. Communication based features derived from individual audio streams (word count, talk time, turn-taking, keyword profile) will be extracted on a per student basis through audio diarization. Next, this information flow will be used to develop class based group dynamics. This solution represents an approach for teachers to monitor student engagement over time in science activity areas, helping teachers identify students who are not verbally engaged in science discourse and quickly assess the impact of changes in classroom practices to improve learning. A number of technology challenges will be addressed for automatic audio stream based voice processing of naturalistic audio data using speech activity detection, speaker diarization based on machine learning models, and keyword spotting for science topic identification and tracking. These research aims will be assessed in classroom settings with teacher feedback on the effectiveness of the resulting solutions. The resulting speech technology advancements would offer new opportunities for future smart classrooms for voice assessment for teachers to better assess student involvement in science vs. infrequent traditional standardized testing. Ultimately, this effort will equip teachers with tools to identify and frequently monitor early indicators of disengagement in science learning, and potentially increase science interest by under-represented student populations and further diversify the STEM workforce.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.
学习概念的能力,特别是基于科学和数学 (STEM) 的学科的概念能力,受到教育工作者的影响,他们启发、激励和创造支持性环境和教学方法,降低学生学习 STEM 科目的进入门槛。全国范围内的教学资源历来受到限制,因为基于 STEM 的科学教育内容随着基于课堂上先前科学接触的学生多样性的增加而不断扩大。学生学习的一个关键方面是评估学生与学生以及教师与学生之间的人际交流质量。在 STEM 学习中,学生如果能够提出正确的问题,知道自己理解什么以及需要帮助,那么教育工作者就可以构建自己的教学方法来帮助学生克服学习挑战。然而,迄今为止,在课堂上收集和测量学生与学生或学生与教师的语音通信几乎是不可能的。此外,当前的语音技术还不足以有效地克服课堂上的多扬声器和自然通信。 该项目将开发课堂音频收集和测量工具,供学生共同解决问题,以及教师与个人/学生小组的参与。音频采集解决方案包括课堂学生子集上的个人录音机,以及每个学生组内的中央智能扬声器麦克风采集单元。我们将开发计算机程序来分析发言者和发言时间,并找出 STEM 主题和学习评估感兴趣的关键词。隐私得到了保护,因为音频分析侧重于高级测量,例如单个学生的字数统计、每个发言者的匿名标记以及连接学生和教师之间的对话轮次。教师驱动的关键字集将用于帮助衡量哪些学生在理解概念时遇到问题。这些个人交流测量术语将集成到仪表板显示中,以便教师能够轻松使用有关学生参与 STEM 学习的反馈。该项目有潜力提高通过课堂交流评估学习的能力,并有可能帮助教师更好地利用他们的时间/专业知识,更有效地改善学生的 STEM 学习。该项目将通过衡量学生与同伴以及教师与学生之间的人际交流参与质量来开发评估课堂学习的方法。研究表明,如果学生与学生以及学生与教师之间在语音通信中进行动态互动,学习效果就会得到改善。该项目在教室中引入了个人录音机,以捕获全天的语音交互。接下来,这些多麦克风录音流被汇集在一起​​,其中将制定语音和语言处理算法来执行“音频二值化”——确定“谁说话、说什么、何时”的过程,并根据课堂情况使用潜在感兴趣的关键词确定的主题。分类输出将推动制定评估沟通参与度的指标。来自各个音频流(字数、通话时间、轮流、关键词配置文件)的基于通信的特征将通过音频分类按每个学生提取。接下来,该信息流将用于开发基于班级的群体动态。该解决方案为教师提供了一种监控学生在科学活动领域的长期参与度的方法,帮助教师识别未口头参与科学讨论的学生,并快速评估课堂实践变化的影响,以改善学习。使用语音活动检测、基于机器学习模型的说话者分类以及用于科学主题识别和跟踪的关键词识别,将解决基于自动音频流的自然音频数据的语音处理的许多技术挑战。这些研究目标将在课堂环境中进行评估,并根据教师对最终解决方案有效性的反馈。由此产生的语音技术进步将为未来智能教室的语音评估提供新的机会,使教师能够更好地评估学生对科学的参与程度,而不是不常见的传统标准化测试。 最终,这项工作将为教师提供工具来识别和经常监测科学学习脱离的早期指标,并有可能提高代表性不足的学生群体的科学兴趣,并进一步使 STEM 劳动力多样化。该奖项反映了 NSF 的法定使命,并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。

项目成果

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Dwight Irvin其他文献

Classroom Sensing Tools: Revolutionizing Classroom-Based Research in the 21st Century
课堂感知工具:21 世纪课堂研究的革命
End-to-end Child-Adult Speech Diarization in naturalistic conditions of preschool 1 classrooms using room-independent ResNet model
使用独立于房间的 ResNet 模型在学前班 1 教室的自然条件下进行端到端儿童-成人语音二值化
  • DOI:
  • 发表时间:
    1970-01-01
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Prasanna V. Kothalkar;John H. L. Hansen;Dwight Irvin;J. Buzhardt
  • 通讯作者:
    J. Buzhardt
Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation.
使用独立于房间的 ResNet 模型和基于自动语音识别的重新分割,在学前教室自然条件下进行儿童-成人语音分类。
Quantifying Engagement in Preschool Classrooms - Conversational Turn-Taking & Topic Initiations
量化学前班课堂的参与度 - 对话轮流

Dwight Irvin的其他文献

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{{ truncateString('Dwight Irvin', 18)}}的其他基金

Collaborative Research: CSL-MultiAD: Assessing Collaborative STEM Learning through Rich Information Flow based on Multi-Sensor Audio Diarization
协作研究:CSL-MultiAD:通过基于多传感器音频二值化的丰富信息流评估协作 STEM 学习
  • 批准号:
    1918012
  • 财政年份:
    2019
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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协作研究:CSL-MultiAD:通过基于多传感器音频二值化的丰富信息流评估协作 STEM 学习
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Collaborative Research: CSL-MultiAD: Assessing Collaborative STEM Learning through Rich Information Flow based on Multi-Sensor Audio Diarization
协作研究:CSL-MultiAD:通过基于多传感器音频二值化的丰富信息流评估协作 STEM 学习
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