Speech Technology Enhanced Assessment of Modeling (STEAM)

语音技术增强建模评估 (STEAM)

• 批准号: 0735673
• 负责人: Philip Vahey
• 金额: $ 39.43万
• 依托单位: SRI International
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-15 至 2012-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0735673&HistoricalAwards=false
• 关键词: Speech; Technology; Enhanced; Assessment; Modeling

This project seeks to achieve more efficient and accurate assessment of students' mathematical reasoning by developing improved speech recognition technology, calibrated to recognize children's speech, and integrating it with a computerized mathematics education environment (SimCalc) involving interactive representations and simulations. Specific questions of interest include:Q1. What are the most promising task-relevant mechanisms for constraining students? spoken responses in such a way that enables valid and reliable speech-based assessments despite the imperfect accuracy of automatic spoken language systems?Q2. What are the most efficient modifications to existing spoken language technologies that achieve acceptable performance with middle-school students engaged in spontaneous speech acts about mathematical models, representations, and simulations?Q3. Which aspects of the computer-based dynamic representation system must be integrated with student speech to provide a more complete representation of student knowledge of the mathematics underlying the model?Q4. Which features of the combined output of the computer-based environment and student speech allow the spoken language understanding engine to reliably assign rubric-based scores to student work?Assessments will address five key aspects of fluency with mathematical models: comprehending, predicting, explaining, improving, and reflecting. A particularly unique focus of this work will be on combining inputs from the speech recognition engine with time-stamped information from a mathematics education environment to reliably score student responses according to a rubric. In addition to its application to education, this research on adolescents' spontaneous mathematical speech will drive advances in spoken language technology.

该项目旨在通过开发改进的语音识别技术，以识别儿童的语音，并将其与涉及交互式表示和模拟的计算机化数学教育环境（SimCalc）集成，从而实现对学生数学推理的更有效和准确的评估。我们感兴趣的具体问题包括：约束学生的最有希望的任务相关机制是什么？尽管自动口语系统的准确性并不完美，但这种方式能够实现有效和可靠的基于语音的评估？什么是对现有口语技术的最有效的修改，以使中学生在数学模型、表征和模拟的自发言语行为中取得可接受的表现？基于计算机的动态表示系统的哪些方面必须与学生的演讲相结合，以便更完整地表示学生对模型基础的数学知识？基于计算机的环境和学生演讲的组合输出的哪些特征允许口语理解引擎可靠地为学生作业分配基于规则的分数？评估将针对数学模型流畅性的五个关键方面：理解、预测、解释、改进和反映。这项工作的一个特别独特的重点将是将来自语音识别引擎的输入与来自数学教育环境的时间戳信息相结合，根据一个标题可靠地对学生的反应进行评分。除了应用于教育之外，这项关于青少年自发数学语言的研究将推动口语技术的进步。