IERI: Collaborative Research: Automating Early Assessment of Academic Standards for Very Young Native and Non-Native Speakers of American English

IERI：合作研究：对美国英语为母语和非母语的幼儿进行学术标准早期评估自动化

• 批准号: 0326228
• 负责人: Shrikanth Narayanan
• 金额: --
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326228&HistoricalAwards=false
• 关键词: IERI; Collaborative; Research; Automating; Early

To help meet the increasing demand for high quality, efficient and accurate diagnostic assessments of children's academic skills, a new paradigm for automatic assessments is proposed. The project aims to advance the state of the art in speech processing, wireless communications, data mining, and human-computer interface (HCI) design so that effective child-friendly conversational interfaces can be designed and developed. These technologies will be researched in the framework of early learning and integrated with a progressive understanding of the components of academic performance to develop a literacy assessment system and explore the use of analogous assessment in math. The impact of the proposed approach will be studied with native speakers of American English (AE) and non-native AE speakers of Mexican-Spanish background in a longitudinal fashion starting from pre-K, and in partnership with the Los Angeles and Long Beach Unified School Districts, and UCLA's University Elementary School. These schools have a highly diverse economic and ethnic student body with more than half of the population being Hispanic. The project will analyze children's speech as they grow; develop speech recognition (ASR) algorithms for automating assessments that measure essential emerging literacy and some math skills; create a query-based, longitudinal database for each student; derive instructional guidance from the analysis of an ongoing professional development program for teachers of native and non-native speakers; and develop a nomadic interface among different computers and a central database. No feedback or tutoring will occur. Instead, teachers will use the results to make more timely and appropriate decisions about curriculum and instructional interventions. Technical Impact: The project will address several fundamental research issues: (a) acoustic modeling: documenting and accounting for inter- and intra-speaker variability cross-sectionally and longitudinally; (b) pronunciation modeling and speaker adaptation techniques that are scalable to children who are 4-8 years old and who are native and non-native English speakers; (c) child-specific language modeling: syntax, non-lexical events, and discourse phenomena; limited-domain natural-language processing (comprehension); (d) novel noise-robust and distributed ASR algorithms; (e) HCI: age-appropriate ways of displaying information and eliciting responses; (f) data mining: mining sequential patterns to discover trends over time, and user-specified associations; and (g) pedagogic issues: investigate early emerging literacy measures for native and non-native speakers, and discover reliable predictors of short- and long-term literacy success.Innovative aspects of the proposed approach include: a focus on literacy assessment that considers not only word recognition, but also phonetic and phonological awareness, comprehension and fluency; an exploratory study of automating math assessment tasks; a longitudinal and cross-sectional acoustic modeling study of very young native and non-native English speakers; extensive and longitudinal validation of the system, children's performance, and teachers' practices; correlating literacy measures to later reading performance; and pioneering research efforts involving system deployment in wired and wireless environments.Educational Impact: The project will foster interdisciplinary activities at: the U. of California, Los Angeles (Electrical Engineering [EE], Computer Science, and Education), U. of Southern California (EE, Linguistics, and Neuroscience), and U. of California, Berkeley (Education), in partnership with local elementary schools. Several renowned experts, including international experts from Mexico and Sweden, from academia and industry will act as advisory board members and consultants. Team members have a track record of working together, and the project will serve as a vehicle to train students, postdocs, and school teachers in novel cross-disciplinary research areas of technological and educational significance. Broader Impact: The proposed project will have a profound impact on relieving much of the burden of testing from teachers (allowing them to focus more on what they do best), automated testing for very young children (allowing a greater leverage point for potential intervention), and inclusion of an increasingly diverse population (enabling unbiased assessment and furthering the goal of universal access). National educational priorities are emphasizing testing to a greater extent than ever before, but increased testing leads to less time for teaching. The proposed system can reduce the test burden and increase the frequency of high-quality, intuitively consumable information about students so that individuals, programs, and schools can evolve more quickly by understanding which methods are working best for which children. Educational policy is also pushing downward to earlier ages to begin formal literacy instruction. This system will provide a useful aid to learn how to help young children succeed and to monitor their progress. The rapid expansion of student groups, reflecting diverse, non-native speakers of English, presents a challenge for fair assessment. The system helps ensure unbiased assessment of competence in a timely and useful way. It is expected that the project will have a profound impact on improving assessment and instructional material in the classroom.

为了帮助满足对高质量、高效和准确的儿童学习技能诊断评估日益增长的需求，提出了一种新的自动评估范式。该项目旨在推动语音处理、无线通信、数据挖掘和人机界面(HCI)设计的最新水平，以便设计和开发有效的儿童友好对话界面。这些技术将在早期学习的框架内进行研究，并与对学习成绩组成部分的逐步了解相结合，以开发一个识字评估系统，并探索在数学中使用类比评估。将与洛杉矶和长滩联合学区以及加州大学洛杉矶分校的大学小学合作，从学前班开始，以纵向方式研究拟议方法的影响，研究对象是以美国英语为母语的人和具有墨西哥-西班牙语背景的非美国英语人。这些学校拥有高度多样化的经济和种族学生群体，其中一半以上的人口是西班牙裔。该项目将分析儿童在成长过程中的语音；开发语音识别(ASR)算法，用于自动评估基本的新兴识字能力和一些数学技能；为每个学生创建一个基于查询的纵向数据库；从对正在进行的针对母语和非母语教师的专业发展计划的分析中获得教学指导；以及开发不同计算机之间的游牧界面和一个中央数据库。不会有任何反馈或辅导。取而代之的是，教师将使用结果来做出关于课程和教学干预的更及时和更适当的决定。技术影响：该项目将解决几个基本研究问题：(A)声学建模：记录和说明说话人之间和说话人内部的横向和纵向变化；(B)发音建模和说话人适应技术，可扩展到4-8岁的母语和非英语母语儿童；(C)针对儿童的语言建模：句法、非词汇事件和话语现象；有限领域自然语言处理(理解)；(D)新颖的抗噪和分布式ASR算法；(E)人机交互：适合年龄的信息显示和激发反应的方式；(F)数据挖掘：挖掘序列模式，以发现随时间推移的趋势和用户指定的联系；(G)教学问题：调查母语和非母语人士的早期扫盲措施，并发现短期和长期扫盲成功的可靠预测因素。拟议方法的创新方面包括：侧重于扫盲评估，不仅考虑单词识别，而且考虑语音和语音意识、理解力和流利性；对数学评估任务的自动化进行探索性研究；对以英语为母语和非母语的非常年轻的人进行纵向和横向声学建模研究；教育影响：该项目将与当地小学合作，在加州大学洛杉矶分校(电气工程[EE]、计算机科学和教育)、南加州大学(EE、语言学和神经科学)和加州大学伯克利分校(教育)促进跨学科活动。来自学术界和工业界的几位知名专家，包括来自墨西哥和瑞典的国际专家，将担任顾问委员会成员和顾问。团队成员有共同工作的记录，该项目将作为一种工具，在具有技术和教育意义的新型跨学科研究领域培训学生、博士后和学校教师。更广泛的影响：拟议的项目将在以下方面产生深远的影响：减轻教师的考试负担(使他们能够更多地专注于他们最擅长的事情)、对幼儿进行自动化考试(为潜在干预提供更大的杠杆点)，以及纳入日益多样化的人口(实现公正的评估和推动普及的目标)。国家教育重点比以往任何时候都更加强调考试，但考试的增加导致教学时间的减少。建议的系统可以减少考试负担，增加关于学生的高质量、直观可用信息的频率，以便个人、项目和学校可以通过了解哪些方法对哪些孩子最有效来更快地发展。教育政策也在向更早的年龄推进，开始正式的识字教育。这一系统将为学习如何帮助幼儿取得成功和监测他们的进步提供有用的帮助。学生群体的迅速扩大反映了不同的非英语母语者，这对公平评估提出了挑战。该系统有助于确保以及时和有用的方式公正地评估能力。预计该项目将对改进课堂评估和教学材料产生深远影响。