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

为了帮助满足日益增长的需求，高质量，高效和准确的诊断评估儿童的学业技能，提出了一种新的范式自动评估。该项目旨在推进语音处理，无线通信，数据挖掘和人机界面（HCI）设计的最新技术，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理解相结合，以开发一个识字评估系统，并探索在数学中使用类比评估。将从学前教育开始，以纵向方式对美国英语（AE）为母语的人和墨西哥-西班牙语背景的非母语AE人进行研究，并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。这些学校有一个高度多样化的经济和种族的学生群体，超过一半的人口是西班牙裔。该项目将分析儿童成长过程中的语言;开发语音识别算法，用于自动评估，以衡量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;从分析母语和非母语教师的持续专业发展计划中获得教学指导;并在不同的计算机和中央数据库之间开发一个游牧界面。没有反馈或辅导将发生。相反，教师将利用这些结果对课程和教学干预做出更及时、更适当的决定。技术影响：该项目将解决几个基本的研究问题：（a）声学建模：记录和解释跨和跨部门和纵向的说话者之间和说话者内部的变化;（B）发音建模和说话者适应技术，可扩展到4-8岁的儿童，母语和非母语为英语的儿童;（c）儿童特定的语言建模：句法、非词汇事件和语篇现象;有限域自然语言处理（理解）;（d）新颖的抗噪声和分布式ASR算法;（e）HCI：显示信息和引发响应的年龄适当方式;（f）数据挖掘：挖掘序列模式，以发现一段时间内的趋势和用户指定的关联;以及（g）教学问题：调查针对母语和非母语人士的早期识字措施，并发现短期和长期扫盲成功的可靠预测因素。注重识字评估，不仅考虑单词识别，而且考虑语音和音韵意识、理解和流利程度;自动化数学评估任务的探索性研究;非常年轻的母语和非母语英语者的纵向和横截面声学建模研究;系统的广泛和纵向验证，儿童的表现和教师的做法;将识字措施与以后的阅读表现相关联;教育影响：该项目将促进跨学科的活动，在：美国;美国加州、洛杉矶（电气工程、计算机科学和教育）、南加州（EE，语言学和神经科学），和U。加州伯克利分校（教育），与当地小学合作。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，将担任咨询委员会成员和顾问。团队成员有共同工作的记录，该项目将作为一种工具，培训学生，博士后和学校教师在新的跨学科研究领域的技术和教育意义。更广泛的影响：拟议的项目将对减轻教师的考试负担（使他们能够更多地关注他们最擅长的事情），对幼儿进行自动化测试（为潜在的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进普及目标）产生深远的影响。 国家教育优先事项比以往任何时候都更强调测试，但测试的增加导致教学时间减少。所提出的系统可以减少测试负担，并增加高质量的频率，直观的消费信息的学生，使个人，程序和学校可以通过了解哪些方法最适合哪些孩子更快地发展。教育政策也在向下推动，使儿童开始正规识字教育。这个系统将提供一个有用的帮助，学习如何帮助幼儿取得成功，并监测他们的进展。学生群体的迅速扩大，反映了不同的，非母语的英语，提出了一个挑战，公平的评估。 该制度有助于确保以及时和有用的方式对能力进行公正的评估。预计该项目将对改进课堂评估和教学材料产生深远影响。

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.