EAGER: Collaborative Research: Computer-Aided Response-to-Intervention for Reading Comprehension Disabilities

EAGER：协作研究：阅读理解障碍的计算机辅助干预响应

• 批准号: 1543639
• 负责人: Chia-LIng Tsai
• 金额: $ 16.58万
• 依托单位: IONA UNIVERSITY
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1543639&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Computer; Aided

The Common Core Learning Standards (CCLS), adopted by 44 states and the District of Columbia, define the skills a student should demonstrate by the end of each grade. One key skill emphasized by CCLS is reading ability, which is the precursor for learning in all content areas. In New York State, students in grades 3-8 take an English Language Arts (ELA) test each spring to measure their CCLS achievement in reading. An ELA test contains both multiple choice questions and open-ended questions based on short text passages; to do well, students should be able to read a passage closely for textual evidence and draw logical inferences from it. To report the results, the number of correct student responses is converted into a scale score; this in turn is divided into four performance levels: NYS Level 1 for well below proficient, NYS Level 2 for partially proficient, NYS Level 3 for proficient, and NYS Level 4 for exceptional in grade-level standards. Schools arrange academic intervention services for students whose performance level is either NYS Level 1 or NYS Level 2. To drive change in students who are at risk for not meeting academic expectations, the Response-to-Intervention model aims to deliver instructions as a function of these assessment outcomes. But the PIs argue that a single performance score as the assessment outcome is often insufficient for identifying underlying learning problems, especially in reading comprehension. In this exploratory project they will focus on discovering error patterns in assessment outcomes at the lexical level, in the expectation these will ultimately lead to improved understanding of how the raw data from a pool of underperforming text-based analytic reading assessments can be transformed into an informative and understandable structure for delivery of effective reading comprehension interventions. Project outcomes will complement the current scoring system by supporting diagnosis at an individual level, and by facilitating grouping of students with similar reading disabilities in the same intervention group in order to optimize school teaching resources. The approach will also support a data-driven instruction framework by maximizing the information gain from each test, which can result in fewer tests taken and more hours for teaching per school year.This is an interdisciplinary collaboration between a computer scientist (Tsai) and an expert in literacy education (Zakierski). PI Tsai will be responsible for computer algorithm development and data analysis, whereas PI Zakierski will be in charge of data collection and evaluation of the proposed approach based on findings in literacy and pedagogy. The team will build a database containing words with lexical properties from literature for children up to grade 3, assessment materials from NYS ELAs, and intervention records. They will annually collect ELA assessment materials from a pool of approximately 120 third grade students with performance at NYS Level 2 or below, for both research development and evaluation. They will develop a computer-aided intervention system that performs data-mining on underperforming individual ELA assessment materials to discover error patterns, which should assist the teacher in identifying a student's underlying reading comprehension problems in order to prepare a more effective instruction plan. And they will evaluate the performance by doing both formative and summative assessments, the former to consist of questionnaires for teachers and mock ELA tests for students taken during the period of intervention, and the latter being the real ELA tests in April following the intervention. From the computer science perspective, the main challenge is the small size of the dataset. The PIs will develop new techniques that are domain-knowledge driven for performing meaningful analysis to discover error patterns in such situations; if successful, the approach will open the door to broad research opportunities in other cases where "small data" is easier to come by. In addition, the exploration of data mining on literacy education itself will constitute a unique contribution, since the marriage of the two fields has not yet received much attention from the research community and there are many interesting questions waiting to be addressed using computational approaches.

由44个州和哥伦比亚特区采用的共同核心学习标准（CCLS）定义了学生在每个年级结束时应该展示的技能。 CCLS强调的一项关键技能是阅读能力，这是学习所有内容领域的先决条件。 在纽约州，3-8年级的学生每年春天都会参加英语语言艺术（ELA）考试，以衡量他们在阅读方面的CCLS成绩。 ELA测试包含多项选择题和基于短文的开放式问题。要想做得好，学生应该能够仔细阅读短文，寻找文本证据，并从中得出逻辑推理。为了报告结果，学生正确回答的数量将转换为量表分数;这又分为四个表现水平：纽约州1级为远低于熟练，纽约州2级为部分熟练，纽约州3级为熟练，纽约州4级为特殊的等级标准。 学校为表现水平为NYS 1级或NYS 2级的学生安排学术干预服务。为了推动那些有可能不满足学术期望的学生的变化，响应干预模型旨在根据这些评估结果提供指导。 但是，PI认为，单一的表现分数作为评估结果往往是不够的，以确定潜在的学习问题，特别是在阅读理解。 在这个探索性的项目中，他们将专注于发现词汇水平评估结果中的错误模式，期望这些最终将导致更好地理解如何将基于文本的分析阅读评估池的原始数据转化为信息丰富且可理解的结构，以提供有效的阅读理解干预。 项目成果将补充目前的评分系统，支持个人层面的诊断，并促进将有类似阅读障碍的学生分组到同一干预组，以优化学校教学资源。该方法还将支持数据驱动的教学框架，最大限度地提高每次考试的信息增益，从而减少考试次数，增加每学年的教学时间。这是计算机科学家（Tsai）和扫盲教育专家（Zakierski）之间的跨学科合作。 PI Tsai将负责计算机算法开发和数据分析，而PI Zakierski将负责数据收集和根据扫盲和教育学的研究结果对拟议方法进行评估。 该团队将建立一个数据库，其中包含3年级以下儿童文学作品中具有词汇特性的单词、纽约州ELA的评估材料以及干预记录。 他们将每年从大约120名三年级学生中收集ELA评估材料，这些学生的表现在NYS 2级或以下，用于研究开发和评估。 他们将开发一个计算机辅助干预系统，对表现不佳的个别ELA评估材料进行数据挖掘，以发现错误模式，这将有助于教师识别学生的潜在阅读理解问题，以便准备更有效的教学计划。 他们将通过形成性评估和总结性评估来评估表现，前者包括在干预期间对教师进行的问卷调查和对学生进行的模拟ELA测试，后者是干预后4月进行的真实的ELA测试。从计算机科学的角度来看，主要的挑战是数据集的大小。 PI将开发领域知识驱动的新技术，用于执行有意义的分析，以发现此类情况下的错误模式;如果成功，该方法将为更容易获得“小数据”的其他情况下的广泛研究机会打开大门。 此外，对扫盲教育数据挖掘的探索本身将是一个独特的贡献，因为这两个领域的结合还没有得到研究界的重视，还有许多有趣的问题等待着使用计算方法来解决。