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: 1543449
• 负责人: Marlene Zakierski
• 金额: $ 9.38万
• 依托单位: The Sage Colleges
• 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=1543449&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.

共同核心学习标准 (CCLS) 已被 44 个州和哥伦比亚特区采用，定义了学生在每个年级结束时应展示的技能。 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 至 8 年级的学生每年春季都会参加英语语言艺术 (ELA) 测试，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段落的开放式问题；为了取得好成绩，学生应该能够仔细阅读一篇文章以获取文本证据并从中得出逻辑推论。为了报告结果，学生正确回答的数量被转换为量表分数；这又分为四个绩效级别：NYS 1 级表示远低于熟练，NYS 2 级表示部分熟练，NYS 3 级表示熟练，NYS 4 级表示在等级标准中表现出色。 学校为成绩水平为 NYS 1 级或 NYS 2 级的学生安排学术干预服务。为了推动面临未达到学术期望风险的学生的改变，干预响应模型旨在根据这些评估结果提供指示。 但 PI 认为，作为评估结果的单一表现分数通常不足以识别潜在的学习问题，尤其是在阅读理解方面。 在这个探索性项目中，他们将重点发现词汇层面评估结果中的错误模式，期望这些最终能够提高对如何将来自表现不佳的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易于理解的结构的理解，以提供有效的阅读理解干预措施。 项目成果将通过支持个人层面的诊断以及促进将具有类似阅读障碍的学生分组到同一干预组中来补充当前的评分系统，以优化学校教学资源。该方法还将通过最大限度地提高每次测试的信息增益来支持数据驱动的教学框架，从而减少每学年的测试次数和增加教学时间。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 将负责数据收集以及根据读写能力和教育学研究结果对拟议方法进行评估。 该团队将建立一个数据库，其中包含来自 3 年级以下儿童文学作品中具有词汇属性的单词、纽约州 ELA 的评估材料以及干预记录。 他们每年都会从大约 120 名成绩处于纽约州 2 级或以下的三年级学生中收集 ELA 评估材料，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，对表现不佳的个人 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潜在的阅读理解问题，以便制定更有效的教学计划。 他们将通过形成性评估和总结性评估来评估表现，前者包括干预期间对教师的调查问卷和对学生进行的模拟ELA测试，后者是干预后4月份的真实ELA测试。从计算机科学的角度来看，主要挑战是数据集的规模较小。 PI 将开发由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，该方法将为其他更容易获得“小数据”的情况打开广泛研究机会的大门。 此外，数据挖掘对识字教育本身的探索将构成独特的贡献，因为这两个领域的结合尚未受到研究界的太多关注，并且有许多有趣的问题等待使用计算方法来解决。