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Usable Measures of Teacher Understanding: Exploring Diagnostic Models and Topic Analysis as Tools for Assessing Proportional Reasoning for Teaching

教师理解的可用措施：探索诊断模型和主题分析作为评估教学比例推理的工具

基本信息

批准号：
1813760
负责人：
Yasemin Copur-Gencturk
金额：
$ 216.86万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813760&HistoricalAwards=false
关键词：
Usable Measures Teacher Understanding Exploring

项目摘要

One of the great challenges related to teachers and their knowledge is measuring their learning in ways that are both formative and meaningful in relation to their likely impact on students. This challenge persists despite efforts to define the knowledge teachers should have and despite previous innovative efforts to create good measures. This project tackles the challenge by specifically aiming to measure the kinds of knowledge developed in professional development (PD) programs that has been shown to matter for teachers' classroom practices and their students' learning. The project aims to develop an assessment that identifies patterns in the teachers' learning in a way that helps drive subsequent professional development. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. The overall goal of this project is to pursue a potentially transformative approach to the assessment of teacher proportional knowledge by developing a measure that is well aligned with the content and skills taught in various PD programs. This instrument will be based on a new approach that builds on emerging psychometric models. Specifically, diagnostic classification models (DCMs) will be utilized to diagnose teachers' learning during a PD program as well as employed to identify the progression in teachers' learning. Statistical topic models (STMs) will be used to look for patterns of understanding that emerge from open-ended responses and provide natural-language insight into teachers' reasoning. A final version of the assessment will be constructed for a national sample based on the results from the predictive validity stage, and this version will be tested with teachers who participate in various types of PD programs targeting proportional reasoning. This project has broad implications for the creation of assessments and for teacher education. It will provide insights about whether there is a clear learning progression for teachers. While much work has been done with students' learning progression, much less is known about how teachers learn. Another implication is that the STM approach allows machine scoring of natural language in a way that highlights strengths and weaknesses in reasoning rather than simply returning a score. For formative use, this is information that is more helpful as it highlights areas for further instruction. A third implication is that DCMs will allow to assess teacher knowledge at a finer-grained understanding than is typically available, thus allowing for careful refinement of PD as well as a tool for showing overall growth in PD. A fourth implication is that a more systematic approach will be followed to capture the kinds of knowledge teachers need. Assessments developed using DCMs and STMs have the potential to serve as models for developing further instruments in other STEM content areas. Such assessments have the potential to not only help identify successful PD programs, but also to provide PD providers with rich data from which they can make instructional decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

与教师及其知识相关的最大挑战之一是，衡量他们的学习方式，既是形成的，也是有意义的，与他们可能对学生产生的影响有关。尽管努力界定教师应该具备的知识，尽管以前为制定良好的措施作出了创新努力，但这一挑战依然存在。该项目通过专门针对在专业发展(PD)计划中发展的已被证明对教师的课堂实践和他们的学生的学习至关重要的知识种类来应对这一挑战。该项目旨在开发一种评估，以确定教师的学习模式，以帮助推动后续的专业发展。探索研究PREK-12计划(DRK-12)旨在通过研究和开发创新资源、模型和工具，显著提高Pre-K-12学生和教师在科学、技术、工程和数学(STEM)方面的学习和教学。DRK-12计划中的项目建立在STEM教育的基础研究和先前的研究和开发努力的基础上，这些研究和开发工作为拟议的项目提供了理论和经验证明。该项目的总体目标是寻求一种潜在的变革性方法来评估教师比例知识，制定一种与各种PD计划中教授的内容和技能很好地结合的措施。该仪器将基于一种新的方法，该方法建立在新兴的心理测量模型基础上。具体地说，诊断分类模型(DCM)将被用来诊断教师在PD计划期间的学习情况，并用于确定教师学习的进展情况。统计主题模型(STM)将被用来寻找从开放式回答中出现的理解模式，并提供对教师推理的自然语言洞察。评估的最终版本将根据预测效度阶段的结果为全国样本构建，并将与参与各种以比例推理为目标的PD计划的教师进行测试。该项目对评估的创建和教师教育具有广泛的影响。它将为教师提供关于是否有明确的学习进展的见解。虽然已经对学生的学习进度做了很多工作，但对教师如何学习的了解要少得多。另一个含义是，STM方法允许对自然语言进行机器评分，这种方式突出了推理的优势和劣势，而不是简单地返回分数。对于形成性使用，这是更有帮助的信息，因为它突出了需要进一步指导的领域。第三个含义是，DCMS将允许以比通常可用的更细粒度的理解来评估教师知识，从而允许仔细改进PD以及显示PD的整体增长的工具。第四个含义是，将遵循更系统的方法来获取教师所需的各种知识。使用DCMS和STMS编制的评估有可能成为在其他STEM内容领域开发进一步工具的模式。这样的评估不仅有可能帮助识别成功的PD项目，而且还可以为PD提供者提供丰富的数据，他们可以根据这些数据做出指导性决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。