Developing and Validating a Scalable, Classroom-focused Measure of Usable Knowledge for Teaching Mathematics: The Classroom Video Analysis Instrument

开发和验证可扩展的、以课堂为中心的数学教学可用知识测量：课堂视频分析仪器

• 批准号: 1720866
• 负责人: Nicole Kersting
• 金额: $ 200.05万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1720866&HistoricalAwards=false
• 关键词: Developing; Validating; Scalable; Classroom; focused

There is widespread agreement that for teachers to effectively teach their students having lots of knowledge is important, but not enough. To benefit instruction and student learning, teachers need to be able to access and flexibly use their knowledge in the classroom in actual teaching situations and teaching tasks. Yet, measures to assess teachers' usable knowledge have remained scarce. We still know little about how the knowledge teachers acquire as part of teacher preparation courses and professional development becomes usable, how it develops over time, and how teachers use it in the process of teaching. To address both assessment needs in this project, The project will develop a set of scalable, classroom-focused measures of usable mathematics teaching knowledge that are aligned with state standards. The new measures will extend the classroom video analysis approach, which is based on teachers' ability to analyze and respond to teaching episodes shown in short video clips of authentic classroom instruction, by aligning video clips and assessment tasks to standards. The new measures, which will be made available online, will be a valuable tool for researchers, policy makers, and school districts to monitor teacher knowledge over time and to gauge teacher preparedness for implementing state standards in mathematics. The measures will also provide new insights into usable knowledge and knowledge use and advance a much-needed theory of teacher knowledge. Finally, the project extends and refines a promising assessment methodology that can be adapted to any future content frameworks or standards and that can also be used for instrument development in other practice-based knowledge domains. The Discovery Research K-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 (RMTs). 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. This project is also supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field.The project will develop a scalable, classroom-focused measure of usable mathematics teaching knowledge that is aligned with the state standards through a classroom video analysis measure (CVA-M)) in three content areas: (a) fractions for grades 4 and 5, (b) ratio and proportions for grades 6 and 7; and (c) variables, expressions, and equations for grades 6 and 7. The project will examine the psychometric properties of the new items and scales, including the reliability of scores, and collect evidence on content, substantive, structural, and external aspects of validity to evaluate the overall construct validity of the CVA-M. The project builds on an innovative and promising assessment methodology that uses video clips of authentic classroom instruction that teachers are asked to view and analyze to elicit their usable knowledge. Teachers analyze the teaching episodes shown in the video clips from different assessment tasks that reflect authentic teaching tasks, such as diagnosing student thinking, generating mathematically targeted teacher question, or relating specific content and mathematical practices to teaching episodes shown in the clips. To develop each of the three scales, video clips will be mapped to state level content and mathematical practice standards. Assessment tasks and rubrics will also be aligned with these standards. To create items, video clips will be combined with analysis prompts that ask for a written answer, multiple-choice or rating scales. To make the constructed response items, which need to be scored by trained raters, easier to use at scale, computational approaches will be employed to develop classifiers to automate scoring. Using responses from large samples of teachers, the psychometric properties of the new CVA-M items and scales will be analyzed using factor analysis, classical test theory and item response theory. A series of validity investigations will be conducted. Teachers' scores on the new CVA-M scales will be compared to their scores on another measure of teacher knowledge, the Mathematics Knowledge for Teaching (MKT) instrument, and each scale's predictive validity will be explored vis-a-vis student learning by relating teachers' CVA-M scores to their students' learning as measured by a pre-post quiz and by students' standardized test scores.

人们普遍认为，对于教师来说，有效地教学生掌握大量知识是重要的，但还不够。为了使教学和学生的学习受益，教师需要能够在实际的教学环境和教学任务中在课堂上访问和灵活使用他们的知识。然而，评估教师可用知识的措施仍然很少。我们对教师在教师准备课程和专业发展中获得的知识如何变得可用，如何随着时间的推移而发展，以及教师如何在教学过程中使用这些知识仍然知之甚少。为了满足这两个评估需求，该项目将开发一套可扩展的，以课堂为中心的可用数学教学知识的措施，符合国家标准。新措施将扩展课堂视频分析方法，该方法基于教师分析和应对真实课堂教学视频短片中显示的教学片段的能力，使视频剪辑和评估任务符合标准。新的措施，这将是一个宝贵的工具，研究人员，政策制定者和学区监测教师的知识随着时间的推移，并衡量教师准备实施国家标准的数学。这些措施也将提供新的见解，有用的知识和知识的使用，并推进急需的教师知识的理论。最后，该项目扩展和完善了一个有前途的评估方法，可以适应任何未来的内容框架或标准，也可以用于其他实践为基础的知识领域的工具开发。探索研究K-12计划（DRK-12）旨在通过研究和开发创新资源，模型和工具（RMT），显着提高学前班学生和教师的科学，技术，工程和数学（STEM）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工作的基础上，这些研究和开发工作为拟议项目提供了理论和经验依据。该项目也得到了NSF的EHR核心研究（ECR）计划的支持。ECR项目强调基础STEM教育研究，产生该领域的基础知识。该项目将开发一个可扩展的，以课堂为中心的可用数学教学知识的衡量标准，通过课堂视频分析措施（CVA-M）在三个内容领域与国家标准保持一致：（a）4年级和5年级的分数，（B）6年级和7年级的比例和比例;以及（c）6年级和7年级的变量、表达式和方程。该项目将检查新项目和量表的心理测量学特性，包括分数的可靠性，并收集内容，实质性，结构和外部效度方面的证据，以评估CVA-M的整体结构效度。该项目建立在一个创新的和有前途的评估方法，使用真实的课堂教学视频剪辑，教师被要求查看和分析，以引出他们的可用知识。教师分析视频剪辑中显示的教学片段，这些片段来自反映真实教学任务的不同评估任务，例如诊断学生思维，生成针对数学的教师问题，或将特定内容和数学实践与视频剪辑中显示的教学片段相关联。为了开发这三个量表中的每一个，视频剪辑将被映射到州一级的内容和数学实践标准。评估任务和规则也将与这些标准保持一致。 为了创建项目，视频剪辑将与要求书面回答、多项选择或评级量表的分析提示相结合。为了使构建的回答项目，这需要由训练有素的评分员，更容易使用的规模，计算方法将被用来开发分类器，自动评分。本研究利用大样本教师的问卷调查，运用因素分析、经典测验理论和项目反应理论对新CVA-M项目和量表的心理测量学特性进行了分析。将进行一系列有效性调查。教师在新的CVA-M量表上的得分将与他们在另一种衡量教师知识的工具--教学数学知识工具上的得分进行比较，并将教师的CVA-M得分与学生的学习联系起来，探讨每个量表对学生学习的预测效度。

项目成果

Using authentic video clips of classroom instruction to capture teachers’ moment-to-moment perceiving as knowledge-filtered noticing

使用真实的课堂教学视频剪辑来捕捉教师的即时感知，即经过知识过滤的注意

• DOI: 10.1007/s11858-020-01201-6
• 发表时间: 2021
• 期刊: ZDM – Mathematics Education
• 作者: Kersting, Nicole B.;Smith, James E.;Vezino, Beau
• 通讯作者: Vezino, Beau

Exploring the affordances of Bayesian networks for modeling usable knowledge and knowledge use in teaching

探索贝叶斯网络对可用知识和教学中知识使用进行建模的可供性

• DOI: 10.1007/s11858-020-01135-z
• 发表时间: 2020
• 期刊: ZDM
• 作者: Kerstmg, N. B.