Collaborative Research: PCK*Lex: Applying Computerized Lexical Analysis to Develop a Cost-Effective Measure of Science Teacher Pedagogical Content Knowledge

合作研究：PCK*Lex：应用计算机词汇分析来开发科学教师教学内容知识的经济有效的衡量标准

• 批准号: 1438739
• 负责人: Kevin Haudek
• 金额: $ 26.67万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1438739&HistoricalAwards=false
• 关键词: Collaborative; Research; PCK; Lex; Applying

Pedagogical content knowledge (PCK) is a critical conceptual tool for distinguishing effective from ineffective teaching, but difficulty measuring it has resulted in minimal application of the concept to enhance actual teaching practice. This project will apply computerized lexical analysis techniques to develop an efficient, valid and reliable measure of science teacher PCK - the type of teacher knowledge that bridges content knowledge and how to effectively teach the content in classrooms. This project will transform an instrument used in multiple NSF STEM projects, but which is limited by being time and resource intensive to score. Application of recent developments in lexical analysis will allow measurement of PCK to be taken to scale with rapid feedback, and will be a vital resource for STEM researchers, evaluators, PD providers, teacher educators, and teachers themselves. This collaborative research project brings together two teams with unique yet complementary expertise. BSCS researchers have experience measuring PCK in multiple federal projects, and recently led an international summit on STEM PCK. The Michigan State University Automated Analysis of Constructed Response research group is working across STEM disciplines to refine approaches to using lexical analysis for formative educational purposes. Broader impacts of this project include developing a measure that will be invaluable to multiple stakeholders in evaluating and enhancing the understandings and abilities of K-12 science teachers. Enhancing the capacity of K-12 teachers will help increase the numbers of underrepresented students who pursue STEM careers, as well as expand the STEM workforce and increase general scientific literacy.

教学内容知识（PCK）是区分有效和无效教学的关键概念工具，但由于测量难度大，导致该概念在实际教学实践中的应用很少。本项目将运用计算机词汇分析技术，开发一种高效、有效和可靠的科学教师PCK测量方法，即教师知识的类型，将内容知识与如何有效地在课堂上教授内容联系起来。该项目将改变多个NSF STEM项目中使用的仪器，但由于时间和资源密集而受到限制。词汇分析的最新发展将使PCK的测量能够快速反馈，并将成为STEM研究人员、评估人员、PD提供者、教师教育工作者和教师本身的重要资源。这个合作研究项目汇集了两个具有独特而互补专业知识的团队。BSCS的研究人员有在多个联邦项目中测量PCK的经验，最近领导了一个关于STEM PCK的国际峰会。密歇根州立大学构建反应自动分析研究小组正在跨STEM学科工作，以改进用于形成性教育目的的词汇分析方法。该项目的更广泛影响包括制定一项对多个利益攸关方在评估和提高K-12科学教师的理解和能力方面非常宝贵的措施。提高K-12教师的能力将有助于增加追求STEM职业的代表性不足的学生人数，并扩大STEM劳动力，提高一般科学素养。