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）是区分有效教学和无效教学的关键概念工具，但难以对其进行测量，导致该概念在增强实际教学实践中的应用很少。该项目将应用计算机词汇分析技术来开发一种高效、有效和可靠的科学教师 PCK 测量方法——连接内容知识和如何在课堂上有效教授内容的教师知识类型。该项目将改造多个 NSF STEM 项目中使用的仪器，但由于评分需要大量时间和资源而受到限制。词法分析最新进展的应用将使 PCK 的测量能够以快速反馈的方式进行扩展，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但互补的专业知识的团队。 BSCS 研究人员拥有在多个联邦项目中测量 PCK 的经验，并且最近领导了一次 STEM PCK 国际峰会。密歇根州立大学建构反应自动分析研究小组正在跨 STEM 学科开展工作，以完善使用词汇分析实现形成性教育目的的方法。该项目的更广泛影响包括制定一项措施，对于多个利益相关者评估和提高 K-12 科学教师的理解和能力而言非常宝贵。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数量，并扩大 STEM 劳动力队伍并提高一般科学素养。