Collaborative Research: Applying Automated Analysis to a Learning Progression for Argumentation

协作研究：将自动分析应用于论证的学习进程

• 批准号: 1561149
• 负责人: Jonathan Osborne
• 金额: $ 34.39万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561149&HistoricalAwards=false
• 关键词: Collaborative; Research; Applying; Automated; Analysis

Argumentation is fundamental to both science and science education. This perspective is reflected in the Next Generation Science Standards where argumentation is presented as one of eight fundamental science and engineering practices through which students learn both the core ideas of science and the crosscutting concepts of science. Argumentation, however, is not measured well using standard multiple choice items. An alternative that is well suited to measuring argumentation skills is the assessment of student written work; but this approach is both expensive and time consuming. This research project addresses this issue through research into using language technology to automate the scoring of student written work to assess their argumentation skills.This project applies lexical analysis and machine learning technologies to develop an efficient, valid, reliable and automated measure of middle school students' abilities to engage in scientific argumentation. The project will build upon prior work that developed high quality assessments for a learning progression for argumentation. However, these assessments are time and resource intensive to score. But when used with automated approaches, such assessments will allow measurement of argumentation to be taken to scale with rapid formative feedback, and will be an invaluable resource for STEM teachers, researchers, and teacher educators. The project brings together BSCS researchers who have experience measuring argumentation; Michigan State University's Automated Analysis of Constructed Response research group which provides expertise across a range of scientific disciplines refining analysis for formative educational purposes; Stanford University which provides expertise in learning progressions for argumentation in science; and Western Michigan University which serves as an external evaluator.

论证是科学和科学教育的基础。 这一观点反映在下一代科学标准中，其中论证被视为八项基本科学和工程实践之一，学生通过这些实践学习科学的核心思想和科学的交叉概念。然而，使用标准的多项选择题并不能很好地衡量论证。另一种非常适合衡量论证能力的方法是评估学生的书面作业;但这种方法既昂贵又耗时。本研究课题针对这一问题，利用语言技术对学生的书面作业进行自动评分，以评估学生的科学论证能力。本研究课题利用词法分析和机器学习技术，开发出一种高效、有效、可靠、自动化的中学生科学论证能力测量方法。该项目将建立在以前的工作，开发了高质量的评估，为学习进展的论证。然而，这些评估是时间和资源密集型的评分。但是，当与自动化方法一起使用时，这种评估将允许通过快速形成反馈来衡量论证，并将成为STEM教师，研究人员和教师教育工作者的宝贵资源。该项目汇集了BSCS的研究人员谁有经验的测量论证;密歇根州立大学的自动分析的建构反应研究小组，提供专业知识，在一系列科学学科细化分析形成的教育目的;斯坦福大学提供专业知识，在学习进展的科学论证;和西密歇根大学作为一个外部评估。