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

协作研究：ArguLex - 将自动分析应用于论证的学习进程

• 批准号: 1561159
• 负责人: Kevin Haudek
• 金额: $ 45万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561159&HistoricalAwards=false
• 关键词: Collaborative; Research; ArguLex; Applying; Automated

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。这种观点反映在《下一代科学标准》中，其中论证被作为八项基础科学和工程实践之一提出，通过这些实践，学生可以学习科学的核心思想和科学的横切概念。然而，使用标准选择题并不能很好地衡量论证能力。另一种非常适合衡量论证能力的方法是对学生书面作业的评估；但是这种方法既昂贵又耗时。这个研究项目通过研究使用语言技术来自动评分学生的书面作业来评估他们的论证能力来解决这个问题。本项目应用词汇分析和机器学习技术，开发一种高效、有效、可靠和自动化的中学生科学论证能力测量方法。该项目将以先前的工作为基础，为论证的学习进展制定高质量的评估。然而，这些评估是时间和资源密集的评分。但是，当与自动化方法一起使用时，这种评估将允许对论证进行测量，并通过快速的形成性反馈进行扩展，并将成为STEM教师、研究人员和教师教育者的宝贵资源。该项目汇集了具有测量论证经验的BSCS研究人员；密歇根州立大学的构建反应自动分析研究小组提供了一系列科学学科的专业知识，用于形成性教育目的的精炼分析；斯坦福大学在科学论证的学习进展方面提供专业知识；以及西密歇根大学作为外部评估机构。