Collaborative Research: Expanding a National Network for Automated Analysis of Constructed Response Assessments to Reveal Student Thinking in STEM

合作研究：扩大构建反应评估自动分析的国家网络，以揭示学生在 STEM 中的思维

• 批准号: 1322851
• 负责人: Michelle Smith
• 金额: $ 18.8万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1322851&HistoricalAwards=false
• 关键词: Collaborative; Research; Expanding; National; Network

This project is being conducted by a large team across 6 institutions that is building on already developed open-ended constructed response versions of well-established concept inventories that can be accurately assessed with already created computer automated analysis resources. The computer-automated analyses are able to predict human ratings of students' work on these topics and have demonstrated higher inter-rater reliability than a group of trained expert human graders. Constructed response assessments reveal more about student thinking and the persistence of misconceptions than do multiple-choice questions, but require more analysis on the part of the educator. In past work, items designed to identify important disciplinary constructs were created based on prior research. The items were then administered via online course management systems where students entered responses. Lexical and statistical analysis software was used to predict expert ratings of student responses. To date, the work has focused primarily in the fields of biology and chemistry in biological contexts. The current project is leveraging the previous research on Automated Assessment of Constructed Response (AACR), and extending the work to other institutions and other STEM disciplines. The specific goals of this project are to: 1. Create a community web portal for the Automated Assessment of Constructed Response (AACR) assessments to expand and deepen collaborations among STEM education researchers, thus providing the infrastructure for expanding the community of researchers and supporting the adoption and implementation of the innovative instructional materials by instructors at other institutions. 2. Propagate the innovations by providing instructors with professional development and long-term, ongoing support to use the assessments. This includes information about common student conceptions revealed by the questions, instructional materials for addressing conceptual barriers, and the opportunity to join a community of practitioners who are using the AACR questions and exchanging materials. 3. Expand the basic research to create and validate AACR questions in introductory chemistry, chemical engineering, and statistics. 4. Engage in ongoing project evaluation for continuous quality improvement and to document the challenges and successes the project encounters. 5. Lay the foundation for sustainability by providing interfaces for e-text publishers, Learning Management System vendors, and Massively Open Online Courses as potential revenue streams to operate and maintain the online infrastructure.Intellectual Merit:Improving STEM education requires valid and reliable instruments that provide insight into student thinking. The automated analysis of constructed response assessments have the potential to assess "big ideas" in STEM in a richer, more multi-faceted manner than multiple-choice instruments. This project is extending the number of these items and provide an online community where instructors may obtain, score, and contribute to the library of items and resources necessary for their analyses. Broader Impacts:The web portal is extending the use of the products created in this project to instructors nationwide. In addition it is providing the foundation for a national collaboration of science and engineering educators interested in developing deeper conceptual assessment tools and supports and mentors postdoctoral research fellows, and graduate research assistants in STEM education research.

该项目是由一个大型团队在6个机构中进行的，这些机构正在基于已经开发的开放式构造的构造响应版本的良好概念清单的响应版本，这些版本可以通过已经创建的计算机自动化分析资源来准确地评估。 计算机自动化的分析能够预测学生在这些主题上工作的评级，并且比一组受过训练的专家人类分布者表现出更高的评价者可靠性。构建的回答评估比做出多项选择的问题更多地揭示了有关学生思维和误解的持久性，但需要对教育工作者进行更多分析。在过去的工作中，根据先前的研究创建了旨在识别重要学科结构的项目。然后通过在线课程管理系统中管理这些项目，学生输入回答。词汇和统计分析软件用于预测学生反应的专家评分。迄今为止，这项工作主要集中在生物学背景下的生物学和化学领域。当前的项目正在利用对构造响应（AACR）自动评估的先前研究，并将工作扩展到其他机构和其他STEM学科。该项目的具体目标是：1。创建一个社区Web门户网站，以自动评估构造响应（AACR）评估，以扩大和加深STEM教育研究人员之间的合作，从而为研究人员社区提供了基础架构，以扩大研究人员的采用和支持其他机构的创新教学材料的采用和实施。 2。通过为讲师提供专业发展和长期，持续支持以使用评估来传播创新。这包括有关问题所揭示的常见学生概念的信息，解决概念障碍的教学材料以及加入使用AACR问题和交换材料的从业者社区的机会。 3。扩展基础研究，以在化学，化学工程和统计数据中创建和验证AACR问题。 4.进行正在进行的项目评估以进行持续的质量改进，并记录项目遇到的挑战和成功。 5。通过为电子文本出版商，学习管理系统供应商提供接口，并大量开放在线课程，作为潜在的收入来源，以运营和维护在线基础架构。智能教育需要有效且可靠的工具为学生思考提供洞察力。对构造响应评估的自动分析具有比多选择仪器更丰富，更多面的方式评估STEM中的“大思想”。该项目正在扩大这些项目的数量，并提供了一个在线社区，教师可以在其中获得，评分并为其分析所需的项目和资源库做出贡献。更广泛的影响：Web门户网站将本项目中创建的产品的使用扩展到全国讲师。此外，它为有兴趣开发更深入的概念评估工具的科学和工程教育者的国家合作为基础提供了基础。