Measuring Complex STEM Thinking Using Epistemic Network Analysis

使用认知网络分析衡量复杂的 STEM 思维

• 批准号: 1247262
• 负责人: David Shaffer
• 金额: $ 249.58万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247262&HistoricalAwards=false
• 关键词: Measuring; Complex; STEM; Thinking; Using

In this project the researchers propose to investigate an alternative model for assessing complex thinking in science, technology, engineering, and mathematics (STEM) education. The method, Epistemic Network Analysis, is said to measure student skills, knowledge, identities, values, and epistemology. Two iterative design cycles over five years are proposed. The investigators will use existing data to do psychometric analyses, develop visualizations, develop a toolkit, and work with and disseminate a toolkit to a community of early adopters. The work will be conducted using data from several engineering education games. Dissemination will be in the form of the toolkit and academic publications.This work is important because it represents a departure from current thought about assessment and the notion of isolating content learning from values, epistemologies, and skills. Because the work proposed by the investigator retains such data and examines their interdependencies, the ENA method represents a potentially transformative approach to assessing complex STEM thinking.

在这个项目中，研究人员建议研究一种替代模型，用于评估科学，技术，工程和数学（STEM）教育中的复杂思维。 该方法，认知网络分析，据说测量学生的技能，知识，身份，价值观和认识论。 两个迭代设计周期超过五年的建议。 研究人员将使用现有数据进行心理测量分析，开发可视化，开发工具包，并与早期采用者社区合作并向其传播工具包。这项工作将使用来自几个工程教育游戏的数据进行。 传播将以工具包和学术出版物的形式进行。这项工作很重要，因为它代表了对当前评估思想的背离，以及将内容学习与价值观、认识论和技能隔离开来的概念。 由于研究人员提出的工作保留了这些数据并检查了它们的相互依赖性，因此ENA方法代表了一种评估复杂STEM思维的潜在变革性方法。

项目成果

Automating the Detection of Reflection-on-Action

• DOI: 10.18608/jla.2017.42.15
• 发表时间: 2017-07
• 期刊: J. Learn. Anal.
• 作者: Jenny Saucerman;A. Ruis;D. Shaffer

Annals and Analytics: The Practice of History in the Age of Big Data

编年史与分析：大数据时代的历史实践

• 发表时间: 2017
• 期刊: Medical history
• 影响因子: 1.4
• 作者: Ruis, A;Shaffer, D.
• 通讯作者: Shaffer, D.

The hands and head of a surgeon: Modeling operative competency with multimodal epistemic network analysis.

• DOI: 10.1016/j.amjsurg.2017.11.027
• 发表时间: 2017-12
• 期刊: American journal of surgery
• 影响因子: 3
• 作者: A. Ruis;A. Rosser;Cheyenne Quandt-Walle;Jay N. Nathwani;D. Shaffer;C. Pugh

Collaborative and Individual Scientific Reasoning of Pre-Service Teachers: New Insights Through Epistemic Network Analysis (ENA)

职前教师的协作和个人科学推理：通过认知网络分析（ENA）的新见解

• 发表时间: 2017
• 期刊: Proceedings of Computer Supported Collaborative Learning
• 作者: Csanadi, A.;Eagan, B.;Shaffer, D.;Kollar, I;Fischer, F.
• 通讯作者: Fischer, F.

Can We Rely on IRR? Testing the Assumptions of Inter-Rater Reliability

我们可以依赖 IRR 吗？

• 发表时间: 2017
• 期刊: Proceedings of Computer Supported Collaborative Learning
• 作者: Eagan, B.;Rogers, B.;Serlin, R.;Ruis, A.;Arastoopur Irgens, G.;Shaffer, D.
• 通讯作者: Shaffer, D.