Transforming STEM assessment methodologies: Research on cyber-enabled measurement of cognitive models of natural selection

转变 STEM 评估方法：自然选择认知模型的网络测量研究

• 批准号: 1340578
• 负责人: Ross Nehm
• 金额: $ 21.93万
• 依托单位: SUNY at Stony Brook
• 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: 2013-03-31 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1340578&HistoricalAwards=false
• 关键词: Transforming; STEM; assessment; methodologies; Research

The investigators propose to develop a form of assessment in natural selection -- and a computer based system to implement such -- that would use Latent Semantic Analysis (LSA) and hot term extraction as a means of transforming rich, open ended student responses into scores that are usable by faculty yet retain the complexities of the learning assessed. The LSA approach will be compared to interview and multiple choice methods of assessment. Some comparative cost analysis will be performed. The project will be performed at OSU with undergraduates (both white students and students from underrepresented racial and ethnic groups).Should this research be successful, it will create a new and more informative way to assess students? knowledge -- one that takes into account all the knowledge he/she brings to bear on the study of STEM education. This could provide instructors, and the students themselves, with more helpful and powerful information to improve teaching methods and learning approaches, respectively.

研究人员建议开发一种自然选择评估形式-以及一种基于计算机的系统来实现这种评估-将使用潜在语义分析（LSA）和热门术语提取作为将丰富的，开放式的学生回答转化为教师可用的分数的手段，但保留了评估学习的复杂性。LSA方法将与面试和多项选择的评估方法进行比较。将进行一些比较成本分析。该项目将在俄勒冈州立大学与本科生（包括白色学生和来自代表性不足的种族和族裔群体的学生）一起进行。知识-一个考虑到所有的知识，他/她带来的影响对干教育的研究。这可以为教师和学生本身提供更有用和更有力的信息，以分别改进教学方法和学习方法。