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

研究人员提议开发一种自然选择中的评估形式--以及一个实现这种评估的计算机系统--它将使用潜在语义分析(LSA)和热词提取作为一种手段，将丰富的、开放式的学生回答转化为教师可以使用的分数，同时保留被评估学习的复杂性。LSA方法将与面谈和选择题评估方法进行比较。将进行一些比较成本分析。该项目将在俄亥俄州立大学的本科生(包括白人学生和来自未被充分代表的种族和民族的学生)中进行。这项研究是否会成功，它将创造一种新的、更有信息量的评估学生的方式？知识--考虑到他/她应用于STEM教育研究的所有知识的知识。这可以为教师和学生自己提供更多有用和强大的信息，分别改善教学方法和学习方法。

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.