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Can Student Characteristics be Used to Effectively Identify Students At-Risk in the Online STEM Environment?

学生特征能否用于有效识别在线 STEM 环境中存在风险的学生？

基本信息

批准号：
1431649
负责人：
Claire Wladis
金额：
$ 71.91万
依托单位：
CUNY Borough of Manhattan Community College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-03-01 至 2019-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1431649&HistoricalAwards=false
关键词：
Student Characteristics Used Effectively Identify

项目摘要

The EHR Core Research Program funds proposals that will help synthesize, build and/or expand research foundations in the following areas of STEM (Science, Technology, Engineering, and Mathematics) Education: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The STEM education pipeline narrows significantly in college. Community colleges serve some of the most diverse audiences, and are increasingly using online learning as a cheaper way to provide STEM instruction; additionally Massive Open Online Courses (commonly known as MOOCs) are being proposed as alternatives to credit-bearing instruction. Prior research shows that online learning environments impact different kinds of students differently. This research project based at a community college asks questions such as the following: Is this move towards STEM learning online at the community college level likely to impact underrepresented groups more than others, and will it have positive or negative impact? Can we identify which students are best served by online vs. face-to-face instruction or conduct interventions for students 'at-risk' in the online environment? This project aims to answer these questions by using two important datasets: one is a dataset to be assembled from six schools in the CUNY (City University of New York) system, which serves one of the most diverse student bodies in the country, and in which over 50,000 students have taken STEM courses online. The second is a large-scale national dataset from the National Center for Education Statistics which contains demographic, academic, personal, and financial variables.Only a small proportion of the research conducted on online learning has controlled for student self-selection into online courses in a rigorous way. This study will explore the extent to which students with particular characteristics fare better or more poorly not only in online STEM courses, but in college afterwards, with a matched comparison to students who take comparable face-to-face STEM courses. The project uses mixed methods. Quantitative analysis will include principal component factor analysis, logistic regression, linear regression, analysis of variance and covariance, generalized linear mixed models, propensity score matching, and sensitivity analysis to examine course and college outcomes including course retention (attendance through the end of the tenth week of classes) and successful course completion (earning a C- or better in the course), whether students re-enrolled in the semester immediately following the course, and persistence at one, two, three, and six years. Overall grade point average, the number of credits accumulated, and transfer and graduation rates at these intervals will also be used. Independent variables and covariates to be modeled include online vs. hybrid vs. offline STEM course format, and a variety of demographic variables including effort capital, social capital, cultural capital, financial capital, human capital, and habitus. Qualitative interviews and in-depth surveys will be used to explore the trends found in the large scale datasets, and a survey will be conducted specifically with online instructors in the CUNY system. Data will be explored to model what variables contribute to differential 'risk' online. The intellectual merit of the project rests on advancing our understanding of how online options differentially help or hinder different kinds of postsecondary STEM students. For broader impacts, the results of the model could be used as the basis for implementation of targeted interventions, either by providing at-risk students with additional mentoring, tutoring, technical support, advisement, or training in skills and behaviors necessary to succeed in an online course; or by advising them to enroll in a comparable face-to-face course instead. These policy implications will be discussed at a culminating one-day conference on elearning hosted by the project.

EHR核心研究计划资助的建议将有助于综合、建立和/或扩大STEM(科学、技术、工程和数学)教育领域的研究基础：STEM学习、STEM学习环境、STEM劳动力发展和扩大STEM的参与。在大学里，STEM教育渠道明显缩小。社区大学服务于一些最多样化的受众，越来越多地使用在线学习作为提供STEM教学的一种更便宜的方式；此外，正在提议将大规模在线公开课(通常称为MOOC)作为学分教学的替代方案。先前的研究表明，在线学习环境对不同类型的学生的影响是不同的。这项以社区大学为基础的研究项目提出了如下问题：在社区大学层面转向STEM在线学习的举措是否可能比其他群体对代表性不足的群体产生更大的影响？它是有积极的影响还是消极的影响？我们能否确定哪些学生最适合在线教学和面对面教学，或者在网络环境中对处于危险中的学生进行干预？该项目旨在通过使用两个重要的数据集来回答这些问题：一个是从纽约城市大学(CUNY)系统中的六所学校收集的数据集，该系统为美国最多样化的学生群体之一提供服务，其中有50,000多名学生在网上参加了STEM课程。第二个是来自国家教育统计中心的大规模国家数据集，其中包含了人口、学术、个人和经济变量。在对在线学习进行的研究中，只有一小部分严格控制了学生自主选择参加在线课程。这项研究将探索具有特定特征的学生在在线STEM课程中表现更好或更差的程度，并与参加类似面对面STEM课程的学生进行匹配比较。该项目使用混合方法。定量分析将包括主成分因子分析、Logistic回归、线性回归、方差和协方差分析、广义线性混合模型、倾向分数匹配和敏感性分析，以检查课程和大学结果，包括课程保留率(到课程第十周结束时的出勤率)和成功完成课程(在课程中获得C-或更好的成绩)，学生是否在课程结束后的那个学期重新注册，以及在一年、两年、三年和六年的持续时间。整体平均绩点、累积的学分数目，以及这些时间间隔内的转学和毕业率也会被使用。要建模的自变量和协变量包括在线、混合和线下STEM课程形式，以及各种人口统计变量，包括努力资本、社会资本、文化资本、金融资本、人力资本和习惯。将使用定性访谈和深入调查来探索大规模数据集中发现的趋势，并将专门与城市大学系统的在线讲师进行调查。我们将探索数据，以模拟哪些变量会对网上的差异化“风险”做出贡献。该项目的智力价值在于促进我们对在线选择如何不同地帮助或阻碍不同类型的中学后STEM学生的理解。对于更广泛的影响，该模型的结果可以作为实施有针对性的干预措施的基础，方法是为高危学生提供额外的指导、辅导、技术支持、建议或成功参加在线课程所需的技能和行为培训；或者建议他们参加类似的面对面课程。这些政策影响将在该项目主办的为期一天的电子学习会议上进行讨论。