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提案将有助于综合、建立和/或扩展 STEM（科学、技术、工程和数学）教育以下领域的研究基础：STEM 学习、STEM 学习环境、STEM 劳动力发展和拓宽参与 STEM。大学里的 STEM 教育渠道显着缩小。社区大学为一些最多样化的受众提供服务，并且越来越多地使用在线学习作为提供 STEM 教学的更便宜的方式；此外，大规模开放在线课程（通常称为 MOOC）也被提议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产生不同的影响。这个以社区大学为基础的研究项目提出了以下问题：社区大学层面的 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？它会产生积极还是消极影响？我们能否确定哪些学生最适合接受在线教学和面对面教学，或者对在线环境中“面临风险”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：一个是由 CUNY（纽约市立大学）系统中的六所学校汇总的数据集，该系统为美国最多元化的学生团体之一提供服务，并且超过 50,000 名学生在线学习了 STEM 课程。第二个是来自国家教育统计中心的大规模全国数据集，其中包含人口、学业、个人和财务变量。在线学习的研究中，只有一小部分控制了学生自主选择在线课程的情况。一种严格的方式。这项研究将探讨具有特定特征的学生不仅在在线 STEM 课程中表现更好或更差的程度，而且在大学毕业后，与参加类似面对面 STEM 课程的学生进行匹配比较。该项目采用混合方法。定量分析将包括主成分因素分析、逻辑回归、线性回归、方差和协方差分析、广义线性混合模型、倾向得分匹配和敏感性分析，以检查课程和大学成果，包括课程保留率（到第十届末的出勤率）周的课程）和成功完成课程（在课程中获得 C- 或更好），学生是否在课程结束后立即重新注册，以及一年、两年、三年和六年的坚持。还将使用这些时间间隔的总平均绩点、累积学分以及转学率和毕业率。要建模的自变量和协变量包括在线、混合、离线 STEM 课程格式，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，并将专门与纽约市立大学系统中的在线讲师进行调查。将探索数据来模拟哪些变量会导致在线差异“风险”。该项目的智力价值在于加深我们对在线选项如何不同地帮助或阻碍不同类型的 STEM 学生的理解。为了产生更广泛的影响，模型的结果可以用作实施有针对性的干预措施的基础，可以通过为高危学生提供额外的指导、辅导、技术支持、建议或成功完成学业所需的技能和行为培训。在线课程；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。