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

Cuny Borough Of Manhattan Community College, New York NY

Investigators

Abstract

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.