Grant: National Research Foundation EHR Core Research Award Award: $719,108
Time Frame: 2015-2018
Primary Investigators:Claire Wladis, Alyse C. Hachey and Katherine M. Conway
Research Associate:Dr. Anthony Picciano, Professor of Urban Education at the CUNY Graduate Center
Research Assistant: Diane McAllister
Advisory Board: Dr. Frances K. Stage, Professor of Administration, Leadership, and Technology at New York University; Dr. Lin Lin
Associate Professor of Learning Technologies at the University of North Texas
Title: Can Student Characteristics Be Used to Effectively Identify Students At-Risk in the Online STEM Environment?
This project addresses the EHR Core Research (ECR) program’s goal to build a research foundation in STEM learning environments by investigating which factors predict poorer outcomes online vs. face-to- face for STEM students, with a particular focus on traditionally underrepresented groups in STEM fields. Specifically, this research is motivated by the following questions:
When controlling for differences in student characteristics, what differences exist between online and face-to-face STEM course and subsequent outcomes; and which student characteristics are the strongest predictors of such differences? In particular, does the online environment impact traditionally underrepresented groups in STEM disciplines differently than other students?
Do statistical models based on student characteristics have predictive validity in identifying which students are most likely to successfully complete online STEM courses, and which online STEM students are most likely persist in college afterwards?
The end product of this research will be a logistic regression equation (or a straightforward recipe for institutions to follow to create their own institution-specific equation) which can be used to pinpoint students at highest risk of dropping out of online STEM courses (or college subsequently), so that effective support services can be targeted at the most at-risk students. This research will not only advance STEM and higher education research, but it will also potentially transform educational practice and policy. These results will impact students considering online courses, faculty designing and teaching online courses, staff implementing online student support structures, administrators determining policies about student access to online courses, and policymakers determining how and when to include online courses in programs to increase student access to, and success in, STEM disciplines.
Grant: Internal Research Award
Time Frame: 2015-2018
Primary Investigators: Claire Wladis, Alyse C. Hachey and Katherine M. Conway
This project is concerned with assessing factors that impact the course and college completion rates of students at BMCC and CUNY in order to inform eLearning policy. The research will support the creation of a CUNY dataset specific to online courses at the university, with information about the percentage of instruction conducted online, as well as variables related to online programs at each campus.
This project will also support analysis on CUNY-wide data and some more general NCES data in order to identify factors that may be influencing online enrollment and course outcomes at BMCC specifically and further, to compare patterns at BMCC to national and CUNY-wide trends. The research will use logistic and ordinary linear regression models, along with propensity score matching and sensitivity analysis, to analyze the impact of student characteristics and eLearning program structures and policies on online course and subsequent college outcomes.