INSPIRE Type 1: Investigating Retention and Persistence with Network Analysis Grant

INSPIRE Type 1: Investigating Retention and Persistence with Network Analysis .

abstract

  • This INSPIRE award is partially funded by the Elementary Particle Physics Program in the Division of Physics in the Directorate for Mathematical and Physical Sciences, the Education and Interdisciplinary Research Program in the Division of Physics in the Directorate for Mathematical and Physical Sciences, the Office of Multidisciplinary Activities in the Directorate for Mathematical and Physical Sciences, and the INSPIRE Program in the Directorate for Education and Human Resources. Supported by this NSF Track 1 award, the Florida International University Physics Education Research Group will integrate the approaches of Network Analysis and Qualitative Education Research to examine, test, and understand the effectiveness of student communities in contributing to student retention and persistence in Physics. They will concentrate their studies at their home institution, a large public and primarily Hispanic-serving institution in south Florida. The proponents propose to examine both academic and social integration as they pertain to classrooms to address one of the most pressing problems in the physics community currently - improving diversity in physics while increasing overall numbers of students in physics. Such issues that affect the physics community are potentially relevant for most of engineering and computer science as well. The program will address several questions in the Formal Learning Environment:1. How does the Modeling Instruction program promote student retention and persistence within the physics major?2. How do differing learning environments shape the formation of student networks, and how do they contribute to student retention and persistence?And the program will address several questions in the Informal Learning Environment:1. What networks are created as a result of participating in a variety of semiformal learning environments? How does participating in these networks correlate with achievement outcomes and persistence?2. Can we utilize cross-sectional analyses to develop a predictive model of student engagement that ultimately can be used to correlate with persistence? What features are most prominent in a predictive model of student engagement?Such questions have broad impact by developing understanding of mechanisms for supporting all students to persist in physics and with the unique student population at FIU, which is historically underrepresented in physics. Further, the development of network methods that are supported by qualitative education research can provide unique opportunities to uncover emergent network structures that support student participation in physics.

date/time interval

  • September 15, 2013 - August 31, 2018

administered by

sponsor award ID

  • PHY-1344247

local award ID

  • AWD000000003538

contributor