INSPIRE Type 1: Investigating Retention and Persistence with Network Analysis

该INSPIRE奖部分由数学和物理科学理事会物理部的基本粒子物理计划，数学和物理科学理事会物理部的教育和跨学科研究计划，数学和物理科学理事会多学科活动办公室，以及教育和人力资源局的INSPIRE方案。在NSF Track 1奖项的支持下，佛罗里达国际大学物理教育研究小组将整合网络分析和定性教育研究的方法，以检查，测试和了解学生社区在促进学生保留和坚持物理学方面的有效性。 他们将集中在他们的家乡机构，一个大型的公共和主要是西班牙裔服务机构在南佛罗里达的研究。 支持者建议审查学术和社会融合，因为它们涉及到教室，以解决目前物理学界最紧迫的问题之一-提高物理学的多样性，同时增加物理学学生的总数。 这些影响物理学界的问题也可能与大多数工程和计算机科学相关。该计划将解决几个问题，在正式的学习环境：1。 建模教学计划如何促进学生在物理专业的保留和坚持？2. 不同的学习环境如何塑造学生网络的形成，以及它们如何有助于学生的保留和坚持？该计划将解决非正式学习环境中的几个问题：1。什么样的网络是由于参与各种半正式的学习环境？ 参与这些网络如何与成就结果和持久性相关？2.我们能否利用横截面分析来开发一个学生参与度的预测模型，最终可以用来与持久性相关联？ 学生参与度预测模型中最突出的特征是什么？这些问题通过发展对支持所有学生坚持物理学的机制的理解，以及对金融情报大学独特的学生群体的理解，产生了广泛的影响。 此外，定性教育研究支持的网络方法的发展可以提供独特的机会来发现支持学生参与物理的新兴网络结构。

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.