NSF Award
0909900
The goal of this research project is to advance knowledge about student and faculty perceptions of connection to community as factors in academic engagement. The three phases of this research will mold basic research results into applied strategies for improvements in engagement in higher education STEM environments. The first phase is a tool development phase which will provide a valid tool box for evaluating target constructs (engagement, participation, affect, cognition, etc.). Three data collection tools will be developed: student surveys, student focus group protocols, and faculty interview protocols. <br/><br/>The second phase focuses on addressing the following research question: What connections to community are contributing to significant differences in academic engagement? and How do institutional characteristics mold the classroom experience toward increased community? Surveys will be administered to students and focus group and interviews conducted with students at five different post-secondary institutions. Data will be analyzed within and across institutions to gauge the effects of higher education institutional characteristics on student affect, perception, cognition, sense of belonging, engagement, and participation. <br/><br/>Finally, the third phase emphasizes applying results to develop strategies by which successful community-building characteristics of the various participating institutions can be transferred elsewhere. In all phases, the research team brings a broad range of diversity of expertise and institutional fabric to the proposed study. <br/><br/>Five different types of institutions are represented in this effort: Research 1 (University of Washington), Comprehensive (Minnesota State), Master's L (Simmons College), Private, Faith-Based (Seattle Pacific), and HBCU (Tuskegee). Each of these institutions has unique historical strengths for undergraduate education; this research will improve understanding of how these strengths impact connections to community via mediating engagement and subsequently predicting student learning.
