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The first year of doctoral study is a critical period for students' academic formation and persistence, especially in engineering disciplines where early integration into research and advising relationships is expected. In the US, for Black and Latiné students—groups historically underrepresented in engineering—this period can be critical as they navigate systemic inequities and social isolation. This longitudinal qualitative study explores how a sense of belonging manifests and evolves for minoritized engineering doctoral students during their first year in U.S. programs. Drawing on focus group data collected across eight sessions with sixteen students from a national summer bridge program, we identify two dominant forms of belonging: merit-contingent belonging, characterized by a need to demonstrate competence and legitimacy, and relational belonging, fostered through supportive advisor and peer relationships. Findings show that while merit-contingent belonging dominates early experiences, it gradually gives way to relational forms of connection as students gain confidence and build community. We call for equity-focused interventions that humanize students, clarify merit, and build trust. 

This full research paper documents assessment definitions from engineering faculty members, mainly from Research 1 universities. Assessments are essential components of the engineering learning environment, and how engineering faculty make decisions about assessments in their classroom is a relatively understudied topic in engineering education research. Exploring how engineering faculty think and implement assessments through the mental model framework can help address this research gap. The research documented in this paper focuses on analyzing data from an informational questionnaire that is part of a larger study to understand how the participants define assessments through methods inspired by mixed method strategies. These strategies include descriptive statistics on demographic findings and Natural Language Processing (NLP) and coding on the open-ended response question asking the participants to define assessments, which yielded cluster themes that characterize the definitions. Findings show that while many participants defined assessments in relation to measuring student learning, other substantial aspects include benchmarking, assessing student ability and competence, and formal evaluation for quality. These findings serve as foundational knowledge toward deeper exploration and understanding of assessment mental models of engineering faculty that can begin to address the aforementioned research gap on faculty assessment decisions in classrooms.