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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. 

This paper reports on a project funded through the Engineering Education and Centers (EEC) Division of the National Science Foundation. Since 2010, EEC has funded more than 500 proposals totaling over $150 million through engineering education research (EER) programs such as Research in Engineering Education (REE) and Research in the Formation of Engineers (RFE), to enhance understanding and improve practice. The resulting archive of robust qualitative and quantitative data represents a vast untapped potential to exponentially increase the impact of EEC funding and transform engineering education. But tapping this potential has thus far been an intractable problem, despite ongoing calls for data sharing by public funders of research. Changing the paradigm of single-use data collection requires actionable, proven practices for effective, ethical data sharing, coupled with sufficient incentives to both share and use existing data. To that end, this project draws together a team of experts to overcome substantial obstacles in qualitative data sharing by building a framework to guide secondary analysis in engineering education research (EER), and to test this framework using pioneering data sets. Herein, we report on accomplishments within the first year of the project during which time we gathered a group of 13 expert qualitative researchers to engage in the first of a series of working meetings intended to meet our project goals. We came into this first workshop with a potentially limiting definition of secondary data analysis and the idea that people would want to share existing datasets if we could find ways around anticipated hurdles. However, the workshop yielded a broader definition of secondary data analysis and revealed a stronger interest in creating new datasets designed for sharing rather than sharing existing datasets. Thus, we have reconceived our second phase as one that is a cohesive effort based on an inclusive “open cohort model” to pilot projects related to secondary data analysis. 

In this Lessons Learned paper, we describe the implementation of an on-campus workshop focused on supporting faculty as they develop metacognitive interventions for their educational contexts. This on-campus workshop at Duke University included faculty from engineering as well as other faculty from campus and was developed and implemented by members of the Skillful Learning Institute Team. First, we describe the purpose and intent of the workshop by the host institution (Duke University) and the workshop development team (Skillful-Learning Institute Team). We then provide the workshop overview across the two day period, including a description of instruction provided and structured breakout sessions. Next, we provide a lessons learned section from the perspectives of the host institution and the workshop developers. Finally, we offer insights into how those lessons learned are being incorporated into the development of future workshops. By providing the two perspectives, our lessons learned should help those who invite speakers in for faculty development and those who are creating faculty development activities. 

Studies on graduate education have shown that underrepresented minorities finish PhDs in engineering at lesser rates and longer timeframes than their majority counterparts. While multiple interventions have been designed for students considering their decision to apply for graduate school or students completing their doctoral journey, few focus on the transition into those doctoral programs. To prepare minoritized doctoral students for this transition to the Ph. D., we developed and researched the Rising Doctoral Institute (RDI). The RDI is a four-day summer workshop for incoming doctoral students who identify as underrepresented in engineering and intend to begin graduate school in the Fall semester. This paper aims to discuss the process through which we developed the RDI and our initial research findings. We conclude with our plan to disseminate these workshops across multiple US institutions using a change-theory informed dissemination model.