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In this research brief, we present our methods research on beginning to define engineering judgment. Judgment is critical to the field of engineering. However, when asked to define engineering judgment, engineers, including faculty, often cannot readily do so. Although judgment is a common term, it has been an elusive notion. This elusiveness may be due to the many conceptions of engineering judgment found in the literature. Unfortunately, given the lack of an agreed-upon, concrete definition for engineering judgment that integrates the various conceptions, its assessment relative to the formation of engineers has been limited. In this research brief, we describe our process to begin the development of an expert, consensus-driven definition for engineering judgment using the Delphi technique and the various conceptions for engineering judgment in the literature. A working or preliminary definition, which is being used to develop assessment and instructional materials for engineering judgment, is presented. 

In a core mechanical engineering course on numerical methods at the University of South Florida in the fall of 2022, students were presented with discussion questions to serve as metacognitive activities. The course consisted of eight topics, and after each topic, the students were asked a single discussion question. While answering these questions was optional for the students, it served as 2% extra credit for the eight questions of the course. This initiative was initially taken to offset any occasional missed 30 online homework assignments, which accounted for 15% of the grade for the semester. These questions were designed to elicit thoughtful and unique responses from the students. To promote learning from others, students were allowed to see posted responses from other students only after they had submitted theirs. The questions ranged from making a meme to describing a difficult or intuitive concept. Despite the opportunity for extra credit and the unique prompts, the participation rate was only 59% of the possible submissions, and no clear trend was observed between the participation of high- or low-performing students. 

In this study, flipped instruction in an undergraduate engineering course in the ‘COVID’ online, remote environment was conducted and compared to onsite flipped instruction (i.e. pre-COVID) to explore potential changes in student perceptions. Student perceptions were gathered via survey instruments and investigated further through instructor interviews. This analysis was done at three universities and made possible by extensive research with the flipped classroom at these three schools as part of a previous NSF-funded study between 2014 and 2016. Results gathered in the online remote setting suggest positive changes in student perceptions of flipped instruction compared to the onsite environment, including the decreased perception of the ‘load’ imposed by the flipped classroom and the ‘effort‘’ required. Some desirable outcomes remained unchanged in the remote setting. The recent and emerging literature has suggested the remote, online environment dictated by the pandemic may be beneficial for flipped teaching and learning. These and other findings from conducting flipped classrooms at three engineering schools in the online environment are presented, including perceptions of the classroom environment (via the College and University Environment Inventory), benefits and drawbacks identified, student motivation levels, and perceived learning. 

Abstract Adaptive learning platforms are increasingly being used as part of varying instructional modalities. Particularly relevant to this paper, adaptive learning is a critical component of personalized, preclass learning in a flipped classroom. Previously inaccessible, data generated by adaptive learning platforms regarding student engagement with the course content provides an invaluable opportunity to gain a deeper understanding of the learning process and improve upon it. We aim to investigate the relationships between adaptive learning platform interactions and overall student success in the course and identify the variables most influential to student success. We present a comprehensive analysis of our adaptive learning platform data collected in a Numerical Methods course, including aggregate statistics, frequency analysis, and Principal Component Analysis, to determine which variables exhibited the most variability and, therefore, the most information in the data. Subsequently, we used the Partitioning Around Medoids clustering approach to investigate naturally occurring clusters of students and how these clusters relate to overall performance in the course. Our results show that overall performance in the course, as measured by the final course grade, is strongly associated with (1) the behavioral interactions of students with the adaptive platform and (2) their performance on the adaptive learning assessments. We also found distinct student clusters (as defined by success in the course) that exhibited distinctly different behaviors. These findings provide qualitative and quantitative information to identify students needing support and to craft an evidence‐based support strategy for these students.