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This work in progress describes an ongoing study of an active, blended, and collaborative (ABC) course environment used in a core mechanical engineering course. This course has built on the growing body of literature citing active learning (Freeman et al., 2014), blended structures (Bowen & Ithaka, 2012), and collaborative engagement (Jeong & Chi, 2007) as positive influences on college and university science, technology, engineering, and math (STEM) outcomes. For the last six years, “Dynamics”, a core mechanical engineering course at a large public university, has utilized in-class activities, frequently-watched problem-solving videos, and a collaborative blog space to realize an ABC environment.

On one key metric of course success, the rate of students who drop, fail, or withdraw from (DFW), the course has experienced near-constant improvements since the ABC structures were introduced. In this study, the authors utilize rigorous longitudinal methods to determine whether this drop in DFW rates can be directly attributed to increased implementation of ABC features. The authors hypothesize that as instructors become accustomed to the ABC environment and increase the level of in-class activity, use of blended resources, and collaboration, the likelihood of DFW in each subsequent year would drop. However, in the same time period, each subsequent class entered with higher levels of performance on proxy measures for prior knowledge.

We therefore build a logistic regression model to predict individual-level DFW and determine whether the anecdotal drops in DFW that we observe can be attributed to the expansion of the ABC environment. More specifically, we predict likelihood of DFW based on students’ prior knowledge (grade in the preceding course, SAT math score), key demographics (gender, race/ethnicity), the semester and year they took Dynamics, their instructor, their year in school, and their major. We test for year fixed effects {year_t, t = 1, 2, ..., 7} to determine whether odds ratios for DFW consistently and significantly decrease over time. We also test for instructor effects, in particular for differences between the instructors who were involved in the design and development of the ABC environment and more independent instructors who only partially implemented the ABC course components. We anticipate results that will provide more rigorous, less biased, and efficient estimates for the individual- and class-level components that explain variance in DFW rates. These results would provide immediate implications for the next phase of our work, as we assess the next on-term implementation of the course in 2016. Our findings would also have long-term significance for other classes in mechanical engineering and related disciplines and for classes at other institutions that are considering implementing a comprehensive ABC learning environment.