Evidence for the effectiveness of a grand challenge-based framework for contextual learning
Student motivation - and associated educational outcomes - can be influenced by the degree to which course material connects to recognizable societal problems. The National Academy for Engineering has established the "Engineering Grand Challenges", a set of 14 fundamental problems whose solutions will require integrated contributions from engineers, scientists, and policy-makers. The current work grafts the Engineering Grand Challenges (EGC) onto a pedagogical framework integrated into courses in several engineering disciplines, assessing whether this framework increased student motivation and, if so, what facets of learning benefit from this approach. The EGC framework, as implemented here, follows a series of six stages that progress from statement of the problem, through exercises that teach a foundational concept using an EGC example, to reflection on the role of engineering in addressing the problem. The framework was implemented in three diverse courses: a computational methods course taken by all first-year engineering students, an upper-level signal-processing elective in electrical engineering, and a design course for upper-level students in environmental engineering. Instructors for each of these courses implemented the EGC framework in a manner appropriate for their course. For example, students in the signal processing course investigated the EGC of "Reverse-Engineering the Brain", which included a lecture/discussion led by a neuroscientist who uses signal processing, followed by a project assignment that applied spectral analysis and filter design to publicly available data from a brain-computer interface contest. For all courses, baseline data were collected from the same classes taught by the same instructors in the previous year. Results from the first year of implementation indicated significant benefits for the EGC framework, as well as differences in effectiveness across settings. Each student provided data that included self-reported ratings of ABET criteria and standard psychological measures of motivation, and those measures were included in structural equation models that predicted interstudent differences in grades. The EGC framework was associated with significantly higher selfreported class effectiveness, as indexed by ABET criteria. Furthermore, in advanced classes the EGC framework enhanced a key measure of student motivation (i.e., situational interest), which in turn was a positive predictor of ABET criteria. This effect was not present in the introductory class examined. No differences between EGC and baseline groups were found in other measures of self-reported motivation (e.g., perceived competence). Collectively, these results provide strong initial evidence that framing course activities around large-scale, societally relevant challenges can have salutary effects upon students' motivation and skill development according to the ABET criteria. Ongoing work examines these effects across multiple semesters of the same courses, as well as across additional courses from throughout engineering curricula. © American Society for Engineering Education, 2014.
