Engineering students are trained to be effective problem solvers. Specifically, engineering students are expected to become skillful at synthesizing and applying information across multiple knowledge domains to generate optimal solutions to problems of varying levels of difficulty. Unfortunately, many engineering students graduate with discernible gaps in their problem solving skills.
In order to be effective problem-solvers, students must learn to construct accurate and appropriate understandings and knowledge about the relationships between task characteristics (i.e., metacognitive knowledge about tasks or MKT) and associated processing demands. Ideally, the metacognitive knowledge about tasks that students develop helps them enact more effective self-regulation, particularly task interpretation, processes. Students’ engagement on a task as a whole, including their active and reflective coordination of cognitive processes in light of metacognitive knowledge and conceptions about problem-solving tasks and the context of academic work, is called self-regulation in action (SRA).
Because engineering problems vary in depth of content, composition, and representation, (i.e., simple to complex problems, well- to ill-structured, and/or low to high problem dynamicity), the challenges that students encounter as they develop and link their MKT to their SRA represent critical obstacles to their development as effective problem solvers. Therefore, we argue that a deeper understanding of the ways in which engineering students’ use MKT as they engage in effective SRA during problem solving is needed.
The purpose of this project is to (1) develop, field-test, and refine research protocols and tools to be used to study students’ metacognitive knowledge about tasks (i.e., task purpose, task structure, and task components) and their self-regulation in action (i.e., interpreting tasks, planning, enacting, monitoring, and evaluating processes) while solving engineering problems of varying levels of difficulty, and (2) develop a better grasp of collecting, analyzing, and interpreting a large amount qualitative data associated with students’ MKT, SRA, and student learning contexts during engineering problem-solving.
A case study approach will be used for this research in order to develop in-depth understanding of undergraduate students’ metacognitive knowledge about tasks and task interpretation processes during engineering problem-solving activities. We situate this study within the Fundamental Electronics for Engineers, one of several second-year engineering courses offered within the college of engineering at a land grant university in the western part of the United States. Two research questions will guide the research: (1) How does students’ metacognitive knowledge about engineering problem-solving tasks inform their self-regulation in action processes while engaged in problem-solving activities?; and (2) How do students’ metacognitive knowledge about engineering problem-solving tasks and self-regulation in action dynamically evolve during problem-solving activities?
The research team will select 3 student volunteers for in-depth study. Purposeful sampling will be used in selecting the student participants for this research to create some variability such as genders and their areas of study in engineering. Because this case study investigates a bounded system holistically, the research team needs to collect, assemble, and relate multiple kinds of evidence (i.e., contextual influences, conceptions about engineering problem-solving tasks, and students’ MKT and engagement in SRA).
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