2024 ASEE Annual Conference & Exposition

To be successful in engineering, students must develop strong problem solving skills. Problem solving skills are normally promoted using cognitive strategies that help students focus on relevant information associated with the problem, identify the structure of the problem, and to solve the problem. However, cognitive strategies are not sufficient to ensure that students become good at solving problems. Cognitive strategies must be paired with metacognitive strategies to promote strong problem solving skills.

Metacognition refers to the processes used to plan, monitor, and assess understanding and performance. Zimmerman’s self-regulated learning (SRL) model is used as a framework in this study to understand self-regulation and metacognitive monitoring. Metacognitive monitoring is a metacognitive strategy that helps students plan, monitor, and modify their problem solving approach.

This work-in-progress paper summarizes a current project in which students enrolled in a first-year engineering program at a mid-Atlantic land grant institution completed an intervention to improve their metacognitive monitoring during problem solving. Seventy engineering students (experimental group) enrolled in college algebra during their first semester at the institution were co-enrolled in an Introduction to Engineering Reasoning course. The intervention was designed to promote students’ metacognitive monitoring and reflection during problem solving and provided repeated applied practice using and improving their metacognitive strategies. To understand the effects of the intervention, we assessed students’ pre-course and post-course metacognitive awareness and monitoring accuracy. We also examined students’ current and subsequent course grades in the mathematics and engineering course sequence. We compared these outcomes with both a comparison group consisting of students enrolled in the Introduction to Engineering Reasoning course but not completing the intervention (n=35) and a matched control group of students not completing the introductory course.

This paper will summarize the implementation of the metacognitive intervention and the results of the implementation of this work in terms of 1) changes in students’ metacognitive monitoring and their problem solving skills across the course and 2) differences in post-course metacognitive and problem-solving skills based on condition. We will use this to support implications for the design and implementation of targeted self-regulated learning interventions for first-year and underrepresented students in engineering.