Jackson L. Autrey, Jennifer Sieber, Zahed Siddique, Arrokh Mistree
We contend that, for the engineer of 2020, the ability to adapt to changing circumstances, technologies, and paradigms willbe among the most important competencies to possess. We further contend that students can develop the competency toadapt by engaging in continuous learning through reflection on doing. Additionally, we hypothesize that this critical self-reflection can be implemented in engineering ‘Design, Build, and Test’ courses. In this paper, we present the ‘LearningStatement’ (LS) as an instrument for learning through reflection while providing instructors an instrument to assess thatlearning holistically. We explore a framework for implementation of the LS in a senior-level mechanical engineering designcourse and a method for evaluating the data collected through its use. Using LS data from 76 students in the Fall 2016AME4163: Principles of Engineering Design course, we implement a version of the bisecting K-Means algorithm to textmine student LSs for patterns in subject matter frequently written about, changes over the course of a design project, andlevels of insight demonstrated. We note that students largely focus in their LSs on keywords linked to principles associatedwith team formationand management as wellas prototype construction and testing.Furthermore, wefind that student LSsfor assignments strongly correspond to targeted themes. Additionally, we find that LSs assessed as more insightfulpreferentially focus on areas related to team organization, concept generation, and critically analyzing the design process.We find that text mining analysis of LSs confirm patterns in student learning identified in our earlier work while greatlyreducing analysis time. Further, we find that students are challenged by the team-based design process and thereby learnlessons dealing with planning, organizational structure, and delegated responsibility in such structures. Finally, we findthat effective self-assessment occurs when students connect their learning to specific future utility.
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