2025 ASEE Annual Conference & Exposition

ABET 7 emphasizes the importance of developing engineering students’ ability to acquire and apply knowledge through effective learning strategies. Reflection is one way to encourage students to evaluate their learning strategies, identify knowledge gaps, and plan ways to achieve their learning objectives. In essence, reflection can facilitate students’ deep thinking about their learning. That said, reflection has been found to be an underused engineering classroom method for developing students’ metacognitive skills and supporting their growth as self-directed learners. The under-utilization of reflection is often due to educators’ lack of familiarity with reflection and comfort with non-technical instruction. For educators who are starting to bring reflection into their classroom, tools are necessary to understand and monitor the impact on students. Such tools can inform educators about what students are taking away from reflection implementation (intended or not) and guide educators to make focused instructional adjustments. This study focuses on the first implementation of reflection in two Biological and Agricultural Engineering (BAE) courses and the use of an instrument to capture students' self-reported knowledge gains.

The first purpose of this study was to gather validity evidence for the Reflective Knowledge Gains Instrument (RKGI, Mejia & Turns, 2021) which is intended to assess the impact of reflection practices on four factors: engineering students’ engineering identity, their course content understanding, their social impact understanding, and their personal growth. The second purpose was to demonstrate the utility of this instrument in two undergraduate BAE courses with different implementations of reflection.

The RKGI was administered in multiple engineering undergraduate courses in which reflection was implemented in Fall 2023 and Spring 2024. An exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) were conducted using 266 RKGI responses to identify the number of latent factors represented by the data and to assess the data fit to the proposed factor structure. Two BAE use cases were developed with data from 47 first-year students enrolled in a course focused on using computer tools to solve biological engineering problems and 41 second-year students enrolled in a biological material properties laboratory course.

Although the factor analyses revealed four factors that were different from the originally proposed RKGI factors, there was strong support for a new four-factor model with good internal consistency and substantial loadings. The new factors were: engineering self, course understanding and work, areas for growth, and personal interest. The use cases demonstrated differences in the impact each BAE course had on students’ knowledge gains that were consistent with the reflection implementation in each course and pointed to improvement needs.

The RKGI is a valuable tool that can guide improvement in reflection instruction and implementation. The ways in which BAE educators can use the RKGI to improve instruction will be discussed.