2023 ASEE Annual Conference & Exposition

Extended Abstract
Much work has been done to assist engineering faculty in higher education as they work to enhance their classroom teaching. While some of this work might be applicable to faculty leading undergraduate research teams, it is unclear which “enhanced teaching methods” might apply in this research setting. Kolb’s cycle is a method that has been used widely in pedagogical settings. The current work is intended to provide a method to facilitate its use with undergraduate research teams. The Kolb model is characterized by a cycle that begins with Concrete Experience (CE), proceeds with Reflective Observation (RO), then Abstract -Conceptualization (AC), and ends, before restarting, with Active Experimentation (AE). Educational environments that incorporate all four parts of the cycle have been shown to span the spectrum of student learning styles more fully, and in general to enhance the overall learning experience. In this current work, a process is provided that allows the use of the Kolb cycle to be applied to an undergraduate research project, with the goal of improving the team’s experience in various ways.
The first stage of the process asks members of the research team to complete an instrument that identifies their preferred Kolb Learning Style (KLS). This learning style is a combination of different pairs of the four Kolb cycle activity types or parameters. The instrument provides ranked learning preferences as either more oriented toward CE or AC and either toward AE or RO. Thus, the learning preference therefore can be seen as a two-dimensional (2-D) vector that has, on the horizontal axis, AE as “-1” and RO as “+1” and on the vertical axis has AC as “-1” and CE as “+1”.
Next, activities are identified that will be completed in the process of the research. Each of these activities are rated in terms of how they are oriented toward CE or AC and toward AE or RO. For example, if creating a prototype is part of a research process, that activity might be given a vector (-0.5, 0.8); meaning that the activity leans toward AE over RO with a strength of 0.5 and aligns with CE over AC with an even stronger correlation of 0.8. This categorization for each research step or activity is done in a group format where the group gives a rating from -1 to 1 for CE vs. AC and again for AE vs. RO. This creates a 2-D vector location for each research activity so that the activity can be plotted on axes with the 4 Kolb parameters as horizontal and vertical axis.
An extensive description of the 4 different parts of the Kolb cycle, is used to assist in giving each research activity a 2-D vector position. Once the different research activities have been associated with a 2-D Kolb vector, the activities are plotted on a 2-D graph with AE – RO being the horizontal axis and AC – CE being the vertical axis. The amount of time spent on a task is also included in the plot as the size of the box identifying the activity.
As mentioned, the students on the research team each take a short survey that defines their “Kolb Learning Style” (KLS). That KSL is quantified as a strength of preference for each of the 4 Kolb parameters. This KLS can be indicated as a location on the same 2-D plot that shows the how the research activities fit within the 4 Kolb designations.
The plot with the research activities and the KLS plotted in accordance with their Kolb affiliation can be studied to show how the KLS for the students align (or do not align) with Kolb plot locations for the research activities. This provides a graphical representation of how the research activities align with KLS preferences for the research team. The potential utility of this work is to provide insight into how research activities might be altered to align them more closely with KLS of the research team of students. As this alignment increases, the quality of the research experience is hypothesized to increase as well. Specifically, a center of gravity (CG) can be computed for the research activities in the 2-D plane of the 4 Kolb designations. In a similar manner a CG can also be computed for the research team’s combined and averaged KLS. The goal is to change certain process parameters in some activities to more closely align these two CGs. Note that the idea is not to add or remove research activities, but to change the manner in which the research activities are accomplished to improve the alignment of the CGs.
One way to get a research activity’s Kolb plot location to move toward a KLS CG is by changing the methodology or process details of the research activity. This can take form in many manners. An example research activity: “individually ideate and down select,” can be improved and made even more suitable for the team as a whole by simply changing how the activity is carried out. For example, if a student on the research team aligns toward the “Abstract Conceptualization” Kolb parameter, they may prefer a less structured or less “step by step” approach. However, if the student were to be aligned more with the other side of the spectrum at “Concrete Experience,” the task may just need some detailed specifications on how to accomplish it. Instead of brainstorming, one could incorporate a more hands-on approach like using sticky notes or creating a mind map on a whiteboard. This would satisfy the CE learning style requirements of the task being a more physical activity that they can engage with. It could also in turn move the 2-D vector location of the activity closer to the desired location (i.e. closer to the team’s KLS CG). An important consideration is whether it is essential to move every task into the comfortable range of the students. Of course, this may not be possible, especially if the students working together do not have similar KLS. It is however reasonable to move the general CG of all the activities closer to the aggregate CG of the students’ Kolb learning style.
By changing the process used to accomplish certain research tasks this research has shown that moving the activity’s CG toward the average of the student’s KLS CG is a helpful way to improve the research team’s efficiency and effectiveness on a given research project. Not only does it help students to work in their respective areas more efficiently and effectively, but it also allows for them to take into account what their colleagues are proficient at; this would allow for certain team members to take a lead on specific tasks while being able to recruit other colleagues to aid them in their process, therefore creating a more positive environment. This process allows any given research project to be better tailored to the participants, while still allowing room for them to grow in activities that require a learning style outside of their comfort zone.