DEVELOPMENT AND APPLICATION OF A STEM WAYS OF THINKING FRAMEWORK

This dissertation is situated within the context of a reform initiative being implemented in

the laboratory component of an introductory physics course for first-year students in a large

mid-western U.S. public university. Research shows that integrating Engineering Design

(ED) into science classrooms has the potential to enhance student learning. Following this

evidence, we integrated ED into the physics laboratory curriculum.

We assessed the educational gains of this approach and adopted a data-driven strategy

to inform future iterations of our interventions. Research indicates that students do not

always consciously engage with science concepts; instead, they often rely on trial-and-error

strategies—an issue referred to as the design-science gap. In response, some researchers have

proposed the use of “Ways of Thinking” frameworks to promote deeper student learning.

Motivated by both the existing literature and our local educational goals, we quali-

tatively explored students’ Ways of Thinking—specifically, design-based thinking, science-

based thinking, mathematics-based thinking, metacognitive reflection, and computational

thinking. This was done through analysis of student artifacts such as group discussion tran-

scripts and written reports submitted as part of engineering design-based tasks within the

laboratory.

Given the often-blurred distinction between the concepts of design and science, we set

out to characterize these notions in the context of engineering design-based (ED-based)

physics tasks. In doing so, we introduced and applied a new framework to physics education

research, called Ways of Thinking for Engineering Design-based Physics (WoT4EDP). This

framework draws on elements from national reform documents and contemporary physics

education research.

We argue that instruction should move beyond the notion of a design-science gap and

instead focus on fostering a design-science connection. To support more effective problem-

solving, students may need: (i) guidance to engage in meaningful metacognition that en-

hances their design thinking; (ii) scaffolded support to promote computational thinking

through Python coding; and (iii) structured guidance for effective problem framing.

Our findings have implications for educational researchers and instructors who wish to

adopt design-based learning as an instructional approach.