[Paper Review] A Framework for Incorporating Model-Based Inquiry into Physics Laboratory Courses

Benjamin M. Zwickl, Noah D. Finkelstein+1 more|arXiv (Cornell University)|Jan 18, 2013

Data Visualization and Analytics3 citations

TL;DR

This paper presents a framework for integrating model-based inquiry into physics lab courses by redesigning a polarization of light experiment to emphasize modeling cycles—constructing, predicting, interpreting data, comparing predictions with measurements, and refining models. The approach naturally embeds systematic error analysis into the inquiry process, enhancing conceptual and quantitative reasoning in lab settings.

ABSTRACT

Implementing a laboratory activity involves a complex interplay among learning goals, available resources, feedback about the existing course, best practices for teaching, and an overall philosophy about teaching labs. Building on our previous work, which described a process of transforming an entire lab course, we now turn our attention to how an individual lab activity on the polarization of light was redesigned to include a renewed emphasis on one broad learning goal: modeling. By using this common optics lab as a concrete case study of a broadly applicable approach, we highlight many aspects of the activity development and show how modeling was used to integrate sophisticated conceptual and quantitative reasoning into the experimental process through the various aspects of modeling: constructing models, making predictions, interpreting data, comparing measurements with predictions, and refining models. One significant outcome is a natural way to integrate an analysis and discussion of systematic error into a lab activity.

Motivation & Objective

• To address the gap in integrating modeling practices into traditional physics laboratory courses.
• To redesign a standard optics lab on light polarization to center on model-based inquiry as a core learning goal.
• To demonstrate how modeling can be systematically embedded in lab activities to promote conceptual and quantitative reasoning.
• To show how systematic error analysis can emerge organically from the modeling cycle rather than being treated as a separate topic.
• To provide a transferable framework applicable to other lab activities across physics curricula.

Proposed method

• Redesigned a standard polarization of light lab to center on the modeling cycle: model construction, prediction generation, data collection, comparison with predictions, and model refinement.
• Integrated formative feedback loops where students interpret discrepancies between predictions and measurements to guide model revision.
• Structured lab activities to require students to articulate models explicitly, including assumptions and limitations.
• Used systematic error analysis as a natural outcome of model comparison, rather than a post-lab add-on.
• Applied best practices in inquiry-based teaching by emphasizing student-driven reasoning and iterative model development.
• Leveraged existing lab resources and equipment to maintain feasibility while transforming pedagogical focus.

Experimental results

Research questions

• RQ1 How can model-based inquiry be systematically incorporated into a traditional physics laboratory activity?
• RQ2 In what ways does the modeling cycle enhance students' conceptual and quantitative reasoning in lab settings?
• RQ3 How can systematic error analysis be naturally integrated into the modeling process rather than treated as a separate task?
• RQ4 What are the observable shifts in student engagement and reasoning when modeling is the central learning goal of a lab?
• RQ5 To what extent can this framework be generalized to other physics lab activities?

Key findings

• The redesigned lab activity successfully embedded modeling as the central framework, with students constructing, testing, and revising models throughout the experiment.
• Students engaged in deeper conceptual reasoning by explicitly comparing predictions with measurements and identifying discrepancies as opportunities for model refinement.
• Systematic error analysis emerged organically during model comparison, particularly when predictions did not match data, leading to meaningful discussions about measurement limitations.
• The modeling cycle enhanced student engagement by making the experimental process more purposeful and iterative.
• The framework proved adaptable to other lab activities, suggesting broad applicability across physics laboratory courses.
• The approach demonstrated that modeling can be integrated into standard lab settings without requiring significant changes to equipment or resources.