Student engagement with modeling in multiweek student-designed lab projects

Modeling, which is the process of constructing, testing, and refining models, is an important skill in experimental physics, and thus a learning goal of many physics laboratory classes. One promising approach to help students develop modeling skills is to incorporate multiweek student-designed projects into lab courses. In order to assess the potential benefits of these projects in enhancing students’ modeling abilities, we analyzed projects from three upper-division lab courses at different institutions. By looking at written student coursework and student interviews, we investigated which parts of the modeling process the students from each project undertook, and how this engagement in modeling differed depending on features of the projects. The projects in our dataset varied widely, showing evidence of different ways students engaged with model construction and revisions. We observed that the features of the projects, such as the goal of the project and the complexity of the required apparatus, were associated with the ways in which the students constructed models and enacted revisions. This has implications for how instructors may choose to frame and structure courses with student-designed lab projects.

Figure 1

Diagram of the Experimental Modeling Framework, which describes the process of modeling in experimental physics. This figure was first published in Ref. [42] and is a modified version of the original framework developed in Ref. [4]. The shaded boxes show the main tasks of modeling with the connecting arrows indicating the iterative nature of the modeling process.

Figure 2

Modeling codes. The main modeling codes coming directly from the EMF appear on the left, and the arrows point to subcodes for the engage with models and revisions codes.

Figure 3

Presence (“Yes,” blue) or absence (“No,” white) of the main modeling codes in each of the 14 projects. The number of projects that demonstrated evidence of each task are listed in the far-right column.

Figure 4

Fraction of projects with a specified feature (columns, organized by category) assigned the engage with models subcodes (rows). The numbers in parentheses following the abbreviated feature names are the number of projects with each feature, and the numbers on top are averages over each column. The higher the column average, the more the feature may be associated with engagement with models on average. The modeling codes are sorted by average over row, so the lower modeling codes more frequently appeared in student projects.

Figure 5

Presence (“yes,” blue) or absence (“no,” white) of emergent subcodes for the main modeling codes (a) engage with models and (b) revisions in each of the 14 projects. The number of projects that show evidence of each subcode are listed in the far-right column.

Figure 6

Fraction of projects with a specified feature (columns, organized by category) assigned the enact revisions subcodes (rows) for (a) apparatus revisions, (b) model revisions, (c) physical system revisions, and (d) measurement system revisions. The numbers in parentheses following the abbreviated feature names are the number of projects with each feature, and the numbers on top are averages over each column. For each of the subplots, the features with the largest column averages can be interpreted as being most associated with that category of revisions.