Quicker method for assessing influences on teaching assistant buy-in and practices in reformed courses

Teaching assistants (TAs) that lead reformed recitations and labs must understand and buy into the design of the course and the research-based instructional strategies that the course requires in order to create high-fidelity implementations. We present a model that outlines possible influences on TAs’ buy-in and their in-class actions coupled with a method, using a Real-time Instructor Observation Tool-based [E. A. West et al. Phys. Rev. ST Phys. Educ. Res. 9, 010109 (2013)] exercise, to measure the effect of these influences that is not only quicker than interviews but also allows one to quantify these effects. We use this method to measure the influences on six graduate TAs teaching algebra-based introductory mechanics and electricity and magnetism recitations and labs (“mini studios”) at the University of Central Florida. The results from the exercise are confirmed by interview responses from the TAs. We find a relatively high degree of buy-in to the design of the course, yet this is not reflected in the TAs’ actions. The TAs’ actions appear to be most influenced by student responses and expectations which do not align with the design of the course. Our study examines the effect of three influences shown in our model, and we argue that our method could be easily adapted to examine additional influences.

Figure 1

A schematic of the influences on a TA’s teaching practices. The interaction of the experiential and class design influences is where initial buy-in is lost or gained. Initial buy-in may change based on systemic influences leading to a mediated buy-in.

Figure 2

The research timeline for interviews and observations.

Figure 3

The RIOT profile for a single TA. Data which fall below the shaded area are categorized as “relatively low.” Data above the shaded area are categorized as “relatively high.” Data within the shaded area are categorized as “medium.” For this TA, there was no occurrence of the Students Talking Serially code, so it would be categorized as “none.”

Figure 4

Average of all TAs’ actual profiles as coded by RIOT.

Figure 5

A representation of the $\mathrm{\Delta }$’s for each group of comparisons. Only the smallest $\mathrm{\Delta }$’s for select groups are shown. Two $\mathrm{\Delta }$’s are shown for the “Actual” group due to the two comparisons having similar $\mathrm{\Delta }$’s (39 and 40). Each section separated by color in each bar represents a TA’s $\delta$. If that section is striped it indicates that this was not the most similar comparison for that group for that TA.

Figure 6

The theoretical model with the aspects that we have measured. Aspects in gray could be measured given the appropriate prompt. Aspects that could be measured are not limited to those shown here. The legend indicates which prompt or comparison measures the corresponding influence.

Figure 7

A copy of the RIOT-based worksheet given to the TAs.

Figure 8

A copy of the definitions for RIOT codes given to the TAs prior to completing the worksheet.

Figure 9

Most similar comparisons for the Design, Helpful TA, Actual, and Agreed Design groups. A striped section means that this is not the most similar comparison for that TA.