Inclusive learning environments can improve student learning and motivational beliefs

We discuss an investigation of students’ motivational beliefs and performance on the Force Concept Inventory (FCI) in a calculus-based introductory physics course at a large public university in the U.S. We investigated how students’ perception of the inclusiveness of the learning environment (including perceived recognition, perceived effectiveness of peer interaction, and sense of belonging) predicts students’ FCI scores and physics motivational beliefs (including self-efficacy, interest, and overall physics identity) at the end of the course after controlling for students’ high school performance and their FCI scores and motivational beliefs at the beginning of the course. We find signatures of noninclusive learning environment in that female students’ mean scores in physics motivational beliefs and perception of the inclusiveness of the learning environment were lower than male students’, and the gender gap in students’ self-efficacy increased from the beginning to the end of the course. Using structural equation modeling, we find that the gender differences in students’ motivational beliefs and FCI scores were mediated by the different components of students’ perception of the inclusiveness of the learning environment. In particular, students’ perceived recognition, e.g., by instructors, was an important predictor of their overall physics identity, and their sense of belonging predicted their self-efficacy and FCI scores. Our findings can be valuable for contemplating guidelines for creating an inclusive learning environment in which all students can excel.

Figure 1

Schematic representation of the theoretical model in which the relation between gender and overall physics identity is mediated through SAT Math scores, high school GPA (HS GPA), and FCI scores as well as peer interaction (Peer Int), perceived recognition (Recog), sense of belonging, self-efficacy (SE), and interest. The solid lines represent regression paths, and the dashed lines represent covariances. From left to right, all possible regression paths were considered, but only some of the paths are shown here for clarity.

Figure 2

Schematic diagram of the path analysis part of the structural equation modeling (model 1) between gender and overall physics identity through SAT Math scores, high school GPA (HS GPA), and FCI scores as well as perceived recognition (Recog), self-efficacy (SE), and interest. The solid lines represent regression paths and the dashed lines represent residual covariances. The regression line thickness corresponds to the magnitude of $\beta$ value (standardized regression coefficient) with $0.01\le p<0.05$ indicated by * and $0.001\le p<0.01$ indicated by **. All the other regression lines show relations with $p<0.001$.

Figure 3

Schematic diagram of the path analysis part of the structural equation modeling (model 2) between gender and overall physics identity through SAT Math scores, high school GPA (HS GPA), and FCI scores as well as peer interaction (Int), perceived recognition (Recog), self-efficacy (SE), and interest. The solid lines represent regression paths and the dashed lines represent residual covariances. The regression line thickness corresponds to the magnitude of $\beta$ value (standardized regression coefficient) with $0.01\le p<0.05$ indicated by * and $0.001\le p<0.01$ indicated by **. All the other regression lines show relations with $p<0.001$.

Figure 4

Schematic diagram of the path analysis part of the structural equation modeling (model 3) between gender and overall physics identity through SAT Math scores, high school GPA (HS GPA), and FCI scores as well as perceived recognition (Recog), sense of belonging, self-efficacy (SE), and interest. The solid lines represent regression paths and the dashed lines represent residual covariances. The regression line thickness corresponds to the magnitude of $\beta$ value (standardized regression coefficient) with $0.01\le p<0.05$ indicated by * and $0.001\le p<0.01$ indicated by **. All the other regression lines show relations with $p<0.001$.

Figure 5

Schematic diagram of the path analysis part of the structural equation modeling (model 4) between gender and overall physics identity through SAT Math scores, high school GPA (HS GPA), and FCI scores as well as peer interaction (Int), perceived recognition (Recog), sense of belonging, self-efficacy (SE), and interest. The solid lines represent regression paths and the dashed lines represent residual covariances. The regression line thickness corresponds to the magnitude of $\beta$ value (standardized regression coefficient) with $0.01\le p<0.05$ indicated by * and $0.001\le p<0.01$ indicated by **. All the other regression lines show relations with $p<0.001$.

Figure 6

Graphs of the distributions of (a) high school GPA, (b) SAT math score, (c) pre-FCI scores, and post-FCI scores.