Effect of gender, self-efficacy, and interest on perception of the learning environment and outcomes in calculus-based introductory physics courses

Students’ motivational beliefs about physics can influence their learning outcomes as well as retention in their majors and career choices. Moreover, due to societal stereotypes and biases about who belongs in physics and can succeed in physics, women often have lower motivational beliefs about physics than men. The expectancy-value theory emphasizes the importance of self-efficacy and value in predicting students’ short- and long-term academic and professional outcomes, but there are few studies focusing on how the learning environment shapes these motivational beliefs of women and men. Investigating how the perception of learning environment in introductory physics courses for the engineering, physical science, and mathematics majors in their first year of college predicts the motivational beliefs of women and men can be useful in making the learning environments equitable and inclusive so that the underrepresented students, e.g., women, are not disadvantaged. In this study, we adapt prior identity framework to investigate how the learning environment (including sense of belonging, perceived peer interaction, and perceived recognition) predicts students’ physics self-efficacy, interest, and identity by controlling for their self-efficacy and interest at the beginning of a calculus-based introductory physics course. We surveyed 1203 students, 35% of whom identified as women. We found signatures of inequitable and noninclusive learning environment in that not only were female students’ physics self-efficacy and interest lower than male students’ at the beginning of the course, but the gender gaps in these motivational constructs became even larger by the end of the course. Analysis revealed that the decrease in students’ physics self-efficacy and interest were mediated by the learning environment and predicted students’ physics identity. We find that the perceived recognition played a major role in predicting students’ physics identity, and students’ sense of belonging in physics played an important role in explaining the change in students’ physics self-efficacy.

Figure 1

Schematic representation of the theoretical framework regarding physics identity. From left to right, all possible regression paths were considered, but only some of the paths are shown here.

Figure 2

Schematic diagram of the path analysis part of the structural equation modeling between gender and being a physics person through self-efficacy, interest, and perceived recognition. 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). All $\beta$ values are significant with $p<0.001$.

Figure 3

Schematic diagram of the path analysis part of the structural equation modeling between gender and being a physics person through self-efficacy, interest, perceived recognition, and peer interaction. 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 $0.01<p<0.05$ indicated by *. Other regression lines show relations with $p<0.001$.

Figure 4

Schematic diagram of the path analysis part of the structural equation modeling between gender and being a physics person through self-efficacy, interest, perceived recognition, and peer interaction. The solid lines represent regression paths, and the dashed lines represent residual covariances. The regression line thickness corresponds to the magnitude of the $\beta$ value (standardized regression coefficient) with $0.01<p<0.05$ indicated by * and $0.001<p<0.01$ indicated by **. Other regression lines show relations with $p<0.001$.

Figure 5

Schematic diagram of the path analysis part of the structural equation modeling between gender and being a physics person through self-efficacy, interest, perceived recognition, peer interaction, and sense of belonging. The solid lines represent regression paths, and the dashed lines represent residual covariances. The regression line thickness corresponds to the magnitude of the $\beta$ value (standardized regression coefficient) with $0.01<p<0.05$ indicated by * and $0.001<p<0.01$ indicated by **. Other regression lines show relations with $p<0.001$.

Figure 6

Schematic diagram of the path analysis part of the structural equation modeling of perception of learning environment models including only peer interaction, only sense of belonging and both peer interaction and sense of belonging. 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 $0.01<p<0.05$ indicated by * and $0.001<p<0.01$ indicated by **. Other regression lines show relations with $p<0.001$.

Figure 7

Schematic diagram of the path analysis part of the SEM model including pre-identity. The solid lines represent regression paths, and the dashed lines represent residual covariances. The regression line thickness corresponds to the magnitude of the $\beta$ value (standardized regression coefficient) with $0.01<p<0.05$ indicated by * and $0.001<p<0.01$ indicated by **. Other regression lines show relations with $p<0.001$.