Principal support, professional learning community, and group-level teacher expectations

ABSTRACT

Although much literature highlights the importance of teacher expectations for students’ academic success, a very small number of studies used large-scale data to examine school-level factors associated with group-level teacher expectations – defined as expectations that teachers have for many students in their class or school, rather than for a specific student – in the US context. Using contextual data provided by mathematics and science teachers participating in the High School Longitudinal Study of 2009, the current study addressed this issue with paying attention to the roles of principal support and professional learning community in group-level teacher expectations. We found that both principal support and professional learning community were positively associated with group-level teacher expectations, even after controlling for other variables. We also found that much of the relationship between principal support and group-level teacher expectations was explained by professional learning community. Theoretical and policy implications are discussed.

KEYWORDS:

• Principal support
• professional learning community
• teacher expectations
• High School Longitudinal Study

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Acknowledgements

The authors thank Kang Ho Lee, Ji-hye Kim, Sangkyoo Kang, and Hee Jin Chung for their editorial suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Joo-Ho Park is professor of educational organizational behavior in the Department of Education at Hanyang University. His research focuses on principal leadership, teacher constructs, and student’s development factors.

Soo-yong Byun is an Associate Professor of Education (Educational Theory and Policy) and Demography at the Pennsylvania State University. His research focuses on the rigorous assessment and evaluation of educational policies and school interventions especially relating to unique populations and contexts, such as socioeconomically disadvantaged students and rural students, using large-scale national and international data.

Notes

1 Brault et al. (2014) measured group-level teacher expectations by teachers’ perceptions of their students’ capacity to succeed in school, using the following three items: Most students at this school have (a) “what it takes, personally and socially, to successfully complete their secondary studies,” (b) “the competencies required to attain the learning objectives for their grade level,” and (c) “the potential to succeed in school” (p. 152).

2 In their study, Sebastian et al. (2017) measured teacher expectations of students for postsecondary education, using the following items: (a) teachers expect most students in this school to go to college, (b) teachers at this school help students plan for college outside of class time, (c) the curriculum at this school is focused on helping students get ready for college, (d) most of our students have the capacity to do college-level work, (e) most of the students in this school are planning to go to college, and (g) teachers in this school feel that it is a part of their job to prepare students to succeed in college. However, they did not use the term “group-level” teacher expectations. Rather, they referred to teacher expectations as part of the learning climate.

3 Sebastian et al. (2017) used the definition of PLC developed by Kruse et al. (1995), which highlights five core practices (i.e., reflective dialogue, teacher collaboration, deprivatized practice, shared norms, and new teacher socialization), and operationalized PLC by using the items developed by Bryk et al. (1999) in a study of Chicago elementary schools, which were combined to capture school-wide professional community.

4 We excluded “unit nonresponses” and “item legitimate skip/NA.” We conducted supplementary analyses by including these cases with multiple imputations and found similar results reported in the current study.

5 Detailed reliability information about this measure of teacher efficacy is available from the authors upon request.

6 HSLS:09 measured students’ mathematics reasoning in algebra and provided the item response theory (IRT)-estimated number-right score in the base-year survey, which consisted of 79 items. The IRT-estimated number-right score is defined as an IRT-based estimate of the number of items that a student would have answered correctly if he or she had taken all of the items in the mathematics assessment (Ingels & Dalton, 2013).

7 Although our reliance on the base-year survey of HSLS:09 does not allow for appropriate mediation tests (Maxwell & Cole, 2007; Maxwell et al., 2011), we conducted supplementary ordinary least squares (OLS) analyses to examine the relationship between principal support and PLC at the school level in order to better address the potential role of PLC as a mediator in linking principal support to group-level teacher expectations, following the recommended steps set forth by Baron and Kenny (1986). We found significant and positive relationships between principal support and PLC for both mathematics and science teachers with and without school-level controls (see Appendix 4). The Sobel test based on the estimates from the final OLS and HLM models suggested that the indirect effect of principal support on group-level teacher expectations via PLC was significantly different from zero for both mathematics and science teachers. Once again, however, the results from our supplementary analyses should be interpreted with great caution because of data limitations.

8 Following Frank (2000), we conducted supplementary analyses to test the robustness of inferences and found that an omitted variable would have to be correlated at .49 and .51 with the group-level teacher expectations for mathematics and science teachers, respectively, and at .49 with the predictor of PLC (conditioning on the observed covariates) to invalidate an inference based on a threshold of .03 for statistical significance (alpha = 0.05). Correspondingly, the impact of an omitted variable (as defined in Frank, 2000) must be .24 and .26 to invalidate an inference for mathematics and science teachers, respectively. Together, these results suggest that our conclusions may be unlikely to change due to omitted variables. Yet, given that school-level organizational constructs are usually highly correlated among each other (e.g., Supovitz et al., 2010), supplementary results should be interpreted with caution.

Additional information

Funding

This research was supported by the Global Research Network program through the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea under Grant number NRF-2017S1A2A2040115. The views expressed in this article are those of the authors and do not necessarily reflect those of the granting agency.