Augmenting the effect of professional development on effective instruction through professional communities

ABSTRACT

This study addresses gaps in the research about the effect of professional development (PD) quality on effective instruction by taking a broad perspective when defining professional learning of teachers and by providing a theory-based explanation for effective instruction. Using a large-scale data set, Teaching and Learning International Survey (TALIS) 2013, the interrelationships between the quality of PD experience, professional communities at the school, and effective instruction were investigated. This theory testing study showed that the effect of PD events on effective instruction was stronger with the spillover effect of professional communities than with its sole effect.

KEYWORDS:

• Professional development quality
• professional communities
• effective instruction
• large-scale data

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

1. We took both theoretical considerations as well as previous research evidence and adapted the data-wise decision making to choose a particular item to add to the scale. CFA was conducted to analyse the measurement model which specifies the relationships of the latent variable (i.e. PD quality) to the observed variables or indicators (i.e. content focus). CFA also was used to assess reliability and provide validity evidence.

2. Mplus and other statistical software are not able to provide global fit indices in complex samples while using BRR (Asparouhov, 2016a; Muthén, 2011). We were unable to report global fit statistics for the models in this study. However, as Kline (2015) points out ‘deciding whether to retain or reject a model should not be based solely on the values of such statistics’ (p. 262). He also underlines the proper emphasis on local fit testing over global fit testing. Therefore, we reported more specific diagnostic information about the models and the scales by providing local fit information. The evaluation and inspection of the models was performed using R² (explained variance-explanatory power of a model), standardised and unstandardised factor loadings, standard errors, modification indices, and residual correlations. The thresholds and benchmarks we embraced (Byrne, 2011; Kline, 2015) were: (1) higher explained variance; (2) standardised factor loadings should be higher than 0.30; (3) modification indices are higher than 3.8; and (4) the absolute value of correlation residuals should be lower than 0.10. Values that are close to the thresholds indicate good local fit.

Additional information

Notes on contributors

Selçuk Dogan

Selçuk Dogan, Ph.D., is an Asistant Professor at the Georgia Southern University. He designs high-quality professional development programs for STEM teachers through blended models and facilitates the development of fully online M.Ed. programs. His research focuses on the effectiveness of professional development activities and professional learning communities.

Alyson Adams

Alyson Adams, Ph.D., is a Clinical Associate Professor and also the Associate Director of Teaching and Teacher Education in the School of Teaching and Learning at the University of Florida. She designs and implements job-embedded, inquiry-focused professional development for teachers in high poverty schools around the state, using whole school reform efforts with professional learning communities as the foundation.