An application of professional learning community approach to enhance language learning among students in higher education institution

Educators need to take action in terms of acquiring, creating and transferring their knowledge to students based on the professional learning community approach. It involves building a learning community in their classrooms. Researchers and professional organizations have cited the Professional Learning Community (PLC) process as having the potential to impact student achievement positively. As the current era of high stakes accountability has left educators struggling to improve the quality of teaching and learning, PLCs have been recommended to foster collaboration and make educator practices public. Therefore, the purpose of this study was to examine educator’ descriptions of their PLCs to determine if the practice of the principles influenced student academic performance in language teaching. A quantitative research design was implemented to explore the extent to which educator training in PLC principles, the actual practice of PLC principles, and student achievement in language teaching were related. A survey was utilized to collect data regarding 365 educators’ perceptions of the existence of three dimensions and seven sub-dimensions of PLCs within their institution. Final years of language course students’ data were examined to measure student achievement in language learning, and inferential statistic by Structural Equation Modeling (SEM) approach was used to look for possible relationships between the variables. Statistical examination indicated PLC members who observed peers, provided feedback on instructional practices, worked with colleagues to judge student work quality, and collaboratively reviewed student work to improve instructional analysis were more likely to improve their quality of teaching. Further results indicated positive correlations between the dimensions belonging to values, vision and mission, a dimension of subject to support: relationship and structural and also dimensions of collective learning and applications. Finally, the model achieved the goodness of fit indices which is resulted in Chisq/df= 2.185; GFI=.980; CFI=.991; TLI=.983; RMSEA=.065)

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