Early Childhood Teachers' Lives in Context: Implications for Professional Development in Under‐Resourced Areas. Issue 3 (29th April 2019) - Schwartz, Kate; Cappella, Elise; Aber, J. Lawrence; Scott, Marc A.; Wolf, Sharon; Behrman, Jere R.
Abstract: This study explores the personal, professional, and contextual conditions faced by early childhood education (ECE) teachers in under‐resourced settings and how these relate to teacher responsiveness to professional development (PD): namely, teacher attrition (a sign of PD failure when occurring shortly after PD), take‐up of offered PD, adherence to PD training/materials, and quality of implementation. We use data from six disadvantaged districts in the Greater Accra Region of Ghana and PD focused on implementation of a national, play‐based curriculum. Descriptive statistics indicate that ECE teachers ( n = 302) face a multitude of barriers to high quality teaching across the bioecological model. Multilevel mixed effects models find that teachers with low job satisfaction are more likely to leave the school within the academic year. Teachers with moderate to severe depression are less likely to attend PD trainings. Senior teachers and those with poverty risks are less likely to adhere to PD material. Teachers with many time demand barriers are more likely to adhere to material. They also implement the content at higher observed quality as do teachers with bachelor's degrees and early childhood development (ECD) training. Take‐up of PD also predicts quality of implementation. Practice and research implications are discussed. Highlights: Teachers' lives are often overlooked and critical to classroom quality and workforce development. ECE teachers face many barriers to effective teaching and responsiveness to professional development. Job satisfaction predicts teacher attrition and depression predicts take up of the teacher training. ECE experience, personal poverty, and time demands predict adherence to training materials. Time demands, higher education, ECD training, and training take‐up predict implementation quality.