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Instructional strategies and linguistic features of kindergarten teachers’ shared book reading: The case of Singapore

Published online by Cambridge University Press:  25 March 2020

He Sun*
Affiliation:
Nanyang Technological University
Weimin Toh
Affiliation:
Nanyang Technological University
Rasmus Steinkrauss
Affiliation:
University of Groningen
*
*Corresponding author. Email: he.sun@nie.edu.sg
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Abstract

Teachers’ language practice during shared book reading may significantly affect the rate and outcome of early language proficiency. The current study has focused on 37 kindergarten teachers and 440 4- to 5-year-old kindergartners during their shared book reading sessions in Singapore, exploring teachers’ variation in instructional strategies and linguistic features, and its relations with children’s language development and teacher’s background. Results demonstrated that teacher’s language strategies and linguistic features varied considerably. Instructional strategies with a medium level of cognitive load were found to be positively related to children’s growth in receptive vocabulary and word reading skills. Teacher’s lexical sophistication was found to be positively associated with children’s vocabulary size. Years of teaching experience was revealed to predict teacher’s variation in medium-level instructions.

Information

Type
Original Article
Copyright
© Cambridge University Press 2020
Figure 0

Table 1. Coding scheme for teachers’ instructional strategies in SBR sessions

Figure 1

Table 2. Raw scores of predictors and outcome variables

Figure 2

Table 3. Correlation matrix for target variables and control variables: Spearman’s rho

Figure 3

Table 4. Fixed-effects part of the mixed-effects model for post-receptive vocabulary

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Table 5. Fixed-effects part of the mixed-effects model for post–word reading performance

Figure 5

Table 6. Best predictors for teachers’ use of medium-level strategies in the multiple regression model

Figure 6

Table 7. Best predictors for teachers’ lexical sophistication in the multiple regression model