Classroom Questioning Tendencies from the Perspective of Big Data1

Abstract

We are now living in an era of big data. From the perspective of data analysis of classroom questioning, the paper chooses three districts in City B that have significant differences. These are educationally developed District D, less developed District F, and developing District M. The study uses the stratified sampling method to choose from three different groups of teachers, namely novice teachers, competent teachers, and experienced teachers, by way of video case analysis, the Item Response Theory (IRT) model method, and the inductive and deductive method, to analyze the characteristics of teachers’ classroom questions. It was found that: (1) In terms of open questioning, all three districts with their different levels in educational development need to improve open questioning levels among their experienced primary school teachers. In middle schools, novice teachers’ open questioning tendency is significantly lower than that of qualified and experienced teachers; (2) From the quantitative study of three kinds of tendencies, the lowest level of the three tendencies in the classroom questions is problem-solving; (3) In the three districts, the experienced and qualified primary school teachers in the developing district has a prominent advantage in raising critical and creative questions, while in the middle schools, novice teachers generally have a lower level than qualified and experienced teachers in raising critical and creative questions. The results of the big data analysis enable us to draw a conclusion about teachers’ value orientation regarding questioning in class. At present, they pay attention to the local value of classroom questioning, but ignore the overall value; pay attention to the instrumental value of classroom questioning, but ignore the objective value; and pay attention to the superficial value of classroom questions, but ignore the underlying value.

Keywords

big data / classroom observation / classroom questioning, IRT model

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2016 Higher Education Press and Brill