An empirical study of a data-driven personalized diagnostic feedback strategy to enhance Chinese primary school pupils’ writing performance

Abstract

ABSTRACT This study proposes a data-driven personalized diagnostic feedback strategy (D-DPDFS) based on data, and its purpose is to explore the effects that this strategy brings on pupils' writing performance, epistemic network structure, and self-efficacy. In this study, the participating pupils were randomly divided into an experimental group (N = 39) and a control group (N = 39), in which the experimental group used the data-driven personalized diagnostic feedback strategy and the control group used the Experience-Driven Teacher Diagnostic Feedback Strategy (E-DTDFS), and the writing activity lasted for 6 weeks. The results of the study showed that the D-DPDFS strategy had higher performance in promoting the four aspects of students' writing and enhanced the self-efficacy of the pupils in the experimental group. Meanwhile, the results based on epistemic network analysis showed that pupils using the DDPDFS focused on describing the action, psychology, and emotional changes in vocabulary use, and their epistemic network structure was more complex. After the interviews, the pupils in the experimental group were able to accurately understand and reflect deeply on the writing problem, and their satisfaction was high. Therefore, we believe that this study brings new insights into improving the teaching and learning of writing.