Remedial Teaching and Learning From a Cognitive Diagnostic Model Perspective: Taking the Data Distribution Characteristics as an Example
Ren He, Ningning Xu, Yuxiang Lin, Shumei Zhang, Tao Yang
Abstract
In response to the big data era trend, statistics has become an indispensable part of mathematics education in junior high school. In this study, a pre-test and a post-test were developed for the six attributes (sort, median, average, variance, weighted average, and mode) of the data distribution characteristic. This research then used the cognitive diagnosis model to learn about the poorly mastered attributes and to verify whether cognitive diagnosis can be used for targeted intervention to improve students' abilities effectively. One hundred two eighth graders participated in the experiment and were divided into two groups. Among them, the intervention materials read by the experimental group students only contained attributes that they could not grasp well. In contrast, the reading materials of the control group were non-targeted. The results of the study showed the following: (1) The variance and the weighted average were poorly mastered by students in the pre-test; (2) compared with the control group, the average test score of the experimental group was significantly improved; (3) in terms of attributes, the experimental group students' mastery of variance and the weighted average was significantly improved than the pre-test, while the control group's mastery was not. Based on this, some teaching suggestions were put forward.