A Study of Accounting Teaching Feature Selection and Importance Assessment Based on Random Forest Algorithm

Abstract

Abstract With the steady progress of China’s education information technology, learners generate massive learning behavior data during classroom interactions, and behind these data lie learners’ implicit behavioral characteristics. In this paper, we use accounting teaching as an example to deeply mine learners’ behavioral data, from which we extract behavioral features related to learning effects to create an experimental dataset. The Random Forest Important Feature Selection Algorithm uses the Gini index to filter out the learning behavior categories with higher importance among the learning behavior feature items. We extensively mine the learning behavior data to construct learning effect prediction models, establish feedback mechanisms, and intervene in the learning process in real time. The learning effect prediction model, which utilizes the Random Forest important feature selection algorithm, increases model prediction accuracy to 85.35% after cross-validation, as shown in the results. The increased accuracy allows for more accurate prediction of students’ learning effects in future periods, effective identification of student problems, and provision of targeted guidance to different student categories. At the same time, teachers can understand students’ learning status in various classes, make timely adjustments to accounting teaching content, and improve the teaching process. Ultimately, accurate education achieves the goal of teaching students according to their abilities.