Uncovering the predictive effect of behaviours on self-directed learning ability

Abstract

Self-direction has become an important skill in the 21st century. To cultivate learners with a high level of self-direction, it is necessary to diagnose their self-directed learning (SDL) ability. This study diagnosed and predicted learners' SDL ability based on their actual SDL behaviours. The study was performed in a self-directed 3D design class lasting 90 minutes. A total of 193 middle school students participated in the study. The results of the Pearson correlation analysis (p < 0.001) showed that the reported perception of SDL ability was significantly correlated with SDL behaviours. The results of the hierarchical multiple linear regression analysis showed that the SDL behaviours explained 84.9% of the variance in SDL ability (adjusted R² = 0.849, p < 0.001). Therefore, SDL behaviours had significant predictive effects on the reported perception of SDL ability. Moreover, based on the random forest algorithm, the study built an SDL ability prediction model with high performance (accuracy = 0.83, precision = 0.82, recall = 0.84) using SDL behaviours as features. The study provides evidence for the design of effective strategies to enhance SDL ability and promote SDL behaviours.