Demographic-Guided Behavior Patterns Contrast for Personality Prediction

In recent years, personality has been considered as a valuable personal factor being incorporated into the provision of personalized learning. Although some studies have endeavored to obtain learners' personalities implicitly from their learning behaviors, they failed to achieve satisfactory prediction performance. On the one hand, most existing approaches ignore the imbalanced distribution of personality classes, which causes the personality classifiers to be biased toward the non-extreme personality class. On the other hand, the related methods normally focus on constructing statistical behavior features, while the sequence information of learning behaviors is ignored, but actually it can reflect learners' behavior patterns more finely. In this paper, inspired by the human learning strategy in the face of small samples, we propose an effective Demographic-Guided Behavior Patterns Contrast (DGBPC) model to classify learners' personalities through the demographic-guided contrast of learners' coarse behavior patterns. Besides, we construct and publish the Personality and Learning Behavior Dataset (PLBD), which should be one of the largest public datasets regarding Big-Five personality and learning behavior sequence according to our knowledge. The experimental results on PLBD demonstrate that our DGBPC model could generate learner representations with higher discrimination and outperform the related methods in terms of balanced accuracy.