Self-Attention Gated Cognitive Diagnosis For Faster Adaptive Educational Assessments

Cognitive diagnosis models map observations onto psychological internal traits, which have been widely used to construct personalized educational assessments. However, the challenges of the latent ability representation, accurate performance with explanatory and cold-start problems caused by limited handcraft skills still exist. In this paper, we introduce a selfadaptive Attention Gate Cognitive Diagnosis Model (AGCDM) based on a multi-layer hierarchical structure. Specifically, in the first two layers, our model embeds the item response logs into a higher expressive latent space, and the specific self-attention mechanism captures the rich information from the inner-dependencies correlation among the examinee, quiz and knowledge. In the next layer, the gate mechanism addresses the challenge of noisy information (slip and guess) over psychological reasons. We verify the empirical performance of our model on different educational tasks and all comparisons study conducted on the same training recipe. On the item response prediction task, the experiment results demonstrate our hierarchical model achieves state-of-the-art performance. Through the estimation of adaptive learning, we also validate the model’s effectiveness in addressing the limited skill problem in real scenarios. The learning tracks show that our model empowered by the meta-learning algorithm can be adaptive faster than the base model to the new different skill distribution set. Furthermore, we present the ablation study on the hierarchical architecture and detailed hyperparameters selections. The comparison results demonstrate the attention blocks yield the scores improvement, and the estimation of the noisy errors shows the gate mechanism boosts the detection accuracy on the slip and guess. Code is available at https://github.com/TerryPei/AGCDM.