Evaluation model of metacognitive ability based on multi-channel self-attention and BiGRU

Metacognition is the critical element of personalized online autonomous learning, but it is not easy to observe or obtain. It is difficult to be monitored continuously in the practice of teaching and learning. The existing model of metacognitive ability is still in theoretical research and lacks effective model construction technology to externalize metacognition. The online learning behavior data contains rich metacognitive information. In contrast, the previous methods based on statistical analysis or traditional machine learning cannot fully extract the internal temporal and semantic features implied in the data. This study uses the self-attention mechanism and the recurrent neural network sequence model to deeply explore and analyze learners' online learning behavior and interactive text. A new evaluation model of metacognitive ability is constructed to represent learners' metacognitive ability. The research takes natural online learners' behavior data as the object to carry out experimental verification and analysis. The results show that the model's accuracy in representing metacognitive ability reaches 85.21%, which verifies the model's effectiveness.