Innovating Educational Assessment: A Hybrid TCN-LSTM Model for Knowledge Tracing

With the rapid development of Internet technology and artificial intelligence, artificial intelligence technology based on deep learning has created new application prospects in the education industry, promoting the innovation and progress of the education system and facilitating the transformation from traditional teaching mode to intelligent education. In this transformation process, knowledge tracing technology is a key tool for assessing student learning behavior. Knowledge tracing aims to trace and assess students' understanding and mastery of individual knowledge points in real-time by analyzing data from students' previous answers to construct models. With the continuous advancement of deep learning technology in recent years, knowledge tracing has evolved into a mainstream approach for modelling students' mastery of knowledge in educational assessment, predominantly employing Recurrent Neural Networks (RNN). Aiming at the challenges RNN face in handling long-term data dependencies, this paper proposes a hybrid knowledge tracing model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). The proposed model is tested and evaluated on three public datasets, ASSISTments2009, ASSISTments2017, and Statics2011. Compared with the existing classical methods, the proposed model shows a significant improvement in the Area Under Curve (AUC) and Accuracy (ACC), which verifies the effectiveness of the proposed method.