The existing Knowledge Tracing （KT） models fail to effectively utilize information about learning behaviors and ignore the differences in contributions of different learning behaviors to question-answering performance. For this reason， a Learning Behavior Boosted Knowledge Tracing （LBBKT） model was proposed. In this model， Gated Residual Network （GRN） was used to encode students’ learning behavior features into four context vectors and embed them into the model， thereby making full use of the learning behavior information （speed of question-answering， number of attempts， and prompts） to better model students’ learning processes. In addition， the students’ learning behavior features were weighted selectively by using variable selection network， and the interference of irrelevant features was suppressed through GRN， so as to enhance the influence of relevant features on students’ question-answering performance， thereby fully considering differential contributions of different learning behaviors to students’ question-answering performance. Experimental results on several public datasets show that LBBKT model outperforms the comparative KT models significantly in terms of prediction accuracy.