Knowledge tracing is the key component in online individualized learning, which is capable of assessing the users' mastery of skills and predicting the probability that the users can solve specific problems. Available knowledge tracing models have the problem that the assessments are not directly used in the predictions. To make full use of the assessments during predictions, a novel model, named deep knowledge tracing embedding neural network ( DKTENN) , is proposed in this work. DKTENN is a synthesis of deep knowledge tracing ( DKT) and knowledge graph embedding ( KGE) . DKT utilizes sophisticated long short-term memory ( LSTM) to assess the users and track the mastery of skills according to the users' interaction sequences with skill-level tags, and KGE is applied to predict the probability on the basis of both the embedded problems and DKT's assessments. DKTENN outperforms performance factors analysis and the other knowledge tracing models based on deep learning in the experiments.