## 5 Conclusions  
For filling the gap of current KT models that express students' learning features insufficiently, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) was put forward. DKT-STDRL extracted the spatial features on the basis of students' exercise history, and then further extracted the temporal features of students' exercise sequence. Firstly, DKT-STDRL used CNN to extract the spatial feature information of students' exercise history. Then, the spatial features were connected with the exercise history features and input into the BiLSTM part as joint learning features. Finally, BiLSTM extracted the temporal feature from the joint learning features to obtain the prediction information of whether the students could answer correctly or not at the next time step. The prediction effect of the DKT-STDRL model was verified on five common public datasets. The experimentations demonstrated that the prediction of the DKT-STDRL outperformed the DKT and CKT. Therefore, DKT-STDRL is effective for promoting the prediction accuracy of the KT model on the basis of DL. Moreover, many experiments were conducted to compare the prediction performance of DKT-STDRL with CKT, DKT, and four variants of DKT-STDRL, which  
Figure 7: Prediction results of DKT-STDRL, CKT, DKT, and the variants of DKT-STDRL on the Statics2011 dataset. showed the different impacts on the prediction effect from the aspects of spatial features, temporal features, prior features, and joint features. Though we have succeeded in advancing the prediction accuracy, there are still some limitations of our work on the complexity and interpretability of the model. First, the structure of the model is complex and has too many parameters. Secondly, the parameters in the DL networks lack interpretability, which limits the significance of the model in practical applications. So, in prospective work, we strive to reduce the complexity of the model to save the computing resources and to improve the interpretability to provide more application value. In addition, in the future, because the model has good prediction accuracy, we can try to integrate the model into LMSs for a better recommendation, so that we obtain more intelligent and caring online learning systems. By using our improved KT algorithm, LMSs can obtain more accurate information about students' knowledge mastery or prediction results of answer performance. Based on this information, LMSs can intelligently recommend learning resources (such as lectures, documents, exercises, and quizzes) that better meet students' needs, thus helping students reduce their learning burden and improve their learning efficiency. Methodology research, L.L. and Z.W. ; model realization, L.L. ; supervision Z.W. ; writing and editing, L.L. and Z.W. ; data collection, L.L., Z.W., H.Y., Z.Y. and Y.L. ; model evaluation, L.L., Z.W., H.Y., Z.Y. and Y.L. ; funding acquisition, L.L., Z.W., H.Y., Z.Y. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Youth Science Foundation of Heilongjiang Institute of Technology (2021Q07), the National Natural Science Foundation of China (Nos. 62177022, 61901165, 61501199), the Collaborative Innovation Center for Informationization and Balanced Development of K-12 Education by MOE and Hubei Province (No. xtzd2021-005), and Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE (No. CCNU20 ZT010), the Natural Science Foundation of Heilongjiang Province (LH2020F047), the Innovation Team Project of Heilongjiang Institute of Technology (2020 CX07), the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCI-2020052), and the Education and Teaching Reform Research Project of Heilongjiang Institute of Technology (JG202109). Not applicable. The ASSISTment2009, ASSISTment2015, and ASSISTchall datasets are from the ASSISTments platform [32].