Design and optimization of an open personalized human-computer interaction system for New Year Painting based on the learner's model

With the rapid development of information technology such as big data and learning analytics, intelligent systems, a product of the deep integration of technology and education, have emerged. In this paper, a humancomputer interaction teaching system for traditional New Year Painting is proposed based on the learner model. Firstly, the attention mechanism based long and short term memory network is used to mine the emotion from the course review text of learners, and the association rule algorithm and ID3 algorithm are used to initialize and dynamically update the text. Constructing a personalized HCI teaching system with the learner as the center. Based on the smart learning model, the functional modules of the human-computer interaction teaching system are analyzed and designed in detail, including online learning, online testing and educational information. The design of the database of the intelligent teaching system is proposed, and the design process of the database is fully demonstrated in terms of both database relationship design and database table structure design, taking into account the security of the database. Finally, the learner model and personalized humancomputer interaction system that incorporate the emotions of this paper are tested for performance, and the results show that the prediction accuracy of this paper's model is about 3 % higher than the standard model DKT on the 2009 dataset, about 3 % higher than the standard model DKT on the AUC index, and about 4 % lower than the standard model DKT on the RMSE index. Students learn through the personalized human-computer interaction system, and their mastery of the traditional art of New Year's Paintings is more thorough, and the learning effect is significantly improved.