Xuan Di, Zhu Delong, Xu Wenhai
Shi Liang School of Law, Changzhou University, Changzhou, China.
KoGuan School of Law, Shanghai Jiao Tong University, Shanghai, China.
Front Psychol. 2021 Oct 29;12:711520. doi: 10.3389/fpsyg.2021.711520. eCollection 2021.
With the increasing attention to the cultivation of legal talents, a new teaching model has been explored through artificial intelligence (AI) technology under educational psychology, which focuses on improving learning initiative, teaching methods, and teaching quality of students. First, the application of AI and deep neural network (DNN) algorithms are reviewed in education, and the advantages and disadvantages of traditional learning material recommendation algorithms are summarized. Then, a personalized learning material recommendation algorithm is put forward based on DNN, together with an adaptive learning system based on DNN. Finally, the traditional user-based collaborative filtering (UserCF) model and lifelong topic modeling (LTM) algorithm are introduced as the control group to verify the performance of the proposed recommendation system. The results show that the best learning rate of model training is 0.0001, the best dropout value is 0.5, and the best batch size is 32. The proposed personalized learning resource recommendation method based on deep learning (DL) still has good stability under various training data scales. The personalized test questions of recommended students are moderately difficult. It is easier to recommend materials according to the acquisition of knowledge points and the practicability of the recommended test questions of students. Personalized learning material recommendation algorithm based on AI can timely feedback needs of students, thereby improving the effect of classroom teaching. Using the combination of AI and DL algorithms in teaching design, students can complete targeted personalized learning assignments, which is of great significance to cultivate high-level legal professionals.
随着对法律人才培养的日益重视，在教育心理学的指导下，借助人工智能（AI）技术探索了一种新的教学模式，该模式着重于提高学生的学习主动性、教学方法和教学质量。首先，回顾了AI和深度神经网络（DNN）算法在教育中的应用，并总结了传统学习材料推荐算法的优缺点。然后，提出了一种基于DNN的个性化学习材料推荐算法以及一个基于DNN的自适应学习系统。最后，引入传统的基于用户的协同过滤（UserCF）模型和终身主题建模（LTM）算法作为对照组，以验证所提出的推荐系统的性能。结果表明，模型训练的最佳学习率为0.0001，最佳随机失活值为0.5，最佳批量大小为32。所提出的基于深度学习（DL）的个性化学习资源推荐方法在各种训练数据规模下仍具有良好的稳定性。推荐给学生的个性化测试题难度适中。根据知识点的掌握情况和学生推荐测试题的实用性来推荐材料更容易。基于AI的个性化学习材料推荐算法能够及时反馈学生的需求，从而提高课堂教学效果。在教学设计中使用AI和DL算法的组合，学生可以完成有针对性的个性化学习任务，这对于培养高水平的法律专业人才具有重要意义。
