Legal Instructional Design by Deep Learning Theory Under the Background of Educational Psychology

This work aims to reform legal teaching in Colleges and Universities (CAUs) and improve law students' comprehensive quality. In the context of Educational Psychology (EPSY) research, Deep Learning (DL) theory is integrated into legal instructional design (ID). Following a theoretical review of EPSY and DL, the current situation and problems of college legal teaching are understood based on the Law School in a University in Shanghai through auditing, communication, and investigation methods. The theoretical research results are integrated into the ID. The teaching content is divided into language information module, wisdom skills module, cognitive module, action skills module, and attitude module. Each module is divided into three teaching methods, and all teaching methods are combined into the proposed legal ID. Finally, the proposed legal ID is applied in the legal classroom of the Law School in a University in Shanghai. Overall, seventy students are recruited and grouped into Class A (experimental group) and Class B (control group). Class A uses the proposed legal ID, and Class B does not. The scores of Classes A and B are compared. After a semester, the average score of Class A has increased from 68 to 71.11 points. The covariance has decreased from 61.66 to 51.42. When the confidence level is set to 0.95, the confidence interval of class A has increased from 65.26-70.74 to 68.62-73.61. By comparison, the average score of Class B dropped from 68.14 to 68.11 points. The covariance has decreased from 60.24 to 41.76. When the confidence level is set to 0.95, the confidence interval of class B has changed from 65.44-70.85 to 65.86-70.37, without significant improvement. Therefore, the proposed legal ID based on DL theory is scientific and effective. This work has certain reference significance for optimizing the ID of CAUs and improving the comprehensive quality of college-student talents.