Network Modelling and Visualisation Analysis of the Undergraduate Dental Curriculum System in China

Abstract

Objectives: This study aims to present the characteristics of the undergraduate dental curriculum system using network modelling and visualisation analysis based on complex network theory, thus providing a theoretical foundation for the course development and curriculum reform. Methods: The correlation coefficient was used to quantify the intensity of the correlation between courses, and a visualisation complex network of the dental curriculum was built to explore the curriculum pattern from a dynamic perspective. Further, the statistical measurements of curriculum network were adopted to express the most relevant topological features. Subsequently, the minimum spanning tree and parallel coordinates plot were drawn to explore the curriculum community structure, quantify the key courses, and present different courses in time and space relationships. Results: The correlation analysis results show that the courses are closely related to each other. The main courses focus on pathology, pathophysiology, oral anatomy and physiology, closely connecting almost all medicine-related courses. The whole course network has an average degree value of 41.53, and a clustering coefficient of 0.78, indicating an obvious small-world characteristic. From the perspective of curriculum attributes, the number of public and theoretical courses was more than that of skills and practical courses. Moreover, the academic performance of skills and practical courses was lower than that of public and theoretical courses. Conclusion: The undergraduate dental courses have a progressive structure from basic professional knowledge to professional skills, which is reasonable for the dental education in China. However, some efforts towards curriculum reform based on this study are needed.

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

“…Furthermore, behaviour-related features, such as total time spent on theoretical and practical contents and forums, can be used to find clusters of procrastination and thus to focus students, e.g., by setting intermediate time goals [ 28 ]. The authors in [ 29 , 30 ] created a network structure of undergraduate courses and applied community detection algorithms to identify the contributions of the courses to students’ learning pathways. Such findings can support the understanding of students’ behaviours in various learning situations [ 31 ] and the identification of potential dropouts at the early stage of the academic year [ 28 ].…”

“…In particular, we construct a network structure based on students’ learning behavioural data to produce more logical and coherent communities in terms of their learning performance. The network structure of undergraduate courses and their contributions to students’ learning pathways have been investigated using the community detection approach and minimum spanning tree [ 29 , 30 ], which are similar to the approach of this research. However, the authors of both studies merely considered the courses’ grades from a relatively small number of students.…”