Affiliations: [a] School of Health Sciences, Xinhua College of Sun Yat-sen University, Guangzhou, China
| [b]
Xinhua College of Sun Yat-sen University, Guangzhou, China
| [c] Center For Faculty Development, Xinhua College of Sun Yat-sen University, Guangzhou, China
Correspondence:
[*]
Corresponding author. Chen Siyan, School of Health Sciences, Xinhua College of Sun Yat-sen University, Guangzhou, China. E-mail: chensiyan_1233@sina.com.
Abstract: The qualitative analysis results of teachers’ abilities are difficult to quantify, and ability problems in the teaching process are difficult to be effectively measured. In order to study methods to improve teachers’ teaching abilities, this paper builds a corresponding teacher competence evaluation model based on machine learning and digital twin technology, establishes a data collection model for teachers’ professional competence, and establishes a data fusion model. It includes data cleaning model based on XML information template, data integration model, multi-index screening mechanism and clustering strategy based on perturbation attributes. On this basis, this paper uses decision tree algorithm, random forest algorithm and neural network algorithm to construct three scheduling rule mining models aiming at teachers’ professional ability. In addition, this paper establishes a digital twin-driven multi-knowledge model scheduling optimization architecture that uses the three scheduling rules mined. The research results show that the model constructed in this paper has good performance.
Keywords: Machine learning, digital twins, teachers, teaching ability
