A practical and efficient multi-assessment system for vocational teaching based on machine learning

Higher vocational education is a self-consistent system for higher education appropriate to the development of productivity and economy in the world. It aims at training skilled talents, which has made great contribution to the economy and industry. Generally, designing courses in high vocational education includes teaching analysis, teaching strategy, teaching practice and teaching assessment. Among the teaching steps, teaching assessment is one of the most important method to improve the quality of course teaching. However, in most high vocational education courses, traditional written exam is still the primary tools of assessments, which can not fulfill the development of high vocational education. In order to improve the quality of high vocational education, it is very urgent to design a practical and efficient system with multiple assessments. We exploit machine learning techniques to design assessment system for high vocation education. Machine learning is a very powerful tool for data analysis and it has been used for education tools in recent years. First, we improve the teaching organization for training skilled talents. Second, we propose a feature selection model based on the improved teaching organization. Third, we propose a machine learning model for teaching assessment. With the main contributions and other improvements, we design a multi-assessment system for vocational teaching based on machine learning. We implement the multi-assessment system by using Python and TensorFlow, which shows that the system can provide practical and efficient multiple assessments for vocational teaching based on training machine learning model. Compared with other assessment methods, machine learning based multi-assessment is more intelligent and automatic. Besides, it can be extended to other fields of education with slight modifications.