Abstract
This paper has developed a sound and practical method to evaluate the key teaching quality including the student academic performance and student satisfaction ratings. The method makes use of the existing data already readily available in a Chinese university, focusing on the identification of key influencing factors affecting the student academic performance and student satisfactions ratings. The data analyses have shown the university student academic performance is significantly affected student gender, age, previous academic performance, settlements and occupations of parents. There is significant difference in the student ratings for different genders and academic positions of teaching staff. The student performance and satisfaction ratings also significantly vary in different years of intakes and different Schools/programs. The student’s university academic performance can be accurately predicted using artificial neural networks with a prediction error of about 7%. This approach can help the university to improve the student academic performance and student satisfactions.
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