Enabled by available educational data and data mining techniques, educational data analysis has become a hot topic. Current researches mainly focus on the prediction of problems and performance rather than revealing the underlying causal relationships. Based on a unique exam data, we extracted the abilities of examinee from HSEE (High School Entrance Exam) based on the knowledge of educational experts, then we measured student growth from middle school to high school in total score and subject scores. We studied the impact of high school ranking and student abilities of HSEE on student growth by multiple linear regression model, in which high school ranking is divided into 5 levels, Level 1 being the best and Level 5 being the poorest. We found that: 1) the higher of the ranking of the high school was, the higher of their student growth in total score was, but there were exceptions in Level 4 and Level 5 schools. The growth in subject scores did not follow the same rule. Level 3 schools performed better than Level 2 schools in student growth in Physics, and Level 2 schools performed better in student growth in Chemistry. 2) Student abilities in HSEE have different impacts on student growth in total score and subject scores. For student growth in total score, the abilities of English memory and Math analysis and solutions have larger positive influences than the other abilities. For student growth in the subject score, most abilities have a negative impact on the growth of the same subject, except for English listening and memory. Our research can not only help educational authorities evaluate the impact of high schools on the variations of student abilities to ensure equity and efficiency, but also help students and parents choose schools based on student abilities and the characteristics of high schools.
|Numero di pagine||11|
|Stato di pubblicazione||Published - 2019|
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)