Abstract
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.
Highlights
With the development of digital technology, all kinds of educational data are saved, which shows great potential for educational analysis and improvement (Dutt, Ismail, and Herawan, 2017; Romero and Ventura, 2017) [30], [31]
For the average order of student ability, we find that English memory (SA7), Math analysis and solution (SA13), Physics comprehension and reasoning (SA17), and English listening (SA6) show stronger positive impacts on the overall student growth, which indicates that these abilities are critical for student growth
We studied the difference between gender, and the impact of students’ abilities in HSEE, high school rankings, the size of high school, and the average score of high school on student growth
Summary
With the development of digital technology, all kinds of educational data are saved, which shows great potential for educational analysis and improvement (Dutt, Ismail, and Herawan, 2017; Romero and Ventura, 2017) [30], [31]. They used student academic achievement both at the beginning and at the end of the educational process for evaluation They compared and explained the measurement results of four models based on Chilean education data. McCaffrey et al (2004) conducted the research based on a longitude data set, using students’ score in every grade They showed a general multi-variable longitudinal hybrid model, which combined the intrinsic complex clustering structure of longitudinal student data linked to teachers, and showed the application of the model in separating the contribution of teachers or schools to student development [17]. Compared with EDM, Learning Analytics focuses on the application of the existed technique which can provide analytical support to the education system and is related to academic analytics, action analytics [25], [26], predictive analytics and may employ social network analysis [14], [27]
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