|
Title:
|
PREDICTIVE SUCCESS FACTORS IN SCHOOL
PERFORMANCE: AN ANALYSIS OF THE LARGE-SCALE
ASESSMENT IN BRAZIL |
|
Author(s):
|
Ivonaldo Vicente da Silva, Márcia Terra da Silva and Saturnina Alves da Silva Martins |
|
ISBN:
|
978-989-8533-90-6 |
|
Editors:
|
Piet Kommers and Guo Chao Peng |
|
Year:
|
2019 |
|
Edition:
|
Single |
|
Keywords:
|
School Performance, SAEB, Data Mining |
|
Type:
|
Full Paper |
|
First Page:
|
161 |
|
Last Page:
|
168 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
With the advent of new technologies and the strengthening of the concept of Industry 4.0, it becomes important to
observe the issue of the need for more qualified and up-to-date professionals concerning the labor market. Thus, there is a
need to investigate the level of school performance, especially among high school graduates, who will be the ones who
will most quickly enter the labor market. In order to compose the research data, the results of the SAEB large-scale
evaluation of the year 2015 in the Portuguese Language and Mathematics proficiency were used. The data were collected,
standardized and the results presented in groupings by levels, obeying a scale of 0 to 10. RapidMiner software was used
for data mining and decision tree construction. The central objective was to identify the best predictive factors for
students' performance equal to or greater than 350 points (level 6). The results showed that there is a significant
percentage of students with scores lower than 225 points in the two proficiencies analyzed, considered by the government
as a concern, since these students did not learn the necessary minimum. The decision trees demonstrated that the level of
schooling of the parents or guardians of students can be considered important and fundamental factors for school
performance |
|
|
|
|
|
|
