This tool has been built in order to prevent primary school dropout in high-risk rural regions of Morocco. Student drop out can be identified as a pattern related to socio-economic backgrounds. It is a pressing issue in developing countries and it can be prevented when tackled early. Our analysis focus only on regios whre level of school dropout is high.
- Higher unemployment rate;
- increased poverty;
- out-of-school students;
- increased violence
Rural environment are often neglected by the government, the tool we created allows to raise awareness and easily identify at-risk students.
baseline_household
work_activity- Work status of the parentsindividual_water_net- Individual water network connectionelectrical_net- Electric connectionmobile_phones- If the family have a mobile phone or nottype_housing- The architectural structure of the house
child_math_test_result
digit_recognition_res- Digit recognition test resultsnumber_recognition- Number recognition test resultssubtraction_res- Subtraction test resultsdivision_res- Division test resulsaverage_math_score- Average score of the 4 math test sections mentioned above
| Variable Name | Encoded Numbers | Description |
|---|---|---|
| mother_alive | 1 | Yes |
| 2 | No | |
| father_alive | 1 | Yes |
| 2 | No | |
| parents_age | - | Age in Years |
| marital_status | 1 | Married |
| 2 | Single | |
| 3 | Divorced | |
| 4 | Widowed | |
| parents_level_ed | 1 | No education |
| 2 | Religious education | |
| 3 | Primary School | |
| 4 | Middle School | |
| 5 | High School | |
| 6 | Higher Education | |
| 7 | Professional Training | |
| work_activity | 1 | Full Time |
| 2 | Part Time | |
| 3 | Unemployed | |
| type_housing | 1 | Adobe/Clay house |
| 2 | Permanent House | |
| 3 | Dry Stone | |
| 4 | Modern/Concrete house | |
| 5 | Other |
Our prediction model has been trained on the following research dataset: Data for Development Initiative. (2019). Morocco CCT Education (Version 1.0) Data set