Data scraped to be used to analyze the impact of a teacher subsidy program on student performance.
Python scripts using web scraping techniques, including packages such as Selenium and bs4, to automate data extraction, transformation, and cleaning of Saudi exam data for all private schools in the country. Each jupyter notebook corresponds to a unique combination of the following features:
- Type of Exam (Capabilities or Achievement)
- School Type (Boy or Girl)
- Exam Specialization (Scientific or Theoretical)
- School Type (Private, in this case)
The 'Cleaning_Appending' notebook brings all datasets together and adds relevant columns. The resulting dataset is named 'exam_scores_complete.csv' and can be found in the data folder along with the individually scraped datasets.
Data from the National Center for Assessment: https://www.qiyas.sa/en/Pages/default.aspx