
Developed an end-to-end data synchronization feature for the AFA-Tooling/GradeSync repository, enabling automated extraction of PrairieLearn gradebook and question-level data and seamless population into configured Google Sheets. Leveraged Python and Pandas to manage data transformation, while Docker was used to containerize the solution for consistent deployment. The implementation included robust error handling to ensure reliable and repeatable integrations, and configuration files were provided to manage API interactions efficiently. By importing proven components from the pl2 repository, the work accelerated delivery and maintained code quality. This feature addressed the need for automated, accurate data integration between PrairieLearn and Google Sheets.
April 2025: Delivered end-to-end PrairieLearn Google Sheets Data Synchronization in AFA-Tooling/GradeSync, enabling automated extraction of PrairieLearn gradebook and question-level data and population into configured Google Sheets. Delivered with a Python script, Dockerfile, and configuration files to manage API interactions, data transformation, and robust error handling for reliable data integration. Brought over pl folder from the pl2 repo to reuse proven components and accelerate delivery.
April 2025: Delivered end-to-end PrairieLearn Google Sheets Data Synchronization in AFA-Tooling/GradeSync, enabling automated extraction of PrairieLearn gradebook and question-level data and population into configured Google Sheets. Delivered with a Python script, Dockerfile, and configuration files to manage API interactions, data transformation, and robust error handling for reliable data integration. Brought over pl folder from the pl2 repo to reuse proven components and accelerate delivery.

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