Batch Grading Analytics Pipeline for Student Assignments

Added Nov 2025 3 design docs

A university processing tens of thousands of assignment submissions each term needs more than spreadsheets to compute grades, detect anomalies, and report cohort statistics. This project recreates that challenge as a distributed data workflow that an intern can build and run end to end. Submission events are simulated with Kafka so assignments arrive as a realistic stream throughout the exercise, and raw submission data is stored durably in HDFS. Scheduled PySpark jobs then process the accumulated submissions in batches, computing grading summaries, score distributions, completion rates, and per-course statistics. Python scripts manage the surrounding workflow, handling ingestion, triggering batch runs, and exporting results as structured data files and reports for instructors and administrators. There is no web or mobile front end; the deliverables are the pipeline itself and the reports it produces, which keeps attention on data correctness and throughput. The intern comes away understanding the streaming-to-batch pattern used across industry big-data platforms: durable ingestion, distributed computation, and reproducible reporting, exercised on a domain where the meaning of every metric is easy to reason about.

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