difficulty-classifier

Automated exam question difficulty classification assigns easy, medium, or hard labels by evaluating hop count, specificity, cognitive level, and distractor quality using Ragas-based metrics. It processes single- and multi-hop questions from educational content or practice banks instantly.

Difficulty classification relies on Ragas-inspired metrics including hop count, specificity, cognitive level, and distractor quality. Evaluating these four dimensions allows the system to output a clear difficulty category for educational quizzes and practice banks.

Yes, educational platforms can use this automated difficulty labeling to auto-tag questions for adaptive testing, review, and curriculum alignment. It evaluates multi-hop questions and provides adjustment suggestions to ensure accurate difficulty scaling.

Analyzing multi-hop question difficulty involves evaluating the question's hop count, specificity, cognitive level, and distractor quality. The system outputs a difficulty category and provides a workflow for automated adjustment suggestions.

Yes, the difficulty classifier provides a workflow for automated adjustment suggestions after labeling. It evaluates cognitive level and distractor quality to output a difficulty category and recommend how to calibrate the question for educational content.

The best way to label quiz difficulty is applying Ragas-based metrics to assess hop count and cognitive level. This assigns easy, medium, or hard categories to practice bank questions, directly supporting curriculum alignment and review processes.