Start by clarifying that 'difficulty' can be subjective and depends on the test-taker's proficiency and goals. Then, propose a data-driven framework that combines psychometric analysis (e.g., item difficulty, discrimination) with user behavior metrics (e.g., completion rates, score distributions) to assess the test's difficulty objectively.
Pro tip: Emphasize that difficulty should be calibrated to the target population's ability to ensure fairness and validity; mention that at Meta, you'd leverage A/B testing and user segmentation to validate difficulty across diverse cohorts.
Clarify what 'difficulty' means for Duolingo: it could refer to the test's ability to differentiate proficiency levels, the average score, or the perceived challenge by users. Align on a definition with stakeholders.
Select quantitative metrics such as item difficulty index (proportion of correct answers), test information function, completion time, and score distribution. Also consider qualitative feedback from users.
Use statistical methods (e.g., Item Response Theory) to estimate item and test difficulty. Segment users by proficiency, device, or demographics to see if difficulty varies across groups.
Compare difficulty against other language tests (e.g., CEFR levels) and validate with external criteria like user performance in real-world language tasks. Conduct A/B tests if changes are proposed.
Continuously monitor difficulty as the user base evolves. Use feedback loops to adjust item pools or scoring algorithms to maintain appropriate challenge and fairness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.