xiaozhi-teach-student-analyzer

To analyze classroom performance data, you upload assessment scores and observation records to generate a class portrait and diagnostic cards. This process identifies knowledge-point weak items and outputs actionable teaching adjustments for differentiated instruction.

A class portrait is a data-driven overview generated from assessments and observations that supports differentiated instruction. It applies knowledge-point heatmaps and weak-item detection to guide targeted teaching adjustments across the entire class.

Yes, you can generate individual diagnostic cards from assignment and assessment scores. The analysis requires input from assessments and classroom records to track trends and identify specific knowledge gaps for each student.

The best way to track learning trends without predictions is using diagnostic analysis on historical classroom performance data. The system explicitly avoids predictions while providing trend tracking and knowledge-point weak item detection for current performance.

Yes, classroom data analysis supports privacy-conscious reporting for parent communication. It generates reports using desensitized data and aggregated metrics to ensure student privacy while still conveying actionable teaching adjustments.

You need to upload class scores and observations to detect knowledge-point weak items. The system processes these inputs to generate heatmaps and identify gaps, though the specific file format requirements depend on the input method used.