Customized Deployment Guide for Education
The following implementation paths are recommended for K12 and adult education scenarios:
- knowledge graph integration::
- Convert the textbook chapters into vectors and store them in the database, run thepython scripts/import_curriculum.py --format=json
- existconfig/education_preset.jsonPre-positioned common learning paths in - cognitive diagnosis::
- pass (a bill or inspection etc)/memory/analyzeAPI Access to Student Error Pattern Analysis
- Enhanced with IRT (Item Response Theory) algorithmsinference_serviceThe difficulty prediction module of the - teaching strategy::
- Configure the SRS (spaced repetition) parameter:"spaced_repetition": {"interval": [1,3,7]}
- Implementing multimodal feedback: when student confusion is detected (via thesentiment_score), which automatically brings up the explainer video
Implementation Example: An online language platform used Deep Recall to increase user retention by 32%. the key to theuser_profileDetailed data such as pronunciation error patterns are stored in the
This answer comes from the articleDeep Recall: an open source tool that provides an enterprise-class memory framework for large modelsThe
May not be reproduced without permission:AI productivity tools " How can Deep Recall be applied in educational scenarios to enable adaptive learning?
