[功能请求]: 适应性长期学习引擎,用于 Mastery Path — 基于保持的间隔重复、统一的学习证据和学习优先级
作者: yepyhun创建于 2026年9月15日更新于 2026年9月15日
标签enhancement
- Spaced repetition currently uses predefined intervals In
deeptutor/learning/scheduler.py, the intervals are currently defined roughly as: MEMORY: [0, 1, 3, 7, 14, 30, 60] CONCEPT: [3, 7, 14] PROCEDURE: [3, 7, 14] DESIGN: [14, 28]schedule_next()moves forward or backward through these lists depending on correctness / consecutive correct answers. This is already useful, but it means that learners with very different memory characteristics still mostly follow the same interval structure. 2. RepetitionState does not model actual memory state Indeeptutor/learning/models.py,RepetitionStatecurrently stores things like: interval_index consecutive_correct consecutive_wrong next_review_at What is missing is a richer model of long-term memory, for example: difficulty stability retrievability / estimated probability of recall desired retention lapse count review count last review timestamp uncertainty/confidence in the estimate This matters because the current system can answer: "Which interval index is this item on?" but not really: "How likely is this learner to remember this knowledge point tomorrow, in one week, or by exam day?" 3. Mastery and retention should be separated Indeeptutor/learning/mastery.py, the current mastery calculation is intentionally simple and based mainly on recent correctness. I think DeepTutor should explicitly distinguish: Mastery: "Has the learner demonstrated understanding?" from: Retention / retrievability: "How likely is the learner to successfully retrieve this knowledge now or later?" These are not the same. Example: A learner may understand the brachial plexus very well but still forget several Latin structure names after a few days. Or they may repeatedly
内容来源: HKUDS/DeepTutor