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adaptive-memory-curve's Introduction

基于 TensorFlow 自适应记忆曲线

  1. 自适应英语单词记忆学习,建立起简单的基于初始数据的自适应学习方向。
  2. 将个性化差异展现在个人模型上,建立几种大体模型应用于单词的自适应学习。
  3. 基于人教版英语单词难度适配、基于小程序获取用户数据。

实现

  • step 1: 数据来源,入门级测试(定级再以此为基础)、日常使用过程中的力(完成度、正确率)
  • step 2: 建立遗忘曲线函数式、确立第一步重复值。
  • step 3: 根据遗忘曲线与完成度叠加学习得到自适应重复记忆需求。
  • step 4: 定期测试并优化记忆曲线。

Step 1 IRT 模型自适应测试

数据来源与数据特征值提取

  • 自适应选题。
  • 三参数模型测试用户能力。

Step 2 建立记忆模型

遗忘曲线函数 + 间隔效应建立

近似公式: R=e^(-t/S) 其中 R 是被记忆的内容,S 是相对记忆强度,t 是时间。 按照学习规划, t = 1 day S: 按照记忆量进行分段 20-1,40-2 ... 将记忆内容 R 丢进次日、七日、一月、三月学习内容库

定义 x 衰减速率

Step 3 训练个性化模型

研究关键,基于已有数据训练模型,采用 DKT(RNN) 模型,基于此模型建立学生行为模型并反馈输出个性化模型自适应记忆曲线

  • 单个学生建模
  • 单个学生数据提取
  • 训练模型

正在起步...GO

adaptive-memory-curve's People

Contributors

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