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Language Reactor ReadLang 1L

Language Reactor ReadLang 1L

Nguyen Hong Phuc

Education免费v2.0.26
App Store
评分

4.7

125 条评分

星级

★★★★★

最近更新

2026年2月3日

发布日期

2025年5月24日

更新内容

v2.0.26

- improved peformance & fixed bugs

应用信息

开发者
Nguyen Hong Phuc
分类
Education
价格
免费
版本
2.0.26
App ID
6740740246

简介

Learn languages with Language Reactor using Active Recall and Spaced Repetition in context. Learn a new word just to forget it minutes later? The problem isn’t you—it’s passive recall. By seeing translations too quickly, your brain never truly locks in new vocabulary. Our app flips the process: you try recalling words first, then check the translation. This active recall approach cements vocabulary into your long-term memory. Ready to stop forgetting and start mastering? Install now! Features include: - Active Recall: Promotes deeper memory retention by requiring you to recall words and phrases rather than passively recognizing them. - Adaptive Learning: Starts with full translations and gradually shifts to giving hints like the first letter of a word, catering to your learning progress. - Seamless Integration: Works intuitively with text and audio on various websites, providing a fluid learning experience without switching contexts. - Customizable Settings: Allows you to adjust the difficulty level and choose which aspects of language learning to focus on. To get started, navigate to any webpage and click on unfamiliar words or listen to dialogues. The extension will facilitate your learning process without interrupting your browsing experience. We support 100+ languages and reader with Voice Aloud Reader and more... We are constantly improving our tools based on user feedback. Please feel free to reach out if you have suggestions or need support. We're excited to be part of your language learning journey! Terms of Use: https://www.apple.com/legal/internet-services/itunes/dev/stdeula/

下载量预测

专业 · 预览

预估总下载量

9K6K18K
保守估计乐观估计

521

低 / 月

772

预估 / 月

1K

高 / 月

基于125 条评分
假设评分率1.4%
应用年龄12 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。