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Compounds

Practice simplified Chinese characters with a structured learning order.

Compounds helps you study common simplified Chinese characters with a learning path that combines character frequency and structural relationships.

01

Optimized learning order

Learn high-value characters first in a progression that builds naturally from what you already know.

02

Writing practice

Practice writing from memory so recognition turns into usable skill.

03

Pronunciation and meaning

Reinforce how characters sound and what they mean as you learn them.

04

Scheduled reviews

Review the right material at the right time so more of it sticks.

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A structured order for learning characters

Compounds introduces high-value simplified Chinese characters in an order designed to improve retention and reduce frustration. The app is built for learners who want a clear study path, whether they are starting from scratch or filling in gaps in their reading and writing.

Each session is designed to help you learn useful characters early, practice writing from memory, reinforce pronunciation and meaning, and review at the right time so you remember more with less effort.

Core Features

  • Optimized learning order. Learn high-value characters first in a progression that builds naturally from what you already know.
  • Writing practice. Practice writing from memory so recognition turns into usable skill.
  • Pronunciation and meaning. Reinforce how characters sound and what they mean as you learn them.
  • Scheduled reviews. Review the right material at the right time so more of it sticks.
  • Private by default. No tracking. No account needed.

Why this order works

Compounds is inspired by research on efficient Chinese character learning strategies, especially work showing that character frequency and structural relationships can be combined to create a more effective progression.

Optimal learning order based on: K. Zhao, D. Zhao, J. Yang, W. Ma and B. Yu, "Efficient Learning Strategy of Chinese Characters Based on Usage Frequency and Structural Relationship," 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China, 2019, pp. 351-355, doi: 10.1109/ICBDA.2019.8713245.

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