Efficient learning strategy of Chinese characters based on network approach

TL;DR

This paper introduces a network-based approach to optimize Chinese character learning by prioritizing characters based on their hierarchical importance and usage frequency, significantly improving learning efficiency.

Contribution

It proposes the distributed node weight (DNW) strategy, a novel method that outperforms existing learning strategies by integrating hierarchical structure and character usage data.

Findings

DNW method significantly outperforms traditional methods

Current textbooks' learning sequences can be optimized

Network analysis reveals key characters for efficient learning

Abstract

Based on network analysis of hierarchical structural relations among Chinese characters, we develop an efficient learning strategy of Chinese characters. We regard a more efficient learning method if one learns the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that takes into account both the weight of the nodes and the hierarchical structure of the network. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.