<record>
  <header>
    <identifier>oai:eurokd.com:article/1244</identifier>
    <datestamp>2025-12-15</datestamp>
  </header>
  <metadata>
    <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
      <dc:title>Learning Chinese as a Second Language: Implications of the Character-Word Dual Function Model</dc:title>
      <dc:description>&lt;p style="text-align: justify;"&gt;Learning new words is fundamental in both first and second-language reading. There are, however, divided opinions on the best instructional approaches. Two widely used approaches across languages are whole-word focus and word-constituent focus. The appropriateness of each approach has varied historically, even within a single language (e.g., the debate between whole-word instruction and phonics in English). In teaching Chinese, both approaches are applied but to different learner groups. Whole-word instruction predominates in teaching Chinese as a second language (L2), while instruction for Chinese children focuses more on character-level mappings. It may seem reasonable in L2 Chinese instructions to focus on direct mappings between Chinese words and their L1 equivalent words. However, this raises a question: Is whole-word instruction the most efficient approach in L2 Chinese instruction? Based on an analysis of the Chinese writing system, we proposed a Character-Word Dual Function model of Chinese and tested its application of a dual-focus approach on both characters and words in L2 Chinese classroom instruction. Empirical findings support the advantage of this new approach compared to conventional whole-word instruction. We discuss the alignment between our findings and the Unified Computational Model and its implications for word instruction across languages.&lt;/p&gt;</dc:description>
      <dc:publisher>EuroKD Publishing</dc:publisher>
      <dc:date>2024-09-28</dc:date>
      <dc:type>Text</dc:type>
      <dc:identifier>https://api.eurokd.com/Uploads/Article/1244/ltrq.2024.44.11.pdf</dc:identifier>
      <dc:identifier>https://doi.org/10.32038/ltrq.2024.44.11</dc:identifier>
      <dc:language>en</dc:language>
      <dc:coverage>Pages 115–128</dc:coverage>
    </oai_dc:dc>
  </metadata>
</record>