Difference between revisions of "Integrated Learning of Chinese"

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== Dependent variables ==
 
== Dependent variables ==
  
[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks
+
*[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks
[[Transfer]]: novel character decision and translation
+
*[[Transfer]]: novel character decision and translation
 
== Independent variables ==
 
== Independent variables ==
 
*Integration of reading and writing vs. reading only
 
*Integration of reading and writing vs. reading only
 +
*Production with automated feedback, production only, vs. no production
 
*
 
*
 
== Hypothesis ==
 
== Hypothesis ==

Revision as of 04:05, 11 January 2008


Summary table

  • Node Title: Integrated Learning of Chinese: reading, perception and production
  • Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu
  • PIs: Ying Liu, Charles Perfetti, Min Wang
  • Others who have contributed 160 hours or more:
  • Post-Docs: Connie Guan
  • Graduate Students: Derek Chan
  • Study Start Date Jan 1, 2008
  • Study End Date Dec 31, 2009
  • LearnLab Site and Courses , CMU Chinese (Classroom and Online)
  • Number of Students: 100
  • Planned Participant Hours for the study: 300
  • Data in the Data Shop: not yet

Abstract

  • Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the integration by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies.


Glossary

Integration; Constituents; Orthography; Phonology; Meaning


Research question

How does integration of language constituents lead to robust learning?

Background

  • Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, in press), tone perception (Wang et al, in preparation), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and cotraining of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.
  • There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The cotraining study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.


Dependent variables

  • Normal post-test: measures of partial character recognition, lexical decision and translation tasks
  • Transfer: novel character decision and translation

Independent variables

  • Integration of reading and writing vs. reading only
  • Production with automated feedback, production only, vs. no production

Hypothesis

  • Integration is crucial for robust learning;
  • Integration is most robust when the involved knowledge components are already refined.

Findings

Explanation

Descendents

Further information