KE, Chuanren: A model for Chinese orthographic awareness
In order to anchor findings from a series of studies on CFL (Chinese as a foreign language) character learning by adult learners of an alphabetic language background within a theoretical framework, Ke proposes a cognitive model of the development of orthographic awareness which states that learners of Chinese acquire this awareness in three successive stages. In the first, pre-component processing stage, learners primarily are adding whole characters to their lexicon without much component processing. They have not yet accumulated enough characters in their mental lexicons to abstract the recurring components. Although they may be accumulating knowledge of various graphic features and character components, they have to rely on creating idiosyncratic mnemonics based on how a character looks or sounds to them. They have to memorize a character as a whole and keep practicing it repetitively. The pattern of character mastery is likely to show a primacy and recency effect. Characters learned at the beginning and end of a learning session tend to be mastered more accurately than those learned in between. In general, the errors these pre-component-processing-stage learners make are not those that native speakers would make. Because, in general, they are unable to decompose characters, they tend to produce characters composed of fewer strokes more accurately than those with more strokes.
The second stage in Ke’s model, referred to as the component processing stage, follows the end of the pre-component processing stage. In this stage, learners habitually apply their knowledge of the structures of the orthographic system to character identification and naming. Learners at this stage already have accumulated a substantial number of characters in their long-term memory and have begun to generate the frequently-used semantic radicals and phonetic components from their relatively extensive lexicons. At the higher end of this stage, learners can guess the meaning and pronunciation of most transparent novel semantic-phonetic compound characters quite accurately. At this stage, the pattern of character acquisition shows a hierarchy in terms of recurring components. Factors contributing to this acquisition hierarchy may include the frequency of occurrence a component has in the Chinese script system, the graphic saliency of a component, and the degree of density that a neighborhood has of similar sounds and graphics. There also will be a competition effect from these parameters of frequency, saliency, and density. At this stage, this acquisition hierarchy appears more prominent than the primacy-recency effect pattern that characterizes character learning in the pre-component processing stage.
The final stage in this sequence can be referred to as the automatic component processing stage. A CFL learner who has completed the component processing stage has reached a threshold in his or her Chinese orthographic acquisition. In other words, there is a quantum leap in orthographic awareness between the component processing stage and the automatic component processing stage. At the high end of this automatic component processing stage, learners' orthographic awareness is native-like. At this stage, learners can recognize and produce characters from the perspective of recurring components, including those components that are not sound or meaning based. In addition to making good guesses about the meaning and pronunciation of most unfamiliar characters, they also can determine quite successfully whether a novel character has a legitimate or impossible combination of character components. At this stage, neither the hierarchy of recurring components nor the pattern of primacy and recency effects that characterize errors made in the two earlier stages is as large. The errors learners make at the automatic component processing stage are likely to be those that native speakers would make. Very often, these errors are phonologically oriented.
This paper will explicate features of this model and will propose future studies for the purpose of verifying the model.
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