Ke S. and Koda K. 2017 . Contributions o
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Contributions of Morphological Awareness to Adult L2 Chinese Word Meaning Inferencing
SIHUI ECHO KE1 and KEIKO KODA2
1
Harbin Institute of Technology, Shenzhen, P. R. China, School of Humanities and Social
Science, G416, HIT Campus, Xili University Town, Shenzhen 518055, P. R. China Emails:
[email protected] or [email protected]
2
Carnegie Mellon University, Department of Modern Languages, 160 Baker Hall, 5000 Forbes
Avenue, Pittsburgh PA 15213 Email: [email protected]
ABSTRACT
The study examined the contributions of morphological awareness (MA) to second language
(L2) word meaning inferencing in English-speaking adult learners of L2 Chinese (N = 50). Three
research questions were posed: Are L2 learners sensitive to the morphological structure of
unknown multi-character words? Does first language (L1) MA contribute to L2 MA over and
above L2 linguistic knowledge? Does L2 MA contribute to L2 word meaning inference over and
above L1 MA and L2 linguistic knowledge? These questions were investigated through the use
of a set of experimental and paper-and-pencil measurements of the aforementioned L1 and L2
reading subskills and L2 linguistic knowledge, as well as working memory. Several significant
results were found. The L2 learners were sensitive to the morphological structure of multicharacter words and more successful in guessing the meanings of novel words containing
affixoids and familiar bases. L1 MA was found to transfer and facilitate the development of L2
MA over and above L2 linguistic knowledge. Subsequently, L2 MA contributed to L2 word
meaning inferencing indirectly through L2 linguistic knowledge. The discussion focuses on the
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intra-lingual and inter-lingual relationships among L1 MA, L2 MA, L2 linguistic knowledge,
and L2 word meaning inferencing in adult L2 reading development.
Keywords: word meaning inferencing; second language; morphological awareness; word
properties; Chinese
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Morphological awareness (MA henceforth) is often defined as learners’ sensitivity to the
morphological structure of printed words (Carlisle, 2000; Koda, 2000; Verhoeven & Perfetti,
2011). Previous research has found that the success of L2 word meaning inferencing depends on
a learner’s MA (e.g., Park, 2004; Zhang, Koda, & Leong, 2016) or structural complexity of the
target unknown word (e.g., Hamada, 2014; Mori & Nagy, 1999). For words to be learned
incidentally during reading, a learner must infer the meaning of an unfamiliar word based on the
information provided by the word and that afforded by the context. To do so, the learner must
have the abilities to analyze the internal structure of a word into its morphological constituents
and to construct the meaning of the word based on familiar morphological elements. Also, adult
L2 readers with limited L2 linguistic knowledge can compensate by using morphological
analysis to retrieve word meanings while reading (Parel, 2004). However, there is a lack of a
consensus regarding the extent and how MA makes intra-lingual and inter-lingual contributions
to L2 word meaning inferencing during reading. The goals of this study were three-fold: (a) to
investigate whether adult L2 learners are sensitive to morphological complexity; (b) to examine
the extent to which MA is related in L1 and L2 with learners whose L1 and L2 are typologically
distinct (i.e., L1 English and L2 Chinese); and (c) to explore how L1 and L2 MA contribute to
L2 word meaning inferencing, and how their joint contributions are affected by L2 linguistic
knowledge.
LITERATURE REVIEW
The Contributions of Morphological Awareness to L2 Word Meaning Inferencing
In this research, we adopted a component approach when examining the contributions of
MA to L2 word meaning inferencing in L2 reading. This approach essentially posits that reading
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entails a set of distinct, yet interdependent, mental operations (Carr & Levy, 1990). Following
Koda (2005, 2007), reading competencies in this study were taken to encompass metalinguistic
awareness (e.g., sensitivity to the abstract structure of language), linguistic knowledge
(knowledge of vocabulary and grammar), and reading subskills (e.g., word form analysis,
retrieving the pronunciation and meaning of known words, inferring the pronunciation and
meaning of unknown words). With regard to metalinguistic awareness, we focus on
morphological awareness. As the smallest functioning unit of a language, morphemes convey
rule-governed grammatical information and arbitrarily assigned functional information. MA, as
their abstract representation, comprises, at the minimum, the internal structure of morphemes in
words (the ability to see the structural difference between ‘incident’ and ‘insecure’), rules of
morpheme concatenation (e.g., prefix + root + suffix), and functional constraints on the
concatenation rules (e.g., the ability to choose a lexically appropriate nominalizer, such as ‘-ure’
for ‘close’ and ‘-ation’ for ‘flirt’) (Koda & Miller, 2017). Reflecting the involvement of
functional knowledge, MA is more varied and language-specific than phonological awareness,
and, thus, more linguistically demanding and dependent. A clear implication of the construct’s
linguistic dependency for L2 reading development is that the utility of L1 MA in the formation
of L2 MA could be constrained by both L2 linguistic knowledge and structural similarity
between the two languages involved. Similarly, the utility of L2 MA in word meaning inference
is also constrained by L2 linguistic knowledge.
In L2 reading, MA can make both intra-lingual and inter-lingual contributions to L2 word
meaning inferencing (e.g., Park, 2004; Zhang, 2012; Zhang, Koda, & Leong, 2015). From the
intra-lingual perspective, substantial evidence suggests that MA plays a dual role in language and
literacy development: (a) to facilitate the transition from oral to written communication for
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learners to map the sound of spoken words and morphemes onto the written symbols that encode
them (e.g., decoding); and (b) to promote an analytical and constructive approach to word
learning (e.g., word meaning inferencing) (Koda, 2000, 2005). From the inter-lingual
perspective, MA has a transfer facilitation effect across languages in second language (L2)
learners who are already literate in their first language (L1) (Ramirez, Chen, & Pasquarella,
2013; Zhang, 2012). However, evidence supporting the inter-lingual contribution of MA to L2
word meaning inferencing has just emerged in recent years (e.g., Park, 2004; Zhang, Koda, &
Leong, 2015). Previous research has investigated how MA facilitates successful inferencing of
unknown words in L2 and explored the interaction among learners’ L1 and L2 resources such as
L1 MA, L2 MA, L2 linguistic knowledge, and L2 word meaning inferencing ability. For
instance, Park’s (2004) study with Korean-speaking English language learners from grade 3 to 5
in the United States, found that L1 MA was related to L2 word meaning inference ability only
indirectly through L2 MA, and that L2 MA facilitated the development of L2 word meaning
inference ability beyond the effect of L2 linguistic knowledge (measured by a listening
comprehension test). A similar pattern was observed in the research by Zhang et al. (2016),
which adopted a longitudinal design. They investigated the impact of MA on the bilingual word
meaning inferencing ability of L1 English–L2 Malay bilingual children in Singapore, where
English is the medium of instruction, at two time points (grade 3 and grade 4). Their findings
suggested that, at Time One, L1 MA only had a significant indirect effect on L2 word meaning
inference via L2 MA; at Time Two, the contribution of L1 MA to L2 word meaning inferencing
was mediated jointly by L1 word meaning inferencing and L2 MA. Both studies seem to suggest
that L1 MA transfers and facilitates the development of L2 MA, which subsequently contributes
to L2 word meaning inferencing.
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The research discussed previously, however, has focused on learners whose L1 and L2
were alphabetic orthographies (for a categorization of various orthographies, see Chang, Plaut, &
Perfetti, 2016). According to previous L2 reading studies with two alphabetic languages, the
explanatory power of L1 and L2 resources in predicting L2 reading ability ranges from 44% to
51% (for a review, see Bernhardt, 2005). For example, in the study reported by Zhang et al.
(2016), L1 MA at Time One explained about 50% of the variance of L2 MA, and concurrently,
L1 word meaning inferencing and L2 MA explained around 35% of the variance of L2 word
meaning inferencing. To date, little is known about how and to what extent MA can affect L2
word meaning inferencing in learners whose L1 and L2 are typologically distant (see an
exception in Koda, 2000). Koda (2000) compared L2 learners’ performances in a semantic
judgment task that entailed the ability to integrate morphological and contextual information,
with two L1 groups (i.e., L1 Korean and L1 Chinese learners of English as a second language). It
was found that Chinese learners, whose L1 orthography is morphosyllabic, were more efficient
than Korean learners were in detecting semantic inconsistency between the information supplied
by the affix of the target word and that conveyed by the context in which the word is embedded.
This implies that L2 learners’ ability to integrate morphological and contextual information in
reading is affected by their L1 literacy experience. Another interesting finding in Koda’s (2000)
study was that when the target words were morphologically simple (e.g., ‘regime’), L1 Chinese
and L1 Korean groups performed similarly. Therefore, it seems that the successful use of
morphological information is also affected by the structural salience of unknown words.
Word Effect on L2 Word Meaning Inferencing
As posited by Nagy, Carlisle, & Goodwin (2014), the importance of MA in word
meaning inferencing can be explained by its capacity for enabling learners to segment unfamiliar
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words, particularly those made up of familiar morphological components, into their constituents.
Logic would suggest that the contribution of MA is greater with morphologically complex
unknown words, smaller with morphologically simple ones. This was supported by Mori and
Nagy (1999) and Hamada (2014) in their studies of word meaning inferencing in L2 Japanese.
Mori and Nagy (1999) conducted a study with English-speaking university learners of L2
Japanese at intermediate and pre-advanced levels, in which they asked the learners to infer the
meanings of novel semantically semi-transparent compound words in a sentence. Compound
words were comprised of familiar kanjis in three conditions (i.e., kanji/word-internal information
only, contextual information only, and the combination of kanji and contextual information).
Important insights have been drawn from their findings: (a) more than half of the L2 Japanese
learners tended to integrate both word-internal and contextual information, and the integrators
were more successful in lexical inferencing than were the non-integrators; (b) the use of wordinternal information and the use of contextual information were not correlated; and (c) the use of
contextual clues depended on L2 proficiency level, yet the use of word-internal information did
not. In a recent study with a similar design, Hamada (2014) expanded the scope by investigating
whether the use of information, morphological or contextual, in lexical inferencing depended
upon the learners’ L2 proficiency and the reliability of the morphological information with
university learners of L2 English at four proficiency levels (beginning, intermediate, highintermediate, and advanced). She found that the choice of information was influenced by the
morphological reliability condition because participants performed similarly across proficiency
levels in the morphology reliable condition but differently in the morphology unreliable
condition. The findings reported by Mori and Nagy (1999) and Hamada (2014) indicated that
there is morphological facilitation in L2 word meaning inferencing, and that the effect of word-
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internal morphological structure is independent from that of L2 linguistic knowledge. Notably,
both studies were based on learners’ overall L2 proficiency at the instructional levels and did not
measure L2 linguistic knowledge. As well, neither study measured the participants’ MA.
To recapitulate, the current study aimed to investigate the contribution of MA to L2 word
meaning inferencing in L1 English-speaking learners of L2 (Mandarin) Chinese, whose L1 and
L2 are typologically distant. This section defines the category ‘word’ and describes the
characteristics of grapheme–morpheme mappings in Chinese printed words. For clarity,
comparisons between English orthography and Chinese orthography are also provided.
Morphological Properties in Chinese
In this research, a word is defined as “an independent occupant of a syntactic form class
slot” (Packard, 2000, p.12). Words in written English are salient because they are separated by
spaces in written texts. English orthography is morphophonemic in that the basic grapheme unit
is an alphabetic letter, but printed words encode both phonemic and morphemic information.
When there is inconsistency in representing phonemes in English words, it is often explained by
the tendency to preserve morphological information in their graphemes (e.g., ‘heal’/‘health,’
‘cats’/‘dogs,’ as cited in Frost, 2012).
In Chinese, written texts are not word-based but character-based, with no spaces inserted
between words. Chinese orthography is morphosyllabic in that the basic grapheme unit is a
character, which, in most cases, maps onto a morpheme that corresponds to a single syllable.
While most characters have their own meanings and can be used independently, the combination
of two or more characters can also form new words. Around 94% of printed words in Chinese
are multi-character words (Lexicon of Common Words in Contemporary Chinese Research
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Team, 2008, as cited in Li et al., 2014). It should be noted that the meaning of each component
character can be either closely related or totally unrelated to the whole word meaning (Li &
Thompson, 1981; as cited in Li & McBride–Chang, 2014). For example, each character in the
two-character word 花生 has its own meanings (‘flower’ for 花 and ‘give birth to’ for 生) and
can be used functionally and independently; yet the word 花生 is a lexicalized expression
mapping onto one morpheme, meaning ‘peanut,’ which cannot be inferred directly from the two
component characters (at least synchronically).
Based on the earlier review, we cannot assume that all multi-character words in Chinese
are morphologically complex, or ‘compound’ words, as cited widely in the extant literature about
Chinese morphology. It is important to differentiate morphologically complex multi-character
words from morphologically simple ones, which would help with an investigation of how
learners of Chinese grasp the fundamental principle on which graphemes map onto morphemes
and to extract morphological information from printed words. In skilled English reading, this
morphological analysis is realized through dissecting strings of letters from a morphologically
complex word and segmenting it into smaller meaningful chunks (e.g., to segment ‘heal’ from
‘health’). In Chinese, intraword segmentation might seem unnecessary because written Chinese
is separated graphically, with each character equally spaced in written texts. Nevertheless,
emerging evidence has suggested that segmentation and chunking are important in reading
Chinese as well. Thus, Zhang et al. (2014) posited that Chinese readers’ sensitivity to
morphological productivity—the extent to which words are formed with certain morphemes
appearing in certain positions—is crucial in identifying known and unknown words, as well as
phrases, when they read character strings with no spaces inserted between words. In a statistical
analysis of two-character words in the Grading Syllabus for Chinese Vocabulary and Chinese
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Characters (GSCVCC ) (Chinese Proficiency Test Center, 2001), Xing (2006) found that only
37% of the characters had fixed positions in forming words, and 63% were not positionally
constrained. To the best of our knowledge, Zeng’s (2008) database is the only available resource
that provides a list of productive word formation morphemes/affixoids in Chinese, accompanied
by morpheme frequency. His database is critical because it provides an explicit description of
four inclusion criteria: (a) productivity, (b) position stability, (c) desemantization (with
weakened lexical meaning), and (d) boundness (cannot be used as an independent lexical unit).
The database includes 34 prefixoids (productive morphemes with fixed positions at the
beginning of multi-character words) and 54 suffixoids (productive morphemes with fixed
positions at the end of multi-character words).
To sum up, there are both commonalities and differences regarding intraword structural
sensitivity in English and Chinese. Chinese and English orthographies thus have at least one
common property, that is, graphemes encode both phonology and morphology; and therefore, the
ability to analyze intraword morphological structure in the two languages reflects readers’
sensitivity to the grapheme–morpheme relationships. In view of the complex interaction among
L1 MA, L2 MA, L2 linguistic knowledge, and L2 word meaning inferencing, and the potential
influence of word properties on the ease with word meaning inferencing, this study aimed to test
three hypotheses: (a) L2 learners are sensitive to the morphological structure of unknown multicharacter words; (b) L1 morphological awareness facilitates the development of L2
morphological awareness; the L1 facilitation effect is affected by L2 linguistic knowledge; (c)
L1 and L2 morphological awareness jointly contribute to L2 word meaning inferencing; the joint
contributions are affected by L2 linguistic knowledge. These hypotheses were translated into
three research questions.
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RQ1.
Are L2 learners sensitive to the morphological structure of unknown multi-character
words?
RQ2. Does L1 MA contribute to L2 MA over and above L2 linguistic knowledge?
RQ3. Does L2 MA contribute to L2 word meaning inference over and above L1 MA and L2
linguistic knowledge?
METHOD
Participants
Fifty English-speaking learners of Chinese as a second language from six American
universities participated in the study. At the time of data collection, they were enrolled in the
fifth or later semesters of their courses. Recruitment criteria included: (a) participants had a
minimum of two years’ experience of learning Chinese; (b) reading was one of the major
learning and instructional components in their affiliated programs; and (c) English was their
native or dominant language. The female:male ratio was 1.4 : 1. Their mean age was 20.3 years.
SAT Reading scores and ACT Reading scores were gathered from 22 and 13 participants
respectively. Two participants reported both. Overall, these participants were fluent reading
comprehenders in English, with SAT Reading scores ranging between 610 and 800 (M = 717.5,
SD = 52.2) and ACT Reading scores ranging between 27 and 36 (M = 32.3, SD = 2.8).
Relating to their L2 Chinese learning experiences, information was collected with regard
to formal learning hours and the amount of time spent reading in Chinese. The participants were
expected to have a command of about 1000 Chinese characters and 2000 vocabularies by the end
of the second year of their Chinese studies. From the 5th semester onward, the participants
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received 135 to 360 hours of instruction within 15 weeks in one semester. They spent an average
of 4.9 hours per week reading course-related materials in Chinese, and an average of 1.0 hour per
week reading course-unrelated materials in Chinese. The participants were also asked to report
whether they had received any explicit or implicit instruction of word problem solving (e.g.,
guessing unknown word meanings). 52.3% replied yes; 18.5% of these respondents had been
taught to analyze semantic and phonetical radicals to retrieve the sound and meaning of a
character, and only 9.2% reported that they had been informed of the strategy to combine
morphological and contextual information to guess unknown word meanings during reading.
Test Batteries
Five tasks, online and offline, were used, including L1 MA, L2 MA, L2 word meaning
inferencing, and L2 linguistic knowledge (constructed after Liu, 2013). Most of the tasks are
presented via the written modality, except for working memory, whose instructions were
provided orally. A post-test background questionnaire (adapted from Liu, 2013) was distributed
after these tasks were completed. A working memory task (Wechsler, 2008) was added for the
purpose of screening anomalous processing performance. The tasks were administered to
participants individually, by the first author in a quiet room. The time to complete all task was
around 50 minutes.
L1 MA. To reiterate, MA was operationalized as sensitivity to the abstract structure
of morphemes, which can be observed in such behaviors as morphological decomposition,
detecting legality/illegality of morphological composition, and judging legality/illegality of base
and morpheme combinations. An online segment shifting task (adapted from Feldman, Frost, &
Pnini, 1995) was used to measure this ability in L1 English. The participants were first presented
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with an affixed word (e.g., PLAYER), asked to strip the affix (e.g., ER) from the source word
(e.g., PLAYER), and attach the stripped segment to the target word (e.g., KICK). They then had
to name the resulting word (KICKER) aloud as rapidly as possible. A lapse between the onset of
the target word’s presentation and the participant’s voice onset was measured in ms together with
oral response accuracy. Shifting performance was compared between two word type conditions:
affixed words (N = 20) (e.g., PLAYER) and nonaffixed ones (N = 20) (e.g., GINGER), which
differ in the morphological structure (complex vs. simple) although sharing the same sequence of
letters (e.g., ER). The prediction was as follows: If the participants were more efficient in
segment shifting with affixed words (e.g., PLAYER), they were considered to be more sensitive
to the morphological structure of printed words. Efficiency scores based on affixed items were
taken as an index of L1 MA in subsequent analysis. Efficiency scores were based on adjusted
reaction times (ARTs), the mean reaction times (RTs) for correct items divided by accuracy.
ART is also known as the inverse efficiency score (Townsend & Ashby, 1983, as cited in
Grainger, Mathôt, & Vitu, 2014). According to Grainger et al. (2014), this measure is not
susceptible to speed/accuracy trade-offs. Because of the way ART was computed, it was
expected that it would have an inverse relationship with accuracy indices (i.e., the total number
of correct responses).
Of the MA measures we found in the literature (for a review, see also Ke & Xiao, 2015),
the segment-shifting task was the most appropriate for measuring MA as defined in this study
because it allowed us to compare efficiency in decomposing morphologically complex and
simple words, as well as manipulating affixes and letter strings.
L2 MA. The L2 MA task was parallel to that for L1 MA. The participants were
asked to strip a designated character (e.g., 多 duō , similar to the English prefix ‘multi-’ ) from
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the source three-character word (e.g., 多功能 duōgōngnéng, meaning ‘multi-function’) and
attach the segment to the target two-character word (e.g., 语言 yǔyán, meaning ‘language’). They
then had to name the resulting multi-character word (多语言 duō yǔyán, meaning ‘multilanguage’) aloud as quickly as possible. The stimuli were constructed in accordance with
language-specific properties. Two types of three-character source words (i.e., words formed with
affixoids versus words formed with nonaffixoids) were constructed for this task (as shown in
Table 1). The procedures for measuring L2 MA were similar to those for L1 MA. Responses due
to foreign accents or non-native-like tones in Chinese were not penalized. Again, ARTs for the
morphologically reliable condition were calculated and used as an indicator of L2 MA.
TABLE 1
Examples of L2 MA Items
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Example
Word type
k
Affixoid
16
Nonaffixoid
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美国式
非正式
Pinyin by
Meaning by
Character
Character
měi
beautiful
guó
country
shì
fēi
format
non-
zhèng
upright
shì
format
Whole Word Meaning
American-style
informal
L2 Word Meaning Inferencing. Following Mori & Nagy (1999), a paper-and-pencil
task was designed to measure L2 word meaning inferencing ability, which was defined as the
ability to infer the meaning of an unknown word embedded in a phrase or a short sentence. The
participants were asked to select the most appropriate word from four choices written in English:
(a) an integrated answer combining the meaning of the word’s morpheme(s) and the context, (b)
the meaning of the word’s morpheme(s) only, (c) the meaning of the context only, and (d) a
distractor/anomalous answer. For instance, the sentence 食物的新鲜度很重要 (shíwù de
xīnxiāndù hěn zhòngyào, literally meaning ‘Food’s freshness is very important’) had the target
unknown word 新鲜度 (xīnxiāndù, ‘freshness’) underlined, followed by four choices: (a)
freshness, (b) degree, (c) taste, and (d) temper. If L2 Chinese readers only resorted to their prior
print vocabulary knowledge, they might have selected (b) because 度 (dù) means ‘degree’. If
they used contextual information only, they might have selected (c). Option (d) was a distractor.
Only when they utilized both the word-internal morphological information and the word-external
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contextual meaning could they successfully infer the meaning of the underlined word and select
(a). Ease with which the meaning of a target word could be inferred was manipulated in two
ways: first by varying the morphological structure of the target word (i.e., affixoid versus
nonaffixoid) and second by varying familiarity with the base word (i.e., whether the participants
knew the meaning of the base word). All lexical items in the phrase/sentence were familiar to the
participants (from Bands One and Two /lowest levels in the GSCVCC), except for the target
unknown words. In total, there were 32 items and fourtwo types of unknown words in the task,
with eight16 items for each word type. One point was awarded for each correct answer. The L2
word meaning inferencing task was based on a revised version of a test piloted to estimate the
reliability (Cronbach’s α = 0.75) in a study with 45 L1 English speakers highly proficient in L2
Chinese (Ke, 2015). The participants’ familiarity with base words was confirmed in a post-test
word checklist, in which they were asked to report how well they knew the words and to provide
corresponding meanings in English.
L2 Linguistic Knowledge. The L2 linguistic knowledge test consisting of two
sections was administered; these sections were L2 vocabulary knowledge and L2 grammar
knowledge. The two sections were adopted from the paper-and-pencil language proficiency tasks
used by Liu (2013). The L2 vocabulary knowledge task had 60 items, including single-character
and two-character words selected from the New HSK (Chinese Proficiency Test). The L2
grammar knowledge task was designed to tap into the participants’ knowledge of the functions of
several grammatical elements, including word order, conjunctions, tense and aspect, and
rhetorical questions in Chinese. This task included 40 items. In both tasks, one point was
awarded for each correct answer.
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Working Memory. In order to gauge the unique contribution of MA, the effect of
working memory needed to be controlled for. Working memory was measured by a backward
digit span task adopted from the Wechsler Adult Intelligence Scale (Wechsler, 2008). The use of
a digit span task was necessary to reduce potential confounding by verbal span tasks (for a
review of working memory tests, see Juffs & Harrington, 2011).
Analysis Procedures
In response to the three research questions, the analysis plan was as follows. First, for the
purpose of examining whether L2 learners were sensitive to intraword structural complexity, a
Repeated Measures ANCOVA was carried out with L2 word meaning inferencing as the
dependent variable, morphological structure (affixoid versus nonaffixoid condition) and base
familiarity (familiar versus unfamiliar condition) as the within-subject independent variables,
and working memory as the covariate. Second, two rounds of hierarchical regression analyses
were carried out to investigate how MA made intra-lingual and inter-lingual contributions to L2
word meaning inferencing. The first round was conducted with L2 MA as the criterion variable,
L1 MA and L2 linguistic knowledge as the predictors, and working memory as the control
variable. The second round was run with L2 word meaning inferencing as the criterion variable,
L1 MA, L2 MA, and L2 linguistic knowledge as the predictors, as well as working memory as
the covariate. The entry orders of the predictors were altered to explore their relative
contributions. All statistical analyses were conducted using SPSS Version 23.
RESULTS
RQ1. Are L2 Learners Sensitive to the Morphological Structure of Unknown Multi-Character
Words?
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Table 2 includes the means and SDs for L2 Chinese word meaning inferencing. A
comparison was made across the four types of unknown words. As shown in Table 2, L2 readers
were more successful at inferring the meanings of complex unknown words formed with
affixoids, than those that were morphologically simple. In addition, the mean accuracy rate was
highest for complex words containing a familiar base. The reliability was 0.85 (Cronbach’s α).
TABLE 2
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Means and SDs for L1 MA, L2 MA, L2 Linguistic Knowledge, L2 Chinese Lexical Inferencing,
and Working Memory (N = 50)
Word Type
Mean
SD
EMA Suffix (ART)
831
141
EMA Suffix (RT)
809
124
EMA Suffix (Accuracy rate)
97.4%
5.7%
CMA Suffixoid (ART)
1488
612
CMA Suffixoid (RT)
1116
249
CMA Suffixoid (Accuracy rate)
75.0%
18.8%
Vocabulary knowledge
36.2 (60.3%)
9.8
(MSP = 60, Cronbach’s α =.92)
Grammar knowledge
26.7 (66.7%)
(16.3%)
6.5
(MSP = 40, Cronbach’s α = .82)
Vocabulary knowledge (z-score)
0.0
(16.4%)
1.0
Grammar knowledge (z-score)
0.0
1.0
L2 linguistic knowledge
0.0
1.0
L1 MA
L2 MA
L2 linguistic knowledge
(Vocabulary + grammar) (z-score)
L2 lexical inferencing (MSP = 32, Cronbach’s α = .85)
Nonaffixoid-familiar base
55.0%
1.4 (17.5%)
Nonaffixoid-unfamiliar base
51.2%
1.3 (16.3%)
Affixoid-familiar base
81.3%
1.5 (18.8%)
Affixoid-unfamiliar base
68.8%
1.3 (16.3%)
Working memory (MSP = 8)
6.0
1.3
Note. ART, adjusted reaction time; RT, reaction time; MSP, Maximum score possible.
The results of RM ANCOVA suggested that there were significant main effects of
morphological structure [F1 (1, 49) = 97.19, p < .001; F2 (1, 31) = 357.21, p = .003] and base
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familiarity [F1 (1, 49) = 14.33, p < .001; F2 (1, 31) = 56.25, p = .008], both by participants (F1
henceforth) and by items (F2 henceforth). The interactional effect between the two approached
significance level in the by-participants analysis, yet was insignificant in the by-items analysis
[F1 (1, 49) = 4.00, p = .051; F2 (1, 31) = 0.00, p = .986]. Post hoc analysis was conducted using
the Bonferroni method, which is best in terms of Type I error rates (Field, 2009). The results are
illustrated in Table 3 and Figure 1.
TABLE 3
Post Hoc Analysis of Effects of Morphological Structure and Base Familiarity
Word Type
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
Nonaffixoid-familiar
55.0%
2.40
50.2%
59.8%
Affixoid-familiar
81.3%
2.70
75.8%
86.7%
Nonaffixoid-unfamiliar
51.8%
2.26
47.2%
56.3%
Affixoid-unfamiliar
68.5%
2.38
63.7%
73.3%
Notes. Familiar, familiar base; unfamiliar, unfamiliar base.
FIGURE 1
Effects of Morphological Structure and Base Familiarity on L2 Word Meaning Inferencing
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100%
Simple
90%
Complex
80%
Accuracy rate
70%
60%
50%
40%
30%
20%
10%
0%
Familiar base
Unfamiliar base
The results illustrated that L2 Chinese learners were sensitive to the morphological
structure of unknown words, as they were able to use the information provided by affixoids when
inferring the meanings of unknown words, and that they more successful at meaning inferencing
with morphologically complex words than simple words. Also, they showed more reliance on the
meaning of the base word given that they were more accurate with novel words formed with
familiar bases. Notably, this performance was not totally random even when the target unknown
word did not provide sufficient information (i.e., words formed with nonaffixoids and unfamiliar
bases). The accuracy rate (51.2%) was well above chance level (25.0%). These findings
confirmed that the participants were best at making inferences when the target word was
morphologically complex and consisted of a familiar base. For subsequent analyses, the subscore
of the affixoid-familiar base condition was taken as an index of L2 word meaning inferencing.
RQ2. Does L1 MA Contribute to L2 MA Over and Above L2 Linguistic Knowledge?
To examine the inter-lingual relationship between L1 and L2 MA, as well as the potential
influence of L2 linguistic knowledge on the shareability of MA, a correlational analysis was
conducted first. As illustrated in Table 4, there were significant correlations between L1 and L2
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MA (r = 0.32), and between L2 linguistic knowledge and L2 MA (r = − 0.45). Notably, working
memory did not correlate significantly with L1 MA (r = − 0.15) or L2 MA (r = − 0.01).
TABLE 4
Bivariate Correlations among L1 MA, L2 MA, L2 Linguistic Knowledge, and Working Memory
(N = 50)
L1 MA
L2 MA
L2 LK
WM
L1 MA
L2 MA
L2 LK
WM
–
.32*
−.03
−.15
–
−.46**
−.01
–
.15
–
Notes. L2 LK, L2 linguistic knowledge; WM, working memory. *, p < .05; **, p < .01.
Hierarchical regression analyses were then conducted to investigate the relative
contributions of L1 MA and L2 linguistic knowledge to L2 MA. As indicated in the correlation
analysis in Table 4, there was no significant correlation between L1 MA and L2 linguistic
knowledge. Therefore, it was decided that there was no interaction between the two predictors.
The results are shown in Table 5: (a) When working memory was controlled for, L1 MA
contributed significantly to L2 MA (ΔR2 = 0.10, p = .024). L1 MA alone explained about 10% of
the outcome variance. When L2 linguistic knowledge was then entered, it was still a significant
predictor of L2 MA, explaining an addition of 23% of the variance (ΔR2 = 0.23, p = .000). (b)
When the entry order was switched, L2 linguistic knowledge was a significant predictor of L2
MA, explaining 21% of the variance (ΔR2 = 0.21, p = .001) beyond working memory. In
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addition, L1 MA made a unique contribution to L2 MA, explaining 12% of the variance beyond
working memory and L2 linguistic knowledge (ΔR2 = 0.12, p = .006).
TABLE 5
Hierarchical Regression Analysis With L2 MA as the Criterion Variable, L1 MA and L2
Linguistic Knowledge as the Predictors, and Working Memory as the Covariate (N = 50)
Model 1
Step Variable
B
R2
ΔR
ΔF
Sig.
2
1
Working memory
−.01
.00 .00
0.00
.947
2
Working memory
.04
.10 .10
5.47*
.024
L1 MA
.33*
Working memory
.11
.33 .23
15.66*** .000
L1 MA
.35**
R2
ΔF
Sig.
3
L2 linguistic knowledge −.48***
Model 2
Step Variable
B
ΔR
2
1
Working memory
−.01
.00 .00
0.00
.95
2
Working memory
.06
.21 .21
12.29**
.001
.33 .12
8.37**
.006
L2 linguistic knowledge −.46**
3
Working memory
.11
L2 linguistic knowledge −.48***
L1 MA
.35**
*, p < .05; **, p < .01; ***, p < .001.
In summary, the results indicate that L1 MA and L2 linguistic knowledge were each
significant and unique predictors of L2 MA, and that L2 linguistic knowledge did not affect the
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transfer facilitation effect of L1 MA. L1 MA alone explained about 10% of the variance in L2
MA.
RQ3. Does L2 MA Contribute to L2 Word Meaning Inference Over and Above L1 MA and L2
Linguistic Knowledge?
The relative contributions of L1 and L2 MA, and L2 linguistic knowledge to L2 word
meaning inferencing were first explored by bivariate correlation analysis. As shown in Table 6,
there was no significant correlation between L1 MA and L2 word meaning inferencing. In
contrast, there were significant correlations between L2 MA and L2 word meaning inferencing
(r = − 0.34, p = .017), and between L2 linguistic knowledge and L2 word meaning inferencing (r
= 0.40, p = .004).
TABLE 6
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Bivariate Correlations Among L1 MA, L2 MA, L2 Word Meaning Inferencing, L2 Linguistic
Knowledge, and Working Memory (N = 50)
L1 MA L2 MA
L1 MA
L2 MA
LI
LK
WM
–
LI
LK
WM
.32*
−.17
.03
−.15
–
−.34*
−.46**
−.01
–
.40**
−.06
–
.15
–
Notes. LI, L2 word meaning inferencing; WM, working memory. *, p < .05; **, p < .01.
Probably because L2 word meaning inferencing demands, language-specific knowledge,
L1 MA did not make any direct contribution; only L2 MA had a significant impact on L2 word
meaning inferencing. Because of the insignificant correlations between L1 MA and L2 word
meaning inferencing, L1 MA was not entered in any of the subsequent regression analyses.
Following the result presented previously, we then focused on the intra-lingual relationship
between L2 MA and L2 word meaning inferencing and whether L2 linguistic knowledge affects
their interrelationship. Hierarchical regression analysis was carried out with L2 word meaning
inferencing as the outcome, L2 MA and L2 linguistic knowledge as the predictors, and working
memory as the covariate. The entry orders of L2 MA and L2 linguistic knowledge were altered.
As shown in Table 7, when entered after working memory, L2 MA was a significant predictor,
explaining about 11% of the variance (ΔR2 = 0.11, p = .018). L2 linguistic knowledge was also a
significant predictor, explaining about 10% of the variance in addition to that of L2 MA and
working memory (ΔR2 = 0.10, p = .025). However, the effect of L2 MA was no longer significant
when entered after L2 linguistic knowledge and working memory (ΔR2 = 0.03, p = .227). L2
linguistic knowledge alone explained about 18% of the outcome variance (ΔR2 = 0.18, p = .002).
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TABLE 7
Hierarchical Regression With L2 Word Meaning Inferencing as the Criterion Variable, L2 MA
and L2 Linguistic Knowledge as the Predictors, and Working Memory as the Covariate (N = 50)
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Model 1
Step Variable
B
R2
ΔR
ΔF
Sig.
2
1
Working memory
−.06
.00 .00
0.19
.661
2
Working memory
−.07
.08 .11
6.00*
.018
L2 MA
−.34
.21 .10
5.38*
.025
R2
ΔF
Sig.
.661
*
3
Working memory
−.12
L2 MA
−.18
L2 linguistic
.35*
knowledge
Model 2
Step Variable
B
ΔR
2
1
Working memory
−.06
.00 .00
0.19
2
Working memory
−.13
.18 .18
10.34** .002
L2 linguistic
.43**
.21 .03
1.50
knowledge
3
Working memory
−.12
L2 linguistic
.35*
.227
knowledge
L2 MA
−.18
**, p < .01; *p < .05.
The results seem to suggest that L2 linguistic knowledge fully mediated the effect of L2
MA in the inferring of morphologically complex unknown words formed with familiar bases.
This mediation effect was tested by a bootstrapping method proposed by Hayes (2013) using
PROCESS (an add-on tool for SPSS). According to Hayes (2013), this method provides point
estimates and confidence intervals by which one can assess the significance or nonsignificance
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of a mediation effect; if zero does not fall between the resulting confidence intervals of the
bootstrapping method, one can confidently conclude that there is a significant mediation effect to
report. The regression results revealed an insignificant direct effect of L2 MA on L2 word
meaning inferencing (b = − 0.20, 95% CI [− 0.49, 0.10]), yet a significant indirect effect of L2
MA (b = − 0.14, 95% CI [− 0.32, − 0.03], R2 mediation effect size = 0.08). Hence, it was
confirmed that when target unknown words were morphologically complex and consisted of
familiar bases, both L2 MA and L2 linguistic knowledge were significant predictors, and L2 MA
contributed to L2 word meaning inferencing indirectly via L2 linguistic knowledge. L2 MA
accounted for about 8% of the variance of L2 word meaning inferencing (as illustrated in Figure
2).
FIGURE 2
Joint Contributions of L2 MA and L2 Linguistic Knowledge to L2 Word Meaning Inferencing
DISCUSSION
Summary of Findings
To recapitulate, this research examined how MA developed in learners’ L1 and L2 jointly
contribute to L2 word meaning inferencing. Specifically, it investigated whether L2 adult
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learners were sensitive to intraword morphological structure when guessing unknown word
meanings, if so, how L1 and L2 MA jointly contributed to L2 word meaning inferencing, and
how L2 linguistic knowledge affected their joint contributions. The major findings are as
follows. First, L1 English-speaking adult L2 learners of Chinese showed sensitivity to structural
complexity when guessing the meanings of novel multi-character words and were most accurate
when the target words were formed with affixoids and familiar bases. Second, in spite of the
typological distance between L1 English and L2 Chinese, L1 MA transferred and facilitated the
development of L2 MA; the transfer facilitation effect was not constrained by L2 linguistic
knowledge. Last, L1 and L2 MA did not contribute jointly to L2 word meaning inferencing.
Only L2 MA contributed to L2 word meaning inferencing. Nevertheless, when L2 linguistic
knowledge was present, the effect of L2 MA was no longer significant; L2 MA contributed
indirectly to L2 word meaning inferencing via L2 linguistic knowledge.
Intra-Lingual and Inter-Lingual Contributions of MA to L2 Word Meaning Inferencing
The findings of the present study can help to specify the unique role of MA in adult L2
word meaning inferencing. They not only corroborate previous research regarding how unknown
word properties affect the ease of inferencing (e.g., Hamada, 2014; Mori & Nagy, 1999), but are
also partially consistent with prior observation of the way in which MA affects L2 word meaning
inferencing ability (e.g., Park, 2004; Zhang et al., 2016). The evidence that L2 Chinese adult
learners were most accurate at guessing the meanings of unknown words formed with affixoids
and a familiar base suggests that, with only a few years’ exposure to college-level Chinese-as-aforeign-language education, L2 Chinese learners are able to develop intraword morphological
sensitivity in the target language. It is very likely that the adult L2 Chinese learners in this study
resorted to their prior literacy resources in L1 and applied them to L2 word meaning inferencing.
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Neither Hamada (2014) nor Mori and Nagy (1999) measured L1 MA or L2 MA, whereas, in this
research, L1 and L2 MA measurement was included. Moreover, the results have confirmed that,
regardless of typological distance between English and Chinese, L1 English MA transfers and
facilitates the development of L2 Chinese MA, beyond L2 linguistic knowledge and L2 working
memory.
Additionally, this study found that a significant intra-lingual effect of L2 MA on L2 word
meaning inferencing via L2 linguistic knowledge, yet no significant inter-lingual effect of L1
MA on L2 word meaning inferencing. In contrast, both Park (2004) and Zhang et al. (2016)
identified a direct effect of L2 MA and an indirect effect of L1 MA on L2 word meaning
inferencing via L2 MA. In Park’s (2004) study, she did not find any significant effect of L2
linguistic knowledge. The absence of any direct effect of L1 MA on L2 word meaning
inferencing in this study did not exclude potential task effect. This research adapted the paperand-pencil word meaning inferencing task from Mori and Nagy (1999) and used a multiplechoice format that manipulates the combination of morphological and contextual information.
This has been a predominant task used in L2 reading studies to examine the accuracy of word
meaning inferencing. While Park (2004) and Zhang et al. (2016) adopted a similar measure of
word meaning inferencing, unknown words were presented in isolation in a stand-alone format.
Therefore, successful unknown word meaning inferencing would not require a substantial
amount of L2 linguistic knowledge. In this research, however, L2 linguistic knowledge would
have played a critical role in selecting the appropriate word meaning consistent with surrounding
text.
It is also noteworthy that the influence of L2 linguistic knowledge does not appear to be
equivalent for the development of L2 MA and L2 word meaning inferencing, because different
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patterns were found regarding how L2 linguistic knowledge can affect the contributions of MA.
Three possible explanations are suggested. The first is that MA and successful word meaning
inferencing require different types and amounts of linguistic knowledge. The former, as a
structural analytical ability, is about form–form (grapheme–morpheme) mappings. Hence, it is
less constrained by L2 linguistic knowledge, measured by vocabulary and grammar in this
research. Successful word meaning inferencing in this study, on the other hand, would impose
operations that require the participants to integrate word-internal and contextual information
from surrounding words. It follows that L2 linguistic knowledge would have played a critical
role in selecting the appropriate word meaning consistent with surrounding text. Hence, it is not
surprising that the development of L2 word meaning inferencing entails more L2 languagespecific operations, and the contribution of MA can be indirect. Another explanation is task
effect, as mentioned previously. The L2 word meaning inferencing ability was tested in an
offline paper-and-pencil task, whereas L1 and L2 MA were measured by an online task that
recorded both speed and accuracy. It is unclear whether and how L1 and L2 MA jointly affect
the efficiency (both speed and accuracy) in guessing unknown word meanings during reading
(see an exception in Koda [2000], who used a semantic inconsistency detection task). Finally, the
participants’ bilingual and biliterate profiles differed from those in the research done by Park and
Zhang et al. (2016), which focused on child learners whose L1 and L2 were both alphabetic
languages. As mentioned earlier, Zhang et al. (2016) observed that L1 English MA accounted for
approximately 50% of the variance of L2 Malay MA. In
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Contributions of Morphological Awareness to Adult L2 Chinese Word Meaning Inferencing
SIHUI ECHO KE1 and KEIKO KODA2
1
Harbin Institute of Technology, Shenzhen, P. R. China, School of Humanities and Social
Science, G416, HIT Campus, Xili University Town, Shenzhen 518055, P. R. China Emails:
[email protected] or [email protected]
2
Carnegie Mellon University, Department of Modern Languages, 160 Baker Hall, 5000 Forbes
Avenue, Pittsburgh PA 15213 Email: [email protected]
ABSTRACT
The study examined the contributions of morphological awareness (MA) to second language
(L2) word meaning inferencing in English-speaking adult learners of L2 Chinese (N = 50). Three
research questions were posed: Are L2 learners sensitive to the morphological structure of
unknown multi-character words? Does first language (L1) MA contribute to L2 MA over and
above L2 linguistic knowledge? Does L2 MA contribute to L2 word meaning inference over and
above L1 MA and L2 linguistic knowledge? These questions were investigated through the use
of a set of experimental and paper-and-pencil measurements of the aforementioned L1 and L2
reading subskills and L2 linguistic knowledge, as well as working memory. Several significant
results were found. The L2 learners were sensitive to the morphological structure of multicharacter words and more successful in guessing the meanings of novel words containing
affixoids and familiar bases. L1 MA was found to transfer and facilitate the development of L2
MA over and above L2 linguistic knowledge. Subsequently, L2 MA contributed to L2 word
meaning inferencing indirectly through L2 linguistic knowledge. The discussion focuses on the
2
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intra-lingual and inter-lingual relationships among L1 MA, L2 MA, L2 linguistic knowledge,
and L2 word meaning inferencing in adult L2 reading development.
Keywords: word meaning inferencing; second language; morphological awareness; word
properties; Chinese
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Morphological awareness (MA henceforth) is often defined as learners’ sensitivity to the
morphological structure of printed words (Carlisle, 2000; Koda, 2000; Verhoeven & Perfetti,
2011). Previous research has found that the success of L2 word meaning inferencing depends on
a learner’s MA (e.g., Park, 2004; Zhang, Koda, & Leong, 2016) or structural complexity of the
target unknown word (e.g., Hamada, 2014; Mori & Nagy, 1999). For words to be learned
incidentally during reading, a learner must infer the meaning of an unfamiliar word based on the
information provided by the word and that afforded by the context. To do so, the learner must
have the abilities to analyze the internal structure of a word into its morphological constituents
and to construct the meaning of the word based on familiar morphological elements. Also, adult
L2 readers with limited L2 linguistic knowledge can compensate by using morphological
analysis to retrieve word meanings while reading (Parel, 2004). However, there is a lack of a
consensus regarding the extent and how MA makes intra-lingual and inter-lingual contributions
to L2 word meaning inferencing during reading. The goals of this study were three-fold: (a) to
investigate whether adult L2 learners are sensitive to morphological complexity; (b) to examine
the extent to which MA is related in L1 and L2 with learners whose L1 and L2 are typologically
distinct (i.e., L1 English and L2 Chinese); and (c) to explore how L1 and L2 MA contribute to
L2 word meaning inferencing, and how their joint contributions are affected by L2 linguistic
knowledge.
LITERATURE REVIEW
The Contributions of Morphological Awareness to L2 Word Meaning Inferencing
In this research, we adopted a component approach when examining the contributions of
MA to L2 word meaning inferencing in L2 reading. This approach essentially posits that reading
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entails a set of distinct, yet interdependent, mental operations (Carr & Levy, 1990). Following
Koda (2005, 2007), reading competencies in this study were taken to encompass metalinguistic
awareness (e.g., sensitivity to the abstract structure of language), linguistic knowledge
(knowledge of vocabulary and grammar), and reading subskills (e.g., word form analysis,
retrieving the pronunciation and meaning of known words, inferring the pronunciation and
meaning of unknown words). With regard to metalinguistic awareness, we focus on
morphological awareness. As the smallest functioning unit of a language, morphemes convey
rule-governed grammatical information and arbitrarily assigned functional information. MA, as
their abstract representation, comprises, at the minimum, the internal structure of morphemes in
words (the ability to see the structural difference between ‘incident’ and ‘insecure’), rules of
morpheme concatenation (e.g., prefix + root + suffix), and functional constraints on the
concatenation rules (e.g., the ability to choose a lexically appropriate nominalizer, such as ‘-ure’
for ‘close’ and ‘-ation’ for ‘flirt’) (Koda & Miller, 2017). Reflecting the involvement of
functional knowledge, MA is more varied and language-specific than phonological awareness,
and, thus, more linguistically demanding and dependent. A clear implication of the construct’s
linguistic dependency for L2 reading development is that the utility of L1 MA in the formation
of L2 MA could be constrained by both L2 linguistic knowledge and structural similarity
between the two languages involved. Similarly, the utility of L2 MA in word meaning inference
is also constrained by L2 linguistic knowledge.
In L2 reading, MA can make both intra-lingual and inter-lingual contributions to L2 word
meaning inferencing (e.g., Park, 2004; Zhang, 2012; Zhang, Koda, & Leong, 2015). From the
intra-lingual perspective, substantial evidence suggests that MA plays a dual role in language and
literacy development: (a) to facilitate the transition from oral to written communication for
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learners to map the sound of spoken words and morphemes onto the written symbols that encode
them (e.g., decoding); and (b) to promote an analytical and constructive approach to word
learning (e.g., word meaning inferencing) (Koda, 2000, 2005). From the inter-lingual
perspective, MA has a transfer facilitation effect across languages in second language (L2)
learners who are already literate in their first language (L1) (Ramirez, Chen, & Pasquarella,
2013; Zhang, 2012). However, evidence supporting the inter-lingual contribution of MA to L2
word meaning inferencing has just emerged in recent years (e.g., Park, 2004; Zhang, Koda, &
Leong, 2015). Previous research has investigated how MA facilitates successful inferencing of
unknown words in L2 and explored the interaction among learners’ L1 and L2 resources such as
L1 MA, L2 MA, L2 linguistic knowledge, and L2 word meaning inferencing ability. For
instance, Park’s (2004) study with Korean-speaking English language learners from grade 3 to 5
in the United States, found that L1 MA was related to L2 word meaning inference ability only
indirectly through L2 MA, and that L2 MA facilitated the development of L2 word meaning
inference ability beyond the effect of L2 linguistic knowledge (measured by a listening
comprehension test). A similar pattern was observed in the research by Zhang et al. (2016),
which adopted a longitudinal design. They investigated the impact of MA on the bilingual word
meaning inferencing ability of L1 English–L2 Malay bilingual children in Singapore, where
English is the medium of instruction, at two time points (grade 3 and grade 4). Their findings
suggested that, at Time One, L1 MA only had a significant indirect effect on L2 word meaning
inference via L2 MA; at Time Two, the contribution of L1 MA to L2 word meaning inferencing
was mediated jointly by L1 word meaning inferencing and L2 MA. Both studies seem to suggest
that L1 MA transfers and facilitates the development of L2 MA, which subsequently contributes
to L2 word meaning inferencing.
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The research discussed previously, however, has focused on learners whose L1 and L2
were alphabetic orthographies (for a categorization of various orthographies, see Chang, Plaut, &
Perfetti, 2016). According to previous L2 reading studies with two alphabetic languages, the
explanatory power of L1 and L2 resources in predicting L2 reading ability ranges from 44% to
51% (for a review, see Bernhardt, 2005). For example, in the study reported by Zhang et al.
(2016), L1 MA at Time One explained about 50% of the variance of L2 MA, and concurrently,
L1 word meaning inferencing and L2 MA explained around 35% of the variance of L2 word
meaning inferencing. To date, little is known about how and to what extent MA can affect L2
word meaning inferencing in learners whose L1 and L2 are typologically distant (see an
exception in Koda, 2000). Koda (2000) compared L2 learners’ performances in a semantic
judgment task that entailed the ability to integrate morphological and contextual information,
with two L1 groups (i.e., L1 Korean and L1 Chinese learners of English as a second language). It
was found that Chinese learners, whose L1 orthography is morphosyllabic, were more efficient
than Korean learners were in detecting semantic inconsistency between the information supplied
by the affix of the target word and that conveyed by the context in which the word is embedded.
This implies that L2 learners’ ability to integrate morphological and contextual information in
reading is affected by their L1 literacy experience. Another interesting finding in Koda’s (2000)
study was that when the target words were morphologically simple (e.g., ‘regime’), L1 Chinese
and L1 Korean groups performed similarly. Therefore, it seems that the successful use of
morphological information is also affected by the structural salience of unknown words.
Word Effect on L2 Word Meaning Inferencing
As posited by Nagy, Carlisle, & Goodwin (2014), the importance of MA in word
meaning inferencing can be explained by its capacity for enabling learners to segment unfamiliar
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words, particularly those made up of familiar morphological components, into their constituents.
Logic would suggest that the contribution of MA is greater with morphologically complex
unknown words, smaller with morphologically simple ones. This was supported by Mori and
Nagy (1999) and Hamada (2014) in their studies of word meaning inferencing in L2 Japanese.
Mori and Nagy (1999) conducted a study with English-speaking university learners of L2
Japanese at intermediate and pre-advanced levels, in which they asked the learners to infer the
meanings of novel semantically semi-transparent compound words in a sentence. Compound
words were comprised of familiar kanjis in three conditions (i.e., kanji/word-internal information
only, contextual information only, and the combination of kanji and contextual information).
Important insights have been drawn from their findings: (a) more than half of the L2 Japanese
learners tended to integrate both word-internal and contextual information, and the integrators
were more successful in lexical inferencing than were the non-integrators; (b) the use of wordinternal information and the use of contextual information were not correlated; and (c) the use of
contextual clues depended on L2 proficiency level, yet the use of word-internal information did
not. In a recent study with a similar design, Hamada (2014) expanded the scope by investigating
whether the use of information, morphological or contextual, in lexical inferencing depended
upon the learners’ L2 proficiency and the reliability of the morphological information with
university learners of L2 English at four proficiency levels (beginning, intermediate, highintermediate, and advanced). She found that the choice of information was influenced by the
morphological reliability condition because participants performed similarly across proficiency
levels in the morphology reliable condition but differently in the morphology unreliable
condition. The findings reported by Mori and Nagy (1999) and Hamada (2014) indicated that
there is morphological facilitation in L2 word meaning inferencing, and that the effect of word-
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internal morphological structure is independent from that of L2 linguistic knowledge. Notably,
both studies were based on learners’ overall L2 proficiency at the instructional levels and did not
measure L2 linguistic knowledge. As well, neither study measured the participants’ MA.
To recapitulate, the current study aimed to investigate the contribution of MA to L2 word
meaning inferencing in L1 English-speaking learners of L2 (Mandarin) Chinese, whose L1 and
L2 are typologically distant. This section defines the category ‘word’ and describes the
characteristics of grapheme–morpheme mappings in Chinese printed words. For clarity,
comparisons between English orthography and Chinese orthography are also provided.
Morphological Properties in Chinese
In this research, a word is defined as “an independent occupant of a syntactic form class
slot” (Packard, 2000, p.12). Words in written English are salient because they are separated by
spaces in written texts. English orthography is morphophonemic in that the basic grapheme unit
is an alphabetic letter, but printed words encode both phonemic and morphemic information.
When there is inconsistency in representing phonemes in English words, it is often explained by
the tendency to preserve morphological information in their graphemes (e.g., ‘heal’/‘health,’
‘cats’/‘dogs,’ as cited in Frost, 2012).
In Chinese, written texts are not word-based but character-based, with no spaces inserted
between words. Chinese orthography is morphosyllabic in that the basic grapheme unit is a
character, which, in most cases, maps onto a morpheme that corresponds to a single syllable.
While most characters have their own meanings and can be used independently, the combination
of two or more characters can also form new words. Around 94% of printed words in Chinese
are multi-character words (Lexicon of Common Words in Contemporary Chinese Research
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Team, 2008, as cited in Li et al., 2014). It should be noted that the meaning of each component
character can be either closely related or totally unrelated to the whole word meaning (Li &
Thompson, 1981; as cited in Li & McBride–Chang, 2014). For example, each character in the
two-character word 花生 has its own meanings (‘flower’ for 花 and ‘give birth to’ for 生) and
can be used functionally and independently; yet the word 花生 is a lexicalized expression
mapping onto one morpheme, meaning ‘peanut,’ which cannot be inferred directly from the two
component characters (at least synchronically).
Based on the earlier review, we cannot assume that all multi-character words in Chinese
are morphologically complex, or ‘compound’ words, as cited widely in the extant literature about
Chinese morphology. It is important to differentiate morphologically complex multi-character
words from morphologically simple ones, which would help with an investigation of how
learners of Chinese grasp the fundamental principle on which graphemes map onto morphemes
and to extract morphological information from printed words. In skilled English reading, this
morphological analysis is realized through dissecting strings of letters from a morphologically
complex word and segmenting it into smaller meaningful chunks (e.g., to segment ‘heal’ from
‘health’). In Chinese, intraword segmentation might seem unnecessary because written Chinese
is separated graphically, with each character equally spaced in written texts. Nevertheless,
emerging evidence has suggested that segmentation and chunking are important in reading
Chinese as well. Thus, Zhang et al. (2014) posited that Chinese readers’ sensitivity to
morphological productivity—the extent to which words are formed with certain morphemes
appearing in certain positions—is crucial in identifying known and unknown words, as well as
phrases, when they read character strings with no spaces inserted between words. In a statistical
analysis of two-character words in the Grading Syllabus for Chinese Vocabulary and Chinese
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Characters (GSCVCC ) (Chinese Proficiency Test Center, 2001), Xing (2006) found that only
37% of the characters had fixed positions in forming words, and 63% were not positionally
constrained. To the best of our knowledge, Zeng’s (2008) database is the only available resource
that provides a list of productive word formation morphemes/affixoids in Chinese, accompanied
by morpheme frequency. His database is critical because it provides an explicit description of
four inclusion criteria: (a) productivity, (b) position stability, (c) desemantization (with
weakened lexical meaning), and (d) boundness (cannot be used as an independent lexical unit).
The database includes 34 prefixoids (productive morphemes with fixed positions at the
beginning of multi-character words) and 54 suffixoids (productive morphemes with fixed
positions at the end of multi-character words).
To sum up, there are both commonalities and differences regarding intraword structural
sensitivity in English and Chinese. Chinese and English orthographies thus have at least one
common property, that is, graphemes encode both phonology and morphology; and therefore, the
ability to analyze intraword morphological structure in the two languages reflects readers’
sensitivity to the grapheme–morpheme relationships. In view of the complex interaction among
L1 MA, L2 MA, L2 linguistic knowledge, and L2 word meaning inferencing, and the potential
influence of word properties on the ease with word meaning inferencing, this study aimed to test
three hypotheses: (a) L2 learners are sensitive to the morphological structure of unknown multicharacter words; (b) L1 morphological awareness facilitates the development of L2
morphological awareness; the L1 facilitation effect is affected by L2 linguistic knowledge; (c)
L1 and L2 morphological awareness jointly contribute to L2 word meaning inferencing; the joint
contributions are affected by L2 linguistic knowledge. These hypotheses were translated into
three research questions.
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RQ1.
Are L2 learners sensitive to the morphological structure of unknown multi-character
words?
RQ2. Does L1 MA contribute to L2 MA over and above L2 linguistic knowledge?
RQ3. Does L2 MA contribute to L2 word meaning inference over and above L1 MA and L2
linguistic knowledge?
METHOD
Participants
Fifty English-speaking learners of Chinese as a second language from six American
universities participated in the study. At the time of data collection, they were enrolled in the
fifth or later semesters of their courses. Recruitment criteria included: (a) participants had a
minimum of two years’ experience of learning Chinese; (b) reading was one of the major
learning and instructional components in their affiliated programs; and (c) English was their
native or dominant language. The female:male ratio was 1.4 : 1. Their mean age was 20.3 years.
SAT Reading scores and ACT Reading scores were gathered from 22 and 13 participants
respectively. Two participants reported both. Overall, these participants were fluent reading
comprehenders in English, with SAT Reading scores ranging between 610 and 800 (M = 717.5,
SD = 52.2) and ACT Reading scores ranging between 27 and 36 (M = 32.3, SD = 2.8).
Relating to their L2 Chinese learning experiences, information was collected with regard
to formal learning hours and the amount of time spent reading in Chinese. The participants were
expected to have a command of about 1000 Chinese characters and 2000 vocabularies by the end
of the second year of their Chinese studies. From the 5th semester onward, the participants
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received 135 to 360 hours of instruction within 15 weeks in one semester. They spent an average
of 4.9 hours per week reading course-related materials in Chinese, and an average of 1.0 hour per
week reading course-unrelated materials in Chinese. The participants were also asked to report
whether they had received any explicit or implicit instruction of word problem solving (e.g.,
guessing unknown word meanings). 52.3% replied yes; 18.5% of these respondents had been
taught to analyze semantic and phonetical radicals to retrieve the sound and meaning of a
character, and only 9.2% reported that they had been informed of the strategy to combine
morphological and contextual information to guess unknown word meanings during reading.
Test Batteries
Five tasks, online and offline, were used, including L1 MA, L2 MA, L2 word meaning
inferencing, and L2 linguistic knowledge (constructed after Liu, 2013). Most of the tasks are
presented via the written modality, except for working memory, whose instructions were
provided orally. A post-test background questionnaire (adapted from Liu, 2013) was distributed
after these tasks were completed. A working memory task (Wechsler, 2008) was added for the
purpose of screening anomalous processing performance. The tasks were administered to
participants individually, by the first author in a quiet room. The time to complete all task was
around 50 minutes.
L1 MA. To reiterate, MA was operationalized as sensitivity to the abstract structure
of morphemes, which can be observed in such behaviors as morphological decomposition,
detecting legality/illegality of morphological composition, and judging legality/illegality of base
and morpheme combinations. An online segment shifting task (adapted from Feldman, Frost, &
Pnini, 1995) was used to measure this ability in L1 English. The participants were first presented
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with an affixed word (e.g., PLAYER), asked to strip the affix (e.g., ER) from the source word
(e.g., PLAYER), and attach the stripped segment to the target word (e.g., KICK). They then had
to name the resulting word (KICKER) aloud as rapidly as possible. A lapse between the onset of
the target word’s presentation and the participant’s voice onset was measured in ms together with
oral response accuracy. Shifting performance was compared between two word type conditions:
affixed words (N = 20) (e.g., PLAYER) and nonaffixed ones (N = 20) (e.g., GINGER), which
differ in the morphological structure (complex vs. simple) although sharing the same sequence of
letters (e.g., ER). The prediction was as follows: If the participants were more efficient in
segment shifting with affixed words (e.g., PLAYER), they were considered to be more sensitive
to the morphological structure of printed words. Efficiency scores based on affixed items were
taken as an index of L1 MA in subsequent analysis. Efficiency scores were based on adjusted
reaction times (ARTs), the mean reaction times (RTs) for correct items divided by accuracy.
ART is also known as the inverse efficiency score (Townsend & Ashby, 1983, as cited in
Grainger, Mathôt, & Vitu, 2014). According to Grainger et al. (2014), this measure is not
susceptible to speed/accuracy trade-offs. Because of the way ART was computed, it was
expected that it would have an inverse relationship with accuracy indices (i.e., the total number
of correct responses).
Of the MA measures we found in the literature (for a review, see also Ke & Xiao, 2015),
the segment-shifting task was the most appropriate for measuring MA as defined in this study
because it allowed us to compare efficiency in decomposing morphologically complex and
simple words, as well as manipulating affixes and letter strings.
L2 MA. The L2 MA task was parallel to that for L1 MA. The participants were
asked to strip a designated character (e.g., 多 duō , similar to the English prefix ‘multi-’ ) from
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the source three-character word (e.g., 多功能 duōgōngnéng, meaning ‘multi-function’) and
attach the segment to the target two-character word (e.g., 语言 yǔyán, meaning ‘language’). They
then had to name the resulting multi-character word (多语言 duō yǔyán, meaning ‘multilanguage’) aloud as quickly as possible. The stimuli were constructed in accordance with
language-specific properties. Two types of three-character source words (i.e., words formed with
affixoids versus words formed with nonaffixoids) were constructed for this task (as shown in
Table 1). The procedures for measuring L2 MA were similar to those for L1 MA. Responses due
to foreign accents or non-native-like tones in Chinese were not penalized. Again, ARTs for the
morphologically reliable condition were calculated and used as an indicator of L2 MA.
TABLE 1
Examples of L2 MA Items
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Example
Word type
k
Affixoid
16
Nonaffixoid
16
美国式
非正式
Pinyin by
Meaning by
Character
Character
měi
beautiful
guó
country
shì
fēi
format
non-
zhèng
upright
shì
format
Whole Word Meaning
American-style
informal
L2 Word Meaning Inferencing. Following Mori & Nagy (1999), a paper-and-pencil
task was designed to measure L2 word meaning inferencing ability, which was defined as the
ability to infer the meaning of an unknown word embedded in a phrase or a short sentence. The
participants were asked to select the most appropriate word from four choices written in English:
(a) an integrated answer combining the meaning of the word’s morpheme(s) and the context, (b)
the meaning of the word’s morpheme(s) only, (c) the meaning of the context only, and (d) a
distractor/anomalous answer. For instance, the sentence 食物的新鲜度很重要 (shíwù de
xīnxiāndù hěn zhòngyào, literally meaning ‘Food’s freshness is very important’) had the target
unknown word 新鲜度 (xīnxiāndù, ‘freshness’) underlined, followed by four choices: (a)
freshness, (b) degree, (c) taste, and (d) temper. If L2 Chinese readers only resorted to their prior
print vocabulary knowledge, they might have selected (b) because 度 (dù) means ‘degree’. If
they used contextual information only, they might have selected (c). Option (d) was a distractor.
Only when they utilized both the word-internal morphological information and the word-external
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contextual meaning could they successfully infer the meaning of the underlined word and select
(a). Ease with which the meaning of a target word could be inferred was manipulated in two
ways: first by varying the morphological structure of the target word (i.e., affixoid versus
nonaffixoid) and second by varying familiarity with the base word (i.e., whether the participants
knew the meaning of the base word). All lexical items in the phrase/sentence were familiar to the
participants (from Bands One and Two /lowest levels in the GSCVCC), except for the target
unknown words. In total, there were 32 items and fourtwo types of unknown words in the task,
with eight16 items for each word type. One point was awarded for each correct answer. The L2
word meaning inferencing task was based on a revised version of a test piloted to estimate the
reliability (Cronbach’s α = 0.75) in a study with 45 L1 English speakers highly proficient in L2
Chinese (Ke, 2015). The participants’ familiarity with base words was confirmed in a post-test
word checklist, in which they were asked to report how well they knew the words and to provide
corresponding meanings in English.
L2 Linguistic Knowledge. The L2 linguistic knowledge test consisting of two
sections was administered; these sections were L2 vocabulary knowledge and L2 grammar
knowledge. The two sections were adopted from the paper-and-pencil language proficiency tasks
used by Liu (2013). The L2 vocabulary knowledge task had 60 items, including single-character
and two-character words selected from the New HSK (Chinese Proficiency Test). The L2
grammar knowledge task was designed to tap into the participants’ knowledge of the functions of
several grammatical elements, including word order, conjunctions, tense and aspect, and
rhetorical questions in Chinese. This task included 40 items. In both tasks, one point was
awarded for each correct answer.
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Working Memory. In order to gauge the unique contribution of MA, the effect of
working memory needed to be controlled for. Working memory was measured by a backward
digit span task adopted from the Wechsler Adult Intelligence Scale (Wechsler, 2008). The use of
a digit span task was necessary to reduce potential confounding by verbal span tasks (for a
review of working memory tests, see Juffs & Harrington, 2011).
Analysis Procedures
In response to the three research questions, the analysis plan was as follows. First, for the
purpose of examining whether L2 learners were sensitive to intraword structural complexity, a
Repeated Measures ANCOVA was carried out with L2 word meaning inferencing as the
dependent variable, morphological structure (affixoid versus nonaffixoid condition) and base
familiarity (familiar versus unfamiliar condition) as the within-subject independent variables,
and working memory as the covariate. Second, two rounds of hierarchical regression analyses
were carried out to investigate how MA made intra-lingual and inter-lingual contributions to L2
word meaning inferencing. The first round was conducted with L2 MA as the criterion variable,
L1 MA and L2 linguistic knowledge as the predictors, and working memory as the control
variable. The second round was run with L2 word meaning inferencing as the criterion variable,
L1 MA, L2 MA, and L2 linguistic knowledge as the predictors, as well as working memory as
the covariate. The entry orders of the predictors were altered to explore their relative
contributions. All statistical analyses were conducted using SPSS Version 23.
RESULTS
RQ1. Are L2 Learners Sensitive to the Morphological Structure of Unknown Multi-Character
Words?
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Table 2 includes the means and SDs for L2 Chinese word meaning inferencing. A
comparison was made across the four types of unknown words. As shown in Table 2, L2 readers
were more successful at inferring the meanings of complex unknown words formed with
affixoids, than those that were morphologically simple. In addition, the mean accuracy rate was
highest for complex words containing a familiar base. The reliability was 0.85 (Cronbach’s α).
TABLE 2
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Means and SDs for L1 MA, L2 MA, L2 Linguistic Knowledge, L2 Chinese Lexical Inferencing,
and Working Memory (N = 50)
Word Type
Mean
SD
EMA Suffix (ART)
831
141
EMA Suffix (RT)
809
124
EMA Suffix (Accuracy rate)
97.4%
5.7%
CMA Suffixoid (ART)
1488
612
CMA Suffixoid (RT)
1116
249
CMA Suffixoid (Accuracy rate)
75.0%
18.8%
Vocabulary knowledge
36.2 (60.3%)
9.8
(MSP = 60, Cronbach’s α =.92)
Grammar knowledge
26.7 (66.7%)
(16.3%)
6.5
(MSP = 40, Cronbach’s α = .82)
Vocabulary knowledge (z-score)
0.0
(16.4%)
1.0
Grammar knowledge (z-score)
0.0
1.0
L2 linguistic knowledge
0.0
1.0
L1 MA
L2 MA
L2 linguistic knowledge
(Vocabulary + grammar) (z-score)
L2 lexical inferencing (MSP = 32, Cronbach’s α = .85)
Nonaffixoid-familiar base
55.0%
1.4 (17.5%)
Nonaffixoid-unfamiliar base
51.2%
1.3 (16.3%)
Affixoid-familiar base
81.3%
1.5 (18.8%)
Affixoid-unfamiliar base
68.8%
1.3 (16.3%)
Working memory (MSP = 8)
6.0
1.3
Note. ART, adjusted reaction time; RT, reaction time; MSP, Maximum score possible.
The results of RM ANCOVA suggested that there were significant main effects of
morphological structure [F1 (1, 49) = 97.19, p < .001; F2 (1, 31) = 357.21, p = .003] and base
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familiarity [F1 (1, 49) = 14.33, p < .001; F2 (1, 31) = 56.25, p = .008], both by participants (F1
henceforth) and by items (F2 henceforth). The interactional effect between the two approached
significance level in the by-participants analysis, yet was insignificant in the by-items analysis
[F1 (1, 49) = 4.00, p = .051; F2 (1, 31) = 0.00, p = .986]. Post hoc analysis was conducted using
the Bonferroni method, which is best in terms of Type I error rates (Field, 2009). The results are
illustrated in Table 3 and Figure 1.
TABLE 3
Post Hoc Analysis of Effects of Morphological Structure and Base Familiarity
Word Type
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
Nonaffixoid-familiar
55.0%
2.40
50.2%
59.8%
Affixoid-familiar
81.3%
2.70
75.8%
86.7%
Nonaffixoid-unfamiliar
51.8%
2.26
47.2%
56.3%
Affixoid-unfamiliar
68.5%
2.38
63.7%
73.3%
Notes. Familiar, familiar base; unfamiliar, unfamiliar base.
FIGURE 1
Effects of Morphological Structure and Base Familiarity on L2 Word Meaning Inferencing
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100%
Simple
90%
Complex
80%
Accuracy rate
70%
60%
50%
40%
30%
20%
10%
0%
Familiar base
Unfamiliar base
The results illustrated that L2 Chinese learners were sensitive to the morphological
structure of unknown words, as they were able to use the information provided by affixoids when
inferring the meanings of unknown words, and that they more successful at meaning inferencing
with morphologically complex words than simple words. Also, they showed more reliance on the
meaning of the base word given that they were more accurate with novel words formed with
familiar bases. Notably, this performance was not totally random even when the target unknown
word did not provide sufficient information (i.e., words formed with nonaffixoids and unfamiliar
bases). The accuracy rate (51.2%) was well above chance level (25.0%). These findings
confirmed that the participants were best at making inferences when the target word was
morphologically complex and consisted of a familiar base. For subsequent analyses, the subscore
of the affixoid-familiar base condition was taken as an index of L2 word meaning inferencing.
RQ2. Does L1 MA Contribute to L2 MA Over and Above L2 Linguistic Knowledge?
To examine the inter-lingual relationship between L1 and L2 MA, as well as the potential
influence of L2 linguistic knowledge on the shareability of MA, a correlational analysis was
conducted first. As illustrated in Table 4, there were significant correlations between L1 and L2
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MA (r = 0.32), and between L2 linguistic knowledge and L2 MA (r = − 0.45). Notably, working
memory did not correlate significantly with L1 MA (r = − 0.15) or L2 MA (r = − 0.01).
TABLE 4
Bivariate Correlations among L1 MA, L2 MA, L2 Linguistic Knowledge, and Working Memory
(N = 50)
L1 MA
L2 MA
L2 LK
WM
L1 MA
L2 MA
L2 LK
WM
–
.32*
−.03
−.15
–
−.46**
−.01
–
.15
–
Notes. L2 LK, L2 linguistic knowledge; WM, working memory. *, p < .05; **, p < .01.
Hierarchical regression analyses were then conducted to investigate the relative
contributions of L1 MA and L2 linguistic knowledge to L2 MA. As indicated in the correlation
analysis in Table 4, there was no significant correlation between L1 MA and L2 linguistic
knowledge. Therefore, it was decided that there was no interaction between the two predictors.
The results are shown in Table 5: (a) When working memory was controlled for, L1 MA
contributed significantly to L2 MA (ΔR2 = 0.10, p = .024). L1 MA alone explained about 10% of
the outcome variance. When L2 linguistic knowledge was then entered, it was still a significant
predictor of L2 MA, explaining an addition of 23% of the variance (ΔR2 = 0.23, p = .000). (b)
When the entry order was switched, L2 linguistic knowledge was a significant predictor of L2
MA, explaining 21% of the variance (ΔR2 = 0.21, p = .001) beyond working memory. In
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addition, L1 MA made a unique contribution to L2 MA, explaining 12% of the variance beyond
working memory and L2 linguistic knowledge (ΔR2 = 0.12, p = .006).
TABLE 5
Hierarchical Regression Analysis With L2 MA as the Criterion Variable, L1 MA and L2
Linguistic Knowledge as the Predictors, and Working Memory as the Covariate (N = 50)
Model 1
Step Variable
B
R2
ΔR
ΔF
Sig.
2
1
Working memory
−.01
.00 .00
0.00
.947
2
Working memory
.04
.10 .10
5.47*
.024
L1 MA
.33*
Working memory
.11
.33 .23
15.66*** .000
L1 MA
.35**
R2
ΔF
Sig.
3
L2 linguistic knowledge −.48***
Model 2
Step Variable
B
ΔR
2
1
Working memory
−.01
.00 .00
0.00
.95
2
Working memory
.06
.21 .21
12.29**
.001
.33 .12
8.37**
.006
L2 linguistic knowledge −.46**
3
Working memory
.11
L2 linguistic knowledge −.48***
L1 MA
.35**
*, p < .05; **, p < .01; ***, p < .001.
In summary, the results indicate that L1 MA and L2 linguistic knowledge were each
significant and unique predictors of L2 MA, and that L2 linguistic knowledge did not affect the
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transfer facilitation effect of L1 MA. L1 MA alone explained about 10% of the variance in L2
MA.
RQ3. Does L2 MA Contribute to L2 Word Meaning Inference Over and Above L1 MA and L2
Linguistic Knowledge?
The relative contributions of L1 and L2 MA, and L2 linguistic knowledge to L2 word
meaning inferencing were first explored by bivariate correlation analysis. As shown in Table 6,
there was no significant correlation between L1 MA and L2 word meaning inferencing. In
contrast, there were significant correlations between L2 MA and L2 word meaning inferencing
(r = − 0.34, p = .017), and between L2 linguistic knowledge and L2 word meaning inferencing (r
= 0.40, p = .004).
TABLE 6
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Bivariate Correlations Among L1 MA, L2 MA, L2 Word Meaning Inferencing, L2 Linguistic
Knowledge, and Working Memory (N = 50)
L1 MA L2 MA
L1 MA
L2 MA
LI
LK
WM
–
LI
LK
WM
.32*
−.17
.03
−.15
–
−.34*
−.46**
−.01
–
.40**
−.06
–
.15
–
Notes. LI, L2 word meaning inferencing; WM, working memory. *, p < .05; **, p < .01.
Probably because L2 word meaning inferencing demands, language-specific knowledge,
L1 MA did not make any direct contribution; only L2 MA had a significant impact on L2 word
meaning inferencing. Because of the insignificant correlations between L1 MA and L2 word
meaning inferencing, L1 MA was not entered in any of the subsequent regression analyses.
Following the result presented previously, we then focused on the intra-lingual relationship
between L2 MA and L2 word meaning inferencing and whether L2 linguistic knowledge affects
their interrelationship. Hierarchical regression analysis was carried out with L2 word meaning
inferencing as the outcome, L2 MA and L2 linguistic knowledge as the predictors, and working
memory as the covariate. The entry orders of L2 MA and L2 linguistic knowledge were altered.
As shown in Table 7, when entered after working memory, L2 MA was a significant predictor,
explaining about 11% of the variance (ΔR2 = 0.11, p = .018). L2 linguistic knowledge was also a
significant predictor, explaining about 10% of the variance in addition to that of L2 MA and
working memory (ΔR2 = 0.10, p = .025). However, the effect of L2 MA was no longer significant
when entered after L2 linguistic knowledge and working memory (ΔR2 = 0.03, p = .227). L2
linguistic knowledge alone explained about 18% of the outcome variance (ΔR2 = 0.18, p = .002).
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TABLE 7
Hierarchical Regression With L2 Word Meaning Inferencing as the Criterion Variable, L2 MA
and L2 Linguistic Knowledge as the Predictors, and Working Memory as the Covariate (N = 50)
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Model 1
Step Variable
B
R2
ΔR
ΔF
Sig.
2
1
Working memory
−.06
.00 .00
0.19
.661
2
Working memory
−.07
.08 .11
6.00*
.018
L2 MA
−.34
.21 .10
5.38*
.025
R2
ΔF
Sig.
.661
*
3
Working memory
−.12
L2 MA
−.18
L2 linguistic
.35*
knowledge
Model 2
Step Variable
B
ΔR
2
1
Working memory
−.06
.00 .00
0.19
2
Working memory
−.13
.18 .18
10.34** .002
L2 linguistic
.43**
.21 .03
1.50
knowledge
3
Working memory
−.12
L2 linguistic
.35*
.227
knowledge
L2 MA
−.18
**, p < .01; *p < .05.
The results seem to suggest that L2 linguistic knowledge fully mediated the effect of L2
MA in the inferring of morphologically complex unknown words formed with familiar bases.
This mediation effect was tested by a bootstrapping method proposed by Hayes (2013) using
PROCESS (an add-on tool for SPSS). According to Hayes (2013), this method provides point
estimates and confidence intervals by which one can assess the significance or nonsignificance
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of a mediation effect; if zero does not fall between the resulting confidence intervals of the
bootstrapping method, one can confidently conclude that there is a significant mediation effect to
report. The regression results revealed an insignificant direct effect of L2 MA on L2 word
meaning inferencing (b = − 0.20, 95% CI [− 0.49, 0.10]), yet a significant indirect effect of L2
MA (b = − 0.14, 95% CI [− 0.32, − 0.03], R2 mediation effect size = 0.08). Hence, it was
confirmed that when target unknown words were morphologically complex and consisted of
familiar bases, both L2 MA and L2 linguistic knowledge were significant predictors, and L2 MA
contributed to L2 word meaning inferencing indirectly via L2 linguistic knowledge. L2 MA
accounted for about 8% of the variance of L2 word meaning inferencing (as illustrated in Figure
2).
FIGURE 2
Joint Contributions of L2 MA and L2 Linguistic Knowledge to L2 Word Meaning Inferencing
DISCUSSION
Summary of Findings
To recapitulate, this research examined how MA developed in learners’ L1 and L2 jointly
contribute to L2 word meaning inferencing. Specifically, it investigated whether L2 adult
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learners were sensitive to intraword morphological structure when guessing unknown word
meanings, if so, how L1 and L2 MA jointly contributed to L2 word meaning inferencing, and
how L2 linguistic knowledge affected their joint contributions. The major findings are as
follows. First, L1 English-speaking adult L2 learners of Chinese showed sensitivity to structural
complexity when guessing the meanings of novel multi-character words and were most accurate
when the target words were formed with affixoids and familiar bases. Second, in spite of the
typological distance between L1 English and L2 Chinese, L1 MA transferred and facilitated the
development of L2 MA; the transfer facilitation effect was not constrained by L2 linguistic
knowledge. Last, L1 and L2 MA did not contribute jointly to L2 word meaning inferencing.
Only L2 MA contributed to L2 word meaning inferencing. Nevertheless, when L2 linguistic
knowledge was present, the effect of L2 MA was no longer significant; L2 MA contributed
indirectly to L2 word meaning inferencing via L2 linguistic knowledge.
Intra-Lingual and Inter-Lingual Contributions of MA to L2 Word Meaning Inferencing
The findings of the present study can help to specify the unique role of MA in adult L2
word meaning inferencing. They not only corroborate previous research regarding how unknown
word properties affect the ease of inferencing (e.g., Hamada, 2014; Mori & Nagy, 1999), but are
also partially consistent with prior observation of the way in which MA affects L2 word meaning
inferencing ability (e.g., Park, 2004; Zhang et al., 2016). The evidence that L2 Chinese adult
learners were most accurate at guessing the meanings of unknown words formed with affixoids
and a familiar base suggests that, with only a few years’ exposure to college-level Chinese-as-aforeign-language education, L2 Chinese learners are able to develop intraword morphological
sensitivity in the target language. It is very likely that the adult L2 Chinese learners in this study
resorted to their prior literacy resources in L1 and applied them to L2 word meaning inferencing.
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Neither Hamada (2014) nor Mori and Nagy (1999) measured L1 MA or L2 MA, whereas, in this
research, L1 and L2 MA measurement was included. Moreover, the results have confirmed that,
regardless of typological distance between English and Chinese, L1 English MA transfers and
facilitates the development of L2 Chinese MA, beyond L2 linguistic knowledge and L2 working
memory.
Additionally, this study found that a significant intra-lingual effect of L2 MA on L2 word
meaning inferencing via L2 linguistic knowledge, yet no significant inter-lingual effect of L1
MA on L2 word meaning inferencing. In contrast, both Park (2004) and Zhang et al. (2016)
identified a direct effect of L2 MA and an indirect effect of L1 MA on L2 word meaning
inferencing via L2 MA. In Park’s (2004) study, she did not find any significant effect of L2
linguistic knowledge. The absence of any direct effect of L1 MA on L2 word meaning
inferencing in this study did not exclude potential task effect. This research adapted the paperand-pencil word meaning inferencing task from Mori and Nagy (1999) and used a multiplechoice format that manipulates the combination of morphological and contextual information.
This has been a predominant task used in L2 reading studies to examine the accuracy of word
meaning inferencing. While Park (2004) and Zhang et al. (2016) adopted a similar measure of
word meaning inferencing, unknown words were presented in isolation in a stand-alone format.
Therefore, successful unknown word meaning inferencing would not require a substantial
amount of L2 linguistic knowledge. In this research, however, L2 linguistic knowledge would
have played a critical role in selecting the appropriate word meaning consistent with surrounding
text.
It is also noteworthy that the influence of L2 linguistic knowledge does not appear to be
equivalent for the development of L2 MA and L2 word meaning inferencing, because different
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patterns were found regarding how L2 linguistic knowledge can affect the contributions of MA.
Three possible explanations are suggested. The first is that MA and successful word meaning
inferencing require different types and amounts of linguistic knowledge. The former, as a
structural analytical ability, is about form–form (grapheme–morpheme) mappings. Hence, it is
less constrained by L2 linguistic knowledge, measured by vocabulary and grammar in this
research. Successful word meaning inferencing in this study, on the other hand, would impose
operations that require the participants to integrate word-internal and contextual information
from surrounding words. It follows that L2 linguistic knowledge would have played a critical
role in selecting the appropriate word meaning consistent with surrounding text. Hence, it is not
surprising that the development of L2 word meaning inferencing entails more L2 languagespecific operations, and the contribution of MA can be indirect. Another explanation is task
effect, as mentioned previously. The L2 word meaning inferencing ability was tested in an
offline paper-and-pencil task, whereas L1 and L2 MA were measured by an online task that
recorded both speed and accuracy. It is unclear whether and how L1 and L2 MA jointly affect
the efficiency (both speed and accuracy) in guessing unknown word meanings during reading
(see an exception in Koda [2000], who used a semantic inconsistency detection task). Finally, the
participants’ bilingual and biliterate profiles differed from those in the research done by Park and
Zhang et al. (2016), which focused on child learners whose L1 and L2 were both alphabetic
languages. As mentioned earlier, Zhang et al. (2016) observed that L1 English MA accounted for
approximately 50% of the variance of L2 Malay MA. In