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]
<A>ABSTRACT
intra-lingual and inter-lingual relationships among L1 MA, L2 MA, L2 linguistic knowledge, and L2 word meaning inferencing in adult L2 reading development. <END OF ABSTRACT>
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.
<A>LITERATURE REVIEW
<B>The Contributions of Morphological Awareness to L2 Word Meaning Inferencing
In this research, we adopted a component approach when examining the contributions of
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.
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
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.
<B>Word Effect on L2 Word Meaning Inferencing
As posited by Nagy, Carlisle, & Goodwin (2014), the importance of MA in word
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 word-internal 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, high-intermediate, 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
word-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).
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).
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 multi-character words; (b) L1 morphological awareness facilitates the development of L2
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?
<A>METHOD
<B>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).
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.
<B>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.
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.
<C>L2 MA. The L2 MA task was parallel to that for L1 MA. The participants were
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
‘multi-language’) 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.
<INSERT TABLE 1 ABOUT HERE>
TABLE 1
Word type k
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.
<C>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
<C>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).
<B>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.
<A>RESULTS
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).
<INSERT TABLE 2 ABOUT HERE>
Means and SDs for L1 MA, L2 MA, L2 Linguistic Knowledge, L2 Chinese Lexical Inferencing,
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.
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.
<INSERT TABLE 3 ABOUT HERE> 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.
<INSERT FIGURE 1 ABOUT HERE>
FIGURE 1
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?
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).
<INSERT TABLE 4 ABOUT HERE> 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 – .32* −.03 −.15
L2 MA – −.46** −.01
L2 LK – .15
WM –
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
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).
<INSERT TABLE 5 ABOUT HERE> 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
2 ΔF Sig.
1 Working memory −.01 .00 .00 0.00 .947
2 Working memory .04 .10 .10 5.47* .024
L1 MA .33*
3 Working memory .11 .33 .23 15.66*** .000
L1 MA .35**
L2 linguistic knowledge −.48***
Model 2 Step Variable B R2 ΔR
2
ΔF Sig.
1 Working memory −.01 .00 .00 0.00 .95
2 Working memory .06 .21 .21 12.29** .001
L2 linguistic knowledge −.46**
3 Working memory .11 .33 .12 8.37** .006
L2 linguistic knowledge −.48***
L1 MA .35**
*, p < .05; **, p < .01; ***, p < .001.
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).
<INSERT TABLE 6 ABOUT HERE>
Bivariate Correlations Among L1 MA, L2 MA, L2 Word Meaning Inferencing, L2 Linguistic Knowledge, and Working Memory (N = 50)
L1 MA L2 MA LI LK WM
Notes. LI, L2 word meaning inferencing; WM, working memory. *, p < .05; **, p < .01.
<INSERT TABLE 7 ABOUT HERE>
TABLE 7
Model 1 Step Variable B R2 ΔR 2
ΔF Sig.
1 Working memory −.06 .00 .00 0.19 .661
2 Working memory −.07 .08 .11 6.00* .018
L2 MA −.34
*
3 Working memory −.12 .21 .10 5.38* .025
L2 MA −.18
1 Working memory −.06 .00 .00 0.19 .661
2 Working memory −.13 .18 .18 10.34** .002
L2 linguistic knowledge
.43**
3 Working memory −.12 .21 .03 1.50 .227
L2 linguistic knowledge
.35*
L2 MA −.18
**, p < .01; *p < .05.
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).
<INSERT FIGURE 2 AROUND HERE>
FIGURE 2
Joint Contributions of L2 MA and L2 Linguistic Knowledge to L2 Word Meaning Inferencing
<A>DISCUSSION
<B>Summary of Findings
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.
<B>Intra-Lingual and Inter-Lingual Contributions of MA to L2 Word Meaning Inferencing
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 paper-and-pencil word meaning inferencing task from Mori and Nagy (1999) and used a multiple-choice 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.
no measurement of L1 word meaning inferencing; (b) typological distance between L1 English and L2 Chinese. It is noted that this study did not examine the possible transfer effect of L1 English word meaning inferencing. We are not sure whether this could have led to the relatively low prediction power of L1 and L2 resources with L2 word meaning inferencing as the outcome. Yet, as indicated earlier, the majority of previous research has focused on two alphabetic
languages. In other words, the distinct writing systems of Chinese (morphosyllabary) and
English (alphabet) could have led to the result. For instance, Liu (2013) examined the acquisition of L2 Chinese word meaning inferencing ability with L1 English university learners and found that L1 reading comprehension, L2 MA, and L2 linguistic knowledge altogether predicted about 40% of the variance of L2 word meaning inferencing, still marginally lower than the estimation (i.e., 50%) based on two alphabetic languages (see also Bernhardt, 2005).
<A>CONCLUSIONS, IMPLICATIONS, AND LIMITATIONS
This study set out to examine how morphological awareness contributed to adult L2 word meaning inferencing with American university learners of L2 Chinese. The findings have
expanded our knowledge of the role of morphological awareness (MA) in L2 word meaning inferencing. Theoretically speaking, the research has provided evidence that specifies the utility of MA: (a) MA is beneficial for guessing unknown word meanings, especially for
languages with English as the target L2. Also, the present study has re-examined the role of L2 linguistic knowledge in cross-linguistic resource sharing.
This research did not involve direct interventions. However, based on the findings some implications can be suggested for college-level foreign language instruction and assessment: (a) It is important for the teaching community to raise teachers’ awareness to optimally utilize L2 learners’ resources in foreign language instruction. As found in this research, L2 linguistic knowledge is necessary, yet in itself insufficient for L2 reading development; morphological awareness is a useful resource available for L2 learners, and it is cross-linguistically sharable between learners’ L1 and L2. Considering the shareability of morphological awareness, this implication is not limited to L1 English–L2 Chinese reading only, but applicable to other
also include learning systematic correspondence between the writing system and the language (including morphology), and that adult L2 readers can pick it up quickly with relatively less linguistic exposure compared to native Chinese speakers/readers.
In spite of the significance of the research, some caution in the interpretation of our findings is appropriate. First, the relatively small sample size may have prevented us from detecting more subtle differences in the linguistic and literacy profiles of the target cohort, that is, American university learners of L2 Chinese. It is noteworthy that the participants in this research are above-average and high-level reading comprehenders based on the reported SAT and ACT scores. As such, the findings may not be generalizable to university L2 Chinese learners with lower levels of L1 English reading comprehension. Also, a larger participant pool is needed in order to run more sophisticated statistical analysis (e.g., path analysis or structural equation modeling) and unpack the four-way interaction between L1 MA, MA, L2 linguistic knowledge, and L2 reading subskills. Second, MA was measured with a computerized segment shifting task adapted from Feldman et al. (1995); there is a need for further research to confirm whether it is a valid and reliable task that measures MA in adult L2 reading. Another reason for caution is the use of a single offline measure of L2 word meaning inferencing in this study. There is a need to investigate further whether the relationship between MA and L2 word
small. To avoid the threat of item effect, the generalizability of the research findings awaits replication. For example, researchers may consider whether there is a balanced representation of familiar and unfamiliar base words in the word meaning inferencing task. In addition, this study has mainly focused on multi-character multisyllabic words that can be either morphologically simple or complex in L2 Chinese item construction. Future studies should pay more attention to possible interaction between word length (i.e., monosyllabic versus multisyllabic words) and morphological structure. Finally, this study did not measure L1 word meaning inferencing. It is crucial for future researchers to explore how learners’ L1 and L2 resources contribute jointly to L2 word meaning inferencing.
ACKNOWLEDGMENTS
We would like to thank the editors and the anonymous reviewers for their insightful comments on a draft of the present article. Our sincere thanks also go to Dr. G. Richard Tucker, Dr. Charles A. Perfetti, and colleagues at Carnegie Mellon UniversityMU and University of
PittsburghPitt for their comments and feedback on our research. We would like to acknowledge,
as well, the internal and external funding support that has helped to enhance this research: the
CMU Carnegie Mellon University Modern Languages Dietrich College Dissertation Completion
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