DR. AHMED MASRAI KING ABDULAZIZ MILITARY ACADEMY a.masrai@hotmail.com Explaining Listening Comprehension in L2 Learners: Individual differences approach DR. AHMED MASRAI  KING ABDULAZIZ MILITARY ACADEMY a.masrai@hotmail.com

PRESENTATION OUTLINE Introduction Background Methodology Results Discussion & Conclusions Pedagogical implication

INTRODUCTION Linguistic knowledge Non-linguistic information Listening comprehension (LC) incorporates several elements involved in understanding and making sense of spoken discourse. These include, but not limited to: Linguistic knowledge Non-linguistic information Memory demand

INTRODUCTION Considering these factors, how can LC be viewed in L2 context? A number of scholars (e.g. Andringa et al., 2012; Wang & Treffer-Daller, 2017) agree that LC is a complicated process, which can pose a serious challenge for L2 learners. Individual differences in learners should play the major role in this mental processing.

INTRODUCTION Explaining individual differences in listening has not been high on the agenda by comparison with studies which look into individual differences in reading comprehension (Andringa et al., 2012). A better understanding of the listener characteristics which determine L2 learners’ success in understanding speech is needed. As pointed out by Graham (2011), “listening is a source of frustration to learners and an area in which it seems difficult to make progress” (p. 113).

INTRODUCTION This study addresses the gap in our understanding of the learner variables which impact on L2 learners’ LC, by providing empirical evidence about the extent to which L2 learners’ aural vocabulary knowledge, written vocabulary knowledge and working memory capacity can explain the variance in L2 learners’ LC. The study, specifically, attempts to model the contribution of these three individual differences in listener characteristics to listening comprehension in one experimental setting. These three factors combined have not yet been examined.

BACKGROUND Factors Explaining Listening Comprehension In a seminal paper by Rubin (1994 ) five groups of factors that influence L2 learners’ LC were mentioned: Text characteristics Task characteristics Interlocutor characteristics Process characteristics Listener characteristics A number of factors are also included within each of these five groups, therefore;

BACKGROUND Factors Explaining Listening Comprehension The current study will focus on individual differences in listener characteristics, since these have received little attention compared with research on individual differences in reading comprehension (Andringa et al., 2012; Wang & Treffers- Daller, 2017).

BACKGROUND Factors Explaining Listening Comprehension There are a number of factors that have been examined in connection with listener characteristics: Vocabulary knowledge (e.g. Bonk, 2000; Stæhr, 2009) General language proficiency (Zuo, 2013) Listening strategy use (Graham et al., 2008) Working memory and processing speed (Andringa et al. 2012) Metacognitive awareness (Vandergrift et al., 2006)

BACKGROUND Factors Explaining Listening Comprehension Most of the LC studies have explored aspects of listening in isolation, and since it is hard to do justice to all the factors mentioned above in a single study, the current paper is an attempt to disentangle the contribution of the most important factors that impact on L2 learners’ performance in LC in one experimental setting. However,

BACKGROUND Vocabulary knowledge and LC Vocabulary knowledge has been reported as one of the most important predictive variables of LC in adult L2 learners (e.g., Andringa et al., 2012; Mecartty, 2000; Stæhr, 2009; Wang & Treffers-Daller, 2017). In a study by Mecartty (2000), vocabulary but not grammar was found to significantly explain the variance in LC in non-native learners. Findings from Clahsen and Felser (2006) also support this by pointing out that in processing listening, non-native speakers are principally guided by lexical and semantic cues but not syntactic cues.

BACKGROUND Vocabulary knowledge and LC As far as lexical knowledge demand for reading and listening comprehension is concerned, figures for adequate comprehension of spoken and written discourse are proposed by a number of researchers. Nation (2006) suggests that in order to adequately understand a spoken discourse, knowledge of 6,000 – 7,000 word families is needed, and to read authentic material, knowledge of 8,000 – 9,000 word families is required. These figures are too ambitious for L2 learners to attain!

BACKGROUND Vocabulary knowledge and LC van Zeeland and Schmitt (2013) suggest a less demanding figure: They argue that a lexical coverage of 95% is probably sufficient for LC. To attain this coverage L2 learners would need 2,000 - 3,000 word families. However, although this figure appears reassuring for L2 learners, I believe a larger volume of vocabulary is required to comprehend academic lectures.

BACKGROUND Vocabulary knowledge and LC Although lexical knowledge has long been recognised as the heart of communicative competence (Meara, 1996), a few studies have explicitly examined the relationship between vocabulary knowledge and LC (e.g., Bonk, 2000; Stæhr, 2009; Wang and Treffers-Daller, 2017). Quantifying the contribution of vocabulary knowledge to LC remains absent in most of those studies. Nonetheless,

BACKGROUND Vocabulary knowledge and LC A prominent methodological issue observed in most of the previous studies, which looked at the relation between vocabulary knowledge and LC, is the use of orthographic measures to explain LC in L2 learners. Phonological and orthographic knowledge are viewed as two sides of the learner’s mental lexicon (Milton et al., 2010). Thus, to appropriately compute the contribution of vocabulary knowledge to LC a test of aural knowledge need to be used.

BACKGROUND Working memory and LC Baddeley (2003, p. 1) defines working memory as a system that “involves the temporary storage and manipulation of information that is assumed to be necessary for a wide range of complex activities”. For over four decades working memory has been suggested to be linked to higher cognitive tasks: Learning abilities Math skills Verbal reasoning skills Language comprehension (Baddeley, 2003; Conway et al., 2005; Just & Carpenter 1992)

BACKGROUND Working memory and LC Reliable correlations have been suggested between learners’ reading and phonological span scores in both L1 & L2, memory span and L2 proficiency (Juffs & Harrington, 2011). Individual differences in WMC should predict language learning attainment (Skehan, 2002). There is also evidence that L2 learners with higher WMC are in a better condition to benefit from interactional feedback than learners with lower WMC (Mackey et al., 2010; Sagarra, 2007).

BACKGROUND Working memory and LC Further evidence for the relationship between working memory and language comprehension comes from studies in individuals with aphasia (e.g., Murray, 2004; Murray et al., 2001; Wright & Fergadiotis, 2012). It can be concluded from these ample evidence that working memory is a necessary construct in daily activities that involve actively holding in mind, manipulating, and integrating information in memory.

BACKGROUND Working memory and LC If working memory plays an important role in human cognition, then it would be expected that variations in individuals’ WMC would relate to performance in the kinds of higher-level cognitive activities, i.e., listening and reading comprehension. Daneman and Carpenter (1980) were the first to publish data on the relationship between individual differences in WMC and language comprehension.

BACKGROUND Working memory and LC In a follow up meta-analysis study, Daneman and Merikle (1996) conclude that measures that tap the combined processing and storage capacity of working memory (e.g., reading span, listening span) are better predictors of comprehension than are measures that tap only the storage capacity (e.g., word span, digit span). Having pointed out the importance of individual differences in vocabulary knowledge and WMC to learners’ performance, investigating the explanatory power of these variables to L2 learners’ LC is worthwhile.

RESEARCH QUESTIONS To this end, two research questions were addressed for the current study: What proportion of the variance in L2 learners’ listening comprehension is explained by aural vocabulary knowledge, written vocabulary knowledge, and WMC? Can written vocabulary knowledge remain a unique predictor of listening comprehension when factored in a unified model including aural vocabulary knowledge?

METHODOLOGY Participants 130 non-native speakers of English from different L1 backgrounds (i.e., Arabic, Brazilian, Chinese, Iranian, Japanese) took part in the study. 66 males (50.8%) and 64 females (49.2%) aged between 22 and 49 (M = 29.63; SD = 6.99)

METHODOLOGY Instruments Written vocabulary size test XK-Lex test (Masrai & Milton, 2012) [10,000 words] Aural vocabulary size test A-Lex test (Milton & Hopkins, 2005) [5,000 words] Working memory capacity test Listening span task (Daneman & Carpenter, 1980) [60 items] Listening comprehension test Listening part of IELTS [out of 9]

METHODOLOGY Procedure Experimental tasks were taken under controlled conditions in a language laboratory equipped with Noise-Cancelling Headphones.

Learners’ performance in AVK, WVK, WMC and LC RESULTS Learners’ performance in AVK, WVK, WMC and LC N Min Max M SD A_Lex 130 1000 4700 2688.46 883.78 XK_Lex 1500 6900 4334.62 1040.77 WMC 20 55 40.95 8.28 IELTS Listening score 4.0 7.0 5.50 .65

Correlation coefficients RESULTS Correlations between the dependent variable (LC) and the independent variables (AVK, WVK and WMC) Variables Correlation coefficients   AVK WVK WMC LC .67** .59** .64** .58** .37** .41** Note. N = 130. **p < 0.01. Correlations are rounded to two decimal points.

RESULTS Contribution of AVK, WVK and WMC to LC: Hierarchical multiple regression analysis Model R R2 Adjusted R2 SEE R2 Change Sig. F Change 1 .59a .35 .53 <.001 2 .72b .52 .51 .46 .17 3 .81c .65 .64 .39 .14 a. Predictors: (Constant), XK-Lex; b. Predictors: (Constant), XK-Lex, A-Lex; c. Predictors: (Constant), XK-Lex, A-Lex, WMC.

RESULTS Contribution of AVK, WVK and WMC to LC: Hierarchical multiple regression analysis Model R R2 Adjusted R2 SEE R2 Change Sig. F Change 1 .67a .45 .49 <.001 2 .72b .52 .51 .46 .06 3 .81c .65 .64 .39 .14 a. Predictors: (Constant), A-Lex; b. Predictors: (Constant), A-Lex, XK-Lex; c. Predictors: (Constant), A-Lex, XK-Lex, WMC.

DISCUSSION & CONCLUSIONS Vocabulary knowledge (i.e., aural and written vocabulary) and WMC provide a powerful model of LC. When combined, they produced a model explaining 65% of the variance in L2 learners’ LC performance. Thus, a componential structure of LC among L2 learners, comprising these three elements, is suggested in this study. Vocabulary knowledge, however, was found to be the most important predictor of LC. This finding supports previous work (e.g., Andringa et al. 2012; van Zeeland & Schmitt, 2013; Wang & Treffers-Daller, 2017).

DISCUSSION & CONCLUSIONS Interestingly, AVK was found to explain unique variance over and above WVK and WMC in LC performance. This variable has been overlooked by previous studies. The contribution of WMC to LC is inconsistent in the literature. The finding from the current study, however, is in line with those which have established a link between the two (e.g., Payne et al., 2009).

DISCUSSION & CONCLUSIONS Can thresholds be identified for learners’ performance in the IELTS listening section? AVK size IELTS Listening score 2000 - 3000 5.5 3000 - 4000 6.0 4000 - 5000 6.5 ? Interpretations of these thresholds should be taken with caution, since a more rigorous empirical work is needed before they become valid.

PEDAGOGICAL IMPLICATION To improve learners’ listening comprehension, in an L2 teaching environment, teachers should pay attention to developing learners’ AVK in particular. Listening skills can be improved with the support of intentional vocabulary learning activities and by increasing the chances for incidental vocabulary acquisition from aural input. Utilising multimedia software to support L2 learners’ vocabulary knowledge for LC. Computer-mediated methods have been suggested to contribute positively to enhancing L2 vocabulary knowledge and LC.

PEDAGOGICAL IMPLICATION To improve WMC, various strategies have suggested (for details see Alloway & Alloway, 2013). Jungle Memory Software has been proposed to significantly improve working memory in learners. However, https://junglememory.com For further reading on memory training see this article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4368783/ NOT FREE THOUGH!!

REFERENCES If you’re interested in the list of references, please email me to send you a copy.

Thank you Questions & Comments are welcome