1. Participants & Procedure
Recognition vocabulary knowledge and intelligence as predictors of academic achievement in EFL context
Ahmed Masrai
Swansea University
Abstract
Participants & Procedure
Results (Continued)
Are AVST and XK-Lex tests measuring the same or different constructs?
Research has shown that vocabulary knowledge (e.g., Milton & Treffers-Daller, 2013) and general intelligence (e.g., Laidra et al., 2007) are good predictors of academic achievement. However, the effect of these factors has mostly been examined separately, so in this study we examine these factors as part of an overall predictive model of academic performance. It will present findings of L1 vocabulary knowledge, L2 vocabulary knowledge, L2 academic vocabulary knowledge, and intelligence (IQ) as predictors of overall academic achievement among learners of English as a foreign language (EFL). Performance on these measures was correlated with Grade Point Average (GPA) as a measure of academic achievement for Arabic L1 users (N= 96) at an English-medium college of languages and translation in Saudi Arabia. The findings show positive significant correlations between all the measures and academic achievement. However, academic vocabulary knowledge shows the strongest correlations (r = .72). To further explore the data, multiple regression analyses were performed. The results show that academic and general vocabulary knowledge combined can explain about 56% of the variance in students’ GPAs. The findings, thus, suggest that, in addition to L1 and L2 vocabulary size, and IQ, knowledge of academic vocabulary is an important factor that explains an additional variance in learners’ academic achievement.
96 undergraduate students from Saudi Arabia took part in the study. They completed the four measures described in the instrument section in two sessions with a short break in between. They first completed the AVST and IQ tests and then the Arabic-Lex and XK-Lex tests. In order to investigate the relationship between the four measures and academic achievement, participants’ GPAs were collected.
Results
Inter-correlations between the variables IQ
Arabic_Lex
XK_Lex
AVST
GPA -
.501**
.340**
.411**
.469**
.512**
.446**
.590**
.782**
683**
.728**
Is AWL learned as a discrete list or subject to the frequency effects?
Aims & Method
**. Correlation is significant at the 0.01 level (2-tailed).
Aims
The study addresses three main research questions:
1. Is there a frequency effect in learning the AWL?
2. Are AWL and size tests testing the same or different knowledge?
3. What are the contributions of AWL, L2 vocabulary size, L1 vocabulary size and IQ to academic success?
Instruments
Four measures were used in the data collection:
Academic vocabulary size test (AVST) (Masrai & Milton, forthcoming).
L2 vocabulary size test (XK-Lex, Masrai & Milton, 2012).
L1 vocabulary size test (Arabic-Lex, Masrai & Milton, in press).
Non-verbal IQ test (Raven’s matrices, 1997).
Regression model summary (individual variables)
Model R R2
Adjusted R2 SE
AVST
.728
.529
.524
.548
XK_Lex
.683
.467
.461
.583
Arabic_Lex
.590
.349
.342
.644 IQ
.469
.220
.212
.705
Conclusions
A number of conclusions can be drawn from the current study:
The AWL may not be as specialist, and as distinct from other vocabulary, as is assumed.
The acquisition of words in the AWL seems to be closely related to frequency and display ranges of difficulty in relation to this.
It has proved very difficult in the work reported in this paper to separate out knowledge of the AWL from knowledge of English vocabulary generally.
General vocabulary size can explain a very sizeable proportion of the variance in GPA scores but the explanatory power of vocabulary knowledge can be improved slightly when knowledge of the AWL is also factored in.
Knowledge of the AWL by itself is not a short-cut or an easy route to academic success, but it does appear useful if built into the far lengthier and more difficult process of mastering the scale of lexicon needed to handle academic discourse easily.
L1 vocabulary size and non-verbal IQ contribute to the predictive model of academic achievement but in a smaller scale (at 34% and 22%, respectively).
Regression model summary (variables combined)
Model R R2
Adjusted R2 SE 1
.797a
.636
.620
.490
a. Predictors: (Constant), IQ, XK_Lex, Arabic_Lex, AVST).
References
Author’s address:
Laidra, K., Pullmann, H., & Allik, J. (2007). Personality and intelligence as predictors of academic achievement: A cross- sectional study from elementary to secondary school. Personality and Individual Differences, 42(3),
Masrai, A., & Milton, J. (2012). The vocabulary knowledge of University students in Saudi Arabia. TESOL Arabia Perspectives, 19(3),
Masrai, A., & Milton, J. (in press). How many words do you need to speak Arabic? An Arabic vocabulary size test. Language learning Journal.
Masrai, A., & Milton, J. (forthcoming). Measuring the contribution of academic and general vocabulary knowledge to learners’ academic achievement. Journal of English for Academic Purposes.
Milton, J., & Treffers-Daller, J. (2013). Vocabulary size revisited: the link between vocabulary size and academic achievement. Applied Linguistics Review, 4(1), 151–172.