The Acquisition of Definiteness and Specificity in English: A Case Study with Saudi-Arabic Learners of English Using an Online Task
- Afnan Aboras
Definiteness with Arabic learners has been explored by many researchers such as Jaensch and Sarko (2009) and Sarko (2009). The majority of previous studies have used an offline task and focused on identifying the types of errors which learners were committing. Conversely, the present study will use an online reaction time task to investigate the learners’ accuracy in judging [±definite and ±specific] in a series of sentences. The aim of the study is to ascertain the accuracy of participants in judging grammatical and ungrammatical sentences in terms of definiteness and specificity in English, and also to identify which factors have the greatest effect on this accuracy. The study will examine the process of article acquisition from the perspective of universal grammar using the following hypotheses: The Representational Deficit hypothesis (RDH) by Hawkins and Chan (1997), the Feature Reassembly hypothesis by Lardiere (2009) and the bottleneck hypothesis by Slabakova (2008, 2009, 2015). Thirty-two Saudi learners have completed a grammatical judgment task that was designed using OpenSesame to incorporate a reaction time test along with two vocabulary tests (Yes/No and Lex30) and a proficiency test. The results showed no effect on definiteness and specificity with the Saudi-Arabic learners. Moreover, the findings demonstrated that there is no difference in reaction time which could be attributed to [±definite and ±specific]. Receptive vocabulary knowledge and proficiency affected the learners’ accuracy in judging article use in English, but no such effect was found for the learners’ productive vocabulary knowledge. Additionally, L1 negative transfer has been observed in Saudi-Arabic learners of English particularly with low-level learners.
h-index (February 2018): 13
i10-index (February 2018): 19
h5-index (February 2018): 8
h5-median (February 2018): 13
- Alice DingEditorial Assistant