The process of language learning involves the mastery of several tasks: making the constituent sounds of the language being learned, learning grammatical patterns, and acquiring the requisite vocabulary for reception and production. While a plethora of computational tools exist to facilitate the first and second of these tasks, a number of challenges arise with respect to enabling the third. This thesis aimed to develop a Computer-Assisted Language Learning (CALL) tool intended to support Arabic language learners with the challenge of understanding the use of âclosed-classâ lexical items; in this case, the correct use of prepositions. While the process of learning open class words is relatively simple and should be possible by means of simple repetition of the word; for example, that the Arabic for âofficeâ isÙ
ÙØªØ¨ (mktb), it is much more difficult to learn and correctly use the Arabic equivalent of the word âonâ, where the choice of the correct Arabic preposition depends on properties of the two items being linked. Therefore, a mechanism for the delivery of diagnostic information regarding specific lexical examples is needed in such a tool, with the aim of clearly demonstrating why a particular translation of a given closed-class item may be appropriate in certain situations, thereby helping learners to understand and use these terms correctly. The process of building this tool involved the implementation of several substantial pieces of software capable of performing two tasks: model building and abductive reasoning. The main system design was based on requirements analysis and the finished tool was piloted to assess the feasibility of its use. The tool was subsequently evaluated in a classroom setting on a sample of 10 Arabic students who were asked to complete two sessions with identical content, which were interspersed by a period of one week in order to assess whether they benefited from using the system. The findings of this study reiterated that providing diagnostic information does indeed seem to help Arabic language learners, with evidence being indicative of the usefulness of this tool, although the data were not of sufficient quantity to form any solid conclusions based on reliable statistical analyses due to the limited sample size. In addition, analysis of user behaviour based on all expected paths of interaction showed a relationship between improved learning outcomes and the use of all available resources provided by the system, including diagnostic messages and text models. These two types of evidence seem to be encouraging and suggest that there is a need for further assessment, and possibly improvement, of the developed tool.
|Date of Award||1 Aug 2017|
- The University of Manchester
|Supervisor||Allan Ramsay (Supervisor) & Goran Nenadic (Supervisor)|