Name
Abstract | ||
|---|---|---|
Arabic language is strongly structured and considered as one of the most highly inflected and derivational languages. Learning Arabic morphology is a basic step for language learners to develop language skills such as listening, speaking, reading, and writing. Arabic morphology is non-concatenative and provides the ability to attach a large number of affixes to each root or stem that makes combinatorial increment of possible inflected words. As such, Arabic lexical (morphological and phonological) rules may be confusing for second language learners. Our study indicates that research and development endeavors on spelling, and checking of grammatical errors does not provide adequate interpretations to second language learners' errors. In this paper we address issues related to error diagnosis and feedback for second language learners of Arabic verbs and how they impact the development of a web-based intelligent language tutoring system. The major aim is to develop an Arabic intelligent language tutoring system that solves these issues and helps second language learners to improve their linguistic knowledge. Learners are encouraged to produce input freely in various situations and contexts, and are guided to recognize by themselves the erroneous functions of their misused expressions. Moreover, we proposed a framework that allows for the individualization of the learning process and provides the intelligent feedback that conforms to the learner's expertise for each class of error. Error diagnosis is not possible with current Arabic morphological analyzers. So constraint relaxation and edit distance techniques are successfully employed to provide error-specific diagnosis and adaptive feedback to learners. We demonstrated the capabilities of these techniques in diagnosing errors related to Arabic weak verbs formed using complex morphological rules. As a proof of concept, we have implemented the components that diagnose learner's errors and generate feedback which have been effectively evaluated against test data acquired from real teaching environment. The experimental results were satisfactory, and the performance achieved was 74.34 percent in terms of recall rate. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1017/S1351324913000223 | NATURAL LANGUAGE ENGINEERING |
Field | DocType | Volume |
Edit distance,Expression (mathematics),Arabic,Arabic verbs,Computer science,Active listening,Proof of concept,Test data,Artificial intelligence,Natural language processing,Spelling,Linguistics | Journal | 21 |
Issue | ISSN | Citations |
2.0 | 1351-3249 | 1 |
PageRank | References | Authors |
0.40 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khaled F. Shaalan | 1 | 506 | 39.80 |
| Marwa Magdy | 2 | 8 | 1.81 |
| Aly Fahmy | 3 | 38 | 4.05 |