ntelligent Language Tutoring Systems

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Shaalan, K., M. Magdy, and A. Fahmy, "Analysis and Feedback of Erroneous Arabic Verbs", Journal of Natural Language Engineering , vol. 21, issue 2, pp. 271-323, 2015. Abstractanalysis_and_feedback_of_erroneous_arabic_verbs.pdfWebsite

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.

Emran, M. A., and K. Shaalan, "A Survey of Intelligent Language Tutoring Systems", Advances in Computing, Communications and Informatics (ICACCI’14), Delhi, India, 25 Septemper, 2014. Abstractsurvey_IELTS.pdf

Intelligent Languages Tutoring Systems (ILTSs) plays a significant role in evaluating students' answers through interaction with them. ILTSs implements Natural Language processing (NLP) techniques in order to allow free input of words and sentences. ILTSs have the capability of identifying the input errors and provide an immediate feedback along with the errors source. It has been observed that ILTSs were not surveyed intensively; the reason that motivates us to conduct this research. Some NLP recent trends such as Latent Sematic Analysis and entailment were demonstrated. Different ILTSs have been discussed with a dedicated section about the development of Arabic ILTSs. Arabic share many of its characteristics with Semitic and morphologically rich languages. In our presentation we point out new trends that have been emerged while conducting survey.