A Cognitive Tutor of Arabic Word Root Extraction Using Artificial Word Generation, Scaffolding and Self-Explanation

Abstract

Extracting the roots (stemming) of Arabic words is one of the most challenging skills taught to Arabic language learners. To address this challenge, this paper proposes the Arabic word Root extraction Tutor (ART). ART is a cognitive tutor intended to teach students production rules needed for Arabic word root extraction. In the passive mode, ART accepts an input word and generates its root with explanation. In the active mode, on the other hand, words are generated by ART and the student is prompted to provide the correct roots. ART integrates several techniques for enhanced tutoring. It provides a positive feedback for a correct answer and a negative one otherwise. In the latter case, Prompting Answer Strategy (PAS) is employed, where the student is guided to detect the error by integrating scaffolding and self-explanation. Scaffolding prompts the student to apply the relevant production rule step by step. In each step, a number of options are given to the student to select the correct one via self-explanation. If the error persists, the correct answer is generated with explanation. In addition to generating real words, artificial words are generated using the production rules. This novel technique is intended to ensure that the student applies the production rules rather than memorizes the roots of common words. Evaluation has shown the effectiveness of ART tutoring process and suggests artificial word generation as a promising technique in language tutoring.