Abstract
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer) which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different questions that deal with the different concepts and have different difficulty levels. Constraint-based student modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain level the different grammar skills through the defined skill structures. The main contribution of this paper is the hierarchal representation of the system's basic grammar skills as domain knowledge. That representation is used as a mechanism for efficiently checking constraints to model the student knowledge and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number of trails the student takes for answering each question and fuzzy logic decision system are used to determine the student learning level for each lesson as a long-term model. The results of the evaluation showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its features and abilities.
Highlights
Computer-Assisted Language Learning (CALL) is a research discipline that concerned with using computers, new media, and information technologies for language learning [1], [2]
Intelligent Tutoring Systems (ITSs), Adaptive Hypermedia systems (AHSs), Collaborative environments (CE), and computer-supported ubiquitous learning (CSUL) are the common adaptive approaches that have been used in Intelligent Computer-Assisted Language Learning (ICALL) [4]
Karaci presented an example in using a fuzzy logic decision system to determine the learning level based on MYCIN certainty factor and the number of times the student takes for answering the question [6]
Summary
Computer-Assisted Language Learning (CALL) is a research discipline that concerned with using computers, new media, and information technologies for language learning [1], [2]. Karaci presented an example in using a fuzzy logic decision system to determine the learning level based on MYCIN certainty factor and the number of times the student takes for answering the question [6]. Another example presents developing parsers to identify grammatical errors by incorporating NLP techniques in analyzing student input [7]. The main contribution is the proposed mechanism for efficiently checking constraints by utilizing the hierarchical structure of the domain knowledge Such a technique facilitates diagnosing student errors and their origins. Sections nine and ten respectively present the evaluation and the conclusion
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