AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Constraint-Based and Fuzzy Logic Student Modeling for Arabic Grammar


Affiliations
1 Informatics Department, Electronic Research Institute, Cairo, Egypt
 

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.

Keywords

Language Tutoring Systems, Student Model, Constraint-Based Modeling, Fuzzy logic.
User
Notifications
Font Size

  • J. Lee, “Review of Language-learner computer interactions: Theory, methodology, and CALL applications.",” Language Learning & Technology, vol. 21, no. 3, pp. 72–75, 2017.

  • L. Kohnke and H. Kong, “Review of New technologies and language learning,” vol. 23, no. 1, pp. 36–39, 2019.

  • J. Gamper and J. Knapp, “A Review of Intelligent CALL Systems,” Computer Assisted Language Learning, vol. 15, no. 4, pp. 329–342, 2002.

  • V. Slavuj, A. Meštrović, and B. Kovačić, “Adaptivity in educational systems for language learning: a review,” Computer Assisted Language Learning, vol. 30, no. 1–2, pp. 64–90, 2017.

  • S. Golonka, Ewa M and Bowles, Anita R and Frank, Victor M and Richardson, Dorna L and Freynik, “Technologies for foreign language learning: a review of technology types and their effectiveness,” Computer assisted language learning, vol. 27, no. 1, pp. 70--105, 2014.

  • A. Karaci, “Intelligent tutoring system model based on fuzzy logic and constraint-based student model,” Neural Computing and Applications, vol. 31, no. 8, pp. 3619–3628, 2019.

  • Heift, Trude. "Intelligent language tutoring systems for grammar practice." Zeitschrift für Interkulturellen Fremdsprachenunterricht 6.2, 2001.

  • M. Feng, N. Heffernan, and K. Koedinger, “Student Modeling in an Intelligent Tutoring System,” Intelligent Tutoring Systems in E-Learning Environments, no. May 2014, pp. 208–236, 2010.

  • P. Holt, S. Dubs, M. Jones, and J. Greer, “The State of Student Modelling,” Student Modelling: The Key to Individualized Knowledge-Based Instruction, pp. 3–35, 1994.

  • N.-T. Le and W. Menzel, “Using Weighted Constraints to Diagnose Errors in Logic Programming--The Case of an Ill-Defined Domain,” International Journal of Artificial Intelligence in Education, vol. 19, no. 4, pp. 381–400, 2009.

  • A. Mitrovic, K. R. Koedinger, and B. Martin, “A Comparative Analysis of Cognitive Tutoring and Constraint-Based Modeling,” pp. 313–322, 2007.

  • S. Ohlsson, “Constraint-Based Modeling: From Cognitive Theory to Computer Tutoring - And Back Again,” International Journal of Artificial Intelligence in Education, vol. 26, no. 1, pp. 457–473, 2016.

  • N. Khodeir, N. Wanas, and H. Elazhary, “Constraint-based student modeling in probability story problems with scaffolding techniques,” International Journal of Emerging Technologies in Learning, vol. 13, no. 1, 2018.

  • K. R. Roll, I., Baker, R. S., Aleven, V., McLaren, B. M., & Koedinger, “User models for adaptive hypermedia and adaptive educational systems,” User modeling- Springer Berlin Heidelberg, vol. 4321, pp. 367–376, 2005.

  • A. Farghaly and K. Shaalan, “Arabic natural language processing: Challenges and solutions,” ACM Transactions on Asian Language Information Processing, vol. 8, no. 4, pp. 1–19, 2009.

  • K. Shaalan, S. Siddiqui, and M. Alkhatib, “Chapter 3,” no. November, 2018.

  • K. Shaalan, “An Intelligent Computer Assisted Language Learning System for An Intelligent Computer Assisted Language Learning System for Arabic Learners,” Computer Assisted Language Learning, vol. 18, no. 1–2, pp. 81--109, 2005.

  • T. Heift, “Web Delivery of Adaptive and Interactive Language Tutoring: Revisited,” International Journal of Artificial Intelligence in Education, vol. 26, no. 1, pp. 489–503, 2016.

  • L. da Costa, F. Bond, and X. He, “Syntactic Well-Formedness Diagnosis and Error-Based Coaching in Computer Assisted Language Learning using Machine Translation,” Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016), pp. 107–116, 2016.

  • C. Troussas, K. Chrysafiadi, and M. Virvou, “An intelligent adaptive fuzzy-based inference system for computer-assisted language learning,” Expert Systems with Applications, vol. 127, pp. 85–96, 2019.

  • S. Hegazi, N and Ali, G and Abed, EM and Hamada, “Arabic expert system for syntax education,” in Proceedings of the Second Conference on Arabic Computational Linguistics, Kuwait, 1989, pp. 596--614.

  • K. F. Shaalan and H. E. Talhami, “Arabic Error Feedback in an Online Arabic Learning System 1,” pp. 203–212, 2006.

  • K. Shaalan, M. Magdy, A. L. Y. Fahmy, K. Shaalan, and M. Magdy, “Analysis and feedback of erroneous Arabic verbs,” Natural Language Engineering, vol. 21, no. 2, pp. 271–323, 2013.

  • Ç. Eroglu et al., “Intelligent Natural Language Processing: Trends and Applications,” vol. 740, no. 2, pp. 380–392, 2018.

  • N. A. Khodeir, M. Hafez, H.Alazhary, “Multi-level Skills-based Student Modeling in an Arabic Grammar Tutor,” System, IEEE#41458-ACCS'017&PEIT'017, Alexandria, Egypt, 2017.

  • Guirao, Josefa Cánovas, Julio Roca de Larios, and Yvette Coyle. "The use of models as a written feedback technique with young EFL learners." System 52 (2015): 63-77.

  • Bo, Yuan, ed. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh. Vol. 6. World Scientific, 1996.


Abstract Views: 313

PDF Views: 129




  • Constraint-Based and Fuzzy Logic Student Modeling for Arabic Grammar

Abstract Views: 313  |  PDF Views: 129

Authors

Nabila A. Khodeir
Informatics Department, Electronic Research Institute, Cairo, Egypt

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.

Keywords


Language Tutoring Systems, Student Model, Constraint-Based Modeling, Fuzzy logic.

References