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International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-9 Issue-45 November-2019
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Paper Title : The implementation of a probabilistic learner model in LMS-LD course creation application COPROLINE
Author Name : Mouenis Anouar Tadlaoui, Fauzi El Moudden and Mohamed Khaldi
Abstract :

In the context of e-Learning systems, we can distinguish between two different types of settings. First, we have adaptable systems, which refer to the property of changing the system settings. The learner can change the behavior of the system. Then, the learner is able to customize the system in a specified way to fit the needs of users. The learner model is the key element to generate the adaptation of the system to each specific user. It’s a representation of the learner information through which the system is based in to make it adaptable. We will present in this paper the implementation of a probabilistic learner model developed based on multi entities Bayesian networks and artificial intelligence, into a Course Creation Application Compatible with LMS-LD. The results presented in this work favor the implementation of a learner model to endorse the adaptation into some learning situations that the learners have followed during a year of testing.

Keywords : Learner model, Learner profile, Adaptive hypermedia, LMS-LD, Multi entity Bayesian networks, Artificial intelligence, Collaborative learning, E-learning personalization, Learning styles.
Cite this article : Tadlaoui MA, El Moudden F, Khaldi M. The implementation of a probabilistic learner model in LMS-LD course creation application COPROLINE. International Journal of Advanced Computer Research. 2019; 9(45):386-396. DOI:10.19101/IJACR.2019.940039.
References :
[1]Tadlaoui MA, Khaldi M, Carvalho RN. Bayesian networks for managing learner models in adaptive hypermedia systems: emerging research and opportunities. IGI Global. 2018.
[Google Scholar]
[2]El Moudden F, Khaldi M, Souhaib A. Designing an IMS-LD model for collaborative learning. International Journal of Advanced Computer Science and Applications. 2015; 6(12):42-8.
[Google Scholar]
[3]Mouenis AT, Mohamed K, Souhaib A. Towards probabilistic ontology based on Bayesian Networks. International Journal of Software and Web Sciences. 2014; 10(1):102-6.
[Google Scholar]
[4]Hrich N, Lazaar M, Khaldi M. Improving cognitive decision-making into adaptive educational systems through a diagnosis tool based on the competency approach. International Journal of Emerging Technologies in Learning. 2019; 14(7):226-35.
[Google Scholar]
[5]Gulbahar Y, Yildirim D. Towards an adaptive learning analytics framework. In society for information technology & teacher education international conference 2019 (pp. 1025-32). Association for the Advancement of Computing in Education.
[Google Scholar]
[6]Normadhi NB, Shuib L, Nasir HN, Bimba A, Idris N, Balakrishnan V. Identification of personal traits in adaptive learning environment: systematic literature review. Computers & Education. 2019; 130:168-90.
[Crossref] [Google Scholar]
[7]Mouenis AT, Souhaib A, Mohamed K. Learner modeling based on Bayesian networks. E-Learning-Instructional Design, Organizational Strategy and Management. 2015:165-87.
[Google Scholar]
[8]Tadlaoui MA, Aammou S, Khaldi M, Carvalho RN. Learner modeling in adaptive educational systems: a comparative study. International Journal of Modern Education and Computer Science. 2016; 8(3):1-10.
[Crossref] [Google Scholar]
[9]Kay J. User modeling for adaptation. User Interfaces for all: Concepts, Methods, and Tools. 2001; 4:271-94.
[Google Scholar]
[10]De Koch NP. Software engineering for adaptive hypermedia systems. PhD Thesis, Verlag Uni-Druck, Munich. 2001.
[Google Scholar]
[11]Pierrakos D, Paliouras G, Papatheodorou C, Spyropoulos CD, Karat J, Karat CM, et al. User modeling and user-adapted interaction. User Modeling and User-Adapted Interaction. 2001; 13 (4): 1-2.
[Crossref]
[12]Castillo G, Gama J, Breda AM. Adaptive Bayes for a student modeling prediction task based on learning styles. In international conference on user modeling 2003 (pp. 328-32). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[13]Han B. Student modelling and adaptivity in web-based learning systems. Massey University New Zealand. 2001.
[Google Scholar]
[14]Henze N, Nejdl W. A logical characterization of adaptive educational hypermedia. New Review of Hypermedia and Multimedia. 2004; 10(1):77-113.
[Crossref] [Google Scholar]
[15]Tadlaoui MA, Khaldi M, Aammou S. Towards a learning model based on Bayesian networks. In EDULEARN14 Proceedings 2014 (pp. 3185-93). IATED.
[Google Scholar]
[16]Tadlaoui MA, Souhaib A, Mohamed K. Development of Bayesian networks from unified modeling language for learner modelling. International Journal of Advanced Computer Science and Applications. 2015; 6(2):139-44.
[Google Scholar]
[17]Laskey KB, Dambrosio B, Levitt TS, Mahoney S. Limited rationality in action: decision support for military situation assessment. Minds and Machines. 2000; 10(1):53-77.
[Crossref] [Google Scholar]
[18]Da Costa PC, Laskey KB, Chang KC. PROGNOS: applying probabilistic ontologies to distributed predictive situation assessment in naval operations. International command and control research and technology symposium. 2009.
[Google Scholar]
[19]Tadlaoui MA, Carvalho RN, Khaldi M. A learner model based on multi-entity Bayesian networks and artificial intelligence in adaptive hypermedia educational systems. International Journal of Advanced Computer Research. 2018; 8(37):148-60.
[Crossref] [Google Scholar]
[20]El-Moudden F, Aammou S, Khaldi M. A tool to generate a collaborative content compatible with IMS-LD. International Journal of Software and Web Sciences. 2014; 11(1):01-8.
[Google Scholar]
[21]El Moudden F, Khaldi M. Towards integration of CopperCore services in application to generate collaborative online content compatible with IMS-LD. International Journal of Current Trends in Engineering & Research. 2016; 2(6):198-204.
[Google Scholar]
[22]Self JA. Formal approaches to student modelling. In student modelling: the key to individualized knowledge-based instruction 1994 (pp. 295-352). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[23]Tadlaoui MA, Carvalho RN, Khaldi M. The initialization of the learner model combining the Bayesian networks and the stereotypes methods. International Journal of Advanced Computer Research. 2017; 7(33):200-12.
[Crossref] [Google Scholar]
[24]Webb GI, Pazzani MJ, Billsus D. Machine learning for user modeling. User Modeling and User-Adapted Interaction. 2001; 11(1-2):19-29.
[Crossref] [Google Scholar]
[25]Park OC, Lee J. Adaptive instructional systems. Educational Technology Research and Development. 2003; 25:651-84.
[Google Scholar]
[26]Simko M, Bielikova M. Lightweight domain modeling for adaptive web-based educational system. Journal of Intelligent Information Systems. 2019; 52(1):165-90.
[Crossref] [Google Scholar]
[27]Leonardou A, Rigou M, Garofalakis JD. Open learner models in smart learning environments. In cases on smart learning environments 2019 (pp. 346-68). IGI Global.
[Google Scholar]