An E-Learning Investigation into Learning Style Adaptivity

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Step-Function Approach for E-Learning Personalization

Personalization is an alternative to improve the learning process for an e-Learning environment. It is a useful strategy to adjust the student' needs based on their characteristics to make learning more effectively. In this study, we propose the step-function approach for personalization in e-learning. It provides the students with adopting the knowledge-ability factor (Novice, Average, or Good category) that matches with their learning materials levels (Level1, Level2, or Level3). The approach implemented into an e-learning which called SCELE-PDE and used as the experimental group in two stages with different scenarios. In the first, without a step-function approach, but the SCELE-PDE can identify an initial of student's ability to knowledge category. The second stage has used the approach to providing students with personalization in e-Learning to adapt learning material based on a knowledge category. As a result, the step-function approach has successfully to improve the student performance in the learning process during the course. Thus, the approach has shown an increase in the level of students’ knowledge. So, it can be used as a guide when designing an e-learning personalization for students to enhance learning and achievement

Factors influencing progression rate in higher education in Oman-data engineering and statistical approach

The paper highlights key issues in student progression in a tertiary level educational system. The colleges operate under the ministry of higher education, ministry of men power. Private universities are affiliated with the external universities from the developed world. Students come from various backgrounds and allocation of students to the education system depends on the performance of the student in the school. The good students get placed in state universities and colleges and those who could not get in the state universities get placed in private universities and colleges. Progression rates in all the type of system is a challenge. We have addressed a way to overcome this problem in this paper. An attempt is made to analyze the factors influencing progression rate in higher education system via data engineering and statistical approach

Design of a recommender system for web based learning

The design of recommender systems is an ongoing research area where several researchers have devised means of incorporating intelligence in web content systems to be able to provide recommendations to learners on the basis of their learning preferences i.e. based on their learning profiles. The paper discusses the design of such a system based mapped to a content ontology and learner profiles created in the system

A Personalized e-Learning Framework

With the advent of web based learning and content management tools, e-learning has become a matured learning paradigm, and changed the trend of instructional design from instructor centric learning paradigm to learner centric approach, and evolved from “one instructional design for many learners” to “one design for one learner” or “many designs for one learner”. Currently, there are mature technologies that can lead to the construction of a personalized e-learning environment, namely: Ontology, Semantic web, learning objects, and content management systems. In this paper, a personalized e-learning framework is proposed, where learning objects are classified according to their suitability for the different types and styles of learning, and where these learning objects are offered to individual learners according to their personal preferences, skills and needs

On the automatic compilation of e-learning models to planning

[EN] This paper presents a general approach to automatically compile e-learning models to planning, allowing us to easily generate plans, in the form of learning designs, by using existing domain-independent planners. The idea is to compile, first, a course defined in a standard e-learning language into a planning domain, and, second, a file containing students learning information into a planning problem. We provide a common compilation and extend it to three particular approaches that cover a full spectrum of planning paradigms, which increases the possibilities of using current planners: (i) hierarchical, (ii) including PDDL (Planning Domain Definition Language) actions with conditional effects and (iii) including PDDL durative actions. The learning designs are automatically generated from the plans and can be uploaded, and subsequently executed, by learning management platforms. We also provide an extensive analysis of the e-learning metadata specification required for planning, and the pros and cons on the knowledge engineering procedures used in each of the three compilations. Finally, we include some qualitative and quantitative experimentation of the compilations in several domain-independent planners to measure its scalability and applicability.This work has been supported by the Spanish MICINN under projects TIN2008-06701-C03 and Consolider Ingenio 2010 CSD2007-00022, by the Mexican National Council of Science and Technology and the regional projects CCG08-UC3M/TIC-4141 and Prometeo GVA 2008/051.Garrido Tejero, A.; Fernandez, S.; Onaindia De La Rivaherrera, E.; Morales, L.; Borrajo, D.; Castillo, L. (2013). On the automatic compilation of e-learning models to planning. Knowledge Engineering Review. 28(2):121-136. https://doi.org/10.1017/S0269888912000380S121136282Garrido A. , Onaindía E. 2010. On the application of planning and scheduling techniques to E-learning. In Proceedings of the 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2010)—Lecture Notes in Computer Science 6096, 244–253. Springer.Ullrich C 2008. Pedagogically founded courseware generation for web-based learning, No. 5260, Lecture Notes in Artificial Intelligence 5260, Springer.Sicilia M.A. , Sánchez-Alonso S. , García-Barriocanal E. 2006. On supporting the process of learning design through planners. CEUR Workshop Proceedings: Virtual Campus 2006 Post-Proceedings. Barcelona, Spain, 186(1), 81–89.IMSLD 2003. IMS Learning Design Specification. Version 1.0 (February, 2003). Retrieved December, 2012, from http://www.imsglobal.org/learningdesign.Sharable Content Object Reference Model (SCORM) 2004. Retrieved December, 2012, from http://scorm.com.Garrido A. , Onaindia E. , Morales L. , Castillo L. , Fernandez S. , Borrajo D. 2009. Modeling E-learning activities in automated planning. In Proceedings of the 3rd International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS-2009), Thessaloniki, Greece, 18–27.Essalmi, F., Ayed, L. J. B., Jemni, M., Kinshuk, & Graf, S. (2010). A fully personalization strategy of E-learning scenarios. Computers in Human Behavior, 26(4), 581-591. doi:10.1016/j.chb.2009.12.010Camacho D. , R-Moreno M.D. , Obieta U. 2007. CAMOU: a simple integrated e-learning and planning techniques tool. In 4th International Workshop on Constraints and Language Processing, Roskilde University, Denmark, 1–11.Fox, M., & Long, D. (2003). PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research, 20, 61-124. doi:10.1613/jair.1129KONTOPOULOS, E., VRAKAS, D., KOKKORAS, F., BASSILIADES, N., & VLAHAVAS, I. (2008). An ontology-based planning system for e-course generation. Expert Systems with Applications, 35(1-2), 398-406. doi:10.1016/j.eswa.2007.07.034Fuentetaja R. , Borrajo D. , Linares López C. 2009. A look-ahead B&B search for cost-based planning. In Proceedings of CAEPIA'09, Murcia, Spain, 105–114.Limongelli C. , Sciarrone F. , Vaste G. 2008. LS-plan: an effective combination of dynamic courseware generation and learning styles in web-based education. In Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Nejdl, W., Kay, J., Pu, P. & Herder, E. (eds.)., 133–142. Springer.Castillo L. , Fdez.-Olivares J. , García-Perez O. Palao F. 2006. Efficiently handling temporal knowledge in an HTN planner. In Proceedings of 16th International Conference on Automated Planning and Scheduling (ICAPS 2006), Borrajo, D. & McCluskey, L. (eds.). AAAI, 63–72.Castillo, L., Morales, L., González-Ferrer, A., Fdez-Olivares, J., Borrajo, D., & Onaindía, E. (2009). Automatic generation of temporal planning domains for e-learning problems. Journal of Scheduling, 13(4), 347-362. doi:10.1007/s10951-009-0140-xUllrich, C., & Melis, E. (2009). Pedagogically founded courseware generation based on HTN-planning. Expert Systems with Applications, 36(5), 9319-9332. doi:10.1016/j.eswa.2008.12.043Boticario J. , Santos O. 2007. A dynamic assistance approach to support the development and modelling of adaptive learning scenarion based on educational standards. In Proceedings of Workshop on Authoring of Adaptive and Adaptable Hypermedia, International Conference on User Modelling, Corfu, Greece, 1–8.IMSMD 2003. IMS Learning Resource Meta-data Specification. Version 1.3 (August, 2006). Retrieved December, 2012, from http://www.imsglobal.org/metadata.Mohan P. , Greer J. , McCalla G. 2003. Instructional planning with learning objects. In IJCAI-03 Workshop Knowledge Representation and Automated Reasoning for E-Learning Systems, Acapulco, Mexico, 52–58.Alonso C. , Honey P. 2002. Honey-alonso Learning Style Theoretical Basis (in Spanish). Retrieved December 2012, from http://www.estilosdeaprendizaje.es/menuprinc2.htm