WELSA: An Intelligent and Adaptive Web-Based Educational System

E-learning systems can be divided in two categories according to the personalization level they offer. LMS and AEHS are representative systems of the above categories. LMS have more capabilities than AEHS but, on the other hand, AEHS consider student's differences and offer personalized learning to achieve better results. Our research findings led us to embed adaptivity techniques in Moodle, with adoption of a hybrid dynamic user model, which is built with techniques that are based both upon knowledge and behaviour of learner.

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“…Our research on some of them [1,2,3] led us to the following findings (Table I). Our system's architecture is illustrated in Fig.…”

Section: Proposed Systemmentioning

confidence: 81%

Comparing LMS and AEHS: Challenges for Improvement with Exploitation of Data Mining

Karagiannis

Satratzemi

2014

2014 IEEE 14th International Conference on Advanced Learning Technologies

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“…Technical details regarding the system, including architectural and implementation issues can be found in Popescu et al . (2009).…”

Section: Adaptivity Mechanism In Welsamentioning

confidence: 99%

Adaptation provisioning with respect to learning styles in a Web‐based educational system: an experimental study

Popescu

2010

Computer Assisted Learning

138

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Personalized instruction is seen as a desideratum of today's e-learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style-based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates some of the limitations of similar systems. The adaptive methods used as well as their implementation in a dedicated system (WELSA) are presented, together with a thorough evaluation of the approach. The results of the experimental study involving 64 undergraduate students show that accommodating learning styles in WELSA has a beneficial effect on the learning process.

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“…The methods are used explicitly or implicitly and the process of detecting learning style and cognitive traits is more complex [41,61]. 3) Reasoning: the most complicated step responsible for inferring a new user's information from the existing information [40,62]. Therefore many researchers urged to follow theses step in order to develop an accurate and reliable adaptive user model [24,63].…”

Section: VIIImentioning

confidence: 99%

“…Moreover many researches call for using applications, tools and learning materials such as "bookmarks, calendar/schedule, searching facilities, portfolio, and context sensitive help, for enhancing the users communication with each other, and their interaction with the system, which will consequently enhance the learning motivation and improve collaborative learning, learning performance and academic achievement, as well as increase the accurate analyses of individual traits through tracking user's behaviour within the system, which consequently enhance the reasoning of user preferences implicitly [62].…”

Section: VIIImentioning

confidence: 99%

Adaptive Educational Hypermedia System using Cognitive style approach: Challenges and opportunities

Ghazal

Yusof

Zin

2011

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics

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E-learning systems have rapidly evolved from generation to another throughout the recent years. Adaptive Educational Hypermedia System (AEHS) provides capabilities to personalize the web-based system according to student's model such as user's interests, needs, preferences, knowledge, and background, which are personal characteristics gathered explicitly or implicitly. The goal of developing AEHS is to achieve an effective and affective learning performance, as well as to get positive impacts on the student through gaining skills and learning experiences. Most AEHS are technologically-driven to transfer knowledge rather than pedagogically-driven focusing on gaining cognitive skills and other experiences. To overcome this gap many researchers considered Cognitive style (CS) as the most important factor for reasoning and predicting individual traits, learning strategies and other preferences. Furthermore many researchers proposed new cognitive factors-rational, intuitive, and interpersonal as the most innate, brain-based, fixed stable preferences to build a robust adaptive student model dynamically and to get positive influence on the affective and effective AEHS. Based on the literature review, we have identified a number of theoretical and technical issues related to the AEHS architecture in relation to the whole brain cognitive style model to achieve an effective learning outcomes .The findings will be used for designing a user model for AEHS.

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WELSA: An Intelligent and Adaptive Web-Based Educational System

Cited by 18 publications

References 8 publications

Comparing LMS and AEHS: Challenges for Improvement with Exploitation of Data Mining

Comparing LMS and AEHS: Challenges for Improvement with Exploitation of Data Mining

Adaptation provisioning with respect to learning styles in a Web‐based educational system: an experimental study

Adaptive Educational Hypermedia System using Cognitive style approach: Challenges and opportunities

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