Intelligent Techniques in Personalization of Learning in e-Learning Systems

Markowska–Kaczmar, Urszula; Kwaśnicka, Halina; Paradowski, Mariusz

doi:10.1007/978-3-642-11224-9_1

Cited by 21 publications

(10 citation statements)

References 30 publications

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“…In the centre of technology development in eLearning is an educational problem that should be resolved in a new way to achieve the required outputs. Examples of novel technologies and intelligent techniques applicable to eLearning are focused on learning personalization (Markowska-Kaczmar et al, 2010), intelligent textbooks creation (Boulanger and Kumar, 2019), using social robots as tutors and peer learners (Belpaeme et al, 2018) and technology for information exchange between intelligent systems and laboratory equipment (Samigulina and Samigulina, 2016) that emerging to resolve available educational problems.…”

Section: Information Technologiesmentioning

confidence: 99%

eLearning Informatics: From Automation of Educational Activities to Intelligent Solutions Building

Ivanova¹

2020

Informatics in Education

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eLearning is fast progressing scientific field proposing novel and specific approaches in a range of domains. It is well established practice in universities, schools and organizations for delivering interactive, adaptive and flexible training, taking advantage of contemporary and emerging technologies. Informatics is a continuously evolving science presenting its theoretical and practical advances applicable in various research areas, including in eLearning. The paper presents an exploration focused on the symbiotic connection between Informatics and eLearning that leads to contemporary and innovative solutions, facilitating and automating a wide variety of activities at information processing. The term eLearning Informatics is conceptualized and explained as a scientific field outlining the current research achievements and further directions for development. The applied research methodology is based on outlining the main vision in the domain eLearning Informatics through utilization of bibliometric approach and construction of bibliometric networks as well as on detailed examination of topic-related scientific papers.

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Section: Information Technologiesmentioning

confidence: 99%

eLearning Informatics: From Automation of Educational Activities to Intelligent Solutions Building

Ivanova¹

2020

Informatics in Education

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“…The author in [30] uses a prediction method based on pattern recognition technique to identify dynamically the best learning style. In [34,36] authors used artificial intelligent techniques to perform automatic personalization in e-learning systems. Bayesian networks are used in [37] to provide a solution of adaption of learning activities.…”

Section: Related Workmentioning

confidence: 99%

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

Essayah¹,

Youssfi²,

Bouattane³

et al. 2019

IJACSA

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In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths. In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective. The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system. Learners' requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph. Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs. In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination. Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.

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“…Markowska-Kaczmar et al [16] have discussed about providing individual learning path through recommendation system in e-learning. Authors have used clustering and classification for defining the learning profiles but not considered any specific learning style model for learning behaviors.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

Kolekar

Pai

2017

Int. J. Emerg. Technol. Learn.

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Abstract-The major requirement of e-learning system is to provide a personalized interface with personalized contents which adapts to the learning styles of the learners. This is possible if the learning styles of the learner is known. In this paper, it is proposed to identify the learning styles of the learner, by capturing the learning behavior of the learner in the e-learning portal using Web Log Mining. The learning styles are then mapped to Felder-Silverman Learning Style Model (FSLSM) categories. Each category of the learner is provided with the contents and interface which are apt for that category. Fuzzy C Means (FCM) algorithm is used to cluster the captured learning behavioral data into FSLSM categories. The learning styles of a learner get changed over a period of time hence the system has to adapt to the changes and accordingly provide the necessary interface and contents. For this, the Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the learner in real-time. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm which calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN.

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Intelligent Techniques in Personalization of Learning in e-Learning Systems

Cited by 21 publications

References 30 publications

eLearning Informatics: From Automation of Educational Activities to Intelligent Solutions Building

eLearning Informatics: From Automation of Educational Activities to Intelligent Solutions Building

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

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