An attribute-based ant colony system for adaptive learning object recommendation

Yang, Yuhong; Wu, Chun-I

doi:10.1016/j.eswa.2008.01.066

Cited by 94 publications

(39 citation statements)

References 14 publications

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“…Several applications tackle attribute-based techniques problems such as prediction and visualization. Attribute-based Ant Colony System (AACS) (Yang and Wu 2009) uses a method of finding learning objects that would be suitable for a learner based on the most frequent learning trails followed by the previous learners. The system updates the trails pheromones from different knowledge levels and different styles of learners to create a powerful and dynamic search mechanism.…”

Section: Content-based Techniquesmentioning

confidence: 99%

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

2015

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With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities.

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Section: Content-based Techniquesmentioning

confidence: 99%

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

2015

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“…Also, they have used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles together with other experts in education. Yang and Wu [28] proposed an attributes-based ant colony recommender system based on an ant colony optimization algorithm to help learners in finding adaptive LOs more effectively. Yang et al [27] designed and implemented a curriculum resources personalized recommendation algorithm based on the semantic web technology as a personalized service in teaching system.…”

Section: E-learning Recommender Systemsmentioning

confidence: 99%

“… The e-learning recommender systems are greatly influenced by pedagogical factors such as the learning history, processes, strategies, knowledge, preferences, styles, patterns, misconceptions, weaknesses, activities, feedback, progress, and expertise [9], [26], [28].…”

Section: E-learning Recommender Systemsmentioning

confidence: 99%

A Framework of Semantic Recommender System for e-Learning

Fraihat¹,

Shambour²

2015

JSW

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Abstract:With the rapid increasing of learning objects (LOs) in a variety of media formats, it becomes quite difficult and complicated task for learners to find suitable LOs based on their needs and preferences. To support personalization, recommender systems can be used to assist learners in finding the appropriate LOs which will be needed for their learning. In this paper, we propose a framework of a semantic recommender system for e-learning in which it will assist learners to find and select the relevant LOs to their field of interest. The proposed framework utilizes the intra and extra semantic relationships between LOs and the learner's needs to provide personalized recommendations for learners. The semantic recommendation algorithm is based on the extension of the query keywords by using the semantic relations, concepts and reasoning means in the domain ontology. The proposed system can be used to reduce the time and effort involved in finding suitable LOs, and thus, improves the quality of learning.Key words: E-learning, learning object, personalization, recommender system, semantic web, semantic indexing system, ontology modeling, semantic query processing. IntroductionWith the rapid growth of Web-based learning applications, e-learning is becoming more and more popular than the traditional educational approaches. Learning management systems (LMSs) are typically employed in large-scale educational institutions to facilitate the delivery and organization of e-learning [1]. LMS can be defined as "the infrastructure that delivers and manages instructional content, identifies and assesses individual and organizational learning or training goals, tracks the progress towards meeting those goals, and collects and presents data for supervising the learning process of an organization as a whole" [2].In general, courses in LMSs consist of LOs. LO can be defined as a digital and reusable piece of content used to achieve a learning objective. LO can be a text document, an audio file, a video, a picture, or a complete website [3]. Commonly, LMSs are considered as one-size-fits-all systems as they deliver the same kind of course structure and LOs to each learner [1], [4]. However, each learner has different characteristics such as levels of expertise, learning styles, prior knowledge, cognitive abilities and interests, and therefore, a one-size-fits-all systems do not support most learners.Personalization is a promising way to deal with this problem by supporting each learner independently based on his/her characteristics. Personalization in LMSs occurs when such systems uniquely address a learner's needs and characteristics. This will help in improving the learner's satisfaction and in overall the quality of learning. To support personalization, recommender systems can be employed to overcome current limitations of LMSs in providing personalization through recommending suitable LOs to learners based on their individual needs and Salam Fraihat * , Qusai Shambour Software Engineering Department, Faculty of Information...

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“…Adaptive learning system provides an alternative to the traditional system which is ignore personal characteristic of students [2]. Several intelligent proposed systems have been developed with different variables for adaptive web based learning system.…”

Section: Introductionmentioning

confidence: 99%