Personalized course generation and evolution based on genetic algorithms

Tan, Xiaohong; Shen, Ruimin; Wang, Yan

doi:10.1631/jzus.c1200174

Cited by 18 publications

(9 citation statements)

References 21 publications

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“…A competency-based approach for comparing and combining the LOs in a distributed system has been presented, based on the dynamic personalization and adaptation of the learning content according to the skills, competencies and knowledge of the learners using Skill Maps and Asset Structures (Albert and Stefanutti 2003). A personalized course generation and evolution scheme has been presented using Genetic Algorithm, which constructs the personalized courses according to the course difficulty level and learner's changing performance in the learning process (Tan et al 2012). By comparing the learning objects we can achieve the evolution of learning resources based on content similarity of the learning object.…”

Section: Learning Objects Evolutionmentioning

confidence: 99%

Learning content design and learner adaptation for adaptive e-learning environment: a survey

Premlatha

Geetha

2015

Artif Intell Rev

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This paper presents a survey about learning content designs and various adaptation levels, in order to adapt the learners' necessities in e-learning environment. Normally, learners have different learning styles, cognitive traits, learning goals and varying progress of their learning over period of time, which affects the learner's performance while providing the same bundle of course to all learners. Hence, there is a need to create adaptive e-learning environment to offer appropriate learning content to all individuals. In general, the adaptation can be done based on learners' characteristics. Here, we explore the adaptation that can be done, not only based on learner context parameters but also on the learning content (learning object) and the configuration of e-learning environment. In this paper, we provide a detail review about the various levels of adaptation, learning object design and process for learning content design, learner context parameters, and models/components of e-learning; moreover, we analyze and portray the associations among the components, necessary to achieve the well-defined adaptation in e-learning environment.

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Section: Learning Objects Evolutionmentioning

confidence: 99%

Learning content design and learner adaptation for adaptive e-learning environment: a survey

Premlatha

Geetha

2015

Artif Intell Rev

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“…At the same time, web-based learning becomes a flexible way to promote personalized learning recently [10]. Currently, there is an increasing demand for personalized online learning to serve huge demands of learners with all ages [34,35]. It is thus challenging to provide personalized online learning that can satisfy all current and future demands.…”

Section: Personalized Online Learningmentioning

confidence: 99%

Smart Learning Environment: Paradigm Shift for Online Learning

Temdee¹

2020

Multi Agent Systems - Strategies and Applications

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Online learning has always been influenced by advanced technology. The role of online learning is expected not only for delivering contents to massive learners anywhere and anytime but also for promoting successful learning for the learners. Consequently, this emerged role has introduced the concept of smart learning environment. More specifically, smart learning environment is developed to promote personalized learning for learners. Personalized learning focuses on individual learner and provides appropriate feedback individually. Currently, the advances of modern technologies and intelligence data analytics have brought the idea of smart learning environment into realization. Machine learning techniques are generally applied to analyze real-time dynamic learner behavior and provide the appropriate response to the right learner. In this chapter, the evolution of online learning environment from different points of technological overviews is first introduced. Next, the concepts of personalized learning and smart learning environment are explained. Then, the essential components of smart learning environment are presented including learner classification and intervention feedback. Learner classification is to understand different learners. Intervention feedback is to provide an individual response appropriately. Additionally, some machine learning techniques widely used in smart learning environment in order to perform smart classification and response are briefly explained.

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“…J. L. Kolodner [11] distinguished between two types of CBR-inspired approaches to education: Goal-Based Scenarios [16] where learners achieve missions in simulated worlds thus confronting themselves with the real world, and Learning By Design [12] in which learners design and build working devices to obtain feedback. CBR is actually well-suited to the latter type of system [9], as well as to other tools from Artificial Intelligence (AI) and Distributed AI (DAI) systems such as Genetic Algorithm (GA) [2], Artificial Neural Network (ANN) [4] and MAS [17]. A. Baylari and G. A. Montazer focused on the adaptation of tests to obtain a personalised estimation of a learner's level [4].…”

Section: Related Workmentioning

confidence: 99%