Intelligent e-Learning Systems: An Educational Paradigm Shift

Bhattacharya, S. D.; Nath, Sayan

doi:10.9781/ijimai.2016.4212

Cited by 15 publications

(11 citation statements)

References 19 publications

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“…wearables, smartphones) that can encourage children to perform outdoor exercise. In this sense, some organizations like Youth Sports Trust, highlighting "the need to include wearable technology and gamification in physical education classes in schools" [59]. Furthermore, they said "in order to get children active from a young age, a more holistic approach to PE is needed, one which integrates technology and the delivery of a seamless, intuitive and digitally enhanced form of physical activity" [58].…”

Section: B Technology For Gamification and Games For Pementioning

confidence: 99%

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Exploring the Benefits of Using Gamification and Videogames for Physical Exercise: a Review of State of Art

González-González¹,

Río²,

Navarro-Adelantado³

2018

IJIMAI

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There is a lack of motivation in children and adolescents to do physical exercise and at the same time a worldwide obesity epidemic. Gamification and active videogames can be used to increase the motivation of young people, promoting healthy habits. In this work we explore different studies on active videogames, eSports and gamification applied to physical exercise and health promotion. Main findings include positive effects in a reduction in body weight and in the promotion to continue performing of physical exercise. It also contributes to increase the motivation in children and adolescents to practice exercise. The personalization of user experience and emerging technologies (big data, wearables, smart technologies, etc.) are presented as promising opportunities to keep the engagement in game-based program and gamification of physical exercise.

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Section: B Technology For Gamification and Games For Pementioning

confidence: 99%

“…But tracked data on the activities and statistical information cannot be enough to transform this data into knowledge, skills, attitudes and behaviors [59], motivating and enhancing physical activity. Coaching support in physical activity can be an important key of success in PE.…”

Section: B Technology For Gamification and Games For Pementioning

confidence: 99%

Exploring the Benefits of Using Gamification and Videogames for Physical Exercise: a Review of State of Art

González-González¹,

Río²,

Navarro-Adelantado³

2018

IJIMAI

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“…A number of virtual assistants exist in different domains. A number of rule-based assistants are working in different domains such as counseling [4][5][6][7], prediction [8][9][10], diagnosis [11][12][13][14][15][16][17][18][19], design [20][21]30], e-learning [28][29] and recommendations [31]. Some of the stochastic assistants are Chatbots, question answering systems and assistants like Apple's Siri, Google's Google Assistant, Amazon's Alexa, Microsoft's Cortana and Facebook's M.…”

Section: Introductionmentioning

confidence: 99%

StuA: An Intelligent Student Assistant

Lodhi¹,

Mishra²,

Jain³

et al. 2018

IJIMAI

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With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help newcomer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a newcomer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate.

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“…The history started with a stand-alone Computer Aided Learning (CAL) and CD-ROM which only provided a set of programmed instructions used for educational purposes. However, such technologies were not able to replace the traditional classroom in which teachers typically consider factors that affect the learning of each student [1]. Therefore, researchers have been studying the adaptation element to be included in CAL system so that the material and the learning session can be personalized to entertain students' heterogeneity [2].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

Idris

Hashim

Samsudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important variations in a particular learning material produced a complex dependency that causes a difficulty in the learning material selection process. Existing rule-based learning material selection approach requires the definition of a huge set adaptation rules. However, this approach usually results in inaccurate and incorrect selection due to the inconsistent, insufficient and confluence of the defined rules. Consequently, the process of learning material selection is hard to be algorithmized, therefore, intelligent methods are applied to handle the complexity challenges. This research proposes a significance weight approach that represents the complex dependency of learning material selection problem to substitute the rules definition in the selection process. In addition, this research proposes an intelligent learning model that combines unsupervised and supervised machine learning techniques to accurately select the learning material for a particular student adaptively. The unsupervised machine learning technique is vital in obtaining a learning material classification and labelling based on the proposed significance weight. Meanwhile, the supervised machine learning technique, the Multilayer Perceptron Artificial Neural Networks conducts the adaptation process that will assign the student to suitable learning materials regarding his performance upon specific DKC. With 98% achievement of classification accuracies, this model can be considered as highly accurate in selecting a correct and suitable learning material based on student's domain knowledge level.

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Intelligent e-Learning Systems: An Educational Paradigm Shift

Cited by 15 publications

References 19 publications

Exploring the Benefits of Using Gamification and Videogames for Physical Exercise: a Review of State of Art

Exploring the Benefits of Using Gamification and Videogames for Physical Exercise: a Review of State of Art

StuA: An Intelligent Student Assistant

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

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