Deep auto encoders to adaptive E-learning recommender system

Gomede, Everton; Barros, Rodolfo Miranda de; Mendes, Leonardo de Souza

doi:10.1016/j.caeai.2021.100009

Cited by 19 publications

(9 citation statements)

References 11 publications

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“…Learning analytics can be used within a smart learning environment to help learners understand their own learning process. Providing feedback serves to inform instructors of the individual learners' affect and suggest non-verbal behaviours to enhance instruction efficiency (Chung et al, 2021;Gomede et al, 2021;Wang et al, 2021). In addition, smart learning environments facilitate learner selfregulation to promote autonomous and lifelong learning by allowing learners to have control over their own learning process (Hui et al, 2020;Kwok & Hui, 2018;T.…”

Section: Characterising Smart Learning Environmentsmentioning

confidence: 99%

The Continuous Pursuit of Smart Learning

Cheung

Wang

Kwok

2021

AJET

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With an emphasis on learning flexibility, effectiveness, efficiency, engagement, adaptivity and reflectiveness, smart learning embraces a variety of concepts, including but not limited to personalised learning, adaptive learning, intelligent tutoring, open online learning, blended learning, and collaborative learning. As new concepts continue to evolve, the pursuit of smart learning is ongoing, mainly in areas pertaining to the design and implementation frameworks, pedagogical theories and practices, learners’ behaviours and learning pattern, learning and assessment strategies and evaluation of learning performance and perception. This editorial gives an overview of smart learning and provides the context on the latest development of smart learning in which the articles in this special issue are located.

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Section: Characterising Smart Learning Environmentsmentioning

confidence: 99%

The Continuous Pursuit of Smart Learning

Cheung

Wang

Kwok

2021

AJET

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“…Finally, the quality ranking is given according to the score. Gomede et al [6] proposed an adaptive learning method for computing student outcomes using a deep autoencoder.…”

Section: Introductionmentioning

confidence: 99%

Research on Teaching Quality Evaluation Model of Physical Education Based on Simulated Annealing Algorithm

Duan

Hou

2021

Mobile Information Systems

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One of the most significant components of the teaching department is the evaluation of teaching quality. The traditional teaching quality evaluation model has the problems of low weight calculation accuracy and long evaluation time. With the development of educational informatization, modern information processing technology can be used to effectively evaluate teachers’ teaching quality. In this article, a physical education teaching quality evaluation model based on the simulated annealing algorithm is proposed. An evaluation index system is established based on the construction principles of the evaluation index system followed by the construction of a judgment matrix to calculate the weight of the evaluation index. The simulated annealing algorithm is employed to effectively optimize the weight of the evaluation index and improve the evaluation accuracy. In addition, the analytic hierarchy process (AHP) is used to test the consistency of the judgment matrix, and the weight ranking results of the evaluation indexes are obtained to complete the teaching quality evaluation of physical education. The experimental results show that the performance of the proposed model in terms of evaluation weight calculation accuracy and evaluation calculation time is higher than that of the existing models. Therefore, the proposed model can better meet the requirements of physical education teaching quality evaluation.

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“…An auto encoder based recommender system [149] [144] which takes user-based or item-based ratings in the rating matrix as input, generates an output through the encoding and decoding process, and optimises model parameters by minimising the reconstruction error. The learning element is based on interactions between learners and objects.…”

Section: E Personalized Recommender Systemmentioning

confidence: 99%

AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

et al. 2022

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A personalized e-learning system is effective in imparting enhanced learning to its users. As compared to a conventional e-learning system, which provides similar contents to each learner, a personalized learning system provides specific learning contents and assessments to the learners. Personalization is based on Artificial Intelligence (AI) based techniques in which appropriate contents for each learner are determined using the level of comprehension of the learner and the preferred modes of learning. This paper presents requirements and challenges for a personalized e-learning system. The paper is focused in elaborating four research questions, which are related to identifying key factors of personalized education, elaborating on state of the art research in the domain, utilizing benefits of AI in personalized education, and determining future research directions. The paper utilizes an extensive survey of current research papers in answering these questions. It provides a comprehensive review of existing solutions in offering personalized e-learning solutions. It also elaborates on different learning models and learning theories, which are significant in providing personalized education. It proposes an efficient framework, which can offer personalized elearning to each learner and identifies significant directions for future research. The paper is beneficial for academicians and researchers in understanding the requirements of such a system, comprehending its methodologies, and identifying challenges which are needed to be addressed.

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Deep auto encoders to adaptive E-learning recommender system

Cited by 19 publications

References 11 publications

The Continuous Pursuit of Smart Learning

The Continuous Pursuit of Smart Learning

Research on Teaching Quality Evaluation Model of Physical Education Based on Simulated Annealing Algorithm

AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

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