Intelligent Cloud Learning Model  for Online Overseas Chinese Education

Chen, Yidong; Huang, Zijian; Shi, Xiaodong

doi:10.3991/ijet.v10i1.4284

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In studies using machine learning techniques, a matrix based on the students' characteristics or navigation data is created. For example, Huang et al (2015) created a matrix with information about the students' interests, preferences, and their browsing history data. Then students were assigned to groups with the matrix created using clustering techniques.…”

Section: Student Modelingmentioning

confidence: 99%

A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

Aydoğdu

2020

Journal of Educational Computing Research

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Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs), which form one group of machine learning techniques, are among the methods most frequently used in learning environments. Convolutional neural networks (CNNs), which are specific types of these networks, are used effectively for complex problems such as image processing, computer vision and speech recognition. In this study, a student model was created using a CNN due to the complexity of the learning process, and the performance of the model was examined. The student modeling technique used was named LearnerPrints. The navigation data of the students in a learning management system were used to construct the model. Training and test data were used to analyze the performance of the model. The classification results showed that CNNs can be used effectively for student modeling. The modeling was based on the students’ achievement and used the students’ data from the learning management system. The study found that the LearnerPrints technique classified students with an accuracy of over 80%.

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Section: Student Modelingmentioning

confidence: 99%

A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

Aydoğdu

2020

Journal of Educational Computing Research

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“…Currently, the research on intelligent education mainly focuses on the realization of such functions as interactive learning and homework review, or the exploration of teaching modes and teaching service strategies [5][6][7]. Huang et al [8] first adopted the literature analysis method to analyze the application and research status of smart learning platforms in higher education and vocational education both at home and abroad, and then proposed a "four-stage and nine-part" smart education and teaching application model, set up an experimental class and a control class to compare the effectiveness of platform applications, and verified that the proposed teaching model could have positive effects on students' learning efficiency and academic performance. Modern intelligent educational robots have integrated electronics, computer, sensing and other technologies.…”

Section: Introductionmentioning

confidence: 99%

A Framework of an Intelligent Education System for Higher Education Based on Deep Learning

Zhang

Cao

2021

Int. J. Emerg. Technol. Learn.

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Intelligent learning platforms and education information application platforms are gaining ground, owing to the wide application of modern technologies such as the Internet of Things, big data analysis, artificial intelligence, and cloud computing. However, the current platforms cannot solve specific teaching problems, and the relevant research mostly focuses on primary and secondary education. Therefore, this paper constructs and analyzes a framework of intelligent education system for higher education based on the deep learning. Firstly, the functional block diagram of the system was built up. Next, a face detection algorithm was proposed based on the multi-task convolutional neural network, a face recognition algorithm was developed based on the improved deep convolutional neural network, and the knowledge learning status of students was tracked based on the memory augmented neural network. Finally, the proposed framework was proved effective and swift through experiments. The research results expand the application scope of the deep learning in education.

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Individual differences and personalized learning: a review and appraisal

Chen

Wang

2020

Univ Access Inf Soc

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Intelligent Cloud Learning Model for Online Overseas Chinese Education

Cited by 3 publications

References 13 publications

A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

A Framework of an Intelligent Education System for Higher Education Based on Deep Learning

Individual differences and personalized learning: a review and appraisal

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