scholarly journals Model detecting learning styles with artificial neural network

2019 ◽  
Vol 9 (1) ◽  
pp. 85 ◽  
Author(s):  
Muhammad Said Hasibuan ◽  
Lukito Edi Nugroho ◽  
Paulus Insap Santosa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Styles ◽  
Prior Knowledge ◽  
Learning Style ◽  
Latent Semantic Indexing ◽  
Knowledge Generation ◽  
Generation Process ◽  
Artificial Neural ◽  
Value Decomposition

Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach.

scholarly journals Self-Organizing and Error Driven (SOED) artificial neural network for smarter classifications

2017 ◽  
Vol 4 (4) ◽  
pp. 282-304 ◽  
Author(s):  
Ruholla Jafari-Marandi ◽  
Mojtaba Khanzadeh ◽  
Brian K. Smith ◽  
Linkan Bian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Styles ◽  
Health Care Systems ◽  
Self Organizing Map ◽  
Care Systems ◽  
Classification Tasks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Abstract Classification tasks are an integral part of science, industry, business, and health care systems; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this paper, motivated by learning styles in human brains, ANN's shortcomings are assuaged and its prediction power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. The proposed method, which we name Self-Organizing Error-Driven (SOED) Artificial Neural Network, shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five different datasets. Highlights A synthesis of MLP and SOM is presented for tackling classification challenges. The superiority of SOED over MLP in addressing 5 classification tasks is presented. SOED is compared with other states of the art techniques such as DT, KNN, and SVM. It is shown that SOED is a more accurate and reliable in comparison with MLP. It is shown SOED is more accurate, reliable and transparent in comparison with MLP.

scholarly journals Artificial neural network for learning personalisation

2017 ◽  
Vol 58 ◽  
Author(s):  
Andrius Berniukevičius ◽  
Eugenijus Kurilovas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Style ◽  
Learning Objects ◽  
Learning Activities ◽  
Artificial Neural

The paper aims to suggest a method of using artificial neural for learning personalisation. Learning personalisation consists form learning style identification then linking it to learning activities and objects which correspond to learning style. But the problem is that learning style identified by questionnaire cannot guarantee adequacy and objectivity. In order to overcome this issue we can use artificial neural network which could analyse learner in learning activities and interactionwith learning objects and correct learning style and scenarios characteristics according to collected information.

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