Cognitive level classification on information communication technology skills for blog

Chalothon Chootong, Jirawan Charoensuk

Abstract


Learners can study and update their knowledge continually due to the rapid growth of online content. The Medium blog is a well-known open platform that encourages authors who want to share their experiences to publish content on various topics in multiple languages. Meanwhile, readers can query interesting content by searching for a related topic. However, finding suitable content is still challenging for learners, especially information communication technology (ICT) content in Thai, and needs to be classified into beginner, intermediate, and advanced cognitive levels. Moreover, ICT blog content is usually a mix of Thai language and technical terms in English. To overcome the challenge of content classification, a deep neural network (DNN) classification model was constructed to classify the ICT content from the Medium blog into three levels based on cognition. We examined and compared the classification results with strong baseline models, including logistic regression, multinomial naïve bayes, support vector machine (SVM), and multilayer perceptron (MLP). The experimental results indicate that the proposed DNN model attained the highest accuracy (0.878), precision (0.882), recall (0.878), and F1-score (0.875).  

Keywords


Cognitive level; Deep neural network; Information communication technology skills; Medium blog; Skill classification

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DOI: http://doi.org/10.11591/ijece.v12i6.pp6387-6396

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578