Feature Selection for Bloom’s Question Classification in Thai Language

Anekboon, Khantharat

doi:10.1007/978-3-030-01174-1_12

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…This framework also focused on the cognitive level and separated learning content into three different levels: 6389 beginner level, which consists of remembering and understanding; intermediate level, which consists of applying and analyzing; and advanced level, which consists of evaluating and creating. Anekboon [6] classified questions into six levels of cognitive taxonomy in Thai. They solved ambiguous Thai words and phrases and focused on feature selection techniques specified by relevance.…”

Section: Cognitive Levelsmentioning

confidence: 99%

“…Most of the current work is focused on English learning content. Although [3], [6] studied the cognitive levels of the Thai language by using Bloom's taxonomy, it cannot be directly applied to ICT skill cognitive levels or the Thai paradigm. Moreover, existing learning content classification systems have been built based on historical user data, which makes the system limited.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive level classification on information communication technology skills for blog

Chootong

Charoensuk

2022

IJECE

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<span lang="EN-US">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). </span>

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Section: Cognitive Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%