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Cognitive Classification Based on Revised Bloom’s Taxonomy Using Learning Vector Quantization | IEEE Conference Publication | IEEE Xplore

Cognitive Classification Based on Revised Bloom’s Taxonomy Using Learning Vector Quantization


Abstract:

The cognitive dimensions of the new bloom taxonomy consist of six categories, namely C1, C2, C3, C4, C5, and C6. The difference is using verbs at each cognitive level. Ba...Show More

Abstract:

The cognitive dimensions of the new bloom taxonomy consist of six categories, namely C1, C2, C3, C4, C5, and C6. The difference is using verbs at each cognitive level. Based on the cognitive level, it appears that the C1 level is the lowest thinking level while C6 is the highest thinking level. However, cognitive classification to develop students' knowledge towards high-level cognitive skills has not been applied to managing students in learning. The focus of this study is to determine the cognitive classification structure using the bloom taxonomy. Learning Vector Quantization (LVQ) is used to classify cognitive levels into three classes, namely Low Cognitive (CL), Medium Cognitive (CM), and High Cognitive (CH). The results showed that the cognitive classification of LVQ succeeded in classifying the cognitive domains into three cognitive classes, namely CL, CM, and CH with an accuracy of 97% through a learning rate of 0.3.
Date of Conference: 17-18 November 2020
Date Added to IEEE Xplore: 24 December 2020
ISBN Information:
Conference Location: Surabaya, Indonesia

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