Validity of figural creativity model development based on robotic learning concept

Hendrik, Billy and Ali, Mohamad Nazlena and Nayan, Norshita Mat (2020) Validity of figural creativity model development based on robotic learning concept. Intenational Journal of Advence and Applied Science, 7 (11). pp. 102-109.

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Abstract

The teaching and learning process is not only related to student learning outcomes but must also be able to stimulate student skills such as creativity skills, problem-solving skills, collaboration skills, communication skills, etc. Creativity skill is one of the skills students need to have in facing the era of the industrial revolution 4.0, so applying the right learning model is very important in achieving the expected learning goals. The purpose of this study is to test the validity of the learning model based on the concept of robotics technology, where the learning model is designed to stimulate students' figural creativity skills. At present, there are several learning models that have been validated and are able to improve the ability to think creatively. However, in this study, the validity testing of this robotics-based learning model was carried out even further, to the building blocks of figural creativity skill. The validity aspects of the figural creativity model based on robot learning were investigated on the four elements of figural creativity, namely: Fluency, flexibility, originality, elaboration, and validity, and were assessed by two psychologists and two education experts. The results showed that the concept of robotic learning was able to fulfill the valid criteria. Based on the validator's evaluation, the cleavage model fulfilled the content validity with an Aiken’s V value>0.92. Learning process by applying figural creativity development models based on robotic learning concepts can improve students' figural creativity skills in all the building blocks of figural creativity. Keywords: Figural creativity Robotic Learning Validity Model

Item Type: Article
Subjects: 0 Research > Ilmu Komputer > Machine Learning
Divisions/ Fakultas/ Prodi: Fakultas Ilmu Komputer
Depositing User: Anggi Anggi A.Md
Date Deposited: 04 Apr 2023 03:35
Last Modified: 04 Apr 2023 03:35
URI: http://repository.upiyptk.ac.id/id/eprint/4596

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