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3. Effect of Design-Based Learning on Elementary Students Computational Thinking Skills in Visual Programming Maker Course

Author

Matere, Isaac M., Weng, Cathy, Astatke, Melese, Hsia, Chih-Hsien, and Fan, Chen-Guo

Abstract

Recently there has been a change in elementary curricula to focus on computational thinking, however, a good curriculum needs to be matched with good learning or teaching strategies. This study explores design-based learning (DBL) in elementary students to examine the development of computational thinking and to support knowledge construction in designing final products. An experimental design was used with 64 students; divided into experimental and control groups. The experimental group used a DBL approach with worksheets aimed at students designing their own final product design, while the control group was taught by the traditional approach with worksheets for redoing, practicing and designing their final product. Pre and posttests were used for data collection and evaluation scheme developed to score the final product. The finding showed no significant difference in the development of computational thinking between the two groups. However, there was a significant difference in designing the final product between the two groups. Therefore, DBL supported elementary students in designing the final product although redesigning curricula by integrating both teaching methods is suggested for the greater achievement of computational thinking.

Published
2023
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47. An Expert Smart Scalp Inspection System Using Deep Learning

Author

Jhong, Sin Ye, Yang, Po-yen, Hsia, Chih Hsien, Jhong, Sin Ye, Yang, Po-yen, and Hsia, Chih Hsien

Abstract

With the advent of the "beauty economic era", in which people are paying more attention to beauty and health, the health of the scalp is being increasingly valued. However, current scalp care services are limited by problems such as they are not automatic and objective, and the results are not significant, which make them unacceptable to the public. Because of these reasons, in this study, we focus on the obstacles that hairdressers face and propose an expert inspection system that is suitable for determining scalp problems by utilizing deep learning, cloud computing techniques, and an embedded system. Dandruff is the most common scalp problem. In this work, we propose a convolutional neural network (CNN)-based method to analyze the severity of dandruff and evaluate the health of the scalp. The convolutional block attention module (CBAM) is adopted to improve the feature extraction performance of the CNN model. The depth separable convolution (DSC) and spinal fully connected (FC) are applied in this work to reduce the number of model parameters. Aside from offering a more effective smart scalp inspection process, this method also enables hairdressers and customers to track their scalp problems easily. In the future, we expect to reduce the stress of hairdressers and enhance customers' trust on scalp care services by using the smart health inspection offered by this system. Last but not the least, it has been shown that the method proposed in this research can achieve an accuracy of 85.03%, which is higher than those achieved by recently proposed methods.

Published
2022

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