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As an important part of core competencies in the 21st century, computational thinking has received a lot of attention from all over the world. In the field of higher education, cultivating the ability of computational thinking has become an important goal of teaching. Previous research has shown that students' learning engagement is related to partial dimensions within computational thinking. However, there was a lack of research on the overall relationship between learning engagement and computational thinking. Therefore, this study aims at constructing an overall relationship model between learning engagement and computational thinking to examine the influence of three dimensions of learning engagement on the five dimensions of computational thinking. The participants were 341 freshmen from central China. The results show that compared with behavioral engagement, both emotional engagement and cognitive engagement had a stronger predictive power for computational thinking. In addition, the learning environment played a significant role in the relationship between learning engagement and computational thinking. On the whole, when compared with traditional multimedia classrooms, the relationship between learning engagement and computational thinking in smart classrooms was closer. A theoretical and empirical study of the relationship between learning engagement and computational thinking presents researchers and education practitioners with a method to improve students' computational thinking by building a learning environment and designing pedagogy. [ABSTRACT FROM AUTHOR]

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, "Inception Score", "Human Rank", and "Inference Time" are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]