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820827. Sweller, J. This process occurs through filtering out irrelevant information and identifying whats most important. The materials for this session is slightly different than the the other three sessions and this is intentional. Cognitive fit: A theory based analysis of the graphs versus tables literature. We certainly look at problem solving methods, often as patterns, and once recognized we apply the certain formulae or practices that lead to a solution. Many people use face recognition in photos when posting to social media. Of course not, your computer just turns itself on. [. Decomposition is simply the idea that youll likely break a complex problem down into more manageable pieces. Underwater cable detection in the images using edge classification based on texture information. This approach is often called computational thinking and is similar, in many ways, to the scientific method where were concerned with making predictions. Cognitive fit: An empirical study of recursion and iteration. articles published under an open access Creative Common CC BY license, any part of the article may be reused without It does not land on any spaces in between these squares. Although the brightness and details of the image enhanced by FE-GAN were restored partially, there is still a large gap from the image style under natural light, which is also the focus of future research. ; Park, T.; Isola, P.; Efros, A.A. Unpaired image-to-image translation using cycle-consistent adversarial networks. The One About Abstraction in Computational Thinking - Learning Abstraction is actually similar to the selective filtering function in our brains that gates the neural signals with which we are constantly bombarded so we can make sense of our world and focus on whats essential to us. PDF Promoting Undergraduate Pre-Service Teacher Computational Thinking Arts: Students generalize chord progressions for common musical genres into a set of general principles they can communicate. Similar to the EUVP dataset, using the trained CycleGAN [, Due to the lack of real underwater images, Silberman et al. in [, We used Pytorch 1.8.0 to implement the FE-GAN model. [. Simultaneously, our model conducted qualitative and quantitative analysis experiments on real underwater images and artificial synthetic image datasets respectively, which effectively demonstrates the generalization ability of the model. 11251134. The results show that our model produces better images, and has good generalization ability and real-time performance, which is more conducive to the practical application of underwater robot tasks. Computational Thinking is a set of techniques for solving complex problems that can be classified into three steps: Problem Specification, Algorithmic Expression, and Solution Implementation & Evaluation.The principles involved in each step of the Computational Thinking approach are listed above and discussed in detail below. and J.Z. Ever find yourself saying, 'where have I seen this before', could be a significant step in computational thinking. Examples of Pattern Recognition in Everyday Life. I can communicate the key features of problems and processes to others. Cognitive load during problem solving: Effects on learning. Decomposition and pattern recognition broke down the complex, and abstraction figures out how to work with the different parts efficiently and accurately. [, For the existing synthetic and real underwater image datasets, many GAN-based methods have been proven to have achieved good results in underwater image enhancement. All representations of a thing are inherently abstract. We will share this in the workshop and discuss under the pattern recognition lens. This is similar to travelling public transport problems. In this process, pattern recognition is Digital literacy refers to the knowledge and ability to use technology effectively and responsibly. Abstraction in coding and computer science is used to simplify strings of code into different functions. That is, she wants to block a single river so that beavers will not be able to travel between all pairs of ponds in the neighbourhood. Like the other elements of computational thinking, abstraction occurs inherently and can be addressed throughout the curriculum with students. I can describe problems and processes as a set of structured steps. In computational thinking, decomposition and pattern recognition break down the complex, while abstraction figures out how to work with the different . Data are the raw facts or observations of nature and computation is the manipulation of data by some systematic procedure carried out by some computing agent. Although computational thinking isnt a formal methodology for reasoning, it does encompass some basic principles that are useful in all fields and disciplines. https://doi.org/10.3390/electronics12051227, Han J, Zhou J, Wang L, Wang Y, Ding Z. FE-GAN: Fast and Efficient Underwater Image Enhancement Model Based on Conditional GAN. In recent years, many learning-based methods used, Structural reparameterization is used in our encoder to speed up inference. We conducted feature fusion experiments between the encoder and decoder utilizing concatenate and aggregation, respectively. Patterns are things that are the same within a problem and between problems. It can also increase effectiveness in the problem-solving process by creating solutions that can be repeated to resolve similar problems or goals. British Machine Vision Conference (BMVC), London, UK, 47 September 2017; Volume 1. Problem Specification: We start by analyzing the problem, stating it precisely, and establishing the criteria for the solution. ; Narasimhan, S.G. Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative adversarial nets. Students summarize a novel into a book review. To further improve the quality of the generated image, we introduce the pixel-level and image-level loss functions into the objective function formulation. 27942802. You ask your smart speaker what the weather will be that 2022 has been an exciting year at Learning.com! Cognitive Science, 12(2), 257285. Fast underwater image enhancement for improved visual perception. Patterns are pieces or sequences of data that have one or multiple similarities. 234241. This process uses inductive thinking and is needed for transferring a particular problem to a larger class of similar problems. Here, we also chose PSNR and SSIM as the evaluation indicators that regard aggregation and concatenate as the connection mode between the encoder and the decoder. methods, instructions or products referred to in the content. The results in the second, fifth, and last columns show that the fuzzy target can be detected in the processed image. Please note, this is not a comprehensive listing but is representative. However, it is more directly cognizant than math per se in its ability to compute and the potential benefits of doing so. All cats have a tail, eyes and fur, and also eat fish and meow. As it sounds, pattern recognition is all about recognizing patterns. Single underwater image enhancement using depth estimation based on blurriness. This helps the system storage by decreasing file size and also utilizes routines that are more efficient in processing. We know that the pattern of process at the timed lights in the area is for the cross-traffic turn lanes to turn next, then straight cross-traffic, the turn lanes in our direction, then finally our light will turn green. For example, you might want to search for students in a class, or who are being taught by a specific teacher all these involve some form of searching, the only thing that differs is what you are searching for. Part of the test results is shown in. The latest iteration of Google Drive call Drive File Streaming is a prime example of how this can be applied to our entire datastore. Ever find yourself saying, 'where have I seen this before', could be a significant step in computational thinking. We automatically process this pattern and can reasonably predict how much time we have before the light will turn green. Learn more about abstraction in computational thinking by downloading our free guide for educators: The Ultimate Guide to Computational Thinking for Educators. More specifically, it is a set of skills and processes that enable individuals to navigate complex Were excited to share that Learning.coms EasyTech has won in this years Tech & Learning Awards of Excellence: Best of 2022 in the Primary Technology is undoubtedly a fixture in students lives. Refs. As technology continues to become more and Texas schools have big changes on the horizon when it comes to digital skills. Decision Sciences, 22(2), 219240. Students conduct a survey of peers and analyze the data to note the key findings, create visualizations, present the findings. Pattern Recognition in Computational Thinking - learning.com Zhang, H.; Sun, L.; Wu, L.; Gu, K. DuGAN: An effective framework for underwater image enhancement. Information not needed is gender, age and date of birth as all this will be obtained from the student search. In this process, pattern recognition is Digital literacy refers to the knowledge and ability to use technology effectively and responsibly. This research was funded by Key R&D plan of Shandong Province (2020JMRH0101), National Deep Sea Center. Information is the result of processing data by putting it in a particular context to reveal its meaning. In computational thinking, one of the integral steps to the problem-solving process is pattern recognition. For them to use technology responsibly, safely and effectively, they need to understand the Digital literacy encompasses the skills required to use technology safely, effectively and responsibly. (2010). Abstraction in learning is the process of taking away or removing certain characteristics of a complex problem to reduce it to its most essential components. Copyright Learning.com 2023. Introduction. a creative chef for a series of smaller problems. While the phrase . Nayar, S.K. The conversion of Data to Information and then Knowledge can be done via computational problem solving. Element interactivity and intrinsic, extraneous, and germane cognitive load. Sinha, A., & Vessey, I. Making predictions based on identified patterns. Decomposition breaks down problems into smaller, more manageable parts. 127 0 obj <>stream Enhancing underwater imagery using generative adversarial networks. In Proceedings of the 2017 IEEE International Conference on Computational Photography (ICCP), Stanford, CA, USA, 1214 May 2017; pp. Or if you see a consistent trend upward in a stock for a number of months, you might decide to buy some shares in that stock. Experiments on different datasets show that the enhanced image can achieve higher PSNR and SSIM values, and the mAP value also achieved significant results in the object detection task. Inspired by this trend, some scholars proposed to use the computing power of convolutional neural networks to calculate the parameters that need to be estimated in the physical imaging model [, The emergence of the GAN (generative adversarial network) opened up another path for image enhancement issues. Structural reparameterization methods improved the ability of the model to extract features while also speeding up inference. TEM Journal. captured are operated to obtain the clear images as the desired output [. % To do this, they type the students surname, click enter, and information is displayed. Chen, R.; Cai, Z.; Cao, W. MFFN: An underwater sensing scene image enhancement method based on multiscale feature fusion network. English Language Arts Students summarize a novel into a book review. [, Yi, Z.; Zhang, H.; Tan, P.; Gong, M. Dualgan: Unsupervised dual learning for image-to-image translation. These general characteristics are called patterns when looking through the lens of computational thinking. Theyre suggestions of ideas youll likely need or require for most efforts but its not some process to pigeonhole your thinking or approach to a solution. future research directions and describes possible research applications. https://doi.org/10.1007/978-3-031-21970-2_26, DOI: https://doi.org/10.1007/978-3-031-21970-2_26. Article metric data becomes available approximately 24 hours after publication online. These images were taken in a poor light environment, and the overall number of this dataset is small, which brings a certain degree of difficulty to training. Cognitive Influences on Learning Programming | SpringerLink This article proposed an underwater image enhancement model FE-GAN (fast and efficient generative adversarial network) to solve these problems. In software engineering and computer science, abstraction is a technique for arranging complexity of computer systems. stream Learn how this concept can be integrated in student learning. Computational thinking is a problem-solving skill that develops an algorithm, or series of steps to perform a task or solve a problem. ; writingreview and editing, J.H. The process of computational thinking typically includes four parts: decomposition, pattern recognition, abstraction and algorithmic thinking. Abstraction helps students return to the larger problem that prompted this whole computational thinking adventure and identify the most important details from the earlier phases. Your alarm on your smart phone wakes you in the morningthats powered by computer science. Educators use abstraction when looking at vast sets of student data to focus on the most relevant numbers and trends. Abstraction in Computational Thinking | Learning.com No special It hides the underlying complexity in a programming language, which makes it simpler to implement algorithms and communicate with digital tools. In learning, we often look for patterns that would aid in knowledge transfer and solving problems in a variety of subjects including languages, music and chemistry. In computational thinking, decomposition and pattern recognition break down the complex, while abstraction figures out how to work with the different parts efficiently and accurately. Electronics | Free Full-Text | FE-GAN: Fast and Efficient Underwater Both of these test sets are from the UIEBD dataset, which is more challenging. most exciting work published in the various research areas of the journal. All of these are needed to come up with the eventual computational solution to the problem. 2023; 12(5):1227. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2126 July 2017; pp. Panetta, K.; Gao, C.; Agaian, S. Human-visual-system-inspired underwater image quality measures. Cognitive characteristics of learning Java, an object-oriented programming language. The pattern types have a similar solution and once you create an algorithm for each you may see some similarities, however recognizing the pattern type of the question helps to create the solution. Computational thinking (CT), recognized as a cognitive skill set for problem-solving (PS ) (), has been regarded as a fundamental capacity for students in the digital society ().Wing (2006) proposed a broad definition, emphasizing the fields of computer science in human endeavors: According to Wing (2006), "computational thinking involves solving problems, designing systems, and . 49524956. Help us to further improve by taking part in this short 5 minute survey, A Fast and Efficient Semi-Unsupervised Segmentation and Feature-Extraction Methodology for Artificial Intelligence and Radiomics Applications: A Preliminary Study Applied to Glioblastoma, Attention-Oriented Deep Multi-Task Hash Learning, https://irvlab.cs.umn.edu/resources/euvp-dataset, https://creativecommons.org/licenses/by/4.0/. Filter out information you do not need and be able to justify this. Pattern recognition as part of computational thinking is the process of identifying patterns in a data set to categorize, process and resolve the information more effectively. https://doi.org/10.3390/electronics12051227, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. Through the learning of paired images, FE-GAN achieved end-to-end underwater image enhancement, which effectively improved the image quality. Behind the scenes, a process will occur to add up the number of times the student was present for a lesson. ; Key Processes - these are the things that are critical to the system - for . (1991). 853862. The details of the hierarchical attention encoder (HAE) are shown in, For the discriminator, we use a Markov discriminator [, The conditional generative adversarial network introduces additional auxiliary information and can learn the mapping. Liu, X.; Gao, Z.; Chen, B.M. We will relate these examples to modern solutions that deal with many more data items. >> ty G ~i-*hd h"uZX{LQ!fbW " z(vW49s7$nZAax9A'21@R%B Nevertheless, our model does not perform well in enhancing darker images, especially in recovering details and textures, which means that it is still challenging in deeper waters, where artificial light sources are needed. Example 1: Can you spot the sequence in these numbers ? We can use this idea of abstraction to do things like make models, such as the map to represent the campus mentioned before. (2023). This helps to simplify or break down the problem to make it easier to resolve. While pattern recognition is most commonly discussed as a step in computational thinking, we automatically use pattern recognition in our everyday lives. interesting to readers, or important in the respective research area. If youre able to make repeated, precise, quantitative predictions, it implies that whichever model youve used or whichever mode of thinking youve employed, its actually working and should likely be re-employed. Our web-based curriculum for grades K-12 engages students as they learn keyboarding, online safety, applied productivity tools, computational thinking, coding and more. The green and blue light with a shorter wavelength will travel farther [, Many scholars have carried out in-depth research on the scattering phenomenon of light propagating in the medium. It may be that there are no common elements but it should still be a stage in the process. In order to be human-readable, please install an RSS reader. This data will be saved in a database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 1823 June 2018; pp. Zeng, L.; Sun, B.; Zhu, D. Underwater target detection based on Faster R-CNN and adversarial occlusion network. Li, C.; Guo, J.; Guo, C. Emerging from water: Underwater image color correction based on weakly supervised color transfer. Generalisation happens when you can spot common themes between patterns. Mathematics: Students conduct a survey of peers and analyze the data to note the key findings, create visualizations, present the findings. Underwater optical imaging: The past, the present, and the prospects. In Early childhood development: Concepts, methodologies, tools, and applications (pp. The new primary curriculum (up to Year 3) and the secondary . Founded in 1999, Learning.com provides educators with solutions to prepare their students with critical digital skills. 5: 1227. 1996-2023 MDPI (Basel, Switzerland) unless otherwise stated. [V9F oCt;pWtDC;m2VOr(xO RA 6Dlo$Qa& Ve ypW# A2Hl (GuzA /K 44809}$LXz#? HIGHLIGHTS who: Kay-Dennis Boom and colleagues from the (UNIVERSITY) have published the research work: Education and Information Technologies (2022) 27:8289-8310 Relationships between computational thinking and the quality of computer programs, in the Journal: (JOURNAL) what: This study examines the relationship between different forms of computational thinking and two different measures of . 2023. Its a drawing of a pipe. The contextualization of data can be considered a first approximation of information and the solution transforms the data to information and then actionable knowledge. For the Mixed dataset, we selected Test-R90 (90 paired images) and Test-C60 (60 unpaired images) as the test sets of paired and unpaired images respectively and compared them with the same methods in qualitative evaluation. Your home for data science. Author to whom correspondence should be addressed. As technology advances and adapts faster and Computational thinking is problem-solving. ; validation, J.H. Educational Psychology Review, 22, 123138. Zhang, H.; Zhang, S.; Wang, Y.; Liu, Y.; Yang, Y.; Zhou, T.; Bian, H. Subsea pipeline leak inspection by autonomous underwater vehicle. This is based on pattern recognition, similar to fingerprints. This can be seen further here. It should be pointed out that because the training set and test set of the Mixed dataset are relatively small, the experimental gap here is not very large. Abstraction is the idea, as alluded to earlier, of ignoring what you deem to be unessential details. Lets consider our Student IMS. >/)gU)FOW_s U}Bgw5]\0QOo, \rz0gx1Ato{C -T/~3IjdzjXM'l2%50TpY?.G/-SYrUT5Af7. Like the other elements of computational thinking, abstraction occurs inherently and can be addressed throughout curriculum with students. The publicly available dataset used in this research can be obtained through the following link: The authors would like to thank the Key R&D plan of Shandong Province (2020JMRH0101), National Deep Sea Center. Patricia is grumpy and wants to build one dam in each neighbourhood that will cause trouble. 694711. Through the structural re-parameterization approach, we design a dual residual block (DRB) and accordingly construct a hierarchical attention encoder (HAE), which can extract sufficient feature and texture information from different levels of an image, and with 11.52% promotion in GFLOPs. Computational Thinking Steps: In order to make predictions using computational thinking, we need to define three steps related to the problem and its solution: I should add a little caveat here: these rules for computational thinking are all well and good but theyre not really rules, per se; instead, think of them more like well-intentioned heuristics, or rules of thumb. positive feedback from the reviewers. Abstraction means hiding the complexity of something away from the thing that is going to be using it. Scientific Reports, 10(1), 110. The appropriateness of scratch and app inventor as educational environments for teaching introductory programming in primary and secondary education. Your task is to create the algorithm that will have the knight visit each square without going off the board. Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for To further verify the generalization ability of FE-GAN, we selected 990 images from the artificially synthesized dataset for testing and compared them with the corresponding ground truth images. Vision in bad weather. %%EOF If that context is the probability of occurrence, we end up with Shannons Information measure. It hides the underlying complexity in a programming language, which makes it simpler to implement algorithms and communicate with digital tools. Mirza, M.; Osindero, S. Conditional generative adversarial nets. ; data curation, L.W. Ignatov, A.; Kobyshev, N.; Timofte, R.; Vanhoey, K.; Van Gool, L. Dslr-quality photos on mobile devices with deep convolutional networks. Compared with the state-of-the-art methods, our model achieved better results. [. QT%^[g5XM.GTFySXX;S$[+?D@_[6E[jmYWNM~jxIoVx2I#UP$0mq'J"e'i[t4B/vdZciYh;'@3B$u$Wq|"60(puvCU We chose fps as a metric to measure inference time, which expresses as, For AUVs and ROVs, during underwater exploration activities, the purpose of improving the image quality is to improve the accuracy of tasks such as object detection and classification. Incorporating computational thinking into how I think about and plan my design projects helps eliminate unnecessary paths that will not work, which is a time saver. and J.Z. Why Is Computational Thinking Important for Students? In this sense, being able to represent the data and then manipulate it is itself a computational solution to a computable problem! In this section, we chose a relatively complete set of real and artificial synthetic underwater images to test the enhancement effect of the proposed model. It works by establishing a level of complexity on which a person interacts with the system, suppressing the more complex details below the current level. To summarise abstraction is the gathering of the general characteristics we need and the filtering out of the details and characteristics that we do not need.. A website providing comprehensive coverage of computer programming. In addition, we downloaded the Aquarium Combined dataset, then trained and tested this dataset on the same hardware environment as the FE-GAN enhancement experiment. Although these are differences, all School and College IMS systems fundamentally need to be able to take a register. Once you have identified a pattern, you can now start to describe it. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 2025 June 2021; pp. Pixel-level: Existing research shows that the, The model we proposed uses paired image training, and an objective function is constructed for this purpose to guide. https://doi.org/10.3390/electronics12051227, Han, Jie, Jian Zhou, Lin Wang, Yu Wang, and Zhongjun Ding. There is not a single reference to "algorithmic thinking" or "computational thinking". Feature papers represent the most advanced research with significant potential for high impact in the field. Abstracting Further As abstraction is a concept often explored in computer science, particularly with students learning to use object-oriented programming (OOP) languages, looking up . They constitute a way of reasoning or thinking logically and methodically about solving any problem in any area! In this paper, we proposed an underwater image enhancement model based on a conditional generative adversarial network. Abstraction is an essential part of computational thinking. The University of Texas at Austin. Using a Google public data site we ask participants to interpret visualization from the data. [, Fabbri, C.; Islam, M.J.; Sattar, J.