Journal articles on the topic 'Computational Learning Sciences'
To see the other types of publications on this topic, follow the link: Computational Learning Sciences.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Computational Learning Sciences.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Willcox, Karen. "Scientific Machine Learning." Aerospace Testing International 2020, no. 2 (June 2020): 14. http://dx.doi.org/10.12968/s1478-2774(22)50190-8.
2
Frank, Michael, Dimitris Drikakis, and Vassilis Charissis. "Machine-Learning Methods for Computational Science and Engineering." Computation 8, no. 1 (March 3, 2020): 15. http://dx.doi.org/10.3390/computation8010015.
Abstract:
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.
3
Birhane, Abeba, and Olivia Guest. "Towards Decolonising Computational Sciences." Kvinder, Køn & Forskning, no. 2 (February 8, 2021): 60–73. http://dx.doi.org/10.7146/kkf.v29i2.124899.
Abstract:
This article sets out our perspective on how to begin the journey of decolonising computational fi elds, such as data and cognitive sciences. We see this struggle as requiring two basic steps: a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and oppressive behaviours and principles towards women of colour; and b) rejection of the idea that centring individual people is a solution to system-level problems. The longer we ignore these two steps, the more “our” academic system maintains its toxic structure, excludes, and harms Black women and other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics and physiognomy. We propose that grappling with our fi elds’ histories and heritage holds the key to avoiding mistakes of the past. In contrast to, for example, initiatives such as “diversity boards”, which can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the work of many women of colour, we hope to advance the dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences — including but not limited to psychology, neuroscience, cognitive science, computer science, data science, statistics, machine learning, and artifi cial intelligence. We aspire to progress away fromthese fi elds’ stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurturesdemographically diverse researchers and ideas that critically challenge the status quo.
4
Nick, Mitchel Res. "Learning Through Computational Modeling." Computers in the Schools 14, no. 1-2 (December 4, 1997): 143–52. http://dx.doi.org/10.1300/j025v14n01_11.
5
Dodig-Crnkovic, G. "Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines." Philosophical Problems of Information Technologies and Cyberspace, no. 1 (July 14, 2021): 4–34. http://dx.doi.org/10.17726/philit.2021.1.1.
Abstract:
The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.
6
Dodig-Crnkovic, Gordana. "Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines." Philosophies 5, no. 3 (September 1, 2020): 17. http://dx.doi.org/10.3390/philosophies5030017.
Abstract:
The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.
7
Thiessen, Erik D. "What's statistical about learning? Insights from modelling statistical learning as a set of memory processes." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1711 (January 5, 2017): 20160056. http://dx.doi.org/10.1098/rstb.2016.0056.
Abstract:
Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274 , 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105 , 2745–2750; Thiessen & Yee 2010 Child Development 81 , 1287–1303; Saffran 2002 Journal of Memory and Language 47 , 172–196; Misyak & Christiansen 2012 Language Learning 62 , 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39 , 246–263; Thiessen et al. 2013 Psychological Bulletin 139 , 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37 , 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'.
8
Schaal, Stefan, Auke Ijspeert, and Aude Billard. "Computational approaches to motor learning by imitation." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1431 (February 17, 2003): 537–47. http://dx.doi.org/10.1098/rstb.2002.1258.
Abstract:
Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees–of–freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking—indeed, one could argue that we need to understand the complete perception–action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions.
9
Chen, Jiming, and Diwakar Shukla. "Integration of machine learning with computational structural biology of plants." Biochemical Journal 479, no. 8 (April 29, 2022): 921–28. http://dx.doi.org/10.1042/bcj20200942.
Abstract:
Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.
10
Rundo, Leonardo, Andrea Tangherloni, and Carmelo Militello. "Artificial Intelligence Applied to Medical Imaging and Computational Biology." Applied Sciences 12, no. 18 (September 8, 2022): 9052. http://dx.doi.org/10.3390/app12189052.
Abstract:
The Special Issue “Artificial Intelligence Applied to Medical Imaging and Computational Biology” of the Applied Sciences Journal has been curated from February 2021 to May 2022, which covered the state-of-the-art and novel algorithms and applications of Artificial Intelligence methods for biomedical data analysis, ranging from classic Machine Learning to Deep Learning [...]
11
Toivonen, Hannu. "Computational creativity beyond machine learning." Physics of Life Reviews 34-35 (December 2020): 52–53. http://dx.doi.org/10.1016/j.plrev.2020.06.007.
12
Oreski, Dijana. "Application of Machine Learning Methods for Data Analytics in Social Sciences." WSEAS TRANSACTIONS ON SYSTEMS 22 (March 7, 2023): 69–72. http://dx.doi.org/10.37394/23202.2023.22.8.
Abstract:
This article addresses the challenges in the application of artificial intelligence methods such as machine learning, computational intelligence and/or soft computing methods in social sciences. The literature review is performed in order to give a review of different approaches and methods that have been applied so far. The most used method in social sciences and management is the SWOT method, for the identification of strengths, weaknesses, opportunities, and threats when making strategic decisions. Two fundamental characteristics of previous approaches are the development of numerical models of utility functions and the possibility of upgrading these models by formalizing the intuition of strategic decision-makers. There are several shortcomings of the existing approaches. The application of computational intelligence and machine learning methods in social sciences is identified as one of the most challenging and promising areas, which could overcome identified shortcomings. The principles of one popular machine learning method, the decision tree, are explained and a demonstration is performed on the case study of churn prediction. Benchmarking data set from the publicly available repository is used to demonstrate the suggested approach Evaluation results measured through model accuracy and reliability gave promising results for further analysis. A developed predictive model could serve as a standalone tool or as support for decision-making in social sciences.
13
Fernández, Jacqueline M., Mariela E. Zúñiga, María V. Rosas, and Roberto A. Guerrero. "Experiences in Learning Problem-Solving through Computational Thinking." Journal of Computer Science and Technology 18, no. 02 (October 9, 2018): e15. http://dx.doi.org/10.24215/16666038.18.e15.
Abstract:
Computational Thinking (CT) represents a possible alternative for improving students’ academic performance in higher level degree related to Science, Technology, Engineering and Mathematics (STEM). This work describes two different experimental proposals with the aim of introducing computational thinking to the problem solving issue. The first one was an introductory course in the Faculty of Physical, Mathematical and Natural Sciences (FCFMyN) in 2017, for students enrolled in computer science related careers. The other experience was a first attempt to introduce CT to students and teachers belonging to not computer related faculties at the National University of San Luis (UNSL). Both initiatives use CT as a mean of improving the problem solving process based on the four following elementary concepts: Decomposition, Abstraction, Recognition of patterns and Algorithm. The results of the experiences indicate the relevance of including CT in the learning problem solving issue in different fields. The experiences also conclude that a mandatory CT related course is necessary for those careers having computational problems solving and/or programming related subjects during the first year of their curricula. Part of this work was presented at the XXIII Argentine Congress of Computer Science (CACIC).
14
Cabrero-Holgueras, José, and Sergio Pastrana. "SoK: Privacy-Preserving Computation Techniques for Deep Learning." Proceedings on Privacy Enhancing Technologies 2021, no. 4 (July 23, 2021): 139–62. http://dx.doi.org/10.2478/popets-2021-0064.
Abstract:
Abstract Deep Learning (DL) is a powerful solution for complex problems in many disciplines such as finance, medical research, or social sciences. Due to the high computational cost of DL algorithms, data scientists often rely upon Machine Learning as a Service (MLaaS) to outsource the computation onto third-party servers. However, outsourcing the computation raises privacy concerns when dealing with sensitive information, e.g., health or financial records. Also, privacy regulations like the European GDPR limit the collection, distribution, and use of such sensitive data. Recent advances in privacy-preserving computation techniques (i.e., Homomorphic Encryption and Secure Multiparty Computation) have enabled DL training and inference over protected data. However, these techniques are still immature and difficult to deploy in practical scenarios. In this work, we review the evolution of the adaptation of privacy-preserving computation techniques onto DL, to understand the gap between research proposals and practical applications. We highlight the relative advantages and disadvantages, considering aspects such as efficiency shortcomings, reproducibility issues due to the lack of standard tools and programming interfaces, or lack of integration with DL frameworks commonly used by the data science community.
15
Ginestet, Cedric. "Semisupervised Learning for Computational Linguistics." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 3 (June 2009): 694. http://dx.doi.org/10.1111/j.1467-985x.2009.00595_2.x.
16
Perez, B., C. Castellanos, and D. Correal. "Measuring the quality of the blended learning approach to teaching computational sciences." Journal of Physics: Conference Series 1587 (July 2020): 012021. http://dx.doi.org/10.1088/1742-6596/1587/1/012021.
17
Xi, Yue, Wenjing Jia, Qiguang Miao, Xiangzeng Liu, Xiaochen Fan, and Jian Lou. "DyCC-Net: Dynamic Context Collection Network for Input-Aware Drone-View Object Detection." Remote Sensing 14, no. 24 (December 13, 2022): 6313. http://dx.doi.org/10.3390/rs14246313.
Abstract:
Benefiting from the advancement of deep neural networks (DNNs), detecting objects from drone-view images has achieved great success in recent years. It is a very challenging task to deploy such DNN-based detectors on drones in real-life applications due to their excessive computational costs and limited onboard computational resources. Large redundant computation exists because existing drone-view detectors infer all inputs with nearly identical computation. Detectors with less complexity can be sufficient for a large portion of inputs, which contain a small number of sparse distributed large-size objects. Therefore, a drone-view detector supporting input-aware inference, i.e., capable of dynamically adapting its architecture to different inputs, is highly desirable. In this work, we present a Dynamic Context Collection Network (DyCC-Net), which can perform input-aware inference by dynamically adapting its structure to inputs of different levels of complexities. DyCC-Net can significantly improve inference efficiency by skipping or executing a context collector conditioned on the complexity of the input images. Furthermore, since the weakly supervised learning strategy for computational resource allocation lacks of supervision, models may execute the computationally-expensive context collector even for easy images to minimize the detection loss. We present a Pseudo-label-based semi-supervised Learning strategy (Pseudo Learning), which uses automatically generated pseudo labels as supervision signals, to determine whether to perform context collector according to the input. Extensive experiment results on VisDrone2021 and UAVDT, show that our DyCC-Net can detect objects in drone-captured images efficiently. The proposed DyCC-Net reduces the inference time of state-of-the-art (SOTA) drone-view detectors by over 30 percent, and DyCC-Net outperforms them by 1.94% in AP75.
18
Guha Majumdar, Mrittunjoy. "Quantum 3.0: Quantum Learning, Quantum Heuristics and Beyond." Current Natural Sciences and Engineering 1, no. 3 (May 29, 2024): 175–87. http://dx.doi.org/10.63015/3a-2425.1.3.
Abstract:
Quantum learning paradigms address the question of how best to harness conceptual elements of quantum mechanics and information processing to improve operability and functionality of a computing system for specific tasks through experience. It is one of the fastest evolving framework, which lies at the intersection of physics, statistics and information processing, and is the next frontier for data sciences, machine learning and artificial intelligence. Progress in quantum learning paradigms is driven by multiple factors: need for more efficient data storage and computational speed, development of novel algorithms as well as structural resonances between specific physical systems and learning architectures. Given the demand for better computation methods for data-intensive processes in areas such as advanced scientific analysis and commerce as well as for facilitating more data-driven decision-making in education, energy, marketing, pharmaceuticals and healthcare, finance and industry.
19
Guha Majumdar, Mrittunjoy. "Quantum 3.0: Quantum Learning, Quantum Heuristics and Beyond." Current Natural Sciences and Engineering 1, no. 3 (May 29, 2024): 175–87. http://dx.doi.org/10.63015/3a-2425.1.1.
Abstract:
Quantum learning paradigms address the question of how best to harness conceptual elements of quantum mechanics and information processing to improve operability and functionality of a computing system for specific tasks through experience. It is one of the fastest evolving framework, which lies at the intersection of physics, statistics and information processing, and is the next frontier for data sciences, machine learning and artificial intelligence. Progress in quantum learning paradigms is driven by multiple factors: need for more efficient data storage and computational speed, development of novel algorithms as well as structural resonances between specific physical systems and learning architectures. Given the demand for better computation methods for data-intensive processes in areas such as advanced scientific analysis and commerce as well as for facilitating more data-driven decision-making in education, energy, marketing, pharmaceuticals and healthcare, finance and industry.
20
Amour, Idrissa S. "STACK for Computational Science, Mathematics and Engineering e-Learners." Tanzania Journal of Engineering and Technology 42, no. 4 (February 23, 2024): 69–80. http://dx.doi.org/10.52339/tjet.v42i4.825.
Abstract:
E-Learning platforms such as Moodle, Blackboard and Canvas have got reasonable attention in teaching and learning processes. However, when it comes to assessment and interactive learning activities they offer little service to science, engineering, and mathematics e-learners. In this work, we present the application of the System for Teaching and Assessment using Computer Algebra Kernel (STACK) as a plugin in a Learning Management System (LMS) to address the issue. Different features of STACK are demonstrated and discussed. As an LMS plugin, STACK can be used for interactive delivery of content as well as an assessment tool. Here, examples from mathematics, physical sciences and engineering are demonstrated. The use of STACK extends the applicability of LMS for a wider range of subjects to address existing inability to handle higher level mathematical and computational skills. Additionally, the use of STACK in an LMS is useful in handling tutorials for large classes especially when a blended delivery mode is preferred.
21
Katai, Zoltan. "Promoting computational thinking of both sciences- and humanities-oriented students: an instructional and motivational design perspective." Educational Technology Research and Development 68, no. 5 (April 3, 2020): 2239–61. http://dx.doi.org/10.1007/s11423-020-09766-5.
Abstract:
Abstract We proposed to investigate whether properly calibrated e-learning environments can efficiently promote computational thinking of both sciences- and humanities-oriented people. We invited two groups of students (sciences- vs. humanities-oriented members) to participate in a six-stage learning session: to watch a folk-dance illustration (s1) and an animation (s2) of the bubble-sort algorithm; to reconstruct the algorithm on the same input (s3); to orchestrate the algorithm on a random input stored in a white(s4)/black(s5) array (visible/invisible sequence) and to watch a parallel simulation of several sorting algorithms as they work side-by-side on different color-scale bars (s6). To assess the current motivation of students we created nine specific questionnaires (Q1–9). The experiment we conducted included the following task sequence: Q1–2, s1, Q3, s2, Q4, s3, Q5, s4, Q6, s5, Q7, s6, Q8–9. We focused on assessing the motivational contributions of the generated (situational factors) emotions, challenge and active involvement during the e-learning experience. Research results revealed that there are no unbridgeable differences in the way these two groups relate to e-learning processes that aim to promote computational thinking. Although sciences-oriented students’ motivational-scores were consistently superior to their humanities-oriented colleagues, there was strong correlation between them; furthermore, differences diminished as both groups advanced with their learning tasks.
22
Angermueller, Christof, Tanel Pärnamaa, Leopold Parts, and Oliver Stegle. "Deep learning for computational biology." Molecular Systems Biology 12, no. 7 (July 2016): 878. http://dx.doi.org/10.15252/msb.20156651.
23
Rosenfeld, Ariel, and Avshalom Elmalech. "Information Science Students’ Background and Data Science Competencies: An Exploratory Study." Journal of Education for Library and Information Science 64, no. 4 (October 1, 2023): 385–403. http://dx.doi.org/10.3138/jelis-2021-0076.
Abstract:
Many Library and Information Science (LIS) training programs are gradually expanding their curricula to include computational data science courses such as supervised and unsupervised machine learning. These programs focus on developing both “classic” information science competencies as well as core data science competencies among their students. Since data science competencies are often associated with mathematical and computational thinking, departmental officials and prospective students often raise concerns regarding the appropriate background students should have in order to succeed in this newly introduced computational content of the LIS training programs. In order to address these concerns, we report on an exploratory study through which we examined the 2020 and 2021 student classes of Bar-Ilan University's LIS graduate training, focusing on the computational data science courses (i.e., supervised and unsupervised machine learning). Our study shows that contrary to many of the concerns raised, students from the humanities performed as well (and in some cases significantly better) on data science competencies compared to those from the social sciences and had better success in the training program as a whole. In addition, students’ undergraduate GPA acted as an adequate indicator for both their success in the training program and in the data science part thereof. In addition, we find no evidence to support concerns regarding age or sex. Finally, our study suggests that the computational data science part of students’ training is very much aligned with the rest of their training program.
24
Kumala, Farida Nur, Arnelia Dwi Yasa, Adam Bin Haji Jait, Aji Prasetya Wibawa, and Laily Hidayah. "Patterns of Computational Thinking Skills for Elementary Prospectives Teacher in Science Learning: Gender Analysis Studies." International Journal of Elementary Education 7, no. 4 (December 28, 2023): 646–56. http://dx.doi.org/10.23887/ijee.v7i4.68611.
Abstract:
The PISA data results show that computational thinking abilities are still lacking. Computational thinking ability is influenced by gender. This research aims to analyze patterns of computational thinking skills of prospective elementary school teachers based on gender at 8 universities in Indonesia. In this research, the components of computational thinking skills analyzed are abstraction, algorithmic, decomposition, and pattern recognition. This research is a mix method research with research subjects as many as 234 prospective elementary school teachers at 8 higher educational institutions. The instruments used were test and interviews. The data analysis technique used is a quantitative data analysis technique using SEM PLS and for qualitative data analysis using miles and Huberman. The research results show that computational thinking skills are still low on the decomposition and pattern recognition components. Based on the SEM PLS test results, it shows that computational thinking abilities are related to gender. In general, the computational thinking ability of female students is slightly higher in all sub-indicators than men and there are differences in the pattern of computational thinking ability between male and female elementary school teacher prospective. The ability of prospective female elementary school teachers to answer in more detail and more structured, while the answers of male prospective teachers are shorter and less comprehensive. Recommendations for developing computational thinking skills by developing problem-based learning, contextual project-based learning and STEAM based learning.
25
Bjorck, Johan, Brendan H. Rappazzo, Qinru Shi, Carrie Brown-Lima, Jennifer Dean, Angela Fuller, and Carla Gomes. "Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 14711–20. http://dx.doi.org/10.1609/aaai.v35i17.17728.
Abstract:
The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species -- invading flora and fauna that are estimated to cause damages in the billions of dollars annually. As an ongoing collaboration with the New York Natural Heritage Program, we consider the use of unsupervised deep learning techniques for dimensionality reduction of remote sensing images, which can reduce sample complexity for downstream tasks and decreases the need for large labeled data sets. We consider spatially augmenting contrastive learning by training neural networks to correctly classify two nearby patches of a landscape as such. We demonstrate that this approach improves upon previous methods and naive classification for a large-scale data set of remote sensing images derived from invasive species observations obtained over 30 years. Additionally, we simulate deployment in the field via active learning and evaluate this method on another important challenge in computational sustainability -- landcover classification -- and again find that it outperforms previous baselines.
26
Filatova, Darya, Charles El-Nouty, and Uladzislau Punko. "HIGH-THROUGHPUT DEEP LEARNING ALGORITHM FOR DIAGNOSIS AND DEFECTS CLASSIFICATION OF WATERPROOFING MEMBRANES." International Journal for Computational Civil and Structural Engineering 16, no. 2 (June 26, 2020): 26–38. http://dx.doi.org/10.22337/2587-9618-2020-16-2-26-38.
Abstract:
The work is devoted to the development of a high-performance deep learning algorithm related to the diagnosis and classification of defects of water-repellent membranes. The mechanism of constructing visual models of the membrane surface is discussed. This allows to get the representative training data set. The proposed methodology consists in the sequent transformations of pixel-image intensities to find defected fragments on the membrane's surface. The computational algorithm is based on the architecture of convolution neural networks. To assess its effectiveness, the "confidence of confidence" criterion is proposed. The presented computations show that the methodology can be successfully applied in material sciences, for example, to study the properties of building materials, or in forensic science when examining the causes of construction catastrophes.
27
R.D., Dhaniya, and Dr Umamaheswari K.M. "Brain Tumor Analysis Empowered with Machine Learning and Deep Learning: A Comprehensive Review with its Recent Computational Techniques." Webology 19, no. 1 (January 20, 2022): 764–79. http://dx.doi.org/10.14704/web/v19i1/web19054.
Abstract:
In driving the medical image research machine-learning and deep-learning algorithm are growing expeditiously. The premature conjecture of disease needs substantial attempts to diagnose the disease. The machine learning algorithm confesses the software application to study from the data and predicts more accurate outcome. The deep learning algorithm drives on extensive dataset imparts on high end machine and clarifies the problem end to end. The primary focus on the survey is to high-spots the machine and deep-learning approaches in medical image analysis that endorses the decision-making practices. The paper provides a plan for the researchers to perceive the extant schemes sustained out for medical imaging with its recognition and hindrances of the machine and deep learning algorithm.
28
Lee, Chia-Jung, Chi-Jen Lu, and Shi-Chun Tsai. "Extracting Computational Entropy and Learning Noisy Linear Functions." IEEE Transactions on Information Theory 57, no. 8 (August 2011): 5485–96. http://dx.doi.org/10.1109/tit.2011.2158897.
29
Ghulam, Ali, Rahu Sikander, and Farman Ali. "AI and Machine Learning-based practices in various domains: A Survey." VAWKUM Transactions on Computer Sciences 10, no. 1 (June 30, 2022): 21–41. http://dx.doi.org/10.21015/vtcs.v10i1.1257.
Abstract:
In several projects in computational biology (CB), bioinformatics, health informatics(HI), precision medicine(PM) and precision agriculture(PA) machine learning(ML) has become a primary resource. In this paper we studied the use of machine learning in the development of computational methods for top five research aeras. The last few years have seen an increased interest in Artificial Intelligence (AI), comprehensive ML and DL techniques for computational method development. Over the years, an enormous amount of research has been biomedical scientists still don’t have more knowledge to handle a biomedical projects efficiently and may, therefore, adopt wrong methods, which can lead to frequent errors or inflated tests. Healthcare has become a fruitful ground for artificial intelligence (AI) and machine learning due to the increase in the volume, diversity, and complexity of data (ML). Healthcare providers and life sciences businesses already use a variety of AI technologies. The review summarizes a traditional machine learning cycle, several machine learning algorithms, various techniques to data analysis, and effective use in five research areas. In this comprehensive review analysis, we proposed 10 ten rapid and accurate practices to use ML techniques in health informatics, bioinformatics, computational and systems biology, precision medicine and precision agriculture, avoid some common mistakes that we have observed several hundred times in several computational method works.
30
Pau, Danilo Pietro, and Fabrizio Maria Aymone. "Mathematical Formulation of Learning and Its Computational Complexity for Transformers’ Layers." Eng 5, no. 1 (December 21, 2023): 34–50. http://dx.doi.org/10.3390/eng5010003.
Abstract:
Transformers are the cornerstone of natural language processing and other much more complicated sequential modelling tasks. The training of these models, however, requires an enormous number of computations, with substantial economic and environmental impacts. An accurate estimation of the computational complexity of training would allow us to be aware in advance about the associated latency and energy consumption. Furthermore, with the advent of forward learning workloads, an estimation of the computational complexity of such neural network topologies is required in order to reliably compare backpropagation with these advanced learning procedures. This work describes a mathematical approach, independent from the deployment on a specific target, for estimating the complexity of training a transformer model. Hence, the equations used during backpropagation and forward learning algorithms are derived for each layer and their complexity is expressed in the form of MACCs and FLOPs. By adding all of these together accordingly to their embodiment into a complete topology and the learning rule taken into account, the total complexity of the desired transformer workload can be estimated.
31
Chanda, Pritam, Eduardo Costa, Jie Hu, Shravan Sukumar, John Van Hemert, and Rasna Walia. "Information Theory in Computational Biology: Where We Stand Today." Entropy 22, no. 6 (June 6, 2020): 627. http://dx.doi.org/10.3390/e22060627.
Abstract:
“A Mathematical Theory of Communication” was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon’s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology—gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.
32
LI, Suiqing, Xinling CHEN, Yuzhu ZHAI, Yijie ZHANG, Zhixing ZHANG, and Chunliang FENG. "The computational and neural substrates underlying social learning." Advances in Psychological Science 29, no. 4 (2021): 677. http://dx.doi.org/10.3724/sp.j.1042.2021.00677.
33
Xavier, Rita, and Leandro Nunes de Castro. "On the use of evolutionary and swarm intelligence algorithms in transfer learning approaches: a review." International Journal of Biosensors & Bioelectronics 8, no. 2 (December 26, 2023): 58–64. http://dx.doi.org/10.15406/ijbsbe.2023.08.00235.
Abstract:
Transfer Learning (TL) utilizes pre-trained models to solve similar problems. The knowledge from the original model is transferred to a new model during training, aiming to leverage previous knowledge in a new task. Natural Computing (NC) algorithms, such as Evolutionary Computation (EC) and Swarm Intelligence (SI), draw inspiration from nature, adapting more easily to new computational problems. This bio-inspired adaptation can enhance the performance of TL techniques, improving generalization and reducing computational costs. We investigate how evolutionary and swarm-intelligence algorithms are applied in TL, their contributions, the addressed problems, and the conducted experiments. We employ a systematic review following the PRISMA protocol, PICOS strategy, and START software to analyze primary studies.
34
Yanti, Yuli, Diah Rizki Nur Kalifah, and Nurul Hidayah. "Implementing Computational Thinking Skills in Socio Scientific Issue (SSI) of Force Material Around Us at Elementary School." E3S Web of Conferences 482 (2024): 04001. http://dx.doi.org/10.1051/e3sconf/202448204001.
Abstract:
Computational thinking is a sophisticated problem-solving approach based on computer science. Implementing computational thinking in elementary schools remains a challenge for education, particularly for teachers and students. Teachers must construct learning experiences using computational thinking to make the learning process more engaging, while students must solve issues logically, systematically, and effectively. This study aims to present an overview of the application of computational thinking in natural and social sciences (IPAS) learning and identify its application in fifth-grade elementary school students. The research employed a qualitative research design with a single one-shot case study. The research subjects were 40 fifth-grade students and four teachers. This investigation was conducted during a single meeting that followed the lesson plan for the force material around us. The findings revealed that (a) analysis of student activity data yielded a percentage of 81.25%; (b) the data analysis of student responses to learning yielded favorable results, approaching 100%. According to interviews and observations of CT (Computational Thinking) in SSI (Socio-scientific Issue) in IPAS subjects, implementing CT learning on the forces around us might bring up aspects of the CT foundation, such as pattern recognition decomposition, abstraction, and algorithms. The learning scenario is that students are requested to assess many types of activities that occur in everyday life. Additionally, students will outline various actions that use force.
35
Merino-Armero, José Miguel, José Antonio González-Calero, Ramón Cózar-Gutiérrez, and Javier del Olmo-Muñoz. "Unplugged Activities in Cross-Curricular Teaching: Effect on Sixth Graders’ Computational Thinking and Learning Outcomes." Multimodal Technologies and Interaction 6, no. 2 (January 28, 2022): 13. http://dx.doi.org/10.3390/mti6020013.
Abstract:
There is a debate about the way to introduce computational thinking (CT) in schools. Different proposals are on the table; these include the creation of new computational areas for developing CT, the introduction of CT in STEM areas, and the cross-curricular integration of CT in schools. There is also concern that no student should be left behind, independently of their economic situation. To this effect, an unplugged approach is the most cost-effective solution. In addition, this topic is interesting in the context of a pandemic situation that has prevented the sharing of materials between students. This study analyzes an unplugged cross-curricular introduction of CT in the Social Sciences area among sixth grade students. A group of 14 students was selected to carry out an unplugged intervention design—where they were required to program an imaginary robot on paper—in the Social Sciences area. Their CT development and academic results were compared to those of 31 students from the control group who continued attending regular classes. Results showed that an unplugged teaching style of CT in Social Sciences lessons significantly increased CT (p < 0.001) and with a large effect size (d = 1.305) without differences in students’ academic achievement. The findings show that children can potentially develop their CT in non-STEM lessons, learning the same curricular contents, and maintaining their academic results.
36
Boldea, Afrodita L. "The impact of teaching computational astronomy on the development of students' computer skills." EPJ Web of Conferences 200 (2019): 02001. http://dx.doi.org/10.1051/epjconf/201920002001.
Abstract:
Some blended methods of teaching-learning were successfully used in teaching the astronomy and astrometry of asteroids to students in Computer Sciences at the University of Craiova, using real astronomical data about celestial objects from our Solar System, obtained from the Astronomical Observatory Isaac Newton (La Palma, Spain). The students were asked to develop some small scripts in order to facilitate the detection and the analysis of data for new discovered asteroids, a request that improved their capacities to understand and apply various modern concepts of Computer Graphics, Data Base and Web design. This approach to learning brings new challenges for the students, new opportunities for the process of professional training in Computer Sciences and provided good result in very short term, the students acquiring very fast the necessary skills to approach both the professional level of Web programming and the modern research area of Computational Astronomy.
37
Levites, Yulia A., Myles Joshua T. Tan, Akshita Gupta, Jamie L. Fermin, Samuel P. Border, Sanjay Jain, John Tomaszewski, Yulia A. Levites Strekalova, and Pinaki Sarder. "89 Bridging Cell Biology and Engineering Sciences: Interdisciplinary Team-based Training in Computational Pathology." Journal of Clinical and Translational Science 7, s1 (April 2023): 25. http://dx.doi.org/10.1017/cts.2023.172.
Abstract:
OBJECTIVES/GOALS: Computational pathology is an emerging discipline that resides at the intersection of engineering, computer science, and pathology. There is a growing need to develop innovative pedagogical approaches to train future computational pathologists who have diverse educational backgrounds. METHODS/STUDY POPULATION: Our work proposes an iterative approach toward teaching master’s and Ph.D. students from various backgrounds, such as electrical engineering, biomedical engineering, and cell biology the basics of cell-type identification. This approach is grounded in the active learning framework to allow for observation, reflection, and independent application. The learners are trained by a team of an electrical engineer and pathologist and provided with eight images containing a glomerulus. They must then classify nuclei in each of the glomeruli as either a podocyte (blue), endothelial cell (green), or mesangial cell (red). RESULTS/ANTICIPATED RESULTS: A simple web application was built to calculate agreement, measured using Cohen’s kappa, between annotators for both individual glomeruli and across all eight images. Automating the process of providing feedback from an expert renal pathologist to the learner allows for learners to quickly determine where they can improve. After initial training, agreement scores for cells scored by both the learner and the expert were high (0.75), however, when including cells not scored by both the agreement was relatively low (0.45). This indicates that learners needed more instruction on identifying unique cells within each image. This low-stakes approach encourages exploratory and generative learning. DISCUSSION/SIGNIFICANCE: Computation medical sciences require interdisciplinary training methods. We report on a robust approach for team-based mentoring and skill development. Future implementations will include undergraduate learners and provide opportunities for graduate students to engage in near-peer mentoring.
38
El Alami, Marc, Nicolas Casel, and Denis Zampunieris. "An architecture for e‐learning system with computational intelligence." Electronic Library 26, no. 3 (June 6, 2008): 318–28. http://dx.doi.org/10.1108/02640470810879473.
39
Howley, Iris K., and Carolyn Penstein Rose. "Towards Careful Practices for Automated Linguistic Analysis of Group Learning." Journal of Learning Analytics 3, no. 3 (December 19, 2016): 239–62. http://dx.doi.org/10.18608/jla.2016.33.12.
Abstract:
This paper reviews work in progress towards bridging the field of linguistics and its operationalizations of discourse, and that of frameworks for studying collaborative learning that are rooted directly in the learning sciences. We begin with the vision of a multi-dimensional coding and counting analysis approach that might serve as a boundary object between the variety of methodological approaches to analysis of collaborative learning that exist within the Learning Sciences. We outline what we have discovered from a combination of hand coding, comparison with alternative analytic approaches including network analytic and qualitative approaches, correlational analyses in connection with learning-relevant extralinguistic variables, and computational modeling. We explore both the contribution of work to date as well as the many remaining challenges.
40
Sikora, Riyaz, and Michael J. Shaw. "A Computational Study of Distributed Rule Learning." Information Systems Research 7, no. 2 (June 1996): 189–97. http://dx.doi.org/10.1287/isre.7.2.189.
41
Czech, Sławomir. "Evaluating the Role of Machine Learning in Economics: A Cutting-Edge Addition or Rhetorical Device?" Studies in Logic, Grammar and Rhetoric 68, no. 1 (December 1, 2023): 279–93. http://dx.doi.org/10.2478/slgr-2023-0014.
Abstract:
Abstract This paper explores the integration of machine learning into economics and social sciences, assessing its potential impact and limitations. It introduces fundamental machine learning concepts and principles, highlighting the differences between the two disciplines, particularly the focus on causal inference in economics and prediction in machine learning. The paper discusses diverse applications of machine learning, from extracting insights from unstructured data to creating novel indicators and improving predictive accuracy, while also addressing challenges related to data quality, computational efficiency, and data ownership. It emphasizes the importance of standardization, transparency, and ethical considerations in prediction tasks, recognizing that machine learning is a powerful tool but cannot replace economic theory. Ultimately, researchers remain optimistic about the transformative potential of machine learning in re-shaping research methodologies and generating new insights in economics and social sciences.
42
Gago, Eduardo. "Teaching and Learning Computational Mathematics with Intensive Application of the Virtual Campus." WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 20 (October 13, 2023): 81–90. http://dx.doi.org/10.37394/232010.2023.20.11.
Abstract:
Nowadays, the field of professional development in engineering requires appropriate and relevant training for a satisfactory insertion in the labor market. This requires that educational institutions provide not only the specific knowledge of the career being studied but also must complement it with other skills necessary for adequate performance in the work environment. Based on these premises, this paper describes the design of a classroom experience to be implemented in the subject of Advanced Calculus (AC), a third-level subject of the Mechanical Engineering course. The proposed activities are developed in the Computer and Multidisciplinary Laboratory of Basic Sciences available at the faculty and seek to encourage the use of the virtual campus, where the mediation between the content of the subject and the concrete applications of simple engineering models are the axis to develop the topic: Analytical Functions of Complex Variables (AFCV).
43
Niazai, Shafiullah, Ariana Abdul Rahimzai, and Hamza Atifnigar. "Applications of MATLAB in Natural Sciences: A Comprehensive Review." European Journal of Theoretical and Applied Sciences 1, no. 5 (September 1, 2023): 1006–15. http://dx.doi.org/10.59324/ejtas.2023.1(5).87.
Abstract:
In the natural sciences, MATLAB is a versatile and essential tool that has revolutionized research across various disciplines, including physics, chemistry, biology, geology, and environmental sciences. This review paper provides a comprehensive overview of MATLAB's applications in data analysis, modeling, simulation, image processing, computational chemistry, environmental sciences, physics, engineering, and data visualization. MATLAB simplifies data analysis by handling complex datasets, performing statistical analyses, and aiding in tasks like curve fitting and spectral analysis. In modeling and simulation, it enables the creation of predictive models for intricate systems, facilitating simulations of physical processes, ecological dynamics, and chemical reactions. In image processing, MATLAB enhances and analyzes images, benefiting fields such as medical imaging and remote sensing. For computational chemistry, MATLAB offers a rich library of tools for exploring molecular structures and simulating chemical reactions. Environmental sciences rely on MATLAB for climate data analysis and ecological modeling. In physics and engineering, it is invaluable for simulating complex systems and analyzing experimental data. Additionally, MATLAB's data visualization capabilities allow scientists to create compelling visuals for effective communication. While challenges like licensing costs exist, efforts are underway to address these issues and enhance integration with other software, including artificial intelligence and machine learning tools. Overall, MATLAB's computational power and versatility are fundamental to advancing natural sciences research, making it an invaluable resource for scientists and researchers across various disciplines.
44
Xiao, Zhifeng, Linjun Qian, Weiping Shao, Xiaowei Tan, and Kai Wang. "Axis Learning for Orientated Objects Detection in Aerial Images." Remote Sensing 12, no. 6 (March 12, 2020): 908. http://dx.doi.org/10.3390/rs12060908.
Abstract:
Orientated object detection in aerial images is still a challenging task due to the bird’s eye view and the various scales and arbitrary angles of objects in aerial images. Most current methods for orientated object detection are anchor-based, which require considerable pre-defined anchors and are time consuming. In this article, we propose a new one-stage anchor-free method to detect orientated objects in per-pixel prediction fashion with less computational complexity. Arbitrary orientated objects are detected by predicting the axis of the object, which is the line connecting the head and tail of the object, and the width of the object is vertical to the axis. By predicting objects at the pixel level of feature maps directly, the method avoids setting a number of hyperparameters related to anchor and is computationally efficient. Besides, a new aspect-ratio-aware orientation centerness method is proposed to better weigh positive pixel points, in order to guide the network to learn discriminative features from a complex background, which brings improvements for large aspect ratio object detection. The method is tested on two common aerial image datasets, achieving better performance compared with most one-stage orientated methods and many two-stage anchor-based methods with a simpler procedure and lower computational complexity.
45
Peel, Amanda, Troy D. Sadler, and Patricia Friedrichsen. "Using Unplugged Computational Thinking to Scaffold Natural Selection Learning." American Biology Teacher 83, no. 2 (February 1, 2021): 112–17. http://dx.doi.org/10.1525/abt.2021.83.2.112.
Abstract:
Computational thinking (CT) is a thought process composed of computer science ideas and skills that can be applied to solve problems and better understand the world around us. With the increase in technology and computing, STEM disciplines are becoming interwoven with computing. In order to better prepare students for STEM careers, computational literacy needs to be developed in K–12 education. We advocate the introduction of computational literacy through the incorporation of CT in core science courses, such as biology. Additionally, at least some of this integration should be unplugged, or without computers, so that all schools can participate in developing computational literacy. These lessons integrate unplugged CT and science content to help students develop CT competencies and learn natural selection content simultaneously through a series of lessons in which unplugged CT is leveraged for natural selection learning within varying contexts. In these lessons, students engage in the creation of handwritten algorithmic explanations of natural selection. Students build CT skills while making sense of the process, resulting in converged learning about CT and science. This article presents a description of CT, the specifics of the classroom implementation and lessons, student work and outcomes, and conclusions drawn from this work.
46
Mitelman, Amichai, Beverly Yang, Alon Urlainis, and Davide Elmo. "Coupling Geotechnical Numerical Analysis with Machine Learning for Observational Method Projects." Geosciences 13, no. 7 (June 28, 2023): 196. http://dx.doi.org/10.3390/geosciences13070196.
Abstract:
In observational method projects in geotechnical engineering, the final geotechnical design is decided upon during actual construction, depending on the observed behavior of the ground. Hence, engineers must be prepared to make crucial decisions promptly, with few available guidelines. In this paper, we propose coupling numerical analysis with machine learning (ML) algorithms for enhancing the decision process in observational method projects. The proposed methodology consists of two main computational steps: (1) data generation, where multiple numerical models are automatically generated according to the anticipated range of input parameters, and (2) data analysis, where input parameters and model results are analyzed with ML models. Using the case study of the Semel tunnel in Tel Aviv, Israel, we demonstrate how this computational process can contribute to the success of observational method projects through (1) the computation of feature importance, which can assist with better identifying the key features that drive failure prior to project execution, (2) providing insights regarding the monitoring plan, as correlative relationships between various results can be tested, and (3) instantaneous predictions during construction.
47
Wu, Linfeng, Huajun Wang, and Huiqing Wang. "A Lightweight Conditional Convolutional Neural Network for Hyperspectral Image Classification." Photogrammetric Engineering & Remote Sensing 89, no. 7 (July 1, 2023): 413–23. http://dx.doi.org/10.14358/pers.22-00130r2.
Abstract:
Deep learning (dl), especially convolutional neural networks (cnns), has been proven to be an excellent feature extractor and widely applied to hyperspectral image (hsi) classification. However, dl is a computationally demanding algorithm with many parameters and a high computational burden, which seriously restricts the deployment of dl-based hsi classification algorithms on mobile and embedded systems. In this paper, we propose an extremely lightweight conditional three-dimensional (3D) hsi with a double-branch structure to solve these problems. Specifically, we introduce a lightweight conditional 3D convolution to replace the conventional 3D convolution to reduce the computational and memory cost of the network and achieve flexible hsi feature extraction. Then, based on lightweight conditional 3D convolution, we build two parallel paths to independently exploit and optimize the diverse spatial and spectral features. Furthermore, to precisely locate the key information, which is conducive to classification, a lightweight attention mechanism is carefully designed to refine extracted spatial and spectral features, and improve the classification accuracy with less computation and memory costs. Experiments on three public hsi data sets show that the proposed model can effectively reduce the cost of computation and memory, achieve high execution speed, and better classification performance compared with several recent dl-based models.
48
Alexandrov, Theodore. "Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence." Annual Review of Biomedical Data Science 3, no. 1 (July 20, 2020): 61–87. http://dx.doi.org/10.1146/annurev-biodatasci-011420-031537.
Abstract:
Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology—imaging mass spectrometry—generate big hyperspectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.
49
Lodi, Michael, and Simone Martini. "Computational Thinking, Between Papert and Wing." Science & Education 30, no. 4 (April 28, 2021): 883–908. http://dx.doi.org/10.1007/s11191-021-00202-5.
Abstract:
AbstractThe pervasiveness of Computer Science (CS) in today’s digital society and the extensive use of computational methods in other sciences call for its introduction in the school curriculum. Hence, Computer Science Education is becoming more and more relevant. In CS K-12 education, computational thinking (CT) is one of the abused buzzwords: different stakeholders (media, educators, politicians) give it different meanings, some more oriented to CS, others more linked to its interdisciplinary value. The expression was introduced by two leading researchers, Jeannette Wing (in 2006) and Seymour Papert (much early, in 1980), each of them stressing different aspects of a common theme. This paper will use a historical approach to review, discuss, and put in context these first two educational and epistemological approaches to CT. We will relate them to today’s context and evaluate what aspects are still relevant for CS K-12 education. Of the two, particular interest is devoted to “Papert’s CT,” which is the lesser-known and the lesser-studied. We will conclude that “Wing’s CT” and “Papert’s CT,” when correctly understood, are both relevant to today’s computer science education. From Wing, we should retain computer science’s centrality, CT being the (scientific and cultural) substratum of the technical competencies. Under this interpretation, CT is a lens and a set of categories for understanding the algorithmic fabric of today’s world. From Papert, we should retain the constructionist idea that only a social and affective involvement of students into the technical content will make programming an interdisciplinary tool for learning (also) other disciplines. We will also discuss the often quoted (and often unverified) claim that CT automatically “transfers” to other broad 21st century skills. Our analysis will be relevant for educators and scholars to recognize and avoid misconceptions and build on the two core roots of CT.
50
García-Martínez, Inmaculada, José María Fernández-Batanero, Jose Fernández-Cerero, and Samuel P. León. "Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis." Journal of New Approaches in Educational Research 12, no. 1 (January 15, 2023): 171. http://dx.doi.org/10.7821/naer.2023.1.1240.
Abstract:
Artificial intelligence (AI) and computational sciences have aroused a growing interest in education. Despite its relatively recent history, AI is increasingly being introduced into the classroom through different modalities, with the aim of improving student achievement. Thus, the purpose of the research is to analyse, quantitatively and qualitatively, the impact of AI components and computational sciences on student performance. For this purpose, a systematic review and meta-analysis have been carried out in WOS and Scopus databases. After applying the inclusion and exclusion criteria, the sample was set at 25 articles. The results support the positive impact that AI and computational sciences have on student performance, finding a rise in their attitude towards learning and their motivation, especially in the STEM (Science, Technology, Engineering, and Mathematics) areas. Despite the multiple benefits provided, the implementation of these technologies in instructional processes involves a great educational and ethical challenge for teachers in relation to their design and implementation, which requires further analysis from the educational research. These findings are consistent at all educational stages.