Computational Ability Research Articles

RELATIONSHIP BETWEEN ATTITUDE TOWARD STEM AND COMPUTATIONAL THINKING ABILITY IN MATHEMATICS LEARNING OF ELEMENTARY SCHOOL STUDENTS

Computational Thinking (CT) plays an important role in Science, Technology, Engineering, Mathematics (STEM) fields. However, whether Attitude toward STEM is related to CT skills is still unknown. The purpose of the study was to determine whether there is a relationship between Attitude Toward STEM and Computational Thinking skills in mathematics learning of elementary school students. This research is a correlational quantitative research that measures the relationship between two variables. The research was conducted at SDN Gandaria Utara 03 in class IVA and IVB. Simple random sampling technique was used to determine the research sample. The instruments in this study used a questionnaire to measure Attitude Toward STEM and a test instrument to measure Computational Thinking ability. The instruments in this study were measured for validity and reliability before use. Data analysis techniques using linear regression statistical tests. The results showed that Attitude toward STEM is related to Computational Thinking ability. From the results of data analysis, the regression model equation is Y = 19.635 + 0.387x. The coefficient of determination or R2 of 0.805 means that the Attitude Toward STEM variable simultaneously has an influence of 80.5% on the Computational Thinking ability variable

Advances in machine learning methods in copper alloys: a review.

Advanced copper and copper alloys, as significant engineering structural materials, have recently been extensively used in energy, electron, transportation, and aviation domains. Higher requirements urge the emergence of high-performance copper alloys. However, the traditional trial-and-error experimental observations and computational simulation research used to design and develop novel materials are time-consuming and costly. With the accumulation of material research and rapid development of computational ability, the thorough application of material genome engineering has sped up the development of novel materials and facilitates the process of systematic engineering application. This review summarizes the benefits of data-driven machine learning techniques and the state of the art of machine learning research in the area of copper alloys. It also displays the widely used computational simulation approaches (e.g., the first-principles calculation, molecular dynamics simulation, phase-field simulations, and finite element analysis) and their combined applications in material design and property prediction. Finally, the limitations of machine learning research methods are outlined, and future development directions are proposed.

Analysis of Gender Issues in Computational Thinking Approach in Science and Mathematics Learning in Higher Education

In the contemporary era, Computational Thinking has emerged as a crucial skill for individuals to possess in order to thrive in the 21st century. In this context, there is a need to develop a methodology for cultivating these skills within a science and mathematics content education framework, particularly among pre-service teachers. This study aimed to investigate the impact of Educational Robotics on the development of Computational Thinking skills, with a particular focus on the role of gender, through a scientific and mathematical content teaching approach. A pre-experimental design with a quantitative approach was employed, and it was implemented with a total of 116 pre-service teachers, 38 males and 78 females. The results demonstrated a notable enhancement between the pre-test (8.11) and post-test (9.63) scores, emphasising specific concepts such as simple functions, while, and compound conditional. With respect to gender, statistically significant differences were identified prior to the intervention, but not following its implementation. The high level of Computational Thinking exhibited by both genders was comparable (53.85% in females and 55.26% in males) following the intervention. This indicates that the intervention is a promising approach for enhancing Computational Thinking proficiency, independent of gender and initial proficiency levels. The implementation of Educational Robotics in the teaching of science and mathematics enables the enhancement of Computational Thinking abilities among pre-service teachers, while reducing the observed gender disparity in this area of skill development.

Improving the Computational Thinking Abilities of Junior High School Students Through Problem-Based Learning

Computational Thinking is a crucial skill that students must master in the 21st century. Computational Thinking encompasses a systematic process that enables individuals to formulate problems, develop strategies, and find effective, efficient, and reusable solutions. This research aims to enhance students' Computational Thinking abilities through the application of the Problem-Based Learning (PBL) model, which is effective in facilitating active learning and problem-solving. The methodology used in this study is Classroom Action Research (CAR) with a spiral approach developed by Stephen Kemmis and Robin McTaggart. Data collection is conducted through post-tests administered at the end of each cycle phase. The obtained data are analyzed using a designed assessment rubric, wherein students are categorized based on their Computational Thinking abilities and foundational Computational Thinking skills. The results indicate that by the end of Cycle II, of students achieved a moderate to high level of Computational Thinking ability. These findings affirm that the implementation of the Problem-Based Learning learning model can significantly improve students' Computational Thinking skills.

Deep Learning-Based Pulmonary Nodule Screening: A Narrative Review

AbstractGiven its capacity to generate three-dimensional pictures, computed tomography is the most effective means of detecting lung nodules with more excellent resolution of detected nodules. Small lung nodules can easily be overlooked on chest X-rays, making interpretation difficult. Artificial intelligence algorithms have recently demonstrated remarkable progress in medical imaging, especially with deep learning techniques such as convolutional neural networks (CNNs). CNN produces excellent results in natural image recognition and classification using abundant available data and the computational abilities of modern computers. It further reduces false-positive pulmonary nodules in medical image processing. This review article provides a detailed and inclusive review of recent advances, challenges, performance comparisons, and possible future directions for the problem of pulmonary nodule screening using deep learning methods.

Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect

Surface temperature distribution is crucial for thermal property-based studies about irregular asteroids in our Solar System. While direct numerical simulations could model surface temperatures with high fidelity, they often take a significant amount of computational time, especially for problems for which temperature distributions are required to be repeatedly calculated. To this end, the deep operator neural network (DeepONet) proves a powerful tool due to its high computational efficiency and generalization ability. In this work, we apply DeepONet to the modeling of asteroid surface temperatures. Results show that the trained network is able to predict temperature with an accuracy of ~1% on average, while the computational cost is five orders of magnitude lower, enabling thermal property analysis in a multidimensional parameter space. As a preliminary application, we analyzed the orbital evolution of asteroids through direct N- body simulations embedded with an instantaneous Yarkovsky effect inferred by DeepONet-based thermophysical modeling. Taking asteroids (3200) Phaethon and (89433) 2001 WM41 as examples, we show the efficacy and efficiency of our AI-based approach.

Development of STEM-Based Science Educational Game Using Scratch to Train Computational Thinking Skill for Socondary School Students

Computational Thinking Skills (CTS) possessed by Indonesian students are not yet in the good category. This skills have not been integrated in learning. This is in accordance with the results of preliminary research at SMP Negeri 8 Semarang that CTS are a new concept for science teachers, including learning media used by teachers have not helped train students' CTS. Through preliminary research, it is known that science teachers give a positive response to the use of technology-based learning media that train students' CTS, one of which is a science educational game using Scratch. The purpose of this study was to analyze the validity and determine the profile of students' CTS after using science educational games. The type of research used is development research with the ADDIE model. The instruments used are expert validation questionnaires, student readability sheets, and tests after using science education games. The results showed that the science education game was valid with a score of 0.89. The readability of science education games is very good with a score of 96.7. Through the test, it was found that the percentage of students with high computational thinking ability was 56.66%, medium 26.66%, and low 16.66%. Based on the research that has been done, it can be concluded that the science education game is valid and can train CTS of junior high school students.

Application of Contextual Approach in Improving Computational Operation Skills in Students with Learning Delays

<table border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="369"><p>This study aims to analyze and determine the effect of applying the contextual approach in improving the ability of calculation operations in students with learning delays. This study uses a quantitative approach with a single-subject experiment method, often called Single Subject Research (SSR), with an A-B-A design. The subject of this study is grade VIII students at SMP Islam Mandiri. The data collection technique in this study was carried out using observation and a test of computational operation ability. The results of the baseline-1 phase showed a stable score and increased when the treatment was carried out in the intervention phase; after the treatment was carried out, the research subjects were then given a test in the baseline-2 phase, and the results obtained increased with a score of 100 in each session. The percentage overlap of the three phases is at the level of 0%. Thus, the results show that applying the contextual approach positively improves the ability to calculate operations in students with learning delays.</p></td></tr></tbody></table>

CNN-Informer: A hybrid deep learning model for seizure detection on long-term EEG

Timely detecting epileptic seizures can significantly reduce accidental injuries of epilepsy patients and offer a novel intervention approach to improve their quality of life. Investigation on seizure detection based on deep learning models has achieved great success. However, there still remain challenging issues, such as the high computational complexity of the models and overfitting caused by the scarce availability of ictal electroencephalogram (EEG) signals for training. Therefore, we propose a novel end-to-end automatic seizure detection model named CNN-Informer, which leverages the capability of Convolutional Neural Network (CNN) to extract EEG local features of multi-channel EEGs, and the low computational complexity and memory usage ability of the Informer to capture the long-range dependencies. In view of the existence of various artifacts in long-term EEGs, we filter those raw EEGs using Discrete Wavelet Transform (DWT) before feeding them into the proposed CNN-Informer model for feature extraction and classification. Post-processing operations are further employed to achieve the final detection results. Our method is extensively evaluated on the CHB-MIT dataset and SH-SDU dataset with both segment-based and event-based criteria. The experimental outcomes demonstrate the superiority of the proposed CNN-Informer model and its strong generalization ability across two EEG datasets. In addition, the lightweight architecture of CNN-Informer makes it suitable for real-time implementation.

From Solid to Fluid: Novel Approaches in Neuromorphic Engineering.

Neuromorphic engineering is rapidly developing as an approach to mimicking processes in brains using artificial memristors, devices that change conductivity in response to the electrical field (resistive switching effect). Memristor-based neuromorphic systems can overcome the existing problems of slow and energy-inefficient computing that conventional processors face. In the Introduction, the basic principles of memristor operation and its applications are given. The history of switching in sandwich structures and granular metals is reviewed in the Historical Overview. Particular attention is paid to the fundamental articles from the pre-memristor era (the 1960s-70s), which demonstrated the first evidence of resistive switching and predicted the filamentary mechanism of switching. Multi-dimensionality in neuromorphic systems: Despite the powerful computational abilities of traditional memristor arrays, they cannot repeat many organizational characteristics of biological neural networks, i.e., their multi-dimensionality. This part reviews the unconventional nanowire- and nanoparticle-based neuromorphic systems that demonstrate incredible potential for use in reservoir computing due to the unique spiking change in conductance similar to firing in neurons. Liquid-based neuromorphic devices: The transition of neuromorphic systems from solid to liquid state broadens the possibilities for mimicking biological processes. In this section, ionic current memristors are reviewed and, the working principles of which bring us closer to the mechanisms of information transmittance in real synapses. Nanofluids: A novel direction in neuromorphic engineering linked to the application of nanofluids for the formation of reconfigurable nanoparticle networks with memristive properties is given in this section. The Conclusion t summarizes the bullet points of the Review and provides an outlook on the future of liquid-state neuromorphic systems.

Photonic reservoir computing for parallel task processing based on a feedback-free spin-polarized VCSEL

Time delayed reservoir computing (RC) is a novel artificial neural network that is easy to implement in hardware due to its extremely simple structure. Because of its time-division multiplexed information processing, laser-based photonic time-delayed RCs usually realize parallel processing with polarization/wavelength multiplexing. However, the performance of two different tasks is difficult to regulate separately and simultaneously in the time delayed RC system, especially for the chip-scale configuration. Here, we propose a feedback-free RC system based on a spin-polarized vertical-cavity surface-emitting semiconductor laser (VCSEL), which simplifies the whole system structure and can process time series prediction and waveform recognition tasks in parallel, with employing the input and output coding to provide the effect from past states. By separately setting the number of past states introduced by the coding for the two tasks, the performance of the two tasks can be adjusted respectively. Furthermore, by appropriately tuning the pump polarization ellipticity which is the unique feature for the spin-polarized VCSEL, the computational ability of the proposed RC can be focused on one of the two parallel tasks. Therefore, the proposed RC system is capable of dealing with different tasks with high performance, and also expected to provide a viable solution for integrated neuromorphic computing systems due to its compact, feedback-free structure.

Computational Thinking Readiness Level of First-Year Students of Mathematics Education

The main objective of this study was to measure the level of computational thinking readiness in prospective first-year mathematics education students. In addition, this study also aims to identify factors that influence their level of readiness towards computational thinking. This research is qualitative and descriptive in nature. This study describes first-year mathematics education students' mathematical computational thinking ability based on the theory of mathematical computational thinking. This study was conducted on first-year mathematics education students in the academic year 2023/2024. There were 16 first-year mathematics education students, all of whom were taken as samples in this study, to obtain more in-depth information about the computational thinking ability of first-year mathematics education students for further research development. The instruments used to collect data on first-year mathematics education students' mathematical computational thinking ability are (1) a mathematical computational thinking ability test and (2) an interview. The data obtained were calculated using statistical tests, and the results will be explained in depth. The mean score of the first-year mathematics education student's computational thinking ability test was 59.68, indicating that students generally have a fairly good level of computational thinking ability.

Exponential Learning Advantages with Conjugate States and Minimal Quantum Memory

The ability of quantum computers to directly manipulate and analyze quantum states stored in quantum memory allows them to learn about aspects of our physical world that would otherwise be invisible given a modest number of measurements. Here we investigate a new learning resource, which could be available to quantum computers in the future—measurements on the unknown state accompanied by its complex conjugate ρ⊗ρ∗. For a certain shadow tomography task, we surprisingly find that measurements on only copies of ρ⊗ρ∗ can be exponentially more powerful than measurements on ρ⊗K, even for large K. This expands the class of provable exponential advantages using only a constant overhead quantum memory, or minimal quantum memory, and we provide a number of examples where the state ρ∗ is naturally available in both computational and physical applications. In addition, we precisely quantify the power of classical shadows on single copies under a generalized Clifford ensemble and give a class of quantities that can be efficiently learned. The learning task we study in both the single copy and quantum memory settings is physically natural and corresponds to real-space observables with a limit of bosonic modes, where it achieves an exponential improvement in detecting certain signals under a noisy background. We quantify a new and powerful resource in quantum learning, and we believe the advantage may find applications in improving quantum simulation, learning from quantum sensors, and uncovering new physical phenomena. Published by the American Physical Society 2024

Enhancing Learning Abilities in Students Using a Cognitive Neuroscience Model Based on Brain-Computer Interface Signal Analysis

Computational intelligence is used to create artificially intelligent systems with the ability to learn, adapt, and solve problems. Learners’ computational abilities can be improved with the help of Cognitive Neuroscience techniques. Computational intelligence refers to the ability of a learner based on the complex operations of the brain in the four relevant dimensions: Visual (V), Aural (A), Kinematic (K), and Reading/Writing (R/W). Our proposed framework consists of electroencephalogram (EEG) data acquisition, signal extraction, and EEG signal categorization to assess students’ cognitive learning abilities. Our proposed approach uses an EEG device equipped with a microprocessor, a Think Gear (TGAM) EEG sensor, and a PCB of 16 dry electrodes. The EEG device and the remote processing device are connected by Bluetooth. The EEG signal provides the students with neurofeedback on their cognitive learning capability. The feedback obtained through the learning process will be endowing to improvise computational intelligence. The statistical derivative, Pearson Co-efficient, is used to find the correlation among the derived attributes. The attributes considered are the learner’s gender, stream, age, and geographical region. The results obtained highlight that gender, stream, and age have no correlation with the detectable learning index, and the most accurate learners are kinesthetic ones. Bi-modal learners, who can maintain focus while reading and using their kinematic abilities, had the second highest learning capacity.

Whisperings From The Ordinary: A Conversational Artificial Intelligence (AI) Approach To Bridging The Human-Object Divide

In a world increasingly dominated by connected devices, one realm remains silent: most inanimate objects that surround us. This stark contrast highlights a critical gap in human-environment interaction, one that the present work aims to bridge through the power of intelligent conversation and shared understanding. Traditional IoT methods, while efficient, raise crucial concerns regarding sustainability. Embedding electronics into every object risk exceeding costs and generating substantial e-waste. This necessitates exploration of alternative paradigms that unlock the communicative potential of everyday items without environmental burden. Enter the confluence of object detection and conversational AI. Object detection, encompassing techniques like image classification, empowers machines to perceive their surroundings and make informed decisions based on identified objects. Conversational AI refers to the ability of computers to engage in a natural, human-like dialogue. By fitting in object detection, conversational AI can transform lifeless objects into active and intelligent participants in our lives, fostering a richer relationship with the spaces we inhabit. This paper proposes an AI-powered mobile application that unlocks the communicative potential of everyday items, transforming them from passive elements into active participants in our lives. We are exploring to enhance domestic routines by streamlining daily tasks with object-initiated reminders, proactive maintenance suggestions, and personalized advice. This research delves into the technical aspects, potential applications, and ethical implications of granting voice to the voiceless.

Machine Learning-Based Approach: Enhancing Social Media Platform

Machine Learning is a technique that aims to learn from data over a task based on certain performance measures. With the flooding of data and increased computational ability, machine learning algorithms have a huge research scope. The task of identifying the correct machine learning algorithm for an application is very challenging without analysing the basic information about the problem such as the domain of the undertaken problem, the features available in the data, etc. The movie industry, a huge part of entertainment industry, has seen a phenomenal growth throughout the globe in recent times. A movie can capture the attention of a viewer and can trigger cognitive and emotional processes in the brain. In this research, the emotional outcome of the viewer was analysed while they watch the movie before its actual release that is, during its preview. Traditionally Functional Magnetic Resonance Imaging device was used to assess human brain activity but proved to be non-feasible and costly so EEG sensors were used to monitor and record the functioning of the brain of volunteers for further analysis. We proposed a model to use the collected data through EEG sensors were analysed using artificial neural network which was Used to find high and low of different brain waves mapping to the emotions depicted in every scene of the movie. Performance measures such as accuracy, precision and recall were calculated to validate our proposed model. Our proposed model resulted in providing assistance to movie makers who could Study the pulse of audience before the actual release and could incorporate changes if necessary.

키워드 네트워크 분석을 활용한 인공지능 융합 영재교육 연구 동향 분석

Objectives The purpose of this study is to analyze trends in artificial intelligence convergence gifted education in elementary and middle schools that have been studied with a focus on artificial intelligence, and to derive important issues, topics, trends, and trends in related research. Methods Research trends were analyzed for 75 literatures related to artificial intelligence and artificial intelligence convergence gifted education research and education programs. Analysis data was collected through Academic Research Information Service (RISS). The collected data was refined using Excel and text, and major keywords were identified, analyzed, and visualized through frequency analysis and keyword network analysis. CONCOR cluster analysis was used to determine the centrality of keywords. Results First, since 2017, research on artificial intelligence convergence gifted education has been steadily expanding in terms of content and methods. Second, the results of frequency analysis of the main keywords in research related to artificial intelligence convergence gifted education showed ‘artificial intelligence’, ‘artificial intelligence education’, ‘gifted education’, ‘computational thinking ability’, and ‘machine learning’.Third, as a result of TF-IDF weight analysis, ‘artificial intelligence’, ‘artificial intelligence education’, ‘gifted education’, ‘computational thinking ability’, ‘science gifted’, ‘artificial intelligence education program’, and ‘information gifted’ were found. In general, it was similar to the results of the frequency analysis of major keywords, but it was confirmed that there was a difference in the rankings. Keywords with relatively high weight compared to their frequency of occurrence are ‘science gifted’, ‘creativity’, and ‘artificial intelligence education program’.Fourth, through semantic network and CONCOR analysis, it can be confirmed that artificial intelligence convergence gifted education research is being conducted focusing on ‘educational content’, ‘educational effect’, ‘teaching method’, and ‘educational target’. Conclusions This study derived the main topics and flows of research based on the results of keyword network analysis of domestic artificial intelligence convergence gifted education-related research.

SYSTEMATIC LITERATURE REVIEW: BAGAIMANA PEMBELAJARAN GEOMETRI 3D DENGAN BERBANTUAN GEOGEBRA

The rapid development of technology can support progress in the field of education, including in mathematics learning. Geogebra is one of the media that can be used in learning, especially in 3D geomtery learning. The purpose of this research with Systematic Literature Review is to provide a description and help solve deeper problems in students' computational abilities when using math software. Acquisition of data through Publish or Perish which contains a Google Scholar database that is reviewed from 200 articles related to 3D geometry learning published in the period 2020-2024 identified by SINTA that can be accessed and is not a foreign language and original research with a final usable 30 articles. The results showed information that learning by using Geogebra is very influential and effective, efficient, and significant so that there is an influence on improving student learning

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two‐dimensional (2D) materials have been extensively investigated as high‐performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α’‐4H‐borophene sheets and metal β12‐borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non‐volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene‐based memories in information storage and in‐memory computing.

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets.

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two-dimensional (2D) materials have been extensively investigated as high-performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α'-4H-borophene sheets and metal β12-borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non-volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene-based memories in information storage and in-memory computing.