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Teachers' Perception and Experiences of computer science Education in K-8 Schools

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Teachers' Perception and Experiences of Computer Science Edu...

Intermountain Engineering, Technology and Computing (IETC)

作者： Vaibhav Anu Sumi Hagiwara Katherine Herbert Kazi Zakia Sultana Minsun Shin Rebecca Goldstein Patricia Virella Department of Computer Science Montclair State University Montclair USA Department of Teaching and Learning Montclair State University Montclair USA Department of Educational Leadership Montclair State University Montclair USA

Supported by a state grant, our team of researchers (consisting of both computer science faculty and Teacher Education faculty) is offering a series of Professional Development sessions to K-8 teachers. These Professional Development (or PD) sessions are meant to help K-8 teachers develop their understanding of core computing concepts (such as algorithms, programming, data analysis, and networks) and thereby develop strong computer science programs for their students. In order to offer effective and meaningful Professional Development (PD) sessions, our research team first intended to understand the perceptions and experiences of K-8 teachers about computer science (CS) and Computational Thinking (CT) education. This paper presents the results of a K-8 teacher survey that we conducted as a pre-cursor to our PD series. The results of this survey provided valuable insights about elementary and middle-school teachers' perceptions of computer science education, self-perception of their ability to teach and learn CS, and understanding of CS discipline and those who typically engage in CS activities. The impact of teachers' perceptions impact how leaders in education and the CS industry can meet the needs of teachers, who in turn can meet the growing demand of CS education in K-8 schools.

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A Study About the Impact of Rewards on Student's Engagement with the Flipped Classroom Methodology

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A Study About the Impact of Rewards on Student's Engagement ...

IEEE Education Engineering (EDUCON)

作者： Jaime Urquiza-Fuentes Department of Computer Science & Statistics ACTIVATE Teaching Innovation Group & Laboratory of Information Technologies in Education - LITE Rey Juan Carlos University Móstoles Madrid Spain

This work describes an incremental design of a course that implements the flipped classroom methodology. The last phase of this implementation was to include rewards based on self-evaluation tests that would impact the students' grades. This should foster students' engagement with the learning methodology. In this work, a deep analysis of the impact of the reward is performed. The analysis of the use of the out-class materials shows that the students' pace does not correspond to the flipped classroom methodology. The students' behaviour is more focused on achieving rewards rather than preparing in-class activities with out-class materials. It is hypothesized that, the main reasons for these behaviours could be that, on the one hand, since the reward is considered by students as another item of the course grade, they work to get the reward. On the other hand, students do not want to change their study methodology because getting involved in the flipped methodology requires from them a significant effort.

关键词： Resistance Electronic learning Education Online services Engineering education Faces Videos

Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

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computers, Materials & Continua 2021年第5期67卷 2141-2160页

作者： Theyazn H.H.Aldhyani Melfi Alrasheed Mosleh Hmoud Al-Adaileh Ahmed Abdullah Alqarni Mohammed Y.Alzahrani Ahmed H.Alahmadi Community College of Abqaiq King Faisal UniversityAl HofufSaudi Arabia Department of Quantitative Methods School of BusinessKing Faisal UniversityAl HofufSaudi Arabia Deanship of E-Learning and Distance Education King Faisal UniversityAl HofufSaudi Arabia Department of Computer Sciences and Information Technology Albaha UniversityAl BahahSaudi Arabia Department of Computer Science and Information Taibah UniversityMadinahKingdom of Saudi Arabia

The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its ***,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of *** this study,a deep learning algorithm and a Holt-trend model are proposed to predict the *** Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death *** real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and *** results suggest that the LSTM models show better performance in predicting the cases of coronavirus *** measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed *** empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three *** far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain ***,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain *** empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these *** findings provide better insights regarding the future of Covid-19 this pandemic in *** results were obtained by applying time series models,which

关键词： Deep learning algorithm holt-trend prediction Covid-19 machine learning

FairTP: A Prolonged Fairness Framework for Traffic Prediction

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arXiv 2024年

作者： Xia, Jiangnan Yang, Yu Shen, Jiaxing Wang, Senzhang Cao, Jiannong School of Computer Science and Engineering Central South University China Centre for Learning Teaching and Technology The Education University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong

Traffic prediction is pivotal in intelligent transportation systems. Existing works mainly focus on improving the overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents' life. Additionally, existing fairness machine learning models fail to preserve fair traffic prediction for a prolonged time. Although they can achieve fairness at certain time points, such static fairness will be broken as the traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introduce two novel fairness definitions tailored to dynamic traffic scenarios, and propose a prolonged fairness traffic prediction framework, namely FairTP. We argue that fairness in traffic scenarios changes dynamically over time and across areas. Each traffic sensor or city area has state that alternates between "sacrifice" and "benefit" based on its prediction accuracy (high accuracy indicates "benefit" state). Prolonged fairness is achieved when the overall states of sensors similar within a given ***, we first define region-based static fairness and sensor-based dynamic fairness. Next, we designed a state identification module in FairTP to discriminate between states of "sacrifice" or "benefit" to enable prolonged fairness-aware traffic predictions. Lastly, a state-guided balanced sampling strategy is designed to select training examples to promote prediction fairness further, mitigating the performance disparities among regions with imbalanced traffic sensors. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing much accuracy degrada

关键词： Intelligent systems

Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides

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arXiv 2024年

作者： Yu, Ziyang Huang, Wenbing Liu, Yang Department of Computer Science and Technology Tsinghua University China Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Molecular Dynamics (MD) is crucial in various fields such as materials science, chemistry, and pharmacology to name a few. Conventional MD software struggles with the balance between time cost and prediction accuracy, which restricts its wider application. Recently, data-driven approaches based on deep generative models have been devised for time-coarsened dynamics, which aim at learning dynamics of diverse molecular systems over a long timestep, enjoying both universality and efficiency. Nevertheless, most current methods are designed solely to learn from the data distribution regardless of the underlying Boltzmann distribution, and the physics priors such as energies and forces are constantly overlooked. In this work, we propose a conditional generative model called Force-guided Bridge Matching (FBM), which learns full-atom time-coarsened dynamics and targets the Boltzmann-constrained distribution. With the guidance of our delicately-designed intermediate force field, FBM leverages favourable physics priors into the generation process, giving rise to enhanced simulations. Experiments on two datasets consisting of peptides verify our superiority in terms of comprehensive metrics and demonstrate transferability to unseen systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

A KALMAN FILTER MODEL FOR SYNCHRONIZATION IN MUSICAL ENSEMBLES

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arXiv 2024年

作者： Carvalho, Hugo T. Li, Min S. di Luca, Massimiliano Wing, Alan M. Department of Statistical Methods Federal University of Rio de Janeiro Brazil Bristol Interaction Group School of Computer Science University of Bristol United Kingdom Virtual Reality Lab School of Psychology University of Birmingham United Kingdom

The synchronization of motor responses to rhythmic auditory cues is a fundamental biological phenomenon observed across various species. While the importance of temporal alignment varies across different contexts, achieving precise temporal synchronization is a prominent goal in musical performances. Musicians often incorporate expressive timing variations, which require precise control over timing and synchronization, particularly in ensemble performance. This is crucial because both deliberate expressive nuances and accidental timing deviations can affect the overall timing of a performance. This discussion prompts the question of how musicians adjust their temporal dynamics to achieve synchronization within an ensemble. This paper introduces a novel feedback correction model based on the Kalman Filter, aimed at improving the understanding of interpersonal timing in ensemble music performances. The proposed model performs similarly to other linear correction models in the literature, with the advantage of low computational cost and good performance even in scenarios where the underlying tempo varies. © 2024, CC BY.

关键词： Kalman filters

AI and IoT in Smart Cities: A Methodology, Transformation, and Challenges 8th

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AI and IoT in Smart Cities: A Methodology, Transformation, a...

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Begishev, Ildar Isavnin, Alexey Nedelkin, Alexey Lydia, E. Laxmi Kumar, K. Vijaya Doctor of Law Institute of Digital Technologies and Law Department of Criminal Law and Procedure Kazan Innovative University named after V. G. Timiryasov Kazan Russia Doctor of Physical-Mathematical Sciences Department of Business-Informatics and Mathematical Methods in Economics Kazan Federal University Naberezhnye Chelny Institute KFU Naberezhnye Chelny Russia Department of Computer Science Plekhanov Russian University of Economics Moscow Russia Vijayawada520007 India GITAM School of Technology Visakhapatnam Campus Visakhapatnam India

ISBN: (纸本)9789819748945

The Internet of Things (IoT) with smart cities and smart homes enables us to progressively sense and alter our surroundings. However, Artificial Intelligence (AI) must leverage such distinguishing abilities and detected data. We are heading in that way with sophisticated IoT technologies. As connected items become more authoritative, we are seeing a smart city movement away from cloud-based IoT systems and toward edge AI and embedded AI. Lower latency, better privacy, and the necessity to process data close to the source have all influenced this decision. According to a current report, smart cities can have 70 billion linked things by 2023. These cities will become smarter as a result of these networked things and introduce hazards and privacy concerns. As a result of the numerous smart city initiatives and plans that have been implemented in current years, we will not only see the anticipated aids but also the hazards that have been presented. The present and future movements in smart cities and IoT are discussed. This chapter provides an overview of several smart city techniques and elements that can be or are now automated with the help of the Internet of Things (IoT), and Artificial Intelligence (AI). Novel components of smart cities of smart parking and smart governance are determined through Artificial Intelligence (AI) in the Internet of Things (IoT). Finally, the IoT's flaws and solutions in the context of smart cities are determined. This work focuses on identifying the transformation and challenges faced in developing a smart city in AI. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence (AI) Challenges Internet of things (IoT) Smart cities Transformation

Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation

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arXiv 2024年

作者： Zhu, Minqin Wu, Anpeng Li, Haoxuan Xiong, Ruoxuan Li, Bo Yang, Xiaoqing Qin, Xuan Zhen, Peng Guo, Jiecheng Wu, Fei Kuang, Kun Department of Computer Science and Technology Zhejiang University China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Center for Data Science Peking University China Department of Quantitative Theory and Methods Emory University United States School of Economics and Management Tsinghua University China Didi Chuxing China

Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent studies predict counterfactual outcomes by learning a covariate representation that is independent of the treatment variable. However, such independence constraints neglect much of the covariate information that is useful for counterfactual prediction, especially when the treatment variables are continuous. To tackle the above issue, in this paper, we first theoretically demonstrate the importance of the balancing and prognostic representations for unbiased estimation of the heterogeneous dose-response curves, that is, the learned representations are constrained to satisfy the conditional independence between the covariates and both of the treatment variables and the potential responses. Based on this, we propose a novel Contrastive balancing Representation learning Network using a partial distance measure, called CRNet, for estimating the heterogeneous dose-response curves without losing the continuity of treatments. Extensive experiments are conducted on synthetic and real-world datasets demonstrating that our proposal significantly outperforms previous methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

Hybrid harmony search algorithm for social network contact tracing of COVID-19

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Soft computing 2023年第6期27卷 3343-3365页

作者： Ala'a Al-Shaikh Basel A Mahafzah Mohammad Alshraideh Learning and Teaching Technology Center Al-Balqa Applied University Al-Salt 19117 Jordan. Department of Computer Science King Abdulla II School of Information Technology The University of Jordan Amman 11942 Jordan.

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs.

关键词： COVID-19 Contact tracing Coronavirus Harmony search algorithm Hill climbing Metaheuristic approach Social networks