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Articles 7081 - 7110 of 63011
Full-Text Articles in Computer Sciences
Research On Operational Protection Area Of Ils Glide Slope, Qingdong Li, Jiaquan Ye, Jian Xu
Research On Operational Protection Area Of Ils Glide Slope, Qingdong Li, Jiaquan Ye, Jian Xu
Journal of System Simulation
Abstract: The scientific protection for the operational protection area of the instrument landing system glide slope is of great significance to ensure the quality of civil aviation navigation space signals, the safety of aircraft approach operations, and the efficiency of airport operations. Combined with the concept evolution of the glide slope operational protection area, a comparative analysis of the delineation of the glide slope operational protection area required by the national civil aviation standards, industry standards, and Annex 10 Volume I of the Convention on International Civil Aviation is carried out. Taking the extra-large aircraft (A380-800) as an example, the …
Air Defense Missile Weapon Target Assignment Based On Multi-Objective Evolutionary Algorithm, Xin Sun, Lining Xing, Rui Wang, Ling Wang, Jianmai Shi, Tianyu Luo
Air Defense Missile Weapon Target Assignment Based On Multi-Objective Evolutionary Algorithm, Xin Sun, Lining Xing, Rui Wang, Ling Wang, Jianmai Shi, Tianyu Luo
Journal of System Simulation
Abstract: An effective weapon target assignment method can reduce the combat losses and improve the defense effect. A reasonable mathematical model is established for the allocation of air defense resources, aiming at the optimization objectives of maximizing target destruction effectiveness and minimizing radar resource consumption, considering multiple constraints such as the upper limit of radar channels, on the basis of multiobjective evolutionary algorithm based on decomposition (MOEA/D), the probability of crossover and mutation is adaptively adjusted to improve the quality of individuals in the process of population evolution, and a set of optimal solution sets for decision makers is obtained. …
Classification Cooperative Scheduling Of U-Automated Container Terminal Based On Container Markers, Fei Wang, Daofang Chang, Furong Wen
Classification Cooperative Scheduling Of U-Automated Container Terminal Based On Container Markers, Fei Wang, Daofang Chang, Furong Wen
Journal of System Simulation
Abstract: To improve the confusing scheduling of U-shaped yard operations due to improper classification of containers, the classification cooperative scheduling process is proposed, in which the classification cooperative scheduling model is established with the constraints of coordinated operation and efficient independent operation. The container classification principle and the container multiple stacking principle are proposed, the stacking point is selected by the weighted calculation based on the container operation mark to design the penalty mechanism, and the classification cooperative heuristic algorithm solution model is designed. Comparative experiments show that the strategy used in the algorithm is superior,which can reduce the equipment …
Optimal Trajectory Of Full-Duplex Uav Relaying Over Hybrid Probability Channels, Tao Wang, Ji Xiaodong
Optimal Trajectory Of Full-Duplex Uav Relaying Over Hybrid Probability Channels, Tao Wang, Ji Xiaodong
Journal of System Simulation
Abstract: A fixed-wing UAV being the full-duplex moving relay, and a hybrid probability channel being the source to send data to the destination, through the flight optimal trajectory design. On the basis of ensuring the total data amount of source-destination communication, the energy consumption of the system is minimized. Two optimization problems of runway shape and mixed trajectory are established, which are non-convex and are difficult to get the closed-form solution. The hybrid probability channels gains are replaced by the average channel gains, and the lower bounds of the received data at the UAV and the destination are calculated by …
Arterial Coordination Optimization Method Based On Vehicle Speed Guidance And Inductive Control, Mingjun Deng, Xinxia Hu, Xiang Li, Liping Xu
Arterial Coordination Optimization Method Based On Vehicle Speed Guidance And Inductive Control, Mingjun Deng, Xinxia Hu, Xiang Li, Liping Xu
Journal of System Simulation
Abstract: Arterial signal coordination is usually based on fixed belt speeds and time-of-day statistical flows. Actually, vehicle speeds and traffic flows are fluctuating, which causes to the mismatch between the signal scheme and the actual optimal belt speeds and traffic flow demands, and affects the intersection's traffic efficiency. Based on the vehicle infrastructure cooperation, by applying Maxband model and the maximum green wave bandwidth, the minimum number of arterial vehicle delays, arterial stops and the minor direction delays being the optimization objectives, a multi-objective optimization model for arterial signal coordination is established. Through using an improved multi-objective particle swarm algorithm …
Curriculum Learning-Based Simulation Of Uav Air Combat Under Sparse Rewards, Jingyu Zhu, Hongli Zhang, Minchi Kuang, Heng Shi, Jihong Zhu, Zhi Qiao, Wenqing Zhou
Curriculum Learning-Based Simulation Of Uav Air Combat Under Sparse Rewards, Jingyu Zhu, Hongli Zhang, Minchi Kuang, Heng Shi, Jihong Zhu, Zhi Qiao, Wenqing Zhou
Journal of System Simulation
Abstract: To address the limited exploration capabilities and sparse rewards of conventional reinforcement learning methods in air combat environment, a curriculum learning distributed proximal policy optimization (CLDPPO) reinforcement learning algorithm is proposed. A reward function informed by professional empirical knowledge is integrated, a discrete action space is developed, and a global observation and local value and decision network featuring separated global and local observations is established. A methodology for unmanned aerial vehicles UAVs is presented to acquire combat expertise through a sequence of fundamental courses that progressively intensify in their offensive, defensive, and comprehensive content. The experimental results show that …
Hybrid Flow Shop Scheduling With Limited Buffers Considering Energy Consumption And Transportation, Tingxin Wen, Tingyu Guan
Hybrid Flow Shop Scheduling With Limited Buffers Considering Energy Consumption And Transportation, Tingxin Wen, Tingyu Guan
Journal of System Simulation
Abstract: Aiming at the untimely production scheduling and excessive energy consumption during processing, a limited buffer hybrid flow shop scheduling optimization model is constructed. To minimize the makespan and total energy consumption of the workshop, the transport time, generalized energy consumption and buffer capacity being the constraints, and the on/off energy saving strategy applied to reduce the standby energy consumption, the feasibility of the optimization model are verified. A lion swarm optimization algorithm is designed, in which a population initialization method combining random generation and greedy selection is used to improve the initial solution quality and solution efficiency, the lion …
Application Of Driving Simulation Technology In Calibration Of Traffic Simulation Parameters, Shikun Liu, Yi Tang, Yonghong Liu
Application Of Driving Simulation Technology In Calibration Of Traffic Simulation Parameters, Shikun Liu, Yi Tang, Yonghong Liu
Journal of System Simulation
Abstract: To address the insufficient accuracy in traffic simulation modeling due to the lack of in-depth consideration of complex driving behaviors, a calibration method for traffic simulation parameters based on driving simulation technology is proposed. The reconstruction and expansion project of Shenzhen Bao'an International Airport Expressway is selected as the case. Using VISSIM simulation software, a comprehensive traffic simulation model of the entire route is constructed, and UC-winRoad software is employed to create the highly realistic driving simulation scenarios. Driving simulation experiments are conducted to extract the typical driving behavior characteristics in complex scenarios. Calibration functions for simulation parameters are …
Development And Application Of Simulation Platform For Aquatic Movement Of An Amphibious Armored Vehicle, Mingzhe Chen, Yunzheng Song, Pei Wang, Lei Zhang
Development And Application Of Simulation Platform For Aquatic Movement Of An Amphibious Armored Vehicle, Mingzhe Chen, Yunzheng Song, Pei Wang, Lei Zhang
Journal of System Simulation
Abstract: In order to design and verify the fire control system(FCS) algorithm of amphibious assault vehicle under heavy wind and wave conditions, a real-time simulation platform is developed. The traditional single rigid body dynamic model can't describe the body-turret-barrel dynamic coupling relationship and it is not suitable for FCS simulation with high dynamic characteristics. Twist-wrench method is used to establish the multiple rigid body dynamic model of vehicle, the buoyancy and the hydrodynamic calculation is carried out according to the body and the moving relationship between visual generated waves, and the hydrodynamic coefficient is obtained by the computational fluid dynamic …
3d Organ-Scale Models Of Tumor Growth And Treatment, Rafael Ramon Bravo
3d Organ-Scale Models Of Tumor Growth And Treatment, Rafael Ramon Bravo
USF Tampa Graduate Theses and Dissertations
To understand the dynamics of cancer, mathematical oncologists have developed models of tumor growth and treatment response. Some models are mechanistic and approach tumor growth at the cell-scale, focusing on the evolution of cancerous cells within the ecology of normal tissue, and are often simulated with agent-based modeling. Other models are more clinically motivated and model tumor growth operating at the organ-scale, using patient data to predict treatment response, and are often simulated with partial differential equations. We developed the Hybrid Automata Library which includes both agent-based modeling and partial differential equations for modeling at either of these scales. We …
The Confluence, Volume3, Issue 2, Full Issue
Unraveling The Versatility And Impact Of Multi-Objective Optimization: Algorithms, Applications, And Trends For Solving Complex Real-World Problems, Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, A. Salih
Unraveling The Versatility And Impact Of Multi-Objective Optimization: Algorithms, Applications, And Trends For Solving Complex Real-World Problems, Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, A. Salih
Journal of Soft Computing and Computer Applications
Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years due to their potential for solving real-world problems in various fields, such as logistics, finance, environmental management, and engineering. These techniques offer comprehensive solutions that traditional single-objective approaches fail to provide. Due to the many innovative algorithms, it has been challenging for researchers to choose the optimal algorithms for solving their problems. This paper examines recently developed MOO-based algorithms. MOO is introduced along with Pareto optimality and trade-off analysis. In real-world case studies, MOO algorithms address complicated decision-making challenges. This paper examines algorithmic methods, applications, trends, and issues in …
Optimization Of Resources Allocation Using Evolutionary Deep Learning, Sanaa Ali Jabber, Soukaena H. Hashem, Shatha H. Jafer
Optimization Of Resources Allocation Using Evolutionary Deep Learning, Sanaa Ali Jabber, Soukaena H. Hashem, Shatha H. Jafer
Journal of Soft Computing and Computer Applications
The Bidirectional Long Short-Term Memory (Bi-LSTM) network structure enables data analysis, enhances decision-making processes, and optimizes resource allocation in cloud computing systems. However, achieving peak network performance relies heavily on choosing the hyperparameters for configuring the network. Enhancing resource allocation improves the Service Level Agreement (SLA) by ensuring efficient utilization and allocation of computational resources based on dynamic workload demands. This paper proposes an approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with deep learning techniques to address this challenge. This approach combines the optimization capabilities of MOEA with the learning predictive models to establish a framework for resource allocation …
Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif
Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif
Journal of Soft Computing and Computer Applications
The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and …
Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali
Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali
Journal of Soft Computing and Computer Applications
Video anomaly detection is one of the trickiest issues in intelligent video surveillance because of the complexity of real data and the hazy definition of anomalies. Since abnormal occurrences typically seem different from normal events and move differently. The global optical flow was determined with the maximum accuracy and speed using the Farneback approach for calculating the magnitudes. Two approaches have been used in this study to detect strangeness in the video. These approaches are Deep Learning (DL) and manuality. The first method uses the activity map's development of entropy to detect the oddity in the video using a particular …
A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy
A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy
Journal of Soft Computing and Computer Applications
The primary elements of Intelligent Transportation Systems (ITSs) have become Vehicular Ad-hoc NETworks (VANETs), allowing communication between the infrastructure environment and vehicles. The large amount of data gathered by connected vehicles has simplified how Deep Learning (DL) techniques are applied in VANETs. DL is a subfield of artificial intelligence that provides improved learning algorithms able to analyzing and process complex and heterogeneous data. This study explains the power of DL in VANETs, considering applications like decision-making, vehicle localization, anomaly detection, traffic prediction and intelligent routing, various types of DL, including Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are …
A Novel Approach To Generate Dynamic S-Box For Lightweight Cryptography Based On The 3d Hindmarsh Rose Model, Ala'a Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi
A Novel Approach To Generate Dynamic S-Box For Lightweight Cryptography Based On The 3d Hindmarsh Rose Model, Ala'a Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi
Journal of Soft Computing and Computer Applications
In lightweight cryptography, the absence of an S-Box in some algorithms like speck, Tiny Encryption Algorithm, or the presence of a fixed S-Box in others like Advanced Encryption Standard can make them more vulnerable to attacks. This study introduces an innovative method for creating a dynamic 6-bit S-Box (8×8) in octal format. The generating process of S-Box passes through two phases. The first is the number initialization phase. This phase involves generating sequence numbers 1, sequence numbers 2, and sequence numbers 3 depending on Xi, Yi, and Zi values generated using the 3D Hindmarsh …
The Robust Digital Video Watermarking Methods: A Comparative Study, Ebtehal Talib, Abeer Salim Jamil, Nidaa Flaih Hassan, Muhammad Ehsan Rana
The Robust Digital Video Watermarking Methods: A Comparative Study, Ebtehal Talib, Abeer Salim Jamil, Nidaa Flaih Hassan, Muhammad Ehsan Rana
Journal of Soft Computing and Computer Applications
Digital data such as images, audio, and video have become widely available since the invention of the Internet. Due to the ease of access to this multimedia, challenges such as content authentication, security, copyright protection, and ownership determination arose. In this paper, an explanation of watermark techniques, embedding, and extraction methods are provided. It further discusses the utilization of artificial intelligence methods and conversion of host media from the spatial domain to the frequency domain; these methods aim to improve the quality of watermarks. This paper also included a classification of the basic characteristics of the digital watermark and the …
Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal
Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal
Journal of Soft Computing and Computer Applications
Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset …
Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy
Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy
Journal of Soft Computing and Computer Applications
Cyberattacks have become one of the most significant security threats that have emerged in the last couple of years. It is imperative to comprehend such attacks; thus, analyzing various kinds of cyberattack datasets assists in constructing the precise intrusion detection models. This paper tries to analyze many of the available cyberattack datasets and compare them with many of the fields that are used to detect and predict cyberattack, like the Internet of Things (IoT) traffic-based, network traffic-based, cyber-physical system, and web traffic-based. In the present paper, an overview of each of them is provided, as well as the course of …
An Inference-Centric Approach To Natural Language Processing And Cognitive Modeling, Animesh Nighojkar
An Inference-Centric Approach To Natural Language Processing And Cognitive Modeling, Animesh Nighojkar
USF Tampa Graduate Theses and Dissertations
Reasoning over natural text is highly nuanced, and interpretations can vary widely depending on cultural background, financial status, age, gender, or even mood. This doctoral dissertation seeks to not only mimic human reasoning behaviors but also improve the task used in natural language processing (NLP) to capture naturalistic reasoning, known as the Natural Language Inference (NLI) task. NLI involves determining whether a hypothesis is true (entailment), false (contradiction), or indeterminate (neutral) based on a given premise. Initially, we will investigate the extent to which NLP systems designed to capture semantic equivalence actually measure meaning equivalence. After establishing that they do …
Reimagining Web Design: Empowering Agency Of Specialized Audiences Through User-Centered Heuristics, Haley Jones
Reimagining Web Design: Empowering Agency Of Specialized Audiences Through User-Centered Heuristics, Haley Jones
USF Tampa Graduate Theses and Dissertations
This research seeks to create an alternative model for website design that interrogates standardized, linear ways of knowing and being by placing the audience at the center of the web design process. This research contributed a reimagined approach to traditional and standardized web design heuristics by considering an audience-centric methodology that was practical and applicable for web design praxis to create equitable user experiences which can empower audiences to recall their own knowledge and experience to make meaning for themselves through a reimagining of knowledge-making processes in a network of digitized information. In perceiving the rhetorical choice in design of …
How Do Preservice Teachers Learn To Teach Integrated Computational Thinking?: Evidence From Planning, Enactment, And Reflection, Rachael Dektor, Samuel Severance, Kip Téllez
How Do Preservice Teachers Learn To Teach Integrated Computational Thinking?: Evidence From Planning, Enactment, And Reflection, Rachael Dektor, Samuel Severance, Kip Téllez
Journal of Computer Science Integration
This study examines pre-service teachers’ (PSTs) beliefs and understandings about computational thinking (CT) integration and lesson implementation over time. Utilizing a design-based research approach, 3 PSTs led the co-design of integrated CT lessons with support from researchers and enacted these CT integrated lessons with K-5 students. All PSTs participated in a whole-group CT workshop and engaged in one-on-one lesson design sessions with a researcher. We utilized a grounded theory approach to qualitatively analyze pre-surveys, semi-structured interviews, and video data of three PSTs enacting their lessons. We found that PSTs’ initial beliefs about CT instruction – including the importance of it …
Addressing Social Inequalities Using Ai, Big Data, And Machine Learning, Erica L. Jensen, Lakell Archer, Sumaya Ali
Addressing Social Inequalities Using Ai, Big Data, And Machine Learning, Erica L. Jensen, Lakell Archer, Sumaya Ali
Journal of Nonprofit Innovation
No abstract provided.
Improving Flextype: Ambiguous Text Input For Users With Visual Impairments, Dylan Gaines, Keith Vertanen
Improving Flextype: Ambiguous Text Input For Users With Visual Impairments, Dylan Gaines, Keith Vertanen
Michigan Tech Publications
We present an improved version of the FlexType interface for nonvisual text input. FlexType enables nonvisual text input on mobile touchscreen devices by allowing users to select from a small number of character groups with gestures instead of targeting letters at specific screen locations. Based on an interview with users who are blind or low vision, we added a letter-entry mode to enable easier entry of difficult words such as proper nouns. We conducted a longitudinal study with users who are legally blind to compare FlexType to users' typical text input methods. While we found FlexType was significantly slower than …
Assessment And Prediction Of Meteorological Drought Using Machine Learning Algorithms And Climate Data, Khalid En-Nagre, Mourad Aqnouy, Ayoub Ouarka, Syed Ali Asad Naqvi, Ismail Bouizrou, Jamal Eddine Stitou El Messari, Aqil Tariq, Walid Soufan, Wenzhao Li, Hesham El-Askary
Assessment And Prediction Of Meteorological Drought Using Machine Learning Algorithms And Climate Data, Khalid En-Nagre, Mourad Aqnouy, Ayoub Ouarka, Syed Ali Asad Naqvi, Ismail Bouizrou, Jamal Eddine Stitou El Messari, Aqil Tariq, Walid Soufan, Wenzhao Li, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted in Morocco’s Upper Drâa Basin (UDB), analyzed data spanning from 1980 to 2019, focusing on the calculation of drought indices, specifically the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as the Mann-Kendall test and the Sen’s Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost Regressor, and K-Nearest Neighbors Regressor, were evaluated to predict the SPEI values for both three …
From Graph Theory For Robust Deep Networks To Graph Learning For Multimodal Cancer Analysis, Asim Waqas
From Graph Theory For Robust Deep Networks To Graph Learning For Multimodal Cancer Analysis, Asim Waqas
USF Tampa Graduate Theses and Dissertations
This dissertation explores the intersection of graph theory and deep learning, focusing on enhancing the robustness of deep neural networks (DNNs) and applying these advancements to complex problems like cancer diagnosis and treatment. We investigate the structural properties of graphs and their influence on neural network performance, particularly in multimodal learning. The work delves into the design space of DNN architectures using graph-theoretic measures, transforming graphs into DNN architectures for various tasks, and examining their robustness against noise and adversarial attacks. The study extends to medical imaging, highlighting advanced DNN architectures like U-Net for brain tumor segmentation. It addresses the …
Transforming Organizational Cyber Security With Artificial Intelligence And Data-Driven Optimization, Soumyadeep Hore
Transforming Organizational Cyber Security With Artificial Intelligence And Data-Driven Optimization, Soumyadeep Hore
USF Tampa Graduate Theses and Dissertations
This dissertation presents a comprehensive framework for enhancing organizational cybersecurity through data-driven intelligence. The research integrates multiple methodologies to tackle challenges in network intrusion detection and vulnerability management within cybersecurity operations centers (CSOCs). First, the research investigates vulnerability prioritization and mitigation techniques currently employed by CSOCs. To further streamline the vulnerability prioritization and mitigation process a machine learning (ML)-based Vulnerability Priority Scoring System (VPSS) is introduced, significantly improving the prioritization and mitigation of context-sensitive vulnerabilities. The VPSS outperforms traditional methods, reducing the cumulative vulnerability exposure score by up to 30% by considering both organizational context and vulnerability severity. Next, the …
Optimizing Cybersecurity Operations Using Data-Driven Intelligence, Jalal Ghadermazi
Optimizing Cybersecurity Operations Using Data-Driven Intelligence, Jalal Ghadermazi
USF Tampa Graduate Theses and Dissertations
Cybersecurity operations centers (CSOCs) play a crucial role in safeguarding organizations from cyber threats. CSOC operations are divided into two main areas: Intrusion detection systems (IDS) and security response team (SRT) operations. Machine learning (ML) and deep learning (DL) advancements have significantly improved IDSs. IDS can be either flow-based, suitable for offline analysis, or packet-based, which analyze traffic in real-time. However, packet-based IDS often treat packets independently, ignoring the sequential nature of network communication. Additionally, recent ML/DL approaches also struggle with capturing global and structural information and novel attack detection due to their reliance on labeled data. The SRT within …
Assessing The Impact Of An Rpp On A Large Urban School District: The Case Of Cafécs, Erin Henrick, Danny Schmidt, Steven Mcgee, Andrew M. Rasmussen, Lucia Dettori, Ronald I. Greenberg, Dale Reed, Don Yanek
Assessing The Impact Of An Rpp On A Large Urban School District: The Case Of Cafécs, Erin Henrick, Danny Schmidt, Steven Mcgee, Andrew M. Rasmussen, Lucia Dettori, Ronald I. Greenberg, Dale Reed, Don Yanek
Computer Science: Faculty Publications and Other Works
This study analyzes the impact of the Chicago Alliance for Equity in Computer Science (CAFÉCS) Research Practice Partnership (RPP) on the Chicago Public School (CPS) Office of Computer Science (OCS). Using a qualitative analysis drawing on data from leadership team meetings, published articles and presentations, and evaluation reports from 11 years of the partnership, we utilized a framework developed by the CAFÉCS leadership team to document the impact on district (1) Programs, (2) Research, (3) Organizational Structures, and (4) Policies leading to (5) Equitable Results for students, condensed as PROSPER. In particular, we explore the role of the RPP in …