Optimising Time for Schoolwide Improvement: Developing a Time-Use Study Process for MTSS Leadership Teams

The current dissertation investigated how professional learning communities (PLCs) can be developed and sustained to support Chinese immersion preschool teachers across diverse roles, including lead teachers (LTs), teaching associates (TAs), and support teachers (STs), in an independent school context.Through the lens of educational equity and teacher empowerment, the study explored how differentiated PLC models can respond to the professional development (PD) needs of immigrant educators while enhancing individualized, child-centered learning practices.Using design-based implementation research (DBIR) methodology and narrative inquiry, the current study drew on stories and experiences spanning from 2020 to February 2025, and formal data collection began in April 2024.Grounded in theoretical frameworks such as communities of practice, complexity leadership theory (CLT), design thinking, multitiered system of supports (MTSS) and universal design for learning (UDL), the research examined three iterative cycles of PLC implementation.The findings illustrated how leadership practices, collaborative structures, and reflective documentation (e.g., learning stories) can foster teacher growth and systemic change, particularly in multilingual, multicultural early learning environments.The current study contributes to the field by proposing a sustainable, inclusive PLC model that bridges hierarchical gaps in teacher roles, integrates culturally responsive pedagogy, and enhances support for all children's developmental needs.The results offer actionable insights for educational leaders and policymakers seeking to improve equity, collaboration, and instructional quality in early childhood education (ECE), especially in bilingual and immersion program.v Preface My upbringing was filled with opportunities to see different perspectives because I came from a low-income family and experienced firsthand how limited resources affect students.In Taiwanese culture of 3-4 decades ago, I was an atypical learner because I could not learn through traditional teacher lectures; I only immersed myself deeply in topics I explored on my own.My family experiences have made me realize deeply the harm caused by discrimination, because since I was in the second grade, my mother has suffered from mental illness, and I heard many judgmental remarks from others.My father, a retired veteran who moved from China to Taiwan with the army after World War II, passed away during my high school years.Without the support of both financial and emotional resources, my family and I endured extremely difficult times.During the early years of college, I struggled to focus on my studies while constantly facing the reality that my mother had attempted suicide.Compared to my sister, I was fortunate to have participated in a government-sponsored experimental education program that allowed me to attend college.My sister, on the other hand, had to work while studying, spending 15 years advancing from vocational school to graduate school.Despite earning her degree, she still does not see herself as a successful learner and continues to be deeply affected by the negative self-image formed during her formative years.My early experiences have taught me one thing: whether child or adult, when one's physical and mental well-being is unstable, one cannot engage in learning.During my educational journey in Taiwan, I was often viewed through a deficit lens by the school system and others.However, when I entered a teacher's college and majored in early

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.KeywordsSensor NetworkSensor NodeWireless Sensor NetworkSteiner TreeSensor ReadingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

From Rio to Iragua--sustainability versus efficiency and equity for preventive health interventions.

Research on time use has seen several major developments in recent years. These include the adoption of exciting new technologies (e.g., smartphones, wearable Global Positioning System devices) that track behavior in real time, as well as a growing international database—the Multinational Time Use Study—that has surpassed one million days’ worth of harmonized time-diary data. These developments are transforming our understanding of the social patterning of everyday behavior. This article provides updates about this area of work, including recent findings regarding foundational sociological issues such as trends in gendered divisions of household labor and over-time, cross-national aggregate estimates of time spent on paid work and leisure. We also highlight new approaches to the study of time use. This includes an overview of advances in the collection and analysis of time-stamped behavioral data, as well as a discussion of methodological advances in the analysis of the temporal sequential structure of everyday activities.

This study sought to analyze the constraints in acquisition and use of non-projected media resources in teaching Geography in public secondary schools in Koibatek S. A sample of 70 Geography teachers, 35 head teachers and 314 students were sampled using purposive and stratified random sampling. Data collection was done by use of questionnaires, observation and content analysis. Data was analysed using descriptive statistical methods. The results were then presented in pie charts, graphs and tables. The study established that most schools had non-projected resources, though a limited supply and were rarely used. However, some schools lacked some important resources such as Geography rooms, facilities for fieldwork, weather station and library services. Constraints emanating from heavy workload, heavy curriculum and inept administration were found to undermine to effective use of these resources in teaching Geography. The study has recommended for an urgent review of curriculum and increased supervision to ensure that curriculum is hilly implemented, and refresher courses be given to teachers on the use of these resources There have been concerns over declining performance in Geography at national examination, especially areas that test students’ knowledge of field work, map work and physical Geography. Coincidentally these are the areas where non-projected media resources are most used. Yet few studies done in Geography have not adequately examined the non-projected media resources despite their critical role in the teaching of Geography.

A Novel Approach to Pediatric Heart Failure Quality Improvement within the ACTION Network

With the high flexibility and low cost of the deployment of UAVs, the application of UAV-assisted data collection has become widespread in the Internet of Things (IoT) systems. Meanwhile, the age of information (AoI) has been adopted as a key metric to evaluate the quality of the collected data. Most of the literature generally focuses on minimizing the age of all information. However, minimizing the overall AoI may lead to high costs and massive energy consumption. In addition, not all types of data need to be updated highly frequently. In this paper, we consider both the diversity of different tasks in terms of the data update period and the AoI of the collected sensing information. An efficient data collection method is proposed to maximize the system utility while ensuring the freshness of the collected information relative to their respective update periods. This problem is NP-hard. With the decomposition, we optimize the upload strategy of sensor nodes at each time slot, as well as the hovering positions and flight speeds of UAVs. Simulation results show that our method ensures the relative freshness of all information and reduces the time-averaged AoI by 96.5%, 44%, 90.4%, and 26% when the number of UAVs is 1 compared to the corresponding EMA, AOA, DROA, and DRL-eFresh, respectively.

Efficient data collection and tracking with flying drones

With the explosive increase of wireless traffic generated by Internet of Things (IoT), it is challenging for the ground communication infrastructures to achieve flexible and efficient data collection. In this paper, we propose an intelligent trajectory optimization and collision avoidance scheme for cooperative multi-UAV data collection in urban IoT environments, where a new Double Deep Q-Network (D3QN) is designed to maximize the total collected data by optimizing UAVs' flying speeds and dynamically adjusting the IoT-UAV associations. Simulations show that the proposed solution achieves the fastest convergence and the lowest collision rate with the average data collection rate of 91.15%, compared to baseline methods. Moreover, 4 UAVs can achieve the data collection rate of 95% compared to that of 50% in single UAV scenario.

Traditional methods of data collection are often expensive and time consuming. We propose a novel data collection technique, called Bayesian Adaptive Sampling (BAS), which enables us to capture maximum information from minimal sample size. In this technique, the information available at any given point is used to direct future data collection from locations that are likely to provide the most useful observations in terms of gaining the most accuracy in the estimation of quantities of interest. We apply this approach to the problem of estimating the amount of carbon sequestered by trees. Data may be collected by an autonomous helicopter with onboard instrumentation and computing capability, which after taking measurements, would then analyze currently available data and determine the next best informative location at which a measurement should be taken. We quantify the errors in estimation and work towards achieving maximal information from minimal sample sizes. We conclude by presenting experimental results that suggest our approach towards biomass estimation is more accurate and efficient as compared to random sampling. Bayesian Adaptive Sampling (BAS) is a methodology that allows a system to examine currently available data in order to determine new locations at which to take new readings. This procedure leads to the identification of locations where new observations are likely to yield the most information about a process, thus minimizing the required data that must be collected. As an example of the application of this methodology, we examine the question of standing woods in the United States. In order to estimate the amount of carbon sequestered by trees in the United States, the amount of standing woods must be estimated with quantifiable uncertainty (Wheeler, 2006). Such estimates come from either satellite images or near ground measurements. The amounts of error in the estimates from these two approaches are currently unknown. To this end, an autonomous helicopter with differential GPS (Global Positioning System), LIDAR (Light Detection and Ranging), stereo imagers, and spectrometers has been developed as a testing platform for conducting further studies (Wheeler, 2006). These instruments are capable of measuring the reflectance data and the location of the Sun and helicopter in terms of the zenith and the azimuth angles (Figure 1). The objective is to develop a controlling software system for this robotic helicopter, which optimizes the required ground O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

The past years have witnessed an increasing interest and concern regarding the development of security monitoring and management mechanisms for Critical Infrastructures, due to their vital role in ensuring the availability of many essential services. This task is not easy due to the specific characteristics of such systems, and the natural resistance of Critical Infrastructures operators against actions implying downtime. Digital Twins, as accurate virtual models of physical objects or processes, can provide a faithful environment for security analysis or evaluation of potential mitigation strategies to be deployed in face of specific situations. Nonetheless, their on-premises deployment can be expensive, implying a significant CAPEX whose return will depend on the ability to plan and deploy a suitable support infrastructure, as well as implementing efficient and scalable data collection and processing mechanisms capable of taking advantage of the acquired resources. This paper presents an off-premises approach to design and deploy Digital Twins to secure critical infrastructures, developed in the scope of the ELEGANT project. Such Digital Twins are built using real-time, high fidelity replicas of Programming Logic Controllers, coupled with scalable and efficient data collection processes, supporting the development and validation of Machine Learning models to mitigate security threats like Denial of Service attacks. The validation approach of ELEGANT, which leveraged from the capabilities of the Fed4Fire federated testbeds evaluated the feasibility of using cloudified Digital Twins, thus converting a significant part of the projected CAPEX for the in-premises model into on-demand, pay-as-you-go OPEX, eventually paving the way for the establishment of a DTaaS (Digital Twin as a Service) paradigm. The achieved results demonstrate that the data pipelines providing support for the ELEGANT Digital Twins have low impact in terms of resource usage in Denial of Service and Distributed Denial of Service attack scenarios, when higher volumes of data are generated.

Considering a wireless sensor network with optimal mobile sink track along which data are transmitted to the nodes, a fraction of nodes in the area of interest generate data packets and send them to the sink. Usually the single path routing is adopted in such scenarios which may cause unnecessary transmissions. We address the problem of how to collect these packets efficiently by using network coding technique. Network coding is a kind of in network computation which has many advantages over traditional routing, such as providing higher network throughput, using bandwidth efficiently and balancing the traffic. In this paper, we propose a data collection strategy by using network coding for efficient data collection in event driven WSNs and explore its performance through theoretical analysis. We present a strategy for network coding algorithm which can guarantee the successful decoding for all the encoded packets. Specifically, our numerical results show the decrease in the total number of transmissions by applying network coding than that by using the single path routing. Also numerical results indicate that the delivery packets rate is significantly improved as the increase of the link loss rate comparing network coding we adopt with single path routing.

A key aspect of collaboration systems, and therefore collaboration technologies is the ability to access concerned information in timely manner. The Internet of things (IoT) is being envisioned as the architecture of the future to assist in this regard. It will be created by combining sensing and communicating devices, which will provide data that can be analyzed and used to initiate automated actions. This structure of IoT has now given a new recognition to Wireless Sensor etworks (WS ) such that WS can be identified as the datacollecting component of the IoT. Efficient data collection from a large scale WS , with limited power supply, bandwidth and packet sizes, is a critical issue. One of the methods of data collection in a WS is through forming multiple clusters of the sensor nodes with one cluster head (CH) in each cluster. The objective of this work is to develop an energy efficient data collection environment for a large scale, randomly deployed cluster based wireless sensor networks by using a virtual grid based mechanism to localize the clusters and stabilize the cluster sizes in the network. This has been done as a precondition to implement the proposed differential data aggregation scheme for the spatially correlated data in a cluster.

Data collection and analysis in the field is critical for operations in domains such as environmental science and public safety. However, field workers currently face data- and platform-oriented issues in efficient data collection and analysis in the field, such as limited connectivity, screen space, and attentional resources. In this paper, we explore how visual analytics tools might transform field practices by more deeply integrating data into these operations. We use a design probe coupling mobile, cloud, and immersive analytics components to guide interviews with ten experts from five domains to explore how visual analytics could support data collection and analysis needs in the field. The results identify shortcomings of current approaches and target scenarios and design considerations for future field analysis systems. We embody these findings in FieldView, an extensible, open-source prototype designed to support critical use cases for situated field analysis. Our findings suggest the potential for integrating mobile and immersive technologies to enhance data's utility for various field operations and new directions for visual analytics tools to transform fieldwork.

Energy conservation and improving the amount of data collected is quite challenging in Wireless Sensor Networks (WSN). This study focuses on survey of various techniques for lowering latency, routing data to Mobile Elements (ME) with motion control mechanisms, scheduling during data collection in WSN-ME and technique for energy conservation by using Fuzzy logic.