Placement of Young English Language Learners’ (ELLs’) in Reading Support : A Question of ELL Status or Learning Disability

Across the United States, increasing numbers of children whose first language, culture, and/or heritage is not English are being served in classrooms where English is the primary language in instruction. English Language Learners (ELLs) represent more than 5 million students in the United States, of which seventy-five percent are only Spanish-speaking. Many ELLs are facing the challenge of overcoming a language barrier to be academically successful, causing a risk of failure in increasing literacy demands. For educators working with ELLs in general education-related settings, their mission is to identify the root cause of their ELL student’s reading difficulties before they are potentially identified as having a learning disability (LD). As this is not an easy process, it is becoming difficult for educators to determine what strategies to use to support the reading difficulties of ELLs with the potential reading disability. This research focuses on the following research questions: (1) examining how educators\u27 beliefs and experiences are related to and impact their teaching of English language learners (ELLs), (2) determining if teachers are currently using evidence-based strategies to support reading achievement in ELL students, and (3) examining the relationship between ELL status and learning disability diagnosis

Formation Flying and Change Detection for the UNSW Canberra Space ‘M2’ Low Earth Orbit Formation Flying CubeSat Mission

The University of New South Wales, Canberra (UNSW Canberra) embarked on an ambitious CubeSatellite research, development, and education program in 2017 through funding provided by the Royal Australian Air Force (RAAF). The program consisted of M1 (Mission 1), M2 Pathfinder, and concludes with the formation flying mission M2. M2 is the final mission comprising two 6U CubeSatellites flying in formation using differential aerodynamic drag control. The M2 satellites were launched in a conjoined 12U form factor on RocketLab’s ‘They Go Up So Fast’ launch in March 2021. On 10th September 2021 the spacecraft divided into two 6U CubeSats (M2-A and M2-B) under the action of a small spring force in their near-circular 550km, 45-degree inclination orbit. The formation is controlled by varying the spacecrafts’ attitude, which creates a large variation in the aerodynamic drag force due to the change in the cross-sectional area from the large, double-deployable, solar arrays located on the zenith face of the spacecraft. This paper presents the outcomes of the Formation Flying and Change Detection primary mission objectives for the mission. The results are generated by collecting and analysing optical and RF (Radio Frequency) space domain awareness sensor data from the ground and validating them against GPS (Global Positioning System) and attitude data downlinked from the spacecraft. The outcomes of the broader mission objectives, which include increasing the Technology Readiness Level for a suite of intelligent on-board optical and RF sensor technologies, will be presented in subsequent publications. The results presented here comprise two major campaigns: 1.) The spacecraft separation campaign when the original 12U form factor deployed following launch split in half to form the M2-A and M2-B satellites, and 2) the demonstration of active formation control of the spacecraft via differential aerodynamic drag. M2-A and M2-B underwent several major configuration changes during the spacecraft separation campaign. The results from ground-based sensors detecting the 12U spacecraft separating into two distinct (6U) objects are presented. The effect of the double-deployable solar arrays deployment on the relative orbital motion of the M2-A and M2-B spacecraft is illustrated and compared to data from optical and RF ground-based measurements taken during this window. The formation control campaign involved actively controlling the spacecraft via differential aerodynamic drag in order to significantly alter the separation distance. The mission demonstrated the capability to switch the leading spacecraft’s position between M2-A and M2-B and to actively control separation distance ranging from 130km down to 1km. Formation control is achieved via open-loop, pre-scheduled, commands issued from the UNSW Canberra Space ground station. A two-stage modelling and simulation process is used to derive the scheduled attitude states. Firstly, a batch least squares orbit determination algorithm is applied to GPS data from a steady-state differential drag actuation period (where one spacecraft is in maximum drag and the other in its minimum drag attitude configuration). The batch least squares orbit determination is conducted out using the NASA General Mission Analysis Tool (GMAT), resulting in precise state estimates for each spacecraft and drag coefficient (Cd) estimates for both the maximum and minimum drag configurations. Predictions of trajectory for various attitude profiles can be produced by tailoring the spacecraft’s drag coefficients between the maximum and minimum values generated by the batch least squares state estimation process. Ground-based optical and RF space domain awareness (SDA) sensor measurements collected during the manoeuvre campaign are compared to the spacecraft’s GPS and attitude telemetry data. The SDA sensors are actively seeking to detect changes in the separation distance between the spacecraft. Initial results from an investigation into whether changes observed in photometric light curve signatures can signal the commencement of a differential drag manoeuvre are presented

An A/B Testing Platform for Fastly’s Compute@Edge

A/B testing has become a common practice that companies use to continually improve and optimize existing products and services due to its ability to evaluate design ideas quickly, precisely, and cheaply. There are multiple A/B testing frameworks and platforms available for companies to use, yet of all the existing ones, currently none use Rust, which is the primary supported language with Fastly’s Compute@Edge (C@E) platform, the serverless execution environment for this project. We designed and developed an end-to-end A/B testing platform compatible with Fastly’s Compute@Edge serverless service offering. The three components of this platform are a Rust Crate (Framework), an API written in Go, and a user interface (UI) written in Javascript using React and Next.js. We thoroughly unit tested these components and system tested the overall platform with an example A/B test, which demonstrated that the platform is a successful minimum viable product. By taking a novel serverless approach to A/B testing, and utilizing the maturity and benefits of the three languages, Fastly can contribute to the business experimentation and serverless communities

Estimation of geosynchronous space objects using finite set statistics filtering methods

The use of near Earth space has increased dramatically in the past few decades, and operational satellites are an integral part of modern society. The increased presence in space has led to an increase in the amount of orbital debris, which poses a growing threat to current and future space missions. Characterization of the debris environment is crucial to our continued use of high value orbit regimes such as the geosynchronous (GEO) belt. Objects in GEO pose unique challenges, by virtue of being densely spaced and tracked by a limited number of sensors in short observation windows. This research examines the use of a new class of multitarget filters to approach the problem of orbit determination for the large number of objects present. The filters make use of a recently developed mathematical toolbox derived from point process theory known as Finite Set Statistics (FISST). Details of implementing FISST-derived filters are discussed, and a qualitative and quantitative comparison between FISST and traditional multitarget estimators demonstrates the suitability of the new methods for space object estimation. Specific challenges in the areas of sensor allocation and initial orbit determination are addressed in the framework. The sensor allocation scheme makes use of information gain functionals as formulated for FISST to efficiently collect measurements on the full multitarget system. Results from a simulated network of three ground stations tracking a large catalog of geosynchronous objects demonstrate improved performance as compared to simpler, non-information theoretic tasking schemes. Further studies incorporate an initial orbit determination technique to initiate new tracks in the multitarget filter. Together with a sensor allocation scheme designed to search for new targets and maintain knowledge of the existing catalog, the method comprises a solution to the search-detect-track problem. Simulation results for a single sensor case show that the problem can be solved for multiple objects with no a priori information, even in the presence of missed detections and false measurements. Collectively, this research seeks to advance the capabilities of FISST-derived filters for use in the estimation of geosynchronous space objects; additional directions for future research are presented in the conclusion