THE USE OF THE EARLY WARNING INDICATOR REPORT TO IMPACT NINTH GRADE PROMOTION RATES IN A LARGE URBAN SCHOOL DISTRICT

Abstract The purpose of this study was to examine how four high schools used an Early Warning Indicator Report (EWIR) to improve ninth grade promotion rates. Ninth grade on-time promotion is an early predictor of a student’s likelihood to graduate (Bornsheuer, Polonyi, Andrews, Fore, & Onwuegbuzie, 2011; Leckrone & Griffith, 2006; Roderick, Kelley-Kemple, Johnson, & Beechum, 2014; Zvoch, 2006). The analysis revealed both similarities and differences in the ways that the four schools used the EWIR. The research took place in a large urban school district in the Mid-Atlantic. Sixteen participants from four high schools and the district’s central office voluntarily participated in face-to-face interviews. The researcher utilized a qualitative case study method to examine the implementation of the EWIR system in Wyatt School District. The interview data was transcribed and analyzed, along with district documents, to identify categories in this cross case analysis. Three primary themes emerged from the data: (1) targeted school structures for EWIR implementation, (2) the EWIR identified necessary supports for students, and (3) the central office support for school staff. The findings revealed the various ways that the target schools implemented the EWIR in their buildings and the level of support that they received from the central office that aided them in using the EWIR to improve ninth grade promotion rates. Based on the findings of this study, the researcher provided a number of key recommendations: (1) Districts should provide professional development to schools to ensure that schools have the support they need to implement the EWIR successfully; (2) There should be increased accountability from the central office for schools using the EWIR to identify impactful interventions for ninth graders; and (3) The district needs to assign dedicated central office staff to support the implementation of the EWIR in high schools across the district. As schools continue to face the challenge of improving ninth grade promotion rates, effective use of an Early Warning Indicator Report is recommended to provide school and district staff with data needed to impact overall student performance.

RELATIONAL MEMORY IN EARLY CHILDHOOD: DOES UNITIZATION HELP?

Young children often experience relational memory failures, which are thought to be due to underdeveloped recollection processes. Manipulations with adults, however, have suggested that relational memory tasks can be accomplished with familiarity, a processes that is fully developed during early childhood. The goal of the present study was to determine if relational memory performance could be improved in early childhood by teaching children a memory strategy (i.e., unitization) shown to increase familiarity in adults. Six- and 8-year old children were taught to use visualization strategies that either unitized or did not unitize pictures and colored borders. Analysis revealed inconclusive results regarding differences in familiarity between the two conditions, suggesting that the unitization memory strategy did not improve the contribution of familiarity as it has been shown to do in adults. Based on these findings, it cannot be concluded that unitization strategies increase the contribution of familiarity in childhood.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Advanced Adhesion Strength Testing Methods of Thin Film Multilayers in Electronic Packaging Systems

With the continued miniaturization and increasing performance of electronic devices, new technical challenges have arisen. One such issue is delamination occurring at critical interfaces inside the device. This major reliability issue can occur during the manufacturing process or during normal use of the device. Proper evaluation of the adhesion strength of critical interfaces early in the product development cycle can help reduce reliability issues and time-to-market of the product. However, conventional adhesion strength testing is inherently limited in the face of package miniaturization, which brings about further technical challenges to quantify design integrity and reliability. Although there are many different interfaces in today's advanced electronic packages, they can be generalized into two main categories: 1) rigid to rigid connections with a thin flexible polymeric layer in between, or 2) a thin film membrane on a rigid structure. Knowing that every technique has its own advantages and disadvantages, multiple testing methods must be enhanced and developed to be able to accommodate all the interfaces encountered for emerging electronic packaging technologies. For evaluating the adhesion strength of high adhesion strength interfaces in thin multilayer structures a novel adhesion test configuration called “single cantilever adhesion test (SCAT)” is proposed and implemented for an epoxy molding compound (EMC) and photo solder resist (PSR) interface. The test method is then shown to be capable of comparing and selecting the stronger of two potential EMC/PSR material sets. Additionally, a theoretical approach for establishing the applicable testing domain for a four-point bending test method was presented. For evaluating polymeric films on rigid substrates, major testing challenges are encountered for reducing testing scatter and for factoring in the potentially degrading effect of environmental conditioning on the material properties of the film. An advanced blister test with predefined area test method was developed that considers an elasto-plastic analytical solution and implemented for a conformal coating used to prevent tin whisker growth. The advanced blister testing with predefined area test method was then extended by employing a numerical method for evaluating the adhesion strength when the polymer’s film properties are unknown.