What Large-Scale, Survey Research Tells Us About Teacher Effects on Student Achievement: Insights From the Prospectus Study of Elementary Schools

This report is about conceptual and methodological issues that arise when educational researchers use data from large-scale, survey research studies to investigate teacher effects on student achievement. In the report, we illustrate these issues by reporting on a series of analyses we conducted using data from Prospects: The Congressionally Mandated Study of Educational Opportunity. This large-scale, survey research effort gathered a rich store of data on instructional processes and student achievement in a large sample of U.S. elementary schools during the early 1990s as part of the federal government\u27s evaluation of the Title I program. We use data from Prospects to estimate the overall size of teacher effects on student achievement and to test some specific hypotheses about why such effects occur. On the basis of these analyses, we draw some substantive conclusions about the magnitude and sources of teacher effects on student achievement and suggest some ways that survey-based research on teaching can be improved

Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems

One of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numerical PDE models, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. This paper proposes a data-driven projection-based model reduction technique to reduce this computational cost. The proposed technique has two distinctive features. First, the model reduction strategy is tailored to inverse problems: the snapshots used to construct the reduced-order model are computed adaptively from the posterior distribution. Posterior exploration and model reduction are thus pursued simultaneously. Second, to avoid repeated evaluations of the full-scale numerical model as in a standard MCMC method, we couple the full-scale model and the reduced-order model together in the MCMC algorithm. This maintains accurate inference while reducing its overall computational cost. In numerical experiments considering steady-state flow in a porous medium, the data-driven reduced-order model achieves better accuracy than a reduced-order model constructed using the classical approach. It also improves posterior sampling efficiency by several orders of magnitude compared to a standard MCMC method

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and

Investigating Multilevel Relationships in Information Systems Research: An Application to Virtual Teams Research Using Hierarchial Linear Modeling

Information Systems researchers are often concerned with empirical questions spanning more than one level of analysis. For example, virtual teams research provides a good illustration because such teams are inherently hierarchical entities involving the situated nature of individuals within teams. Despite the importance of multilevel research questions to Information Systems research, the literature has yet to fully engage appropriate techniques for multilevel investigations. Using hierarchical linear modeling (HLM) as a statistical tool that can appropriately test cross-level relationships, we provide an illustration of the differences and advantages of using a multilevel technique over ordinary least squares (OLS) regression. Using data from a study of global virtual teams, we demonstrate that substantive research conclusions differ based on the use of HLM versus OLS regression. Using HLM, we find a significant relationship between individual level task liking and affective commitment; we also find a significant relationship between individual level task liking and satisfaction with the virtual team. When testing the moderating effects of team characteristics, we found a significant positive moderating effect of team work processes on the relationship between task liking and satisfaction. We conclude with recommendations for future research and provide a comparison of empirical techniques available for IS researchers testing relationships at single and multiple levels of analysis

Development and identification of hierarchical nonlinear mixed effects models for the analysis of dynamic systems: identification and application of hierarchical nonlinear mixed effects models for the determination of steady-state and dynamic torque responses of an SI engine

Multi-level or hierarchical models present various features for dealing with data grouped at several levels. The majority of applications of hierarchical models use clustered data that is static in nature and collected over a long period of time. The purpose of this study is investigating hierarchical models for application with highly dynamic systems. Steady-state data are conventionally employed for engine torque mapping purposes. The data takes much time to collect and the dynamics of the system are routinely ignored. This valuable information could be used for better control of the system.In this study, an innovative transient spark-sweep approach is developed for collecting dynamic torque data more efficiently. The means of data collection implies a structure for which a multi-level model is best suited. A multi-model augmented D-optimal design is created, and the experimental data collected. Spark excitation is applied at speed/load points using Amplitude Modulated Pseudo Random Signal (AMPRS), and the torque response over the operating space is thus obtained. Conditional first-order linearization is used within the identification process for determining the hierarchical model parameters. The level-1 Nonlinear Auto Regressive eXogenous (NARX) models are separately determined using an Iterative Generalized Least Square (IGLS) method and the results are employed for initialisation of the covariance matrix and the model level-2 parameters. A novel gradient optimiser was established to facilitate the dynamic hierarchical model identification. Additionally, the uncertainty associated with model selection was mitigated using a multi-model approach. The model identified is evaluated and compared with experimental dynamic and steady-state data. It shows behaviour, both dynamic and steady state, providing prediction over a wider extrapolated spark range than conventional approaches. The new approach is eight time faster than current state-of-the-art approaches.</div

Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions

Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.Comment: 18 page