Evaluating Digital Math Tools in the Field

Many school districts have adopted digital tools to supplement or replace teacher-led instruction, usually based on the premise that these tools can provide more personalized or individualized experiences for students and at lower cost. Rigorously evaluating whether such initiatives promote better student outcomes in the field is difficult as most schools and teachers are unwilling to enforce rigorous study designs such as randomized control trials. We used study designs that were feasible in practice to assess whether two digital math tools, eSpark and IXL, were associated with improvements in 3rd – 6th grade student test scores in math. We also investigated the resource requirements and costs of implementing eSpark and IXL to assess whether these tools represent a valuable use of resources. We find that while IXL is substantially less costly to implement than eSpark, its use is not significantly associated with students’ math performance

The Class Size Controversy

[Excerpt] When we ask whether class size matters for achievement, it is essential to ask also, how class size matters. This is important for three reasons. First, if we can observe not only achievement differences, but also the mechanisms through which the differences are produced, this will increase our confidence that the differences are real, and not an artifact of some unmeasured or inadequately controlled condition. Second, the effects of class size may vary in different circumstances, and identifying how class size affects achievement will help us to understand why the effects of class size are variable. Third, the potential benefits of class size reduction may be greater than what we observe. For example, suppose class size reductions aid achievement, but only when teachers modify instructional practices to take advantage of the smaller classes. If a few teachers make such modifications, but most do not, then understanding how class size affects achievement in some cases will help reveal its potential effects, even if the potential is generally unrealized

A rating scale for measuring the validity of reading instructional materials for the use of minority groups

Thesis (Ed.M.)--Boston Universit

Learning from Teen Childbearing Experiences of Close Friends: Evidence Using Miscarriages as a Natural Experiment

We examine peer effects in teen childbearing among close friends, using miscarriages as a natural experiment. We use 775 women from the core sample of Add Health who had a friend with a teen pregnancy. We find a sizable negative treatment effect – a close friend\u27s teen birth is associated with a 6 percentage point reduction in the likelihood of own teen pregnancy and childbearing. There is evidence that this effect operates through a learning mechanism by changing beliefs regarding early childbearing. Effects of teen pregnancy prevention policies may be partially offset by reductions in the opportunities for social learning

Joint Material and Illumination Estimation from Photo Sets in the Wild

Faithful manipulation of shape, material, and illumination in 2D Internet images would greatly benefit from a reliable factorization of appearance into material (i.e., diffuse and specular) and illumination (i.e., environment maps). On the one hand, current methods that produce very high fidelity results, typically require controlled settings, expensive devices, or significant manual effort. To the other hand, methods that are automatic and work on 'in the wild' Internet images, often extract only low-frequency lighting or diffuse materials. In this work, we propose to make use of a set of photographs in order to jointly estimate the non-diffuse materials and sharp lighting in an uncontrolled setting. Our key observation is that seeing multiple instances of the same material under different illumination (i.e., environment), and different materials under the same illumination provide valuable constraints that can be exploited to yield a high-quality solution (i.e., specular materials and environment illumination) for all the observed materials and environments. Similar constraints also arise when observing multiple materials in a single environment, or a single material across multiple environments. The core of this approach is an optimization procedure that uses two neural networks that are trained on synthetic images to predict good gradients in parametric space given observation of reflected light. We evaluate our method on a range of synthetic and real examples to generate high-quality estimates, qualitatively compare our results against state-of-the-art alternatives via a user study, and demonstrate photo-consistent image manipulation that is otherwise very challenging to achieve

Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

Impervious surface estimation using remote sensing images and gis : how accurate is the estimate at subdivision level?

Impervious surface has long been accepted as a key environmental indicator linking development to its impacts on water. Many have suggested that there is a direct correlation between degree of imperviousness and both quantity and quality of water. Quantifying the amount of impervious surface, however, remains difficult and tedious especially in urban areas. Lately more efforts have been focused on the application of remote sensing and GIS technologies in assessing the amount of impervious surface and many have reported promising results at various pixel levels. This paper discusses an attempt at estimating the amount of impervious surface at subdivision level using remote sensing images and GIS techniques. Using Landsat ETM+ images and GIS techniques, a regression tree model is first developed for estimating pixel imperviousness. GIS zonal functions are then used to estimate the amount of impervious surface for a sample of subdivisions. The accuracy of the model is evaluated by comparing the model-predicted imperviousness to digitized imperviousness at the subdivision level. The paper then concludes with a discussion on the convenience and accuracy of using the method to estimate imperviousness for large areas

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America