Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.

We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks. Content

Abstract:We perform an up-to-date global fit of top quark effective theory to experimental data from the Tevatron, and from LHC Runs I and II. Experimental data includes total cross-sections up to 13 TeV, as well as differential distributions, for both single top and pair production. We also include the top quark width, charge asymmetries, and polarisation information from top decay products. We present bounds on the coefficients of dimension six operators, and examine the interplay between inclusive and differential measurements, and Tevatron/LHC data. All results are currently in good agreement with the Standard Model.

In this paper we present a global fit of beyond the Standard Model (BSM) dimension six operators relevant to the top quark sector to all currently available top production cross-section measurements, namely parton-level top-pair and single top production at the LHC and the Tevatron. Higher order QCD corrections are modelled using differential and global K-factors, and we use novel fast-fitting techniques developed in the context of Monte Carlo event generator tuning to perform the fit. This allows us to provide new, fully correlated and model-independent bounds on new physics effects in the top sector from the most current direct hadron-collider measurements in light of the involved theoretical and experimental systematics. As a by-product, our analysis constitutes a proof-of-principle that fast fitting of theory to data is possible in the top quark sector, and paves the way for a more detailed analysis including top quark decays, detector corrections and precision observables.

This study compares teaching and learning activities in 4th and 5th grade classrooms that were permanently equipped with one laptop for each student and classrooms that share a cart of laptops that create a 1:1 laptop environment on a temporary basis. The study originated from a question posed to us by Andover Public Schools (MA): “How does teaching and learning differ when upper elementary students (4th and 5th graders) are provided with their own laptop computers?” In response to this question, we undertook an intensive two month study that employed a mixed methodology that included student surveys, student drawings, teacher interviews, and 56 structured classroom observations. The findings summarized in this article provide evidence of several differences in teaching and learning activities between the two settings. Classrooms that were fully equipped with 1:1 laptops showed more technology use across the curriculum, more use of technology at home for academic purposes, less large group instruction, and nearly universal use of technology for writing.

Over the past decade, investment in technology for schools has increased at a dramatic rate. Although policy makers are eager to understand the ways in which technology use in schools is affecting student learning, we believe that a critical preliminary step toward assessing the impacts of technology on teaching and learning requires the examination of the varied uses of technology in schools as well as the contexts that are likely to affect the use of technology in the classroom as a teaching and learning tool. Previous research examining technology use has focused on teacher characteristics and has neglected to explore the potentially alterable, organizational characteristics that may be affecting the adoption and use of technology in the classroom. In light of this argument and using survey data collected from 1490 elementary classroom teachers in 96 schools in 22 Massachusetts districts, this research examines how technology is being used by elementary school teachers, and examines the school and district organizational characteristics that are associated with increased use of technology as a teaching and learning tool. In addition to examining technology-use as a multi-faceted construct, using multilevel regression techniques this study provides evidence that schools’ organizational characteristics are associated with teachers’ use of technology in the classroom. Organizational characteristics such as districts’ and schools’ leadership practices and emphasis on technology, the type and amount of technology-related professional development available to teachers, as well as the amount of technology-related restrictive policies in place were found to be associated with the four measures of teachers’ use of technology examined in this study. Individual teacher characteristics such as constructivist beliefs, higher confidence using technology and positive beliefs about the efficacy of technology were each found to be associated with increased use of technology in the classroom.

In this article, Walt Haney, Michael Russell, and Damian Bebell summarize a decade of work using student drawings as a way to both document and change education and schooling. After a brief summary of more than one hundred years of literature on children's drawings, the authors point out that drawings have been little recognized as a medium of educational research in recent decades. Next they explain how the work reported here has evolved, recounting how they have used student drawings as a way to document educational phenomena. They then present reliability and validity evidence to support such use on a macro level. The authors go on to relate examples at the micro level of how drawings have been used to inform and change education and learning. Finally, they argue that student drawings, though only one form of inquiry, help illustrate the fundamental point that, if educational reforms are to succeed, we must treat teachers and students not just as the objects, but also as the agents, of reform and improvement.

As access to computer-based technology in schools and classrooms increases, greater emphasis has been placed on preparing teachers to use technology for instructional purposes. Survey data collected from 2,894 teachers in 22 Massachusetts districts were analyzed to examine the extent to which technology is used in and out of the classroom for instructional purposes. In addition to defining six specific categories of instructional use of technology, this study provides evidence that teachers generally use technology more for preparation and communication than for delivering instruction or assigning learning activities that require the use of technology. Important differences, however, were found among teachers who were new to the field compared with their more experienced colleagues. Although new teachers reported higher levels of comfort with technology and use it more for preparation, more experienced teachers report using technology more often in the classroom when delivering instruction or having students engage in learning activities.