106,620 research outputs found

    Continued Progress: Promising Evidence on Personalized Learning

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    The findings are grouped into four sections. The first section on student achievement finds that there were positive effects on student mathematics and reading performance and that the lowest-performing students made substantial gains relative to their peers. The second section on implementation and the perceptions of stakeholders finds that adoption of personalized learning practices varied considerably. Personalized learning practices that are direct extensions of current practice were more common, but implementation of some of the more challenging personalized learning strategies was less common. The third section relates implementation features to outcomes and identifies three elements of personalized learning that were being implemented in tandem in the schools with the largest achievement effects. Finally, the fourth section compares teachers' and students' survey responses to a national sample and finds some differences, such as teachers' greater use of practices that support competency-based learning and greater use of technology for personalization in the schools in this study with implementation data

    Ravensbourne College of Design and Communication

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    Curriculum Guidelines for Undergraduate Programs in Data Science

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    The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

    Making a Difference?: The Effects of Teach for America in High School

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    Uses longitudinal data from North Carolina to estimate the effectiveness, in terms of gains in student test scores, of TFA teachers relative to traditional teachers. Focuses on math and science teachers in the first study of TFA effects in high schools

    2009-10 statistics derived from ILR data for the monitoring and allocation of funding in FECs

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    "This document describes: how we used 2009-10 Data Service learner data to inform 2011-12 funding allocations how we used 2009-10 learner data to monitor returns made to HEFCE the responses required from colleges to these monitoring processes." - Page 2

    Selection of Statistical Software for Solving Big Data Problems: A Guide for Businesses, Students, and Universities

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    The need for analysts with expertise in big data software is becoming more apparent in today’s society. Unfortunately, the demand for these analysts far exceeds the number available. A potential way to combat this shortage is to identify the software taught in colleges or universities. This article will examine four data analysis software—Excel add-ins, SPSS, SAS, and R—and we will outline the cost, training, and statistical methods/tests/uses for each of these software. It will further explain implications for universities and future students

    MatLab vs. Python vs. R

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    Selection of Statistical Software for Solving Big Data Problems for Teaching

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    The need for analysts with expertise in big data software is becoming more apparent in 4 today’s society. Unfortunately, the demand for these analysts far exceeds the number 5 available. A potential way to combat this shortage is to identify the software sought by 6 employers and to align this with the software taught by universities. This paper will 7 examine multiple data analysis software – Excel add-ins, SPSS, SAS, Minitab, and R – and 8 it will outline the cost, training, statistical methods/tests/uses, and specific uses within 9 industry for each of these software. It will further explain implications for universities and 10 students (PDF

    Charter-School Management Organizations: Diverse Strategies and Diverse Student Impacts

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    Examines the growth of charter school management organizations, characteristics of students served, and use of resources; CMO practices; impact on students, including middle school test scores; and structures and practices linked to positive outcomes
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