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Perfect Opportunity
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Diversifying Digital Learning : Online Literacy and Educational Opportunity
How does the digital divide affect the teaching and learning of historically underrepresented students?Many schools and programs in low-income neighborhoods lack access to the technological resources, including equipment and Internet service, that those in middle- and upper-income neighborhoods have at their fingertips.This inequity creates a persistent digital divide—not a simple divide in access to technology per se, but a divide in both formal and informal digital literacy that further marginalizes youths from low-income, minoritized, and first-generation communities. Diversifying Digital Learning outlines the pervasive problems that exist with ensuring digital equity and identifies successful strategies to tackle the issue.Bringing together top scholars to discuss how digital equity in education might become a key goal in American education, this book is structured to provide a framework for understanding how historically underrepresented students most effectively engage with technology—and how institutions may help or hinder students’ ability to develop and capitalize on digital literacies. This book will appeal to readers who are well versed in the diverse uses of social media and technologies, as well as less technologically savvy educators and policy analysts in educational organizations such as schools, afterschool programs, colleges, and universities.Addressing the intersection of digital media, race/ethnicity, and socioeconomic class in a frank manner, the lessons within this compelling work will help educators enable students in grades K–12, as well as in postsecondary institutions, to participate in a rapidly changing world framed by shifting new media technologies. Contributors: Young Whan Choi, Zoë B. Corwin, Christina Evans, Julie Flapan, Joanna Goode, Erica Hodgin, Joseph Kahne, Suneal Kolluri, Lynette Kvasny, David J.Leonard, Jane Margolis, Crystle Martin, Safiya Umoja Noble, Amanda Ochsner, Fay Cobb Payton, Antar A.Tichavakunda, William G. Tierney, S. Craig Watkins
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Programming Machine Learning : From Coding to Deep Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool.But where to start? It's easy to be intimidated, even as a software developer.The good news is that it doesn't have to be that hard.Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system.Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty.Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning.Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work.Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on.Iterate on your design, and add layers of complexity as you go.Build an image recognition application from scratch with supervised learning.Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning.Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.Train and refine those networks with backpropagation and batching.Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.Start from the beginning and code your way to machine learning mastery.What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly.Apart from that, you'll only need your computer, and your code-adept brain.
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Learning for Work : How Industrial Education Fostered Democratic Opportunity
Founded in 1883, the Chicago Manual Training School (CMTS) was a short-lived but influential institution dedicated to teaching a balanced combination of practical and academic skills.Connie Goddard uses the CMTS as a door into America’s early era of industrial education and the transformative idea of “learning to do.” Rooting her account in John Dewey’s ideas, Goddard moves from early nineteenth century supporters of the union of learning and labor to the interconnected histories of CMTS, New Jersey’s Manual Training and Industrial School for Colored Youth, North Dakota’s Normal and Industrial School, and related programs elsewhere.Goddard analyzes the work of movement figures like abolitionist Theodore Weld, educators Calvin Woodward and Booker T.Washington, social critic W.E.B. Du Bois, Dewey himself, and his influential Chicago colleague Ella Flagg Young.The book contrasts ideas about manual training held by advocate Nicholas Murray Butler with those of opponent William Torrey Harris and considers overlooked connections between industrial education and the Arts and Crafts Movement. An absorbing merger of history and storytelling, Learning for Work looks at the people who shaped industrial education while offering a provocative vision of realizing its potential today.
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Is learning programming and software development very challenging?
Learning programming and software development can be challenging for some people, as it requires logical thinking, problem-solving skills, and attention to detail. However, with dedication, practice, and the right resources, it is definitely achievable. Breaking down complex concepts into smaller, more manageable parts and seeking help from online tutorials, courses, and communities can make the learning process easier and more enjoyable. Ultimately, the level of challenge will vary depending on the individual's background, experience, and learning style.
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Which programming languages are used in software development?
There are many programming languages used in software development, including popular languages such as Java, Python, C++, JavaScript, and Ruby. Each language has its own strengths and is used for different purposes in software development. For example, Java is commonly used for building enterprise-level applications, while Python is known for its simplicity and versatility. C++ is often used for system software and game development, while JavaScript is essential for web development. Overall, the choice of programming language depends on the specific requirements of the software being developed.
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What is the difference between equal opportunity and equity of opportunity?
Equal opportunity refers to the idea that everyone should have the same chances and access to resources, regardless of their background or circumstances. On the other hand, equity of opportunity recognizes that not everyone starts from the same place and therefore may require different levels of support or resources to achieve the same opportunities. Equity of opportunity aims to level the playing field by addressing the systemic barriers and providing additional support to those who have been historically disadvantaged. In essence, while equal opportunity focuses on treating everyone the same, equity of opportunity focuses on ensuring that everyone has the same chance to succeed.
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Is every day a new beginning and an opportunity for further development?
Yes, every day is a new beginning and an opportunity for further development. Each day presents a chance to learn, grow, and improve ourselves. By approaching each day with a positive mindset and a willingness to embrace new experiences, we can continue to evolve and reach our full potential. It's important to seize the opportunities that each day brings and use them to propel ourselves forward on our journey of personal growth and development.
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Unisex Triple Opportunity Sunglasses
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Unisex 3l Opportunity Sunglasses
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Opportunity, Mobility and Inequality
Research on Economic Inequality is a well-established publication of quality research.Volume 31 provides original research on intergenerational earnings mobility, the factors determining adult health inequality, the evolution of wealth inequality in different regions of the world, inequality of employment opportunities, and the effects of the type of labour agreement on wage inequality and on an extended measure (income plus wealth) of well-being. Opportunity, Mobility and Inequality presents newly published, rich datasets and deepens our understanding of these issues, uncovering novel innovations around how we conceive and measure such economic entities.
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Perfect Opportunity
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Which opportunity was missed?
The opportunity missed was to establish a stronger presence in the emerging market. By not investing in marketing and product development in the region, the company failed to capitalize on the potential growth and profitability that could have come from expanding into this market. This could have allowed the company to gain a competitive advantage and increase its market share in the region.
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What are opportunity costs?
Opportunity costs refer to the benefits or values that are foregone when a decision is made to pursue a particular course of action over another. It is the value of the next best alternative that is sacrificed in order to choose one option over another. Understanding opportunity costs is important in decision-making as it helps individuals and businesses evaluate the trade-offs involved in choosing one option over another. By considering opportunity costs, one can make more informed decisions that maximize the benefits gained from the resources available.
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What is the difference between software development and programming?
Software development is a broader term that encompasses the entire process of creating software, including planning, designing, testing, and maintaining software applications. Programming, on the other hand, refers specifically to the act of writing code to instruct a computer to perform certain tasks. While programming is a key component of software development, software development involves a more comprehensive approach that includes various stages beyond just writing code.
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Is artificial intelligence an opportunity or a risk for learning in school?
Artificial intelligence can be both an opportunity and a risk for learning in school. On one hand, AI can personalize learning experiences, provide instant feedback, and offer additional resources to support student learning. This can enhance student engagement and understanding. However, there are also concerns about the potential for AI to replace teachers, compromise student privacy, and perpetuate biases in the learning process. Therefore, it is important for educators to carefully consider how to integrate AI into the classroom in a way that maximizes its benefits while minimizing its risks.
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