Products related to Learning:
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Toward Trustworthy Adaptive Learning : Explainable Learner Models
This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI.It aims to provide readers with a comprehensive understanding of how these models can enhance adaptive learning systems. Chapters cover a wide range of topics, including the development and optimization of explainable learner models, the integration of these models into adaptive learning systems, and their implications for educational equity.It also discusses the latest advancements in AI explainability techniques, such as pre-hoc and post-hoc explainability, and their application in intelligent tutoring systems.Lastly, the book provides practical examples and case studies to illustrate how explainable learner models can be implemented in real-world educational settings. This book is an essential resource for researchers, educators, and practitioners interested in the intersection of AI and education.It offers valuable insights for those looking to integrate explainable AI into their educational practices, as well as for policymakers focused on promoting equitable and transparent learning environments.
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Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent
With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications.However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions.This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency.The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning.The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications.The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.
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Practicing Trustworthy Machine Learning : Consistent, Transparent, and Fair AI Pipelines
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy.Many books on the subject offer deep dives into theories and concepts.This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems.With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn:Methods to explain ML models and their outputs to stakeholdersHow to recognize and fix fairness concerns and privacy leaks in an ML pipelineHow to develop ML systems that are robust and secure against malicious attacksImportant systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
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Challenging Professional Learning
Teachers and trainers are dual-professionals – they are required to have up-to-date industry skills and also skills in teaching and learning.The issue of professional identity, and the promotion of maintaining and building pedagogic expertise in relation to their vocational work, is therefore an extremely important one.This book argues that quality teaching and learning is very much dependent upon teachers and trainers undergoing continuing professional development (CPD), engaging actively in professional learning activities, generating professional learning communities and building their level of professionalism to meet increasing teaching standards.Unfortunately, CPD is battling a context of intensification of work, pressure of time and economic restrictions.The completion of CPD under such conditions can often become tokenistic and hitherto there has been very little research or evidence base for determining what approaches to CPD are most effective and efficient.Challenging Professional Learning draws on a wealth of recent research and evidence on what ingredients are necessary for effective and efficient (crucial at a time of such fiscal constraints) professional learning.It also explores the wider implications of these findings and the concept of learning as a collective activity.It argues that real professionalism cannot be achieved in isolation but instead takes place in a context that has political, social and cultural influences.The book brings together research from the Institute for Learning and practice around professional learning to link both individual and collective professional learning to organisational learning, leadership and the management of change whilst offering practical suggestions for improving these practices.It will be of great interest to teacher educators and their students at undergraduate and post-graduate levels, as well as anyone who works in higher education and with professional development.
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Does the brain become more efficient when learning?
Yes, the brain becomes more efficient when learning. When we learn new information or skills, the brain forms new neural connections and strengthens existing ones, which allows for more efficient processing and retrieval of that information in the future. This process, known as neuroplasticity, enables the brain to adapt and reorganize itself in response to learning experiences, ultimately leading to increased efficiency in processing and retaining new knowledge. Additionally, as we practice and repeat new skills or information, the brain becomes more efficient at performing those tasks, requiring less cognitive effort over time.
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Is MMOGA trustworthy/reliable?
MMOGA has a mixed reputation in the gaming community. While some users have reported positive experiences with their services, others have complained about issues such as delayed delivery, poor customer service, and fraudulent transactions. It is important to exercise caution and do thorough research before making any purchases from MMOGA to ensure a safe and reliable transaction.
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Which blogs are good and reliable for learning about passive income?
Some good and reliable blogs for learning about passive income include Smart Passive Income by Pat Flynn, The College Investor by Robert Farrington, and Making Sense of Cents by Michelle Schroeder-Gardner. These blogs offer a wealth of information on various passive income streams, such as affiliate marketing, online courses, and real estate investing. They also provide practical tips, case studies, and success stories to help readers understand and implement passive income strategies effectively.
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Is the HSE method for language learning by Marco Haak reliable?
The HSE method for language learning by Marco Haak is considered reliable by many learners and language educators. The method focuses on using high-frequency words and phrases to build a strong foundation in a language, and it emphasizes the importance of consistent practice and exposure to the language. Many learners have reported success in using the HSE method to improve their language skills, and Haak himself has demonstrated proficiency in multiple languages. However, as with any language learning method, individual results may vary, and it's important for learners to find the approach that works best for them.
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Transdisciplinary Professional Learning and Practice
This book presents thinking about and through transdisciplinary and professional development as an educative process.Rather than focusing on the delineation of the approaches offered, an analysis of these contributions points to commonality in those problems that benefit from a transdisciplinary perspective. The core elements of transdisciplinarity can lead to what might be called metanoia - another way of knowing; a knowing which is ‘beyond that which is creative and transformative. This poses challenges for the practice of all professionals and is the core issue that this book addresses. The book brings together the constituting views of transdisciplinarity, or metanoia, and focus them on current professional practice.?The book is structured in two parts and five sections. The first part deals with key issues in Transdisciplinarity; its actuality and how it creates knowledge.Section 1 has three original papers which look at Transdisciplinarity from a different lens. Especially, the Islamic voice has not been heard in this context before.Section 2 considers the knowledge aspect of Transdisciplinarity and how this might be confronted with existing disciplinary knowledge. Part 2 of the book is directly focused on professionals and their education. The third section considers research pedagogy and graduate education for the professional. This is followed in section 4 which offers a discussion on team work.In the final section six chapters present the transdisciplinary practitioner in different contexts.
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Assessing Impact : Evaluating Professional Learning
Design high-impact professional learning programs with results-based evaluations With increasing accountability pressure for evidence-based strategies and ever-tightening budgets, you want to make sure that the time, effort, and resources you are investing in your professional learning programs is truly making an impact on educator effectiveness and student achievement. In this third edition of Assessing Impact, Joellen Killion guides administrators, professional learning leaders, school improvement teams, and evaluators step by step through the rigors of producing an effective, in-depth, results-based analysis of your professional learning programs.A recognized expert in professional learning, Killion emphasizes the critical role of evaluation in bolstering effectiveness and retaining stakeholder support for ongoing educator development.The methods outlined here help you: Adhere to changes in federal and state policy relating to professional learning and educator developmentFacilitate the use of extensive datasets crucial for measuring feasibility, equity, sustainability, and impact of professional learningProduce more powerful, data-driven professional learning programs that benefit both students and educatorsEvaluate the effectiveness and impact of professional learning to make data-informed decisions and increase quality and results Assessing Impact is a vital resource for staff developers and educational leaders seeking to improve the effectiveness and sustainability of professional learning, while retaining the support of parents and the public alike. Praise for the Second Edition: "Anyone who reads this book has to feel obligated to 'set their world on fire.' The text not only forces the reader to see how we are failing our children and their teachers, it provides the means for each of us to do better." —Michael J.Ford, Superintendent Phelps-Clifton Springs CSD, Clifton Springs, NY
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Reliable Machine Learning : Applying SRE Principles to ML in Production
Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization.You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D.Sculley, Todd Underwood, and featured guests show you how to run an efficient ML system.Whether you want to increase revenue, optimize decision-making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. You'll examine:What ML is: how it functions and what it relies onConceptual frameworks for understanding how ML "loops" workEffective "productionization," and how it can be made easily monitorable, deployable, and operableWhy ML systems make production troubleshooting more difficult, and how to get around themHow ML, product, and production teams can communicate effectively
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Power Up Blended Learning : A Professional Learning Infrastructure to Support Sustainable Change
Take Blended Learning to the Next Level by Building an Effective Professional Learning Community! Technology is permeating classrooms and offering teachers and students opportunities to reimagine teaching and learning.Many schools and districts are shifting from traditional teacher-led instruction to blended learning with the goal of placing students at the center of learning.It’s time to power up your approach to professional learning to ensure teachers feel inspired and supported as they make this shift and embrace technology.This book provides an effective and actionable framework for school leaders looking to implement a long-term professional learning plan that extends professional development beyond a handful of days each year to create "coaching culture" that supports teachers as they move toward blended learning. Blended Learning expert Catlin Tucker provides a clear path for professional learning that is embedded in the school’s culture, blending targeted training (the "spark"), 1:1 coaching, and professional learning communities.This comprehensive approach to professional learning ensures that teachers in a blended environment work continually to improve learning outcomes for students, stay up-to-date on current technology, and learn with their colleagues.This text is full of concrete resources and strategies for leaders and blended learning coaches.It includes Coaching protocols Virtual coaching strategiesTemplates for feedback Lesson planning resources for designing blended lessons Rubrics for evaluation Stories and tips from blended learning coaches Vignettes from teachers who have successfully shifted to a blended learning model Reflection questions for leading book studies This book provides the tools and tips you’ll need to support teachers as they move toward blended learning.From articulating the why behind a shift to blended learning, to creating and implementing an actionable roadmap for building a professional learning infrastructure, this must-have resource will take your PD plans to the next level!
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What does learning experiences mean in personal and professional terms?
Learning experiences in personal and professional terms refer to the opportunities and situations that allow individuals to acquire new knowledge, skills, and insights. In personal terms, learning experiences can include anything from traveling to new places, trying new activities, or engaging in meaningful conversations with others. In professional terms, learning experiences can involve formal education, on-the-job training, mentorship, or professional development activities. These experiences contribute to personal growth and development, as well as professional advancement and expertise.
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Why deep learning compared to machine learning?
Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.
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Is distance learning as recognized as traditional learning?
Distance learning is becoming increasingly recognized and accepted as a legitimate form of education, especially with the advancements in technology and the widespread availability of online courses. However, traditional learning still holds a higher level of recognition and credibility in many circles, such as certain industries or academic institutions. Ultimately, the recognition of distance learning versus traditional learning can vary depending on the context and the perceptions of individuals or organizations.
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Is distance learning worse than on-campus learning?
The effectiveness of distance learning versus on-campus learning depends on individual preferences, learning styles, and the specific course or program. Distance learning can offer flexibility and convenience for those with busy schedules or other commitments, while on-campus learning may provide more opportunities for in-person interaction and hands-on experiences. Both modalities have their own set of advantages and disadvantages, and the quality of the learning experience ultimately depends on the resources, support, and engagement available to the student in either setting.
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