The Handbook of Research-Based Practices for Educating Students with Intellectual Disability provides an integrated, transdisciplinary overview of research-based practices for teaching students with intellectual disability. This comprehensive volume emphasizes education across life stages, from early intervention in schools through the transition to adulthood, and highlights major educational and support needs of children and youth with intellectual disability. The implications of history, recent research, and existing information are positioned to systematically advance new practices and explore promising possibilities in the field. Driven by the collaboration of accomplished, nationally recognized professionals of varied approaches and philosophies, the book emphasizes practices that have been shown to be effective through multiple methodologies, so as to help readers select interventions based on the evidence of their effectiveness.

Digital technologies hold immense potential to transform the field of rehabilitation and enable greater community participation for individuals with disabilities. In the Handbook of Research on Advances in Digital Technologies to Promote Rehabilitation and Community Participation, a comprehensive exploration of these cutting-edge technologies and their impact is presented, and it delves into various digital solutions, such as virtual reality, tele-rehabilitation, mobile apps, rehabilitation platforms, and more. The book sheds light on the applications to promote rehabilitation and enhance community involvement. It provides a profound understanding of how these technologies can facilitate remote rehabilitation, foster self-management of illnesses, support independent living, and enhance communication and social participation. Furthermore, it emphasizes the accessibility of information and resources that digital technologies provide, unlocking new possibilities for individuals with disabilities. However, this research goes beyond mere exploration and also examines the challenges and opportunities associated with these digital advancements. The ethical considerations that arise in the utilization of these technologies are addressed, emphasizing the need for responsible and considerate implementation.

The Monte Carlo method is a numerical technique to model the probability of all possible outcomes in a process that cannot easily be predicted due to the interference of random variables. It is a technique used to understand the impact of risk, uncertainty, and ambiguity in forecasting models. However, this technique is complicated by the amount of computer time required to achieve sufficient precision in the simulations and evaluate their accuracy. This book discusses the general principles of the Monte Carlo method with an emphasis on techniques to decrease simulation time and increase accuracy.

Teacher education programs serve traditional and non-traditional students and develop teachers to enter a range of teaching environments. Approaching teacher education through community involvement and learning objectives helps to effectively prepare teachers to serve local and community needs. The Handbook of Research on Service-Learning Initiatives in Teacher Education Programs provides emerging research on the methods and techniques for educators to strengthen their knowledge regarding the intersection of service learning and field placements. While highlighting topics, such as cultural competency, teacher development, and multicultural education, this book explores the benefits, challenges, and opportunities for employing community service as the driving framework for field experiences. This publication is a vital resource for practitioners, educators, faculty, and administrators seeking current research on the opportunity of field involvement to enhance teacher candidates’ experiences and provide a channel for meaningful learning.

This book addresses the experimental calibration of best-estimate numerical simulation models. The results of measurements and computations are never exact. Therefore, knowing only the nominal values of experimentally measured or computed quantities is insufficient for applications, particularly since the respective experimental and computed nominal values seldom coincide. In the author’s view, the objective of predictive modeling is to extract “best estimate” values for model parameters and predicted results, together with “best estimate” uncertainties for these parameters and results. To achieve this goal, predictive modeling combines imprecisely known experimental and computational data, which calls for reasoning on the basis of incomplete, error-rich, and occasionally discrepant information. The customary methods used for data assimilation combine experimental and computational information by minimizing an a priori, user-chosen, “cost functional” (usually a quadratic functional that represents the weighted errors between measured and computed responses). In contrast to these user-influenced methods, the BERRU (Best Estimate Results with Reduced Uncertainties) Predictive Modeling methodology developed by the author relies on the thermodynamics-based maximum entropy principle to eliminate the need for relying on minimizing user-chosen functionals, thus generalizing the “data adjustment” and/or the “4D-VAR” data assimilation procedures used in the geophysical sciences. The BERRU predictive modeling methodology also provides a “model validation metric” which quantifies the consistency (agreement/disagreement) between measurements and computations. This “model validation metric” (or “consistency indicator”) is constructed from parameter covariance matrices, response covariance matrices (measured and computed), and response sensitivities to model parameters. Traditional methods for computing response sensitivities are hampered by the “curse of dimensionality,” which makes them impractical for applications to large-scale systems that involve many imprecisely known parameters. Reducing the computational effort required for precisely calculating the response sensitivities is paramount, and the comprehensive adjoint sensitivity analysis methodology developed by the author shows great promise in this regard, as shown in this book. After discarding inconsistent data (if any) using the consistency indicator, the BERRU predictive modeling methodology provides best-estimate values for predicted parameters and responses along with best-estimate reduced uncertainties (i.e., smaller predicted standard deviations) for the predicted quantities. Applying the BERRU methodology yields optimal, experimentally validated, “best estimate” predictive modeling tools for designing new technologies and facilities, while also improving on existing ones.

Education is expanding to include a stronger focus on the practical application of classroom lessons in an effort to prepare the next generation of scholars for a changing world economy centered on collaborative and problem-solving skills for the digital age. The Handbook of Research on Technology Tools for Real-World Skill Development presents comprehensive research and discussions on the importance of practical education focused on digital literacy and the problem-solving skills necessary in everyday life. Featuring timely, research-based chapters exploring the broad scope of digital and computer-based learning strategies including, but not limited to, enhanced classroom experiences, assessment programs, and problem-solving training, this publication is an essential reference source for academicians, researchers, professionals, and policymakers interested in the practical application of technology-based learning for next-generation education.