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Deep learning in visual computing and signal processing
\"This new volume, Deep Learning in Visual Computing and Signal Processing, covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more. Key features : covers both the fundamentals and the latest concepts in deep learning, presents some of the diverse applications of deep learning in visual computing and signal processing, and includes over 90 figures and tables to elucidate the text. An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this valuable resource will serve as a guide for researchers, engineers, and students who want to have a quick start on learning and/or building deep learning systems. It provides a good theoretical and practical understanding and complete information and knowledge required to understand and build deep learning models from scratch\"-- Provided by publisher.
BOOK
Principles of Blended Learning
The rapid migration to remote instruction during the Covid-19 pandemic has expedited the need for more research, expertise, and practical guidelines for online and blended learning. A theoretical grounding of approaches and practices is imperative to support blended learning and sustain change across multiple levels in education organizations, from leadership to classroom. The Community of Inquiry is a valuable framework that regards higher education as both a collaborative and individually constructivist learning experience. The framework considers the interdependent elements of social, cognitive, and teaching presence to create a meaningful learning experience. In this volume, the authors further explore and refine the blended learning principles presented in their first book, Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry, with an added focus on designing, facilitating, and directing collaborative blended learning environments by emphasizing the concept of shared metacognition.
eBook
Digital learning designs in physiotherapy education: a systematic review and meta-analysis
Background
Digital learning designs have the potential to support teaching and learning within higher education. However, the research on digital learning designs within physiotherapy education is limited. This study aims to identify and investigate the effectiveness of digital learning designs in physiotherapy education.
Methods
The study was designed as a systematic review and meta-analysis of randomized and non-randomized trials. A search of eight databases on digital learning designs and technology was conducted. Study selection, methodology and quality assessment were performed independently by three reviewers. The included studies were mapped according to the types of digital interventions and studies. For similar interventions, the learning effects were calculated using meta-analyses.
Results
Altogether, 22 studies were included in the review (17 randomized controlled trials and five cohort studies). A blended learning design was used in 21 studies, a flipped classroom model in five and a distance learning design in one. Altogether, 10 of the 22 articles were included in meta-analyses, which showed statistically significant effects for flipped classrooms on knowledge acquisition (standardized mean difference [SMD]: 0.41; 95% confidence interval [CI]: 0.20, 0.62), for interactive websites or applications (apps) on practical skills (SMD: 1.07; 95% CI: 0.71,1.43) and for students self-produced videos on a practical skill in a cervical spine scenario (SMD: 0.49; 95% CI: 0.06, 0.93). Overall, the effects indicated that blended learning designs are equally as or more effective than traditional classroom teaching to achieve learning outcomes. Distance learning showed no significant differences compared to traditional classroom teaching.
Conclusions
The current findings from physiotherapy education indicate that digital learning designs in the form of blended learning and distance learning were equally or more effective compared to traditional teaching. The meta-analyses revealed significant effects on student learning in favour of the interventions using flipped classrooms, interactive websites/apps and students self-produced videos. However, these results must be confirmed in larger controlled trials. Further, research should investigate how digital learning designs can facilitate students’ learning of practical skills and behaviour, learning retention and approaches to studying as well as references for teaching and learning in digital learning environments.
Journal Article
Practical machine learning and image processing : for facial recognition, object detection, and pattern recognition using Python
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You?ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You?ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you?ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects.
Book
Digital Learning Demand for Future Education 4.0—Case Studies at Malaysia Education Institutions
The rapid growth of the Industrial Revolution (IR) 4.0 has prompted the Malaysian Education Institution to transform the current education system into the future education system 4.0. The impact of IR 4.0 has opened a new paradigm for the Malaysian Educational Institution to ensure that all lecturers are capable of using information and communication technologies (ICT) in teaching and learning. However, there is a challenge in identifying appropriate digital learning platforms and tools to engage students in learning at their own pace. In this paper, we aimed to investigate the demand for digital learning platforms and tools according to the needs of students in Polytechnic Malaysia. The study was conducted randomly among 320 students from various fields of study in selected polytechnics. The analysis method used in this study was a quantitative method using questionnaires as an instrument. The results of our study indicated that e-learning platforms were the highest demand students’ preferred compared to other learning platforms and tools. Hence, the implications of this study could be useful as a guideline to assist Malaysian Polytechnic lecturers in strengthening the practice of using digital learning and develop digital proficiency for enabling education 4.0 in the future.
Journal Article
The high-impact digital library : innovative approaches for outreach and instruction
\"This book explores background information on outreach and instruction efforts by digital library practitioners, detailed survey results from practitioners themselves, and instructional ideas such as drop-in class sessions, course-integrated instruction, training, and ways digital library practitioners can contribute to the Open Educational Resources (OER) and open pedagogy movements\"-- Provided by publisher.
BOOK
Resilience, Confidence-Building, and Performance
Adaptive digital learning courseware is becoming part of the instructor tool kit to support student performance and ultimately reduce DFWI rates. However, past studies of the effectiveness of adaptive digital learning platforms in elevating student performance on summative assessment have shown promising yet at times mixed reviews (e.g. Yarnall et al., 2016). This case study integrates adaptive digital learning to address the challenge of promoting reading and concept application outside of class and analyzes its impacts on students’ engagement in class, perceived learning, and performance on summative assessment. Such an analysis, which considers mediating factors not previously analyzed together in adaptive digital learning studies, such as individual rather than aggregate performance, digital learning platform design differences, resiliency factors, and in-class activities, is an important step in clarifying some of the previously mixed results. Drawing on data collected in two sections of the same general education social science course taught by the same instructor in the same semester, this study illustrates the varying potential of adaptive digital learning to increase student confidence in the material and how it can translate into increased student performance if aligned and coupled in certain ways with in-class active learning. This study also provides evidence that illustrates how digital learning that is designed for greater degrees of editability by faculty can maximize learning benefits for students.
Journal Article
Resilience, Confidence-Building, and Performance
Adaptive digital learning courseware is becoming part of the instructor tool kit to support student performance and ultimately reduce DFWI rates. However, past studies of the effectiveness of adaptive digital learning platforms in elevating student performance on summative assessment have shown promising yet at times mixed reviews (e.g. Yarnall et al., 2016). This case study integrates adaptive digital learning to address the challenge of promoting reading and concept application outside of class and analyzes its impacts on students’ engagement in class, perceived learning, and performance on summative assessment. Such an analysis, which considers mediating factors not previously analyzed together in adaptive digital learning studies, such as individual rather than aggregate performance, digital learning platform design differences, resiliency factors, and in-class activities, is an important step in clarifying some of the previously mixed results. Drawing on data collected in two sections of the same general education social science course taught by the same instructor in the same semester, this study illustrates the varying potential of adaptive digital learning to increase student confidence in the material and how it can translate into increased student performance if aligned and coupled in certain ways with in-class active learning. This study also provides evidence that illustrates how digital learning that is designed for greater degrees of editability by faculty can maximize learning benefits for students.
Journal Article