In this era of rapid technological evolution, the landscape of education continually transforms. This compilation is a timely endeavor, capturing the intersection of technology and learning, a ...synergy pivotal for the future. This anthology, a mosaic of innovation and insight, beckons readers to witness the dawn of a transformative epoch in education.
This book reviews the literature on reflection, explores the relationship between reflection and learning, and discusses how reflection can be used to improve learning and professional practice. The ...following are among the topics discussed: (1) common-sense and developmental stage approaches to reflection; (2) the philosophies of Dewey and Habermas as "backbone philosophies" of reflection; (3) reflection in experiential learning (Kolb's experiential learning cycle, action research); (4) the work of Donald Schon and reflection in professional practice (reflection-in-action versus reflection-on-action, reflection in professional education); (5) a theoretical stance on reflective practice in the professions (reflection and professional characteristics); (6) a practical stance on reflective practice in the professions; (7) the role of reflection in counseling, therapy, and personal development (empowerment, emancipation); (8) taking stock of reflection; (9) fundamentals of learning (constructivism); (10) mapping learning; (11) the place of reflection in learning; (12) the conditions for reflection; (13) two case studies of reflection in professional situations; (14) learning journals as tools for learning through reflection; and (15) more ways and means of learning through reflection (generating reflection by having learners reflect on their own learning and by teaching critical thinking and philosophy). Eleven tables/figures are included. (Contains 272 references.) (MN)
Augmented reality (AR) deepens learning interactions by imposing digital information on top of physical settings. This study implemented an AR-enhanced theme-based contextualized learning and aimed ...to examine the effects of captions (non-caption, English caption and Chinese caption) and English proficiency (less proficient and proficient) on junior high school students' English learning effectiveness, motivation and attitude. Six classes of ninth-graders voluntarily participated in the experimental learning sessions using tablets. A factorial design was employed, and the participants' learning performance, motivation and attitude were evaluated. The results indicated that captions did not affect knowledge comprehension, but English proficiency played a significant role in it. The effects of captions and English proficiency on knowledge application indicated that English captions placed high cognitive load and hindered less proficient learners' knowledge application, but proficient learners performed equally under different caption conditions. Generally, students demonstrated positive motivation toward learning from the AR-enhanced contextualized learning. The proficient learners were more motivated in terms of self-efficacy, proactive learning and learning value. All learners expressed positive attitude toward learning, among whom, those who learned without captions showed greater degrees of confidence and preferences, and the proficient learners showed greater degrees of confidence, preferences, learning process and learning strategy but lower degrees of anxiety.
The electromyogram (EMG), also known as an EMG, is used to assess nerve impulses in motor nerves, sensory nerves, and muscles. EMS is a versatile tool used in various biomedical applications. It is ...commonly employed to determine physical health, but it also finds utility in evaluating emotional well-being, such as through facial electromyography. Classification of EMG signals has attracted the interest of scientists since it is crucial for identifying neuromuscular disorders (NMDs). Recent advances in the miniaturization of biomedical sensors enable the development of medical monitoring systems. This paper presents a portable and scalable architecture for machine learning modules designed for medical diagnostics. In particular, we provide a hybrid classification model for NMDs. The proposed method combines two supervised machine learning classifiers with the discrete wavelet transform (DWT). During the online testing phase, the class label of an EMG signal is predicted using the classifiers’ optimal models, which can be identified at this stage. The simulation results demonstrate that both classifiers have an accuracy of over 98%. Finally, the proposed method was implemented using an embedded CompactRIO-9035 real-time controller.
Age of Entanglement explores the connections that linked German and Indian intellectuals from the nineteenth century through the Second World War as they shared ideas, formed networks, and studied ...one another's worlds. But, as Kris Manjapra shows, transnational intellectual entanglements are not inherently liberal or conventionally cosmopolitan.
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images ...(e.g. 256 × 256, 384 × 384). For gigapixel whole-slide imaging (WSI) in computational pathology, WSIs can be as large as 150000 × 150000 pixels at 20 × magnification and exhibit a hierarchical structure of visual tokens across varying resolutions: from 16 × 16 images capturing individual cells, to 4096 × 4096 images characterizing interactions within the tissue microenvironment. We introduce a new ViT architecture called the Hierarchical Image Pyramid Transformer (HIPT), which leverages the natural hierarchical structure inherent in WSIs using two levels of self-supervised learning to learn high-resolution image representations. HIPT is pretrained across 33 cancer types using 10,678 gigapixel WSIs, 408,218 4096 × 4096 images, and 104M 256 × 256 images. We benchmark HIPT representations on 9 slide-level tasks, and demonstrate that: 1) HIPT with hierarchical pretraining outperforms current state-of-the-art methods for cancer subtyping and survival prediction, 2) self-supervised ViTs are able to model important inductive biases about the hierarchical structure of phenotypes in the tumor microenvironment.
This book describes the Moulster and Griffiths nursing model and demonstrates how learning disability nurses can use it in practice. It provides an effective framework to assess, plan, reflect on and ...evaluate person-centred care, considering the complex needs of people who have learning disabilities, their families and their carers.
Lung and Colorectal (LC) cancer is life-threatening and rapidly developing cancers. According to World Health Organization (WHO), approximately 4.14 million lung and colorectal cancer cases were ...newly diagnosed, with 2.7 million fatalities. An International Agency for Research on Cancer (IARC) reported that there will be more than 3 million additional instances of colorectal cancer worldwide between 2020 and 2040. Early diagnosis of LC cancer is very helpful for treatment and can save the precious human life. The conventional diagnosis methods are expensive and time consuming. In this work, we present an accurate and efficient model for the classification of Lung and Colorectal (LC) cancer. We utilize two well-known pre-trained deep learning models, ResNet50 and EfficientNetB0, and fine-tuned the both models based on the addition and removal of layers. After the fine-tuning, manual hit and trail based hyperparameters are initialized. Later on, the deep transfer learning was performed and obtained the trained models. Two different feature vectors have been extracted from both models and fused using a priority based serial approach. To further improve the performance of extracted features, Normal Distribution based Gray Wolf Optimization algorithm is employed and obtained the best features that given as input to five classifiers. The output of these five classifiers is then utilized by soft voting technique to generate the final prediction. Experimental results show that the proposed architecture achieved an overall 98.73% accuracy on LC25000 dataset. Furthermore, prediction time was reduced by 19.14%. Comparison with the state-of-the-art techniques shows that the proposed technique obtained the improved performance results.
In recent years, many researchers have been engaged in the development of educational computer games; however, previous studies have indicated that, without supportive models that take individual ...students' learning needs or difficulties into consideration, students might only show temporary interest during the learning process, and their learning performance is often not as good as expected. Learning styles have been recognized as being an important human factor affecting students' learning performance. Previous studies have shown that, by taking learning styles into account, learning systems can be of greater benefit to students owing to the provision of personalized learning content presentation that matches the information perceiving and processing styles of individuals. In this paper, a personalized game-based learning approach is proposed based on the sequential/global dimension of the learning style proposed by Felder and Silverman. To evaluate the effectiveness of the proposed approach, a role-playing game has been implemented based on the approach; moreover, an experiment has been conducted on an elementary school natural science course. From the experimental results, it is found that the personalized educational computer game not only promotes learning motivation, but also improves the learning achievements of the students.
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