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Savvy internet consumption starts right here! Teachers and students are constantly inundated with information, yet lack the organizational skill necessary to effectively utilize it. From Twitter hashtags to online communities, this handy guide will help you to find, store, and share the best information and resources found on the web today--and teach your students to do the same. Real-world tips, tools, in-depth examples and lesson plans help you systematically: Understand the curation process Find, collect, and share reliable web-based information Build students' information literacy skills Help students research and organize problem-based learning projects Use cutting-edge curation tools like Evernote, Diigo, and Pocket.

'provides an excellent synopsis of a range of different aspects of student learning ... a thorough and thought-provoking book ...' TES 'If I had to choose just one book to teach best practice for learning across the curriculum, then Getting the Buggers to Learn would be a hot contender. It is also an excellent resource for any thinking skills programme ... I wish I had had access to this book when I developed a research model for students at my school ... The book is clearly structured and sequenced [and] it is easy to navigate your way round and find information quickly ... Don't walk, run to your local bookshop and order a copy of this book immediately.' Teacher review The new edition of this successful book is an invaluable guide for teachers, containing a variety of strategies to develop students' learning skills. Covering everything from traditional learning approaches to more innovative methods, such as how technology and the media can be used to great effect, Duncan Grey writes accessibly and entertainingly. Brimming with top tips and innovative advice, this book will prove extraordinarily helpful to teachers everywhere. This edition features fully-updated sections on assessment, teaching and learning styles and thinking skills.

\"There is a necessary balance between knowledge and knowing how to find out - between having the key facts in your head, having the understanding of how to use them, and having the skill to draw on extra resources too.

The rapid development of urban systems and rising requirements for sustainable development lift resource management issues in smart communities. A fundamental problem for contemporary communities involves effectively using energy and water resources and waste management systems under environmental limitations. Artificial intelligence (AI) techniques at an advanced level deliver new methods that optimize resource management systems. The research builds and examines a deep-learning framework that optimizes the management of smart community resources. The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. The system focuses on two main objectives, which include better forecasting precision, optimum resource distribution, and efficient detection of operational problems. Research validation employed data from the Amsterdam Open Data Platform and Singapore Government Open Data Portal joined by crowdsourced platforms FixMyStreet and OneService. The preprocessing phase involved three stages, i.e., cleaning and normalization and feature engineering steps, before model training and testing phases. Predictive models received assessment based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². A comparison with traditional methods revealed the proposed approach delivered superior performance results. The deep learning framework demonstrated superior performance, achieving an average reduction of 18.7% in resource consumption and a 16.2% reduction in operational costs. The models outperformed baseline methods, with LSTMs achieving an MAE of 1.8 for water demand prediction and autoencoders detecting anomalies with an F1-score of 95.5%. Due to its effective capabilities, the proposed framework solves challenges in resource management for smart communities while showing the potential of AI-driven solutions for sustainable urban development. Research results demonstrate that integrating sophisticated deep-learning methods yields more significant potential for optimizing resource utilization while improving operational effectiveness.

Learning Analytics can provide powerful tools for teachers in order to support them in the iterative process of improving the effectiveness of their courses and to collaterally enhance their students' performance. In this paper, we present the theoretical background, design, implementation, and evaluation details of eLAT, a Learning Analytics Toolkit, which enables teachers to explore and correlate learning object usage, user properties, user behavior, as well as assessment results based on graphical indicators. The primary aim of the development of eLAT is to process large data sets in microseconds with regard to individual data analysis interests of teachers and data privacy issues, in order to help them to self-reflect on their technology-enhanced teaching and learning scenarios and to identify opportunities for interventions and improvements.

\"A global pandemic hit our world, and education has forever changed. But have your instructional practices changed? Teachers must now leverage technology to provide students with high-quality teaching and learning experiences that transcend a traditional classroom's walls. This is a historic opportunity to abandon antiquated teaching practices and reimagine instruction in ways that boost learning outcomes and prepare students for living and working in the digital age. This book offers guidance for creating and sustaining rigorous and engaging blended learning solutions. Opening with lessons learned from the pandemic, the book addresses impacts on lesson design and delivery, student engagement, assessment, and teacher training and PD. The following chapters build on and address these experiences, with each chapter featuring practical examples on how to implement effective blended instructions as well as examples from the field to illustr

By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by significant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a comprehensive systematic literature mapping review that overcomes some limitations of the traditional ones and provides a broader overview of the content and main topics studied in the specialized literature devoted to MOOCs. The sample consisted of 6320 publications automatically classified within six research topics, denominated by human experts: institutional approach, pedagogical approach, evaluation, analytics, participation, and educational resources. The content analysis of the topics identified was conducted using visual network analysis, which supported the identification of different thematic sub-clusters and endorsed the classification. Results from the review show that the lowest production of MOOC papers is within the topics of the pedagogical approach and educational resources. In contrast, participation and evaluation are the most frequent ones. In addition, the most cited papers are on the topics of analytics and resources, being the pedagogical approach and the institutional approach the less cited. This highlights the need for more MOOC research from a pedagogical perspective and calls upon the presence of educators.

Background Virtual simulation in medical education is often fragmented, lacking a continuous pathway from theory to clinical practice. The Mini-Clinical Evaluation Exercise (Mini-CEX) offers a competency-based assessment framework that could address this gap. This study aimed to develop and evaluate a four-stage virtual simulation teaching platform integrating the Mini-CEX framework for clinical medicine training. Methods The four-stage platform (theoretical instruction, faculty demonstration, virtual simulation practice, bedside training) was developed and implemented with 120 final-year clinical medicine interns. A 15-item questionnaire, developed by the research team, assessed students’ learning experiences (10 items; 10-point Likert scale) and the platform’s functional demands (5 items; 0–1 scale). Data were analyzed using descriptive statistics (mean, SD) in SPSS 26.0. Results Student acceptance of the model was high, with mean scores for all experience items exceeding 7.1. The highest-rated aspects were flexibility in using fragmented time (8.3 ± 0.5) and platform ease of use (8.3 ± 0.3). The most desired platform functions were access to course materials (0.82) and assessment information (0.80). Conclusion The Mini-CEX-oriented four-stage virtual simulation platform is a feasible and well-accepted model that effectively integrates clinical knowledge and skills training. It provides a structured pathway for competency development, supporting its potential for broader implementation in medical education reform.

Student Engagement in the Digital University challenges mainstream conceptions and assumptions about students' engagement with digital resources in Higher Education. While engagement in online learning environments is often reduced to sets of transferable skills or typological categories, the authors propose that these experiences must be understood as embodied, socially situated, and taking place in complex networks of human and nonhuman actors. Using empirical data from a JISC-funded project on digital literacies, this book performs a sociomaterial analysis of student-technology interactions, complicating the optimistic and utopian narratives surrounding technology and education today and positing far-reaching implications for research, policy and practice.

Technology advancements promote a redefinition of traditional instructional methodologies, as well as the roles of teachers and learners towards an efficient e-learning ecosystem. To date, all existing solutions are combined with the conventional face-to-face learning process. However, the latter can be unexpectedly hindered in some emergency cases, like the Coronavirus (COVID-19) pandemic. To handle such unexpected scenarios, this paper presents NOTA, a novel online teaching and assessment scheme that takes advantage of Blockchain technology to maintain the expected teaching quality and assessment fairness while respecting the courses’ and examinations’ schedule. Besides, NOTA also motivates both learners and teachers to persist in their endeavours, even from home, through Blockchain’s incentive strategies. The preliminary results taken during the CoronaVirus period showed a very high satisfaction ratio, exceeding the 90%. This made us feel very optimistic about the potential of our proposal when deployed at a larger scale.