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\"Supplying a foundation for understanding the development of the brain and the learning process, this text examines the physical and environmental factors that influence how we acquire and retain information throughout our lives. The book also lays out practical strategies that educators can take directly into the classroom\"-- Provided by publisher.

From launching a student on the route to self-directed learning to using group processes, self-assessment, the life line experience, and developing a learning contract, this encyclopedia comprehensively covers all aspects of distributed learning. The overarching goal is to collect together in a single resource the best practices for adults engaged in continuing education at the graduate level, in corporate settings, in open university settings, and in similar learning environments. Topics covered: } administrative processes } technical tools and supports } policy, finance, and governance } social and cultural perspectives } student and faculty issues } teaching and learning processes and technologies.

There is a rich body of encyclopaedic writing which survives from the two millennia before the Enlightenment. This book sheds new light on that material. It traces the development of traditions of knowledge ordering which stretched back to Pliny and Varro and others in the classical world. It works with a broad concept of encyclopaedism, resisting the idea that there was any clear pre-modern genre of the 'encyclopaedia', and showing instead how the rhetoric and techniques of comprehensive compilation left their mark on a surprising range of texts. In the process it draws attention to both remarkable similarities and striking differences between conventions of encyclopaedic compilation in different periods, with a focus primarily on European/Mediterranean culture. The book covers classical, medieval (including Byzantine and Arabic) and Renaissance culture in turn, and combines chapters which survey whole periods with others focused closely on individual texts as case studies.

This encyclopaedia showcases the explanatory power of Marxist educational theory and practice. The entries have been written by 51 leading authors from across the globe. The 39 entries cover an impressive range of contemporary issues and historical problematics. The editor has designed the book to appeal to readers within the Marxism and education intellectual tradition, and also those who are curious newcomers, as well as critics of Marxism. The Encyclopaedia of Marxism and Education is the first of its kind. It is a landmark text with relevance for years to come for the productive dialogue between Marxism and education for transformational thinking and practice.

In the higher education sector, a new era has begun with the advent of ubiquitous learning environments. Ubiquitous learning tools allow improving context-aware as well as learning experiences by offering seamless availability regardless of location all the time. They also help in establishing effortless interaction between authentic and digital learning resources and at the same time offering personalised learning opportunities as well. There are numerous available ubiquitous e-learning tools that can be employed in higher education. E-learning tools also offer training and higher education to many students that have different higher educational levels and come from diverse cultural backgrounds. However, if the capabilities of e-learning are underestimated, these may not be successful in higher education. Some of the people lack understanding about the limitations and weaknesses of e-learning, while some may have superfluous expectations. In this paper, various e-learning tools like Wikipedia, MOODLE, Web 2.0, Web 3.0 and Blackboard have been evaluated. We also comment on key aims regarding each tool and investigate the disadvantages and advantages. Based on this analysis, a global view regarding the current as well as future tendencies pertaining to ubiquitous e-learning tools is obtained and thus possible key comments are provided for employing e-learning tools like MOODLE, Web 2.0 and Web 3.0 in the classroom. Based on our teaching experience, MOODLE was found to be efficient in the development of e-learning. MOODLE was favoured by a majority of authors and practitioners rather than Blackboard. However, MOODLE cannot be considered a fully pure social software since it does not include social networks. In this review, the scope of employing ubiquitous learning environments has been presented in higher education contexts. However, it increases the requirement for transparent research that shows practical implications to generalise future development processes. Moreover, it was shown that e-learning 3.0 is one amongst the key trends employing Web 3.0 tools for social learning. Also, on the Internet, quick incorporation of new services into existing applications like integrating Wiki with Web 3.0 can be done easily. The primary risk here would be the fact that lecturers and students are not fully aware that these web services are not controlled by their universities. Since these servers have been installed in many different countries, the principles and privacy laws vary from country to country.

Background In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Previously proposed methods for NER are dictionary- or rule-based methods and machine learning approaches. However, these traditional approaches are heavily reliant on large-scale dictionaries, target-specific rules, or well-constructed corpora. These methods to NER have been superseded by the deep learning-based approach that is independent of hand-crafted features. However, although such methods of NER employ additional conditional random fields (CRF) to capture important correlations between neighboring labels, they often do not incorporate all the contextual information from text into the deep learning layers. Results We propose herein an NER system for biomedical entities by incorporating n-grams with bi-directional long short-term memory (BiLSTM) and CRF; this system is referred to as a contextual long short-term memory networks with CRF (CLSTM). We assess the CLSTM model on three corpora: the disease corpus of the National Center for Biotechnology Information (NCBI), the BioCreative II Gene Mention corpus (GM), and the BioCreative V Chemical Disease Relation corpus (CDR). Our framework was compared with several deep learning approaches, such as BiLSTM, BiLSTM with CRF, GRAM-CNN, and BERT. On the NCBI corpus, our model recorded an F-score of 85.68% for the NER of diseases, showing an improvement of 1.50% over previous methods. Moreover, although BERT used transfer learning by incorporating more than 2.5 billion words, our system showed similar performance with BERT with an F-scores of 81.44% for gene NER on the GM corpus and a outperformed F-score of 86.44% for the NER of chemicals and diseases on the CDR corpus. We conclude that our method significantly improves performance on biomedical NER tasks. Conclusion The proposed approach is robust in recognizing biological entities in text.

Background One of the major challenges in precision medicine is accurate prediction of individual patient’s response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model. Results We presented here a novel deep-learning model that integrates gene expression, genetic mutation, and chemical structure of compounds in a multi-task convolutional architecture. We applied our model to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. We selected relevant cancer-related genes based on oncology genetics database and L1000 landmark genes, and used their expression and mutations as genomic features in model training. We obtain the cheminformatics features for compounds from PubChem or ChEMBL. Our finding is that combining gene expression, genetic mutation, and cheminformatics features greatly enhances the predictive performance. Conclusion We implemented an extended Graph Neural Network for molecular graphs and Convolutional Neural Network for gene features. With the employment of multi-tasking and self-attention functions to monitor the similarity between compounds, our model outperforms recently published methods using the same training and testing datasets.

The flood of information brought to us by advancing technology is often accompanied by a distressing sense of \"information overload,\" yet this experience is not unique to modern times. In fact, says Ann M. Blair in this intriguing book, the invention of the printing press and the ensuing abundance of books provoked sixteenth- and seventeenth-century European scholars to register complaints very similar to our own. Blair examines methods of information management in ancient and medieval Europe as well as the Islamic world and China, then focuses particular attention on the organization, composition, and reception of Latin reference books in print in early modern Europe. She explores in detail the sophisticated and sometimes idiosyncratic techniques that scholars and readers developed in an era of new technology and exploding information.

Data are a crucial raw material of this century. The amount of data that have been created in materials science thus far and that continues to be created every day is immense. Without a proper infrastructure that allows for collecting and sharing data, the envisioned success of big data-driven materials science will be hampered. For the field of computational materials science, the NOMAD (Novel Materials Discovery) Center of Excellence (CoE) has changed the scientific culture toward comprehensive and findable, accessible, interoperable, and reusable (FAIR) data, opening new avenues for mining materials science big data. Novel data-analytics concepts and tools turn data into knowledge and help in the prediction of new materials and in the identification of new properties of already known materials.

NRC publication: Yes