Problem Solving With Language

Although Content and Language Integrated Learning (CLIL) is a popular teaching method, research on CLIL has nearly exclusively focused on aspects of language learning. Besides that, we are still lacking any cognitively well-grounded theory about the special features of contexts in which the focus is on content learning, but in which a foreign language is used as the medium of communicating information. This book re-examines the basis for CLIL from a cognitive perspective and investigates how the use of a foreign language as a working language influences the processing of content. It summarizes findings from cognitive psychology on thinking, problem solving and conceptual processing, and integrates them with models of language-specific mental activities such as speech processing and text composition. This provides a theoretically well-grounded basis for the understanding of the special features of CLIL, and promotes a Cognitive Linguistic perspective on CLIL pedagogy. The theoretical considerations form the basis for an empirical study that offers the first insights into what CLIL learners actually do when they solve content-focused tasks while using an L2. Through spontaneous verbalization of thought, detailed verbal protocols were elicited and analysed into language and content focused cognitive processes. The analysis shows that both language and conceptual thought interact closely and that a focus on language in general has positive effects on the processing of semantic content; the use of an L2 as working language can enhance this effect. Additionally, the study offers a thorough reflection and new perspectives on verbal protocols as research tools, in particular in L2 contexts.

Automata and natural language theory are topics lying at the heart of computer science. Both are linked to computational complexity and together, these disciplines help define the parameters of what constitutes a computer, the structure of programs, which problems are solvable by computers, and a range of other crucial aspects of the practice of computer science. In this important volume, two respected authors/editors in the field offer accessible, practice-oriented coverage of these issues with an emphasis on refining core problem solving skills.

Offering a solid, research-based approach along with sound practical advice, this book equips you with the skills you need to analyse your own contexts and develop your practice, whether through formal study or alone. Badger explores teaching English as a problem-solving activity addressing three fundamental questions: what aspect of language do students needs to learn, how do they learn it, and how can teachers support this learning. This new edition includes updated references, a chapter on pragmatics, coverage of concepts such as translanguaging, CLIL, EMI, English as a lingua franca and sections on digital learning. Topics covered include: · Psychological and social learning processes · TESOL teaching methods and approaches · Lesson planning and classroom management · English teacher professional development The book also includes chapter summaries, activities for students and key readings recommendations, and online resources such as video case studies, additional exercises and multiple choice quizzes to consolidate learning. The book is ideal for both trainee and practicing teachers who want to develop their practice.

Quantitative Methods for Second Language Research introduces the approaches to and techniques for quantitative data analysis in second language research (SLR), with a primary focus on second language learning and assessment research. It takes a conceptual, problem-solving approach, emphasizing the understanding of statistical theory and its application to research problems and pays less attention to the mathematical side of statistical analysis. A range of common statistical analysis techniques that can be employed in SLR, presented and illustrated through applications of the IBM Statistical Package for Social Sciences (SPSS) program, are presented and discussed. These include tools for descriptive analysis (e.g. means and percentages) as well as inferential analysis (e.g. correlational analysis, t-tests, and analysis of variance (ANOVA)). It provides conceptual explanations of quantitative methods through the use of examples, cases and published studies in the field. Additionally, a companion website to the book hosts review exercises and answer keys for each chapter, SPSS files, and a manual. Practical and lucid, this book is the ideal resource for data analysis for graduate students and researchers in applied linguistics.

The fun and simple problem-solving guide that took Japan by storm Ken Watanabe originally wrote Problem Solving 101 for Japanese schoolchildren. His goal was to help shift the focus in Japanese education from memorization to critical thinking, by adapting some of the techniques he had learned as an elite McKinsey consultant. He was amazed to discover that adults were hungry for his fun and easy guide to problem solving and decision making. The book became a surprise Japanese bestseller, with more than 370,000 in print after six months. Now American businesspeople can also use it to master some powerful skills. Watanabe uses sample scenarios to illustrate his techniques, which include logic trees and matrixes. A rock band figures out how to drive up concert attendance. An aspiring animator budgets for a new computer purchase. Students decide which high school they will attend. Illustrated with diagrams and quirky drawings, the book is simple enough for a middleschooler to understand but sophisticated enough for business leaders to apply to their most challenging problems.