Interpreting Language Learning Data
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Interpreting language-learning data
This book provides a forum for methodological discussions emanating from researchers engaged in studying how individuals acquire an additional language. Whereas publications in the field of second language acquisition generally report on empirical studies with relatively little space dedicated to questions of method, the current book gave authors the opportunity to more fully develop a discussion piece around a methodological issue in connection with the interpretation of language-learning data. The result is a set of seven thought-provoking contributions from researchers with diverse interests. Three main topics are addressed in these chapters: the role of native-speaker norms in second-language analyses, the impact of epistemological stance on experimental design and/or data interpretation, and the challenges of transcription and annotation of language-learning data, with a focus on data ambiguity. Authors expand on these crucial issues, reflect on best practices, and provide in many instances concrete examples of the impact they have on data interpretation.
Interpreting language-learning data
Author: Amanda Edmonds
language: en
Publisher: BoD – Books on Demand
Release Date: 2020-09-28
This book provides a forum for methodological discussions emanating from researchers engaged in studying how individuals acquire an additional language. Whereas publications in the field of second language acquisition generally report on empirical studies with relatively little space dedicated to questions of method, the current book gave authors the opportunity to more fully develop a discussion piece around a methodological issue in connection with the interpretation of language-learning data. The result is a set of seven thought-provoking contributions from researchers with diverse interests. Three main topics are addressed in these chapters: the role of native-speaker norms in second-language analyses, the impact of epistemological stance on experimental design and/or data interpretation, and the challenges of transcription and annotation of language-learning data, with a focus on data ambiguity. Authors expand on these crucial issues, reflect on best practices, and provide in many instances concrete examples of the impact they have on data interpretation.
Learning to See Data
This book is associated with the 'Data Literacy Level 1' on-demand online course: https://dataliteracy.com/courses/data-literacy-level-1 For most of us, it's rare to go a full day without coming across data in the form of a chart, map or dashboard. Graphical displays of data are all around us, from performance indicators at work to election trackers on the news to traffic maps on the road. But few of us have received training or instruction in how to actually read and interpret them. How many times have we been misled simply because we aren't aware of the pitfalls to avoid when interpreting data visualizations. Learning to See Data will teach you the different ways that data can be encoded in graphical form, and it will give you a deeper understanding of the way our human visual system interprets these encodings. You will also learn about the most common chart types, and the situations in which they are most appropriate. From basic bar charts to overused pie charts to helpful maps and many more, a wide array of chart types are covered in detail, and conventions, pitfalls, strengths and weaknesses of each of them are revealed. This book will help you develop fluency in the interpretation of charts, an ability that we all need to hone and perfect if we are to make meaningful contributions in the professional, public and personal arenas of life. The principles covered in it also serve as a critical background for anyone looking to create charts that others will be able to understand. "This book is clear and evocative, thorough and thoughtful, and remarkably readable: a marvelous launchpad into the world of data." –Tamara Munzner, Professor, University of British Columbia Computer Science "Everyone of us needs good data literacy skills to survive in the modern world. Without them, it's hard to succeed at work, or survive the onslaught of information (and misinformation) across all our media. Ben's book provides the necessary building blocks for a strong foundation. From that foundation, Ben's approach will inspire you to own the process of developing your skills further." –Andy Cotgreave, Technical Evangelism Director, Tableau