Book Announcement: Implementing Mobile Language Learning Technologies in Japan
New book: Implementing Mobile Language Learning Technologies in Japan
by Steve McCarty, Hiroyuki Obari, and Takeshi Sato
Publisher: Springer Singapore / SpringerBriefs in Education (107 pages)
Table of Contents
Chapter 1 Introduction: Contextualizing Mobile Language Learning in Japan
Chapter 2 Mobile Language Learning Pedagogy: A Sociocultural Perspective
Chapter 3 Tokyo University of Agriculture and Technology Case Study:
Smartphone App LINE for EFL Peer Learning
Chapter 4 Osaka Jogakuin University Case Study:
Mobilizing the EFL Curriculum and Campus Infrastructure with iPods and iPads
Chapter 5 Aoyama Gakuin University Case Study:
Blended Learning and Flipped Classrooms utilizing Mobile Devices
Chapter 6 Conclusion: Implementing Language Learning in a Mobile-Oriented Society
Abstract
This book explores theoretical and practical aspects of implementing mobile language learning in university classrooms for English as a Foreign Language in Japan. The technologies utilized, such as smartphones, iPads, and wi-fi, integrate students’ hand-held devices into the campus network infrastructure. The pedagogical aims of ubiquitous mobile learning further incorporate social media, blended learning, and flipped classroom approaches into the curriculum. Chapter 1 defines mobile language learning within dimensions of e-learning and technology-assisted language learning, prior to tracing the development of mobile learning in Japan. Chapter 2 documents the sociocultural theory underpinning the authors’ humanistic approach to implementation of mobile technologies. The sociocultural pedagogy represents a global consensus of leading educators that also recognizes the agency of Asian learners and brings out their capability for autonomous learning. Case studies of universities, large and small, public and private, are organized similarly in Chapters 3 to 5. Institutional/pedagogical and technological context sections are followed by detailed content on the implementation of initiatives, assessment of effectiveness, and recommendations for other institutions. Distinct from a collection of papers, this monograph tells a story in brief book length about theorizing and realizing mobile language learning, describing pioneering and original initiatives of importance to practitioners in other educational contexts.
Authors
Steve McCarty lectures for Kansai University, Osaka Jogakuin University, KIC Graduate School of IT, and the government agency JICA.
Hiroyuki Obari, PhD in Computer Science, is a Professor at the Aoyama Gakuin University College of Economics in Tokyo.
Takeshi Sato is an Associate Professor at the Division of Language and Culture Studies, Tokyo University of Agriculture and Technology.
Seidel, V. P., & Fixson, S. K. (2013). Adopting Design Thinking in Novice Multidisciplinary Teams: The Application and Limits of Design Methods and Reflexive Practices: Adopting Design Thinking in Novice Teams. Journal of Product Innovation Management, 30, 19–33. https://doi.org/10.1111/jpim.12061
Razavi, M. N., & Iverson, L. (2007). Designing for privacy in personal learning spaces. New Review Of Hypermedia & Multimedia, 13(2), 163-185. doi:10.1080/13614560701709861
Jung, I., & Latchem, C. (2011). A model for e-education: Extended teaching spaces and extended learning spaces. British Journal Of Educational Technology, 42(1), 6-18. doi:10.1111/j.1467-8535.2009.00987.x
In scholarly and scientific publishing, altmetrics are non-traditional metrics[2] proposed as an alternative[3] to more traditional citation impact metrics, such as impact factor and h-index.[4] The term altmetrics was proposed in 2010,[1] as a generalization of article level metrics,[5] and has its roots in the #altmetrics hashtag. Although altmetrics are often thought of as metrics about articles, they can be applied to people, journals, books, data sets, presentations, videos, source code repositories, web pages, etc. They are related to Webometrics, which had similar goals but evolved before the social web. Altmetrics did not originally cover citation counts.[6] It also covers other aspects of the impact of a work, such as how many data and knowledge bases refer to it, article views, downloads, or mentions in social media and news media.[7][8]
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more on analytics and metrics in education in this IMS blog
EBSCO Information Services has debuted Stacks, a hosted content management system for libraries, and Stacks Mobile, a native app for iOS and Android devices.
Social media integration, including Goodreads, Facebook, Twitter and LinkedIn;
W3Schools – Fantastic set of interactive tutorials for learning different languages. Their SQL tutorial is second to none. You’ll learn how to manipulate data in MySQL, SQL Server, Access, Oracle, Sybase, DB2 and other database systems.
Treasure Data – The best way to learn is to work towards a goal. That’s what this helpful blog series is all about. You’ll learn SQL from scratch by following along with a simple, but common, data analysis scenario.
10 Queries – This course is recommended for the intermediate SQL-er who wants to brush up on his/her skills. It’s a series of 10 challenges coupled with forums and external videos to help you improve your SQL knowledge and understanding of the underlying principles.
TryR – Created by Code School, this interactive online tutorial system is designed to step you through R for statistics and data modeling. As you work through their seven modules, you’ll earn badges to track your progress helping you to stay on track.
Leada – If you’re a complete R novice, try Lead’s introduction to R. In their 1 hour 30 min course, they’ll cover installation, basic usage, common functions, data structures, and data types. They’ll even set you up with your own development environment in RStudio.
Advanced R – Once you’ve mastered the basics of R, bookmark this page. It’s a fantastically comprehensive style guide to using R. We should all strive to write beautiful code, and this resource (based on Google’s R style guide) is your key to that ideal.
Swirl – Learn R in R – a radical idea certainly. But that’s exactly what Swirl does. They’ll interactively teach you how to program in R and do some basic data science at your own pace. Right in the R console.
Python for beginners – The Python website actually has a pretty comprehensive and easy-to-follow set of tutorials. You can learn everything from installation to complex analyzes. It also gives you access to the Python community, who will be happy to answer your questions.
PythonSpot – A complete list of Python tutorials to take you from zero to Python hero. There are tutorials for beginners, intermediate and advanced learners.
Read all about it: data mining books
Data Jujitsu: The Art of Turning Data into Product – This free book by DJ Patil gives you a brief introduction to the complexity of data problems and how to approach them. He gives nice, understandable examples that cover the most important thought processes of data mining. It’s a great book for beginners but still interesting to the data mining expert. Plus, it’s free!
Data Mining: Concepts and Techniques – The third (and most recent) edition will give you an understanding of the theory and practice of discovering patterns in large data sets. Each chapter is a stand-alone guide to a particular topic, making it a good resource if you’re not into reading in sequence or you want to know about a particular topic.
Mining of Massive Datasets – Based on the Stanford Computer Science course, this book is often sighted by data scientists as one of the most helpful resources around. It’s designed at the undergraduate level with no formal prerequisites. It’s the next best thing to actually going to Stanford!
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners – This book is a must read for anyone who needs to do applied data mining in a business setting (ie practically everyone). It’s a complete resource for anyone looking to cut through the Big Data hype and understand the real value of data mining. Pay particular attention to the section on how modeling can be applied to business decision making.
Hadoop: The Definitive Guide – As a data scientist, you will undoubtedly be asked about Hadoop. So you’d better know how it works. This comprehensive guide will teach you how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Make sure you get the most recent addition to keep up with this fast-changing service.
Online learning: data mining webinars and courses
DataCamp – Learn data mining from the comfort of your home with DataCamp’s online courses. They have free courses on R, Statistics, Data Manipulation, Dynamic Reporting, Large Data Sets and much more.
Coursera – Coursera brings you all the best University courses straight to your computer. Their online classes will teach you the fundamentals of interpreting data, performing analyzes and communicating insights. They have topics for beginners and advanced learners in Data Analysis, Machine Learning, Probability and Statistics and more.
Udemy – With a range of free and pay for data mining courses, you’re sure to find something you like on Udemy no matter your level. There are 395 in the area of data mining! All their courses are uploaded by other Udemy users meaning quality can fluctuate so make sure you read the reviews.
CodeSchool – These courses are handily organized into “Paths” based on the technology you want to learn. You can do everything from build a foundation in Git to take control of a data layer in SQL. Their engaging online videos will take you step-by-step through each lesson and their challenges will let you practice what you’ve learned in a controlled environment.
Udacity – Master a new skill or programming language with Udacity’s unique series of online courses and projects. Each class is developed by a Silicon Valley tech giant, so you know what your learning will be directly applicable to the real world.
Treehouse – Learn from experts in web design, coding, business and more. The video tutorials from Treehouse will teach you the basics and their quizzes and coding challenges will ensure the information sticks. And their UI is pretty easy on the eyes.
Learn from the best: top data miners to follow
John Foreman – Chief Data Scientist at MailChimp and author of Data Smart, John is worth a follow for his witty yet poignant tweets on data science.
DJ Patil – Author and Chief Data Scientist at The White House OSTP, DJ tweets everything you’ve ever wanted to know about data in politics.
Nate Silver – He’s Editor-in-Chief of FiveThirtyEight, a blog that uses data to analyze news stories in Politics, Sports, and Current Events.
Andrew Ng – As the Chief Data Scientist at Baidu, Andrew is responsible for some of the most groundbreaking developments in Machine Learning and Data Science.
Bernard Marr – He might know pretty much everything there is to know about Big Data.
Gregory Piatetsky – He’s the author of popular data science blog KDNuggets, the leading newsletter on data mining and knowledge discovery.
Christian Rudder – As the Co-founder of OKCupid, Christian has access to one of the most unique datasets on the planet and he uses it to give fascinating insight into human nature, love, and relationships
Dean Abbott – He’s contributed to a number of data blogs and authored his own book on Applied Predictive Analytics. At the moment, Dean is Chief Data Scientist at SmarterHQ.
Practice what you’ve learned: data mining competitions
Kaggle – This is the ultimate data mining competition. The world’s biggest corporations offer big prizes for solving their toughest data problems.
Stack Overflow – The best way to learn is to teach. Stackoverflow offers the perfect forum for you to prove your data mining know-how by answering fellow enthusiast’s questions.
TunedIT – With a live leaderboard and interactive participation, TunedIT offers a great platform to flex your data mining muscles.
DrivenData – You can find a number of nonprofit data mining challenges on DataDriven. All of your mining efforts will go towards a good cause.
Quora – Another great site to answer questions on just about everything. There are plenty of curious data lovers on there asking for help with data mining and data science.
Meet your fellow data miner: social networks, groups and meetups
Facebook – As with many social media platforms, Facebook is a great place to meet and interact with people who have similar interests. There are a number of very active data mining groups you can join.
LinkedIn – If you’re looking for data mining experts in a particular field, look no further than LinkedIn. There are hundreds of data mining groups ranging from the generic to the hyper-specific. In short, there’s sure to be something for everyone.
Meetup – Want to meet your fellow data miners in person? Attend a meetup! Just search for data mining in your city and you’re sure to find an awesome group near you.
Data storytelling is the realization of great data visualization. We’re seeing data that’s been analyzed well and presented in a way that someone who’s never even heard of data science can get it.
Google’s Cole Nussbaumer provides a friendly reminder of what data storytelling actually is, it’s straightforward, strategic, elegant, and simple.
Students, teachers, and organizations will join together online to celebrate and demonstrate global collaboration on September 15, 2016. On Global Collaboration Day, educators and professionals from around the world will host connective projects and events and invite public participation. This event is brought to you by VIF International Education,Google for Education, iEARN-USA and Edmodo.
The primary goals of this 24-hour, worldwide event are to:
demonstrate the power of global connectivity in classrooms, schools, institutions of informal learning and universities around the world
introduce others to the collaborative tools, resources and projects that are available to educators today
to focus attention on the need for developing globally competent students and teachers throughout the world
Global Collaboration Day will take place on September 15 in participant time zones. Classrooms, schools, and organizations will design and host engaging online activities for others to join. Events will range from mystery location calls to professional development events to interviews with experts. All events will be collated in an online calendar viewable in participants’ individual time zones. Participants will be connected on Twitter via the hashtag #globaled16.
An optional new activity this year will be the Great Global Project Challenge. Between now and October 1, 2016, global educators will design collaborative projects using a variety of platforms in which other students and teachers may participate during the course of the 2016-2017 school year. The objective is to create and present as many globally connective projects for students and educators as possible. The final deadline for submissions into our project directory is October 1, but participants are also encouraged to do an introductory activity for their project on Global Collaboration Day as well.
Global Collaboration Day is a project of the Global Education Conference Network, a free online virtual conference that takes place every November during International Education Week. GCD, along with Global Education Day at ISTE and Global Leadership Week, are events designed to connect educators and keep global conversations going year round.
Help us spread the word. Here are some sample Tweets:
Join us for Global Collaboration Day! Details here: http://bit.ly/2016GCD #globaled16
YOUR ORG’S TWITTER HANDLE is pleased to partner with @GlobalEdCon and educators around the globe for Global Collaboration Day: http://bit.ly/2016GCD
Are you an education leader? Inspire global collaboration on Global Collaboration Day 9/15. http://bit.ly/2016GCD #globaled16
Learn more about participating in the Global Collaboration Day celebration: http://bit.ly/2016GCD #globaled16
Project hosts are sought for Global Collaboration Day. Details here: http://bit.ly/2016GCD #globaled16
Logos and Badges for Participants, Hosts, Partners and Sponsors are located here:http://bit.ly/gcdimages
Interested in serving as an outreach partner?
Send an email to Lucy Gray (lucy@globaledevents.com) indicating your interest. Include information on how you can help us get the word out to networks with 5000 members or more.
As you may be aware that TERI is a global think-tank knowledge driven organisation working in the field of Energy, Environment and Sustainable Development. TERI is organising it’s one of the flagship event ICDL 2016 from
13 to 16 December, 2016 at India Habitat Center, Lodhi Road, New Delhi. The theme of the conference is “Smart Future: Knowledge Trends that will Change the World”. (URL: http://www.teriin.org/events/icdl/)
As we understand that in the current scenario all enterprises are heading towards Digital Transformation, which derives business value for an effective decision making process. To be a part of this transformation strategy, all stakeholders at various levels should be aware of certain pertinent components, which are mentioned below. This conference is a unique platform to brainstorm and network with leading speakers and digital luminaries. Some of the major thrust areas to be covered are:
Innovation and Knowledge Management
Big Data and Analytics
Social Media and Analytics
Internet of Things (IoT)
To get yourself and your team to engage in one of these issues, we would request you to kindly share your skills, expertise and experiences with audiences in this thought provoking and stimulating interactive platform of ICDL 2016.
the topics of privacy pertaining technology is becoming ubiquitous.
If you feel that the content of your class material can benefit of such discussions, please let us know.
Please have some titles, which can help you brainstorm topics for discussions in your classes:
While badging and digital credentialing are gaining acceptance in the business world and, to some extent, higher education, K-12 educators — and even students — are slower to see the value.
That’s when the MacArthur Foundation highlighted the winning projects of its Badges for Lifelong Learning competition at the Digital Media and Learning Conference in Chicago. The competition, co-sponsored by the Bill & Melinda Gates Foundation and the Mozilla Foundation, had attracted nearly 100 competitors a year earlier. The winners shared $2 million worth of development grants.
Evidence of Lifelong Learning
A digital badge or credential is a validation, via technology, that a person has earned an accomplishment, learned a skill or gained command of specific content. Typically, it is an interactive image posted on a web page and connected to a certain body of information that communicates the badge earner’s competency.
Credly is a company that offers off-the-shelf credentialing and badging for organizations, companies and educational institutions. One of its projects, BadgeStack, which has since been renamed BadgeOS, was a winner in the 2013 MacArthur competition. Virtually any individual or organization can use its platform to determine criteria for digital credentials and then award them, often taking advantage of an open-source tool like WordPress. The credential recipient can then use the BadgeOS platform to manage the use of the credential, choosing to display badges on social media profiles or uploading achievements to a digital resume, for instance.
Finkelstein and others see, with the persistently growing interest in competency-based education (CBE), that badging is a way to assess and document competency.
There are obstacles, though, to universal acceptance of digital credentialing. For one, not every community, company or organization sees a badge as something of value.
When a player earns points for his or her success in a game, those points have no value outside of the environment in which the game is played. For points, badges, credentials — however you want to define them — to be perceived as evidence of competency, they have to have portability and be viewed with value outside of their own environment.
