What Happens to Student Data Privacy When Chinese Firms Acquire U.S. Edtech Companies?
Between the creation of a social rating system and street cameras with facial recognition capabilities, technology reports coming out of China have raised serious concerns for privacy advocates. These concerns are only heightened as Chinese investors turn their attention to the United States education technology space acquiring companies with millions of public school users.
A particularly notable deal this year centers on Edmodo, a cross between a social networking platform and a learning management system for schools that boasts having upwards of 90 million users. Net Dragon, a Chinese gaming company that is building a significant education division, bought Edmodo for a combination of cash and equity valued at $137.5 million earlier this month.
Edmodo began shifting to an advertising model last year, after years of struggling to generate revenue. This has left critics wondering why the Chinese firm chose to acquire Edmodo at such a price, some have gone as far as to call the move a data grab.
as data becomes a tool that governments such as Russia and China could use to influence voting systems or induce citizens into espionage, more legislators are turning their attention to the acquisitions of early-stage technology startups.
NetDragon officials, however, say they have no interest in these types of activities. Their main goal in acquiring United States edtech companies lies in building profitability, says Pep So, NetDragon’s Director of Corporate Development.
In 2015, the firm acquired the education technology platform, Promethean, a company that creates interactive displays for schools. NetDragon executives say that the Edmodo acquisition rounds out their education product portfolio—meaning the company will have tools for supporting multiple aspects of learning including; preparation, instructional delivery, homework, assignment grading, communication with parents students and teachers and a content marketplace.
NetDragon’s monetization plan for Edmodo focuses on building out content that gets sold via its platform. Similar to tools like TeachersPayTeachers, So hopes to see users putting up content on the platform’s marketplace, some free and others for a fee (including some virtual reality content), so that the community can buy, sell and review available educational tools.
As far as data privacy is concerned, So notes that NetDragon is still learning what it can and cannot do. He noted that the company will comply with Children’s Online Privacy Protection Act (COPPA), a federal regulation created in order to protect the privacy of children online, but says that the rules and regulations surrounding the law are confusing for all actors involved.
Historically, Chinese companies have faced trust and branding issues when moving into the United States market, and the reverse is also true for U.S. companies seeking to expand overseas. Companies have also struggled to learn the rules, regulations and operational procedures in place in other countries.
Iran and Huawei top agenda as Pompeo meets Merkel for 45 minutes in Berlin
Merkel to Ratchet up Huawei Restrictions in Concession to Hawks
more on data privacy in this IMS blog:
The ACM/IEEE Joint Conference on Digital Libraries in 2018 (JCDL 2018L:
https://2018.jcdl.org/) will be held in conjunction with UNT Open Access
Symposium 2018 (https://openaccess.unt.edu/symposium/2018) on June 3 – 6, 2018
in Fort Worth, Texas, the rustic and artistic threshold into the American
West. JCDL welcomes interesting submissions ranging across theories, systems,
services, and applications. We invite those managing, operating, developing,
curating, evaluating, or utilizing digital libraries broadly defined, covering
academic or public institutions, including archives, museums, and social
networks. We seek involvement of those in iSchools, as well as working in
computer or information or social sciences and technologies. Multiple tracks
and sessions will ensure tailoring to researchers, practitioners, and diverse
communities including data science/analytics, data curation/stewardship,
information retrieval, human-computer interaction, hypertext (and Web/network
science), multimedia, publishing, preservation, digital humanities, machine
learning/AI, heritage/culture, health/medicine, policy, law, and privacy/
General Instructions on submissions of full papers, short papers, posters and
demonstrations, doctoral consortium, tutorials, workshops, and panels can be
found at https://2018.jcdl.org/general_instructions. Below are the submission
• Jan. 15, 2018 – Tutorial and workshop proposal submissions
• Jan. 15, 2018 – Full paper and short paper submissions
• Jan. 29, 2018 – Panel, poster and demonstration submissions
• Feb. 1, 2018 – Notification of acceptance for tutorials and workshops
• Mar. 8, 2018 – Notification of acceptance for full papers, short papers,
panels, posters, and demonstrations
• Mar. 25, 2018 – Doctoral Consortium abstract submissions
• Apr. 5, 2018 – Notification of acceptance for Doctoral Consortium
• Apr. 15, 2018 – Final camera-ready deadline for full papers, short papers,
panels, posters, and demonstrations
Please email firstname.lastname@example.org if you have any questions.
Report: Tech Companies Are Spying on Children Through Devices and Software Used in Classroom
By Richard Chang 04/17/17
according to a new report from the nonprofit Electronic Frontier Foundation (EFF), “Spying on Students: School-Issued Devices and Student Privacy”
shows that state and federal laws, as well as industry self-regulation, have failed to keep up with a growing education technology industry.
One-third of all K–12 students in the United States use school-issued devices running software and apps that collect far more information on kids than is necessary.
Resource-poor school districts can receive these tools at deeply discounted prices or for free, as tech companies seek a slice of the $8 billion ed tech industry. But there’s a real, devastating cost — the tracking, cataloging and exploitation of data about children as young as 5 years old.
Our report shows that the surveillance culture begins in grade school, which threatens to normalize the next generation to a digital world in which users hand over data without question in return for free services
EFF surveyed more than 1,000 stakeholders across the country, including students, parents, teachers and school administrators, and reviewed 152 ed tech privacy policies.
“Spying on Students” provides comprehensive recommendations for parents, teachers, school administrators and tech companies to improve the protection of student privacy. Asking the right questions, negotiating for contracts that limit or ban data collection, offering families the right to opt out, and making digital literacy and privacy part of the school curriculum are just a few of the 70-plus recommendations for protecting student privacy contained in the report.
more on students and privacy
more on copyright in this IMS blog:
Learn data mining languages: R, Python and SQL
– 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.
– 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.
– 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.
– 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.
– 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.
– 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.
– 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.
– 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!
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
– 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 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.
– 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.
– 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.
– 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.
– 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
– Chief Data Scientist at MailChimp and author of Data Smart, John is worth a follow for his witty yet poignant tweets on data science.
– Author and Chief Data Scientist at The White House OSTP, DJ tweets everything you’ve ever wanted to know about data in politics.
– He’s Editor-in-Chief of FiveThirtyEight, a blog that uses data to analyze news stories in Politics, Sports, and Current Events.
– As the Chief Data Scientist at Baidu, Andrew is responsible for some of the most groundbreaking developments in Machine Learning and Data Science.
– He might know pretty much everything there is to know about Big Data.
– He’s the author of popular data science blog KDNuggets
, the leading newsletter on data mining and knowledge discovery.
– 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
– 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
– This is the ultimate data mining competition. The world’s biggest corporations offer big prizes for solving their toughest data problems.
– 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.
– With a live leaderboard and interactive participation, TunedIT offers a great platform to flex your data mining muscles.
– You can find a number of nonprofit data mining challenges on DataDriven. All of your mining efforts will go towards a good cause.
– 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
– 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.
– 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.
– 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.
8 fantastic examples of data storytelling
8 fantastic examples of data storytelling
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.
more on text and data mining in this IMS blog
The EU just told data mining startups to take their business elsewhere
By enabling the development and creation of big data for non-commercial use only, the European Commission has come up with a half-baked policy. Startups will be discouraged from mining in Europe and it will be impossible for companies to grow out of universities in the EU.
more on copyright and text and data mining in this IMS blog