How @SyracuseU and Other Universities Are Adapting to the #IoT Revolution

By Adam Popescu

George Bernard Shaw once said, “He who can, does. He who cannot, teaches.” The tongue-in-cheek phrase is a common insult in academia, but when it comes to advances in the field of the Internet of Things (IoT), it couldn’t be farther from the truth.

The academic world is in many ways leading the way in innovation – both in the classroom and through research.

To Arif Ansari, associate professor of clinical data sciences and operations at the University of Southern California (USC), this shift couldn’t come soon enough. “The United States faces a shortage of 140,000 to 190,000 people with deep data analytics skills, and 1.5 million managers and analysts to make business decisions based on their findings,” said Ansari.

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To help bridge that gap, Ansari put together an undergraduate class addressing big data and analytics at the Marshall School of Business at USC. He’s trying to lead and shape the minds of young students and notes “there is more work to be done, and it is being implemented.”

Similar innovation is going on at Syracuse University in New York, where students and faculty have been researching machine-to-machine (M2M) communication for the past decade. The school is supported by the National Science Foundation Partnerships for Innovation program, and is at work on the Wireless Grid Innovation Testbed project.

“More than 100 partner campuses, companies and communities have been engaged in the efforts, which have led to a number of patented innovations, new companies and diverse novel applications,” explained Lee W. McKnight, a professor of entrepreneurship and innovation at Syracuse. Those applications include a social radio, which debuted at TEDxHarlem, as well as “cloud to edge” services, or what McKnight describes as “edgeware.”

“What will be most useful is the ability to combine several of these disciplines and to create rapid prototypes of new things,” noted Kelly Lux, McKnight’s colleague and the School of Information Studies’ director of social media. “We are not just following industry trends,” clarified McKnight, an outspoken advocate for IoT. “Students and faculty are actively experimenting with a wide variety of new IoT and M2M applications and open specifications.”

Also at Syracuse, Dan Pacheco, a chair in journalism innovation in the S.I. Newhouse School of Public Communications, teaches a tech for new media course. He’s working with students on using Arduino microcontrollers and sensors to “measure everything from air quality to temperature,” he says. “When you connect the Arduino to the Internet, it can upload data to a database so that others can see it and compare to their own data. These citizen sensor networks provide an alternative to government-reported data sources,” he says.

Initiatives like those at USC and Syracuse demonstrate the opportunities that can be forged when academia works to keep pace with industry. Far from reinforcing Shaw’s famous dictum, they offer a welcome opportunity to rewrite it – just in time for the IoT age.

Adam Popescu is a freelance writer based in Los Angeles. His work has appeared in the BBC, Fast Company, Mashable, LA Weekly, Marketplace Radio, and Los Angeles Magazine.

Image Credit: John Marino/Flickr Creative Commons

The 10 Hottest Disruptive Technologies in #HigherEd

The annual EDUCAUSE conference is where innovative higher education CIOs go to learn about new industry trends and compare notes on the latest breakthroughs. This year was no exception as 7,300 IT leaders from more than 50 countries gathered in Orlando along with 260 educational technology exhibitors. Discussions took place in session rooms, on the exhibition floor, after the keynotes, and throughout the hallways. These are the common threads that permeated those discussions; the ten hottest topics for CIOs in higher education.

1. Campus Wi-Fi
Wireless capacity is a passionate topic for two reasons. It is now universally understood that the quality of the student computing experience has become an important decision factor for students in selecting a college. The challenge is to provide Wi-Fi density and coverage to adequately accommodate the three or more devices, many of them streaming, that each student is bringing on campus. This burgeoning demand for Wi-Fi on campus is severely taxing the IT infrastructure. Residence hall Wi-Fi can get congested quickly, so wired access is often used to provide bandwidth relief for devices like gaming consoles. Many schools have started to charge an extra fee to charge uber users who consume more than 20GB per week. When it comes to guest Wi-Fi access, schools run the gamut of open-connection, charging for use, sponsored guest access, or a combination of these. Here are 50 incredible WiFi market trends and statistics that are truly staggering.

The Campus Computing Project’s 2014 survey was revealed at the conference and reported that senior higher education IT officers identify “implementing/supporting mobile computing” as a top IT priority, yet only 17% rate mobile services at their institution as “excellent.” One informal poll at the conference showed that about 30% of schools are in the process of migrating to the latest Wi-Fi standard, 802.11ac.

Strategic CIOs in higher education are investing in WiFi infrastructure to improve the student, faculty and administration’s overall campus experience.

2. The Importance Of Being Social

EDUCAUSE CIO panel – Sound Off: To Be or Not to Be “Social”; with Michael Berman, California State University – Channel Islands; Raechelle Clemmons, St. Norbert College; Jack Seuss, University of Maryland, and Melody Childs University of Alabama Hunstville,

Social media is a game changer for higher education CIOs. Social media is taking on a growing role at EDUCAUSE and throughout higher education. Here is a list of the top 50 social higher education CIOs on Twitter. There was more live tweeting this year than ever before. The social media feeds enabled attendees and even those unable to attend to have a virtual presence, absorbing content from across the conference. The social media feeds were captured on Storify: #EDU14 Daily Wrap-up day 2 and#EDU14 Daily Wrap-up day 1.The session CIOs Sound Off: To Be or Not to Be “Social” provided a point-counterpoint discussion of the pros and cons of social media for university CIOs. The audience actively participated via Twitter (#EDU14socialcio, captured on Storify) and interactive poll questions, and provided crowd-sourced tips for more effective use of social media in higher education.

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Educause in-session poll indicated the attendees are indeed active on social media

3. Digital Badges
Digital badges as validated indicators of specific competencies and their connection to competency-based education were heavily discussed at EDUCAUSE 2014. Just before the conference, EDUCAUSE published the 7 Things You Should Know About BadgingFor Professional Development. As another indicator of the growing significance of digital badges, 60% of the 1,900 people who participated in the Extreme Networksdigital badge survey believe that badges will either entirely replace diplomas and course certificates, or be used in combination with them. I recently published a presentation about the use of digital badges to improve employee engagement.

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Sondra Smith of EDUCAUSE talks about the use of digital badges for professional development.

4. Business Analytics
The use of analytics as a means to drive critical institutional outcomes has grown rapidly as the associated technology has improved. This year there were no less than seven panels and 26 sessions dealing with learning analytics, data-driven decision making, and predictive analytics at the EDUCAUSE conference. The session, Analytics That Inform: The University Challenge, articulated the different contexts for analytics in education. Two analogies were made: one with business intelligence, now a $15B market, and one with physician diagnostic tools. There is general agreement on the need to present student analytics in the form of a dashboard, for use by both administration and students. Such a dashboard can help improve student outcomes as well as improve student retention.

In addition to student analytics, another type of campus analytics relates to network infrastructure. Presenters from Fontys Hogescholen described how they use network analytics to track student activities across the campus and are able to correlate demographic data with behavior and even effect change.

5. Google Glass and Wearables
The session, Prepare to Wear! Exploring Wearable Technologies in the Learning Environment, generated phenomenal enthusiasm and discussions that carried well past the conference. Many of the 14 Google Glass Innovative Uses In Educationthat I wrote about with Brian Rellinger earlier this year were in evidence. It is clear that all types of wearable computers including Google Glass, fitness bands, clothing, fashion wearables, and the forthcoming Meta holographic eyewear will have a dramatic impact on higher education.

6. Drones
Drones are finding growing usage in education. Colgate University’s poster session, Just Don’t Call It a Drone, showed how to use hobbyist quadcopters and Arduino technology in student research programs to capture photography and other environmental observations. The project had amazing results for both learners and researchers. I predict there will be more sessions on this topic next year, as drones find many new uses within higher education (see my blog, 10 Uses of Drones in Higher Education [Slideshare]).

7. 3D Printing
As listed in the description of one of sessions on the topic, “the era of 3D printing has arrived.” For those eager to enter this era, a number of sessions and exhibition demonstrations showed how to integrate 3D printing, and complementary 3D scanning, into the curriculum. Popularity of the printers is highest in art, design and engineering programs. Many schools are acquiring one high-end consumer-grade or low-end enterprise-grade 3D printer per department. Stay tuned as prices of consumer 3D printers are likely to be aggressively driven downward.

8. Digital Courseware
The two emerging aspects of digital courseware are Competency-Based Education (CBE) and Adaptive Learning. The concept behind Competency-Based Education (CBE) is to enable students to master skills and knowledge at their own pace, via multiple pathways that generally make better use of technology. The Bill and Melinda Gates Foundation had a hand in elevating the topic this year with a $20M investment in next generation courseware related to adaptive learning and CBE. Last year, a similar grant gave a major boost to Integrated Planning and Advisory Services (IPAS).

CBE can help meet the needs of all students regardless of their learning abilities, and can lead to more efficient student outcomes. With CBE, students earn competency units rather than credit hours. So far, large community colleges have taken a leadership role in the field.

Adaptive learning, closely related to CBE, is an educational method that uses computers and electronic text books as interactive teaching devices. The presentation of educational material is dynamically adapted to students’ learning needs, as indicated by their responses to questions and tasks as they progress.

A number of young CBE and adaptive learning technology vendors demonstrated their wares during EDUCAUSE 2014, including Flat World Education, eLumen(demo), Regent Education (presentation), Pathbrite, Public Agenda, CCKF, andAcrobatiq. Many of these vendors emphasize a mobile-first approach. The feedback from Salt Lake Community College and the University System of Georgia highlighted the need for integration with existing products, comprehensive dashboards, and a mechanism for social interaction. Schools in general are watching to see what kind of results adaptive learning generates.

9. Small Private Online Courses (SPOCs)
This is the year that Massive Open Online Courses (MOOCs) lost the limelight. The issue is the extremely low completion rates of students who sign up for MOOCs. A Gartner poster of the education hype cycle that was on display at EDUCAUSE marked MOOC as “obsolete before plateau”. The Campus Survey 2014 noted that less than two-fifths of the survey respondents now agree that MOOCs offer a viable model for the effective delivery of online instruction, down from 53% in the fall of 2013.

Clayton Christensen did not mention “MOOC” even once during his opening keynote, though the best known MOOCs, EDx and Coursera, had often been considered as disruptive to higher education. It is now realized that MOOCs lack most of the markers for disruptive innovation; they do not target non-consumers and they lack a viable business model. On the other hand, the Christensen Institute does believe that competency-based education may prove to be disruptive.

Picking up where MOOCs left off is the concept of Small Private Online Courses (SPOCs). These were discussed at the one MOOC conference session. This session also discussed the future of MOOCs – for advanced placement courses, remedial classes, professional development, and to serve the community. More importantly, the technology infrastructure created by the MOOC providers like EDx, Kahn Academy and Coursera will very likely provide the platform for the full range of online courses into the future.

10. Virtual Reality
Immersive and augmented reality have the ability to completely re-invent education. When ready for general use, products like Oculus Rift and Magic Leap are capable of transporting the student to almost any learning environment imaginable. As with many emerging educational technologies, virtual reality has application both to higher education and K-12 education. At the ISTE K-12 conference earlier this year, there were no less than 14 sessions discussing how to apply VR in education. The technology enables students to travel with their professors to any virtual learning environment imaginable: far-off lands and planets, inside the atom, ancient civilizations, to the beginning of the universe. On a limited scale, some of these capabilities are already here. At the rate the technology is progressing, VR could be fully integrated into our teaching within five years.

The Disruption of Higher Education
The Disruption of Higher Education was perhaps the hottest topic before, during, and after EDUCAUSE. It was the subject of Clayton Christensen’s keynote. Higher education is undeniably at a transition point. With student debt now over $1 trillion and economists like Robert Reich questioning the value of college, industry leaders are searching for a path to maintain higher education’s relevancy. In his talk, Christensen asked the audience to “pray for Harvard”, given the upheavals already underway in higher education.

Markers of disruption are already appearing in higher education, including new entrants and start-ups selling low feature-set products to previous non-consumers. Examples of this include not just online colleges, but more importantly corporate in-house academies like Perdue University (think chickens not boilermakers), General Assembly, GE Crotonville, and Intel University. A technological core is forming with video courseware, competency-based education and learning analytics, as well as new interactive collaborative capabilities that provide something approaching a classroom experience remotely. Modularity, another important marker of disruption, has emerged in the packaging of courses and the awarding of certificates of completion and digital badges. These aspects represent an overall trend toward the unbundling of higher education.

A college president had pointed out to Christensen that the most generous alumni at his university felt their lives had been dramatically changed by their college experiences. The lasting impact was due not to the course material, but rather to the motivating performance of a memorable professor; a different professor in each case. In response Christensen asks, are colleges taking this into account as they recruit faculty, or is recruiting based more on academic publishing history?

This post was co-authored by Robert Nilsson, Director of Marketing, Extreme Networks.

Big Data for Education

Written by Saga Briggs and published by InformedEDU

When learners interact with content in your course, they leave behind ‘digital breadcrumbs,’ so to speak, which offer clues about the learning process. We’re now able to collect and track this data through learning management systems (LMSs), social networks, and other media that measure how students interpret, consider, and arrive at conclusions about course material.

The good news is that this information–called Big Data–can do wonders for personalized instruction, especially within the e-learning industry. The not-so-good news is that the rise of Big Data brings with it many risks and ethical dilemmas, all of which need to be addressed before we move forward with this new approach.

What is Big Data?

Big Data refers to the large amount of information that flows through various channels – usually online – each second. It’s data that is too large, complex, and dynamic for any conventional tools to capture and manage. The term originated in the open source community, where specialists were trying to find faster and more scalable solutions to store and process immense amounts of data. Thanks to advancements in technology, this data can now be interpreted and analyzed, providing great benefits to the healthcare, government, retail manufacturing, e-learning, and other data-driven industries.

What makes Big Data “big” (both in size and significance) is that it allows for the analysis and prediction of behavior across a huge variety of demographics, personal backgrounds, learning styles, thinking processes, IQ levels, academic intentions, genetic predisposition, environmental factors, skills, potentials–anything you can think of measuring.

In education, these data points are now being used to help design instructional strategies, evaluate the impact of these strategies on both students and teachers, fuel an evidence-based approach to experimentation, and create personalized learning environments.

The term “Big Learning Data” encompasses three aspects of learning data: volume, velocity, and variety. [Editor’s Note: Check out our piece on Learning Analytics]

1. Volume: Big Data can yield information about thousands of learners taking the same course or having the same instructional experience. It can also shed light on multiple data points, over time, about a single learner. Because of its scalability, Big Data might someday bring together learning data from hundreds of organizations to provide a global perspective on education.

2. Velocity: Big Learning Data enables learners and organizations to have rapid access to data even in real time. Imagine a student entering a wrong answer into an assessment exam. Velocity instantly would provide her with remedial and enrichment options based on her historical learning patterns and successful strategies from thousands of other learners who also failed that question. It would also allow instructors to make adjustments to content delivery, based on rapid analysis of user experience, on a continual basis.

3. Variety: Big Learning Data connects the dots, weaving together a wider variety of information from students with different backgrounds. It allows us to see the correlations between performance and environment. Without it, we have traveling expenses and limited representation.

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What are the Benefits of Big Data to the E-Learning Industry?

The 2013 World Innovation Summit for Education (WISE) began in October with a plea for radical change. Leading educators, policy makers, and governments from over 100 countries congregated in Qatar, with 84 percent claiming that the way learning happens today will not adequately prepare young people for the world of tomorrow.

But one topic which led to considerable optimism among the delegates was Big Data.

Pearson’s CEO John Fallon presented the notion that “big data, and the disruptions it can lead to, have led to one of the most creative periods in history in terms of innovation.” Time to harness that in education, he said.

Speaking later in the day at WISE, Fallon also stressed that Big Data is only as powerful as our willingness to share it with each other.

“We have to become more willing to share what’s working and not working. In return, all organisations that are trying to tackle big intractable problems in education should be more generous with each others’ ideas and evidence.”

The following are a few ways Big Data is expected to help education in the near future:

1. Feedback: Big learning data can be informative from a feedback and context perspective. Because somebody often might fail at a topic but not know why he is failing, it becomes interesting when the learner can look not just at himself, but at other people who have had the same experience. He may certainly get an insight either that would explain it so he is not frustrated or that he could use to correct it so that he could succeed again.

2. Motivation: If you implemented big data in a comprehensive way, learners potentially become invested in inputting data to the process because they see the impact of how it works.

3. Personalization: Big Data will change the way we approach e-learning design by enabling developers to personalize courses to fit their learners’ individual needs. This will allow e-learning professionals to continue to raise the standard for effective and exceptional e-learning courses.

4. Efficiency: Big Data can save us hours upon hours of time and effort when it comes to realizing our goals and the strategies we need to achieve them. Say someone wants to take job B, having done job A for a year. Big data would indicate, first of all, the number of people who did job A and who then got to job B. Of the people who got job B, what preparation did they have? It also would indicate which learning programs were most effective, and what the timing was for when they attempted to change to job B.

5. Collaboration: More often than not, specialists from multiple departments must come together to keep a Learning Management System functioning at its best. This encourages cooperation, collaboration, and interdisciplinary thought processes.

6. Tracking: Big Data can help us understand the real patterns of our learners more effectively by allowing us to track a learner’s experience in an e-learning course. In examining the digital footprints or ‘breadcrumbs’ learners leave behind, we’re able to track their journey throughout the entire learning experience.

7. Understanding the learning process: By tracking Big Data in e-learning, we can see which parts of an assignment or exam were too easy and which parts were so difficult that the student got stuck. Other parts of the journey we can now track and analyze include pages revisited often, sections recommended to peers, preferred learning styles, and the time of day when learning operates at its best.

Still, when discussing Big Learning Data, we must honestly consider the risks that it raises, which in some cases may outweigh the rewards.

Big Mistake?

In the wrong hands, Big Data can do more harm than help. Regardless of whether it’s being purposely or naively mishandled, it can undermine an entire educational system with the click of a button.

Here are some of the risks and hurdles involved in using Big Data in education:

1. Privacy: As companies like Google have extended the services they offer to include email, document storage and processing, news, Web browsing, scheduling, maps, location tracking, video and photo sharing, voice mail, shopping, social networking and whatever else might be of interest to their users, they gain access to even more personal data, which they collect, store, and cross-reference.

Even information that is accessible to the public, when assembled from different sources into a comprehensive dossier, can create a revealing picture of a person. A simple Google search can turn up an enormous amount of information about an individual, though the accuracy of much of it is questionable. As one researcher put it, “while the quantity of publicly available information about individuals to be found online is vast, it is riddled with inaccuracies.”

Certain young children may be judged “at risk” because of the personal profiles the school or the state has developed on them, and placed in school accordingly. That designation–whether accurate or not–could then follow those children through school, denying them the chance to develop normally with their peer group.

2. Dehumanization: Apart from the obvious potential for error and prejudice, this use of profiling is objectionable because it dehumanizes those being judged, as well as those making the judgments. It substitutes calculation for human judgment on what should be very sensitive human issues, and thus treats those profiled as objects, as collections of facts, rather than as persons.

3. Deception by Numbers: Cyril Burt of the University of London was the man responsible for the introduction of the standardised 11+ exam in the UK. Burt was subsequently discredited for publishing largely in a journal that he himself edited, falsifying not only the data upon which he based his work, but also co-workers on the research. The correlation coefficients on IQs in Burt’s twin studies were the same to three decimal places, across articles, despite the fact that new data had been added twice to the sample of twins. Leslie Hearnshaw, Burt’s friend and official biographer, claimed that most of Burt’s data after World War II were fraudulent or unreliable.

This is just one of many standardised tests that have become common in education but many believe that tests of this type serve little useful purpose and are unnecessary, even socially divisive. Many argue that standard tests have led to a culture of constant summative testing, which has become a destructive force in education, demotivating and acting as an end-point and filter, rather than a useful mark of success. Narrow academic assessment has become almost an obsession in some countries.

4. Correlation vs. Causation: Have you ever heard the phrase, “Correlation does not prove causation”? If you’re a good scientist, all of your efforts will be based in recognizing the difference between these two terms. If you’re an effective user of Big Data, you will be careful not to jump to conclusions–or, worse, act on your impulses–when you see a pattern.

5. Claims Beyond the Data: Take university rankings, for example. University rankings are used by politicians, universities, parents, and students alike. But oftentimes, where they claim to ‘rank’ universities, they tell you very little about about teaching. What may be labeled as ‘measures’ on teaching is actually data drawn from proxies, such as employment and research activity–offering no direct evidence of teaching quality itself.

Recommendations

“The problem with learning data, historically, is that we’ve always gone for the low-hanging fruit,” says Elliott Masie for the American Society for Training and Development. “Learning professionals have collected inexpensive, easily acquired data from people while they are in our domain, usually the classroom or program. In a big learning data world, we will need to rethink our data sources.”

Since big learning data is just evolving, it is difficult to be prescriptive about such issues. Part of the innovation process is an active and open dialogue, along with collaboration on these risks. However, to add to this discussion, here are a few approaches that you might consider to better align big learning data with these concerns.

1. Transparency. Learners have the right to know how learning data will be used, shared, stored, or leveraged. We should develop a clearly stated system so that there are no surprises.

2. Privacy. Who gets to see the aggregated data of 1,000 learners? Who gets to see a single learner’s data? Levels of privacy, as well as designated access to them, should be carefully considered.

3. Value to the learner. Big learning data can provide great value back to the learner. What have other learners who have taken the same program found most difficult? What are the types of questions that learners most often get wrong? What remedial actions have been most successful for other learners who failed that question or program?

4. Depth of measurement. We have looked at whether learners passed an exam, but more valuable data might include the answer, as well as characteristics of how learners answer the question. For example, how long it took them to answer and whether their mouse hovered over a wrong answer for a while.

7. Expense. Some data that we will use in big learning data will be more expensive to get than what we have traditionally used. But what we easily collect tends to be superficial or inaccurate. Collecting data through interviews with managers of learners, says Masie, costs more but yields much more data.

8. Many factors influence learning. We need to have an anthropological view of the learning process to understand that there are many factors that may influence learning. We need to realize that learning may influence or may support or destroy the impact of learning, thus broadening our view of potentially relevant data.

9. Presenting data. We need to adopt a strategic approach to presenting data. How do we display data so that it brings meaning to people? If you are given this data, what do you do with it strategically and how do you handle it?

10. Readiness. This refers to the extent to which individuals making decisions are ready to operate with a massively enhanced set of data.

12. Infrastructure. Institutions will need to upgrade, alter, or change learning systems To prepare for big data use.

13. Openness. We need to understand where, how, and in what way it’s appropriate to share and use that data, simply because it can yield such powerful results.

Technology is revolutionizing the way learning and development practitioners do their work. Leveraging big data is the next logical step in this evolution. We now have access to volumes of data, but we must understand what it can tell us, what is does tell us, and as importantly what it can’t and doesn’t tell us. Going forward, we need to recognize the potential and risks. We also need to respect the views of our fellow colleagues, whether they are fearful, low risk, or deeply correct in their concerns.

Being open to all perspectives is the only way to safely handle this evolving approach.

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Cited From: http://www.opencolleges.edu.au/informed/features/big-data-big-potential-or-big-mistake/#ixzz2qfUI0GLZ

Educause ECAR Student & Technology Research Study 2013

Educause 2013 (ECAR Study of Undergraduate Students and Information Technology)

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The three R’s; Paper, Pencil and Google Apps for Edu ??

Reposted from EdTech Digest October 8, 2013; written by Rob May CEO of Backupify

More than seven million K-12 students throughout the country use Google Apps for Edu and they aren’t alone. Seventy-two of the top 100 Universities (according to U.S. News & World Report) use Google Apps too – including seven of the eight Ivy League institutions.  The service is quickly gaining traction as connectivity increasingly becomes an essential part of everyday learning.  Students and teachers can easily collaborate in real time through a web application, and the vast majority are already familiar with the Google platform, meaning adoption is fairly seamless. The service is also compelling for IT departments working on tight budgets with limited infrastructure resources.

While the influx of Google Apps in the education sector provides huge opportunities for collaboration and cost-cutting, this new era also comes with its own set of challenges – specifically around holding student-related data in the cloud. For example, Gmail accounts help students easily work together but they can also introduce cyber bullying. Google Docs is a simple way to share an assignment but new excuses now arise in the form of “I swear I did it, Google must have lost it.”

The fact of the matter is that data stored in SaaS services like Google Apps for Education is not fully protected. Forty-seven percent of SaaS data loss is a result of end-user deletion (something Google cannot recover). Many teachers and IT admins at schools that are adopting Google Apps, however, are unaware of the limitations that Google has around data backup. Although Google is one of the safest productivity suites in the world, it does not protect against user error (e.g., a student overwriting or deleting an important assignment in Google Docs)

While enterprise organizations are also faced with new IT challenges related to backing up the cloud, schools, in particular, have their own unique set of hurdles to overcome, including:

Massive amounts of personal student data. As teachers use Google Apps in their classroom day to day, they begin to accumulate massive amounts of data – much of it personal student-related information. With this kind of data saved in cloud services, schools can face a long list of regulatory requirements (depending on the state) that call for strong efforts to meet compliance. Schools stress the importance of privacy but Google does not always guarantee it.

Cyber bullying. Statistics show that about half of young people have experienced some form of cyber bullying, and 10 to 20 percent experience it regularly. Google Apps for Edu can unfortunately be another avenue for students to bully one another – a problem that is already a challenge for many schools. Schools therefore must have a quick and easy way to recover emails and documents that might be used for reference in legal cases around bullying – even if they were deleted from Google.

Loss of control. IT managers give up the reins a bit when they move to the cloud through Google Apps for Edu. They may feel a loss of control since they no longer have to maintain on-premise physical hardware and keep track of bundles of software updates. Using cloud-to-cloud backup provides a fix for IT managers who want to ensure they have the last say on all the data in the cloud. Creating a second copy allows these professionals to rest assured that they will have any data they need, when they need it, without relying on Google

Taking data to the cloud can be a scary transition in any industry and the education sector is no different. As more and more schools implement Google Apps for Education, it is more important than ever to determine what supporting technologies are necessary to make the transition a smooth one. There are many resources available to newbies jumping into the world of Google Apps, including education technology associations, LinkedIn Groups or even the Google Education Summit. Cloud-to-cloud backup is just one of the many technologies that can help schools ensure that their data in the cloud is protected – and make students leave the “Google Ate My Homework” excuse at home.

Rob May is the CEO of Backupify, makers of Backupify for Education, a leading cloud-to-cloud backup solution allowing schools to retain control over their critical data, prevent data loss and adhere to data compliance requirements. Write to: rob@backupify.com

 

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@UMUC – #BigData Revolution Is Here

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