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Ray Fleming's take on what's interesting in Education IT in Australia

  • Education

    How to make Windows 10 rock - Silly Competition Time

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    LollyJar1

    Yesterday our office celebrated the arrival of Windows 10 with jars of Windows 10 sugary confections. My colleagues here in Australia call it a lolly jar, and I’d call it seaside rock - it’s the kind of sweet that’s made with huge volumes of sugar, and then coloured and rolled, ending up with the words “I Red heart Windows 10” running through every sweet (and now you know ‘How to make Windows 10 rock’ Smile)

    LollyJar2

    Windows 10 Sweet Competition

    In the past, when we’ve launched new products I’ve often tried to get my hands on early copies and given them away in silly competitions on my blog or Twitter. But this time almost everybody is going to get a free Windows 10 upgrade anyway, so instead I collected 10 jars of sweets, and threw in my brand new Targus Bex laptop sleeve, and so we can still have a silly competition!

    I’ll give the prize for the closest guess for how many individual sweets there are in 10 jars of Windows 10 sweets by the 4PM Friday in Sydney.

    To enter, just tweet me with your guess - I’m @rayfleming on Twitter

    Tweet your answer here

    I’ll post them out to a winner in Australia first thing Monday (sorry folks, but I can’t post sweets abroad Sad smile)

  • Education

    Case study - Applying Azure Machine Learning in education to student dropout

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    Having recently written two articles about the theory of applying Machine Learning in Education - “Two ways to use Azure Machine Learning in education” and “Making machine learning in education easier for every day users” - I think it’s time to dive into a specific example of machine learning in education where it is being used to support education outcomes in schools. The story comes from my colleagues on the Machine Learning blog.

    Tacoma Public Schools logo

    The example is from Washington State, in the US, where Tacoma Public Schools has been using it as part of their ongoing initiative to prevent student dropout for school students. The district has delivered a dramatic turnaround - eight years ago five of the high schools were described as “dropout factories”, and five years ago just 55% of students graduated on time, compared to 81% nationally. But last year that had been boosted to 78%, ensuring that the district is recognised nationally for its educational achievements. The district is now developing the next level of data-driven improvement with the help of Machine Learning.

    Starting the data-driven journey

    The long journey started several years ago when new leaders joined the school district’s board. The new leaders expressed a desire to be more transparent with data and to use the data to address any shortcomings. They resoundingly embraced the value of data-driven analytics for the benefit of the district and its students.

    The board also asked themselves a radical question: What if they could process all their data to predict whether or not a student was likely to disengage and ultimately drop out? This was in the days before Machine Learning in education, and so the project team worked with Microsoft to create a data warehouse with student grades, attendance, health records and other data. And teachers and administrators were given access to data through their tools, like SharePoint and Excel. As the board President, Scott Heinz, described it:

      We now have this world-class data system for teachers to use. They want to know what is going on in their classrooms.  

    Getting predictive

    It is only recently that the board have been able to apply Azure Machine Learning to predict future dropout risks - turning historical data into an engine for predicting future success. As Shaun Taylor, the district’s CIO said:

     

    By using predictive analytics, we thought we would be able to intervene earlier and work closely with those at-risk students. Then we would be able to reach our ultimate goal: getting that graduation number close to 100 percent.

    When we saw Azure Machine Learning, we started to see how it could be possible for us to realize our vision

     

    The Microsoft team worked with the district to create a proof of concept, using Azure Machine Learning to create a model using five years of demographic, academic and student performance information to predict whether there was a risk of a student dropping out in the next semester. Using the Azure data services in the cloud, and going through a number of iterations of creating a predictive model (what’s key to this is understanding what factors might influence student dropout, and making that historical data available for the analysis) they were able to give a dashboard to board members to see the details of students at-risk of dropping out. Christopher Baidoo-Essien, at the school district summarised their journey:

      When we started this POC, we didn’t know if any predictive analytics would be attainable. As we progressed and used more historical data, the model proved to be almost 90 percent accurate.”  

    Turning conventions upside down

    As well as working on developing more real-time, or near-time, data sources for the analysis (using data that’s a month old risks missing key signals) and providing more regular analysis and reports, there is a focus on changing traditional, but incorrect, perceptions about the reasons for students’ struggles. As Christopher says:

      Often, students are seen as fitting certain profiles that indicate a potential lack of success, but none of those profiles are supported by analytical data. We wanted to use data to change that perception. And eventually, we want to predict what the key indicators are for kids disengaging.  

    There’s plenty of work still to be done, but the journey so far has proven that there is significant value in both the data dashboards, as well as the predictive analytics.

    You can read the full case study on the Azure Machine Learning blog

    Applying machine learning in education in Australia

    We will soon start to see examples within Australia of using Azure Machine Learning in education, as similar work is starting here. One of the data science teams behind this work in the US is working with S1 Consulting in Australia to fine tune a model for student dropout from universities, using the Azure Machine Learning service, and there’s another team at Literatu working on predicting opportunities for support and intervention for high school students, based on their performance in school assessments, as well as external assessments, such as NAPLAN and PAT assessments.

  • Education

    Making machine learning in education easier for every day users

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    Last week I wrote “Two ways to use Azure Machine Learning in education”, which started exploring the use of algorithms, alongside cloud-based machine learning in education to solve some of the key challenges facing education institutions. The problem is that it all sounds so very geeky. Hey, I just wrote “algorithms” and “machine learning” in the first sentence, which kind of proves the geekiness. Although this kind of technology is making huge differences to our online lives (like protecting us from spam email and giving us just the 3 out of 100 emails that aren't spam) it’s also something that has been the domain of technical wizards. To make a difference, machine learning in education has to be simpler.

    But we’re moving into a world where we’re going to be able to use this technology to solve real-world problems that don’t involve huge numbers of data scientists, and where the real knowledge sits inside the heads of business users in our organisations. Not the IT department and the data analysts.

    So how do we make it easier for every day users to be able to apply their expertise to analyse their own data?

    Part Two: Making it easier for every day users to use intelligent analytics and machine learning

    Missed Part One: Building and sharing algorithms? Here it is.

    If we’re recognising that there’s just a bit too much rocket surgery involved in today’s work with data, how do we make it easier to work with, for mere mortals like you and I? Well, there’s some smart teams working on that across Microsoft.

    Patrice Simard, a Microsoft Distinguished Engineer, is leading a new machine teaching research project at Microsoft Research, which plans to focus on how to make the tools and UI possible for non-experts to create helpful and valuable machine learning capable systems - rather than just focusing on how to make machine learning algorithms more accurate - through a project call ‘machine teaching’. Before you react with shock, this isn’t about machines teaching, but about users teaching machines!

    As Patrice says “No one has really built a machine learning tool for the layman” - and as more uses are found for machine learning, there’s a growing deficit between demand and the availability of data scientists with the right skills. There just aren’t enough people with machine learning expertise to do all the projects businesses and organizations want.

    You can read more about this work on the next evolution of machine learning: Machine teaching here

    But there are already some practical examples that you can look at to see what the future of Machine Learning could resemble for every day users, in Project Oxford, revealed earlier this year. Project Oxford allows developers to create smarter apps, which can do things like recognise faces and interpret natural language even if the app developers are not experts in those fields.

    Project Oxford currently includes four main components:

    • Face recognition: This automatically recognises faces in photos; groups faces that look alike; and verifies whether two faces are the same.
    • Speech processing: This can recognise speech and translate it into text, and vice versa. A developer might use it for hands-free tools such as the ability to dictate text or to have an automated voice read out instructions or other necessary functions
    • Visual tools: This can analyse visual content to look for things like inappropriate content or a dominant colour scheme. It also can detect and understand text in photos, such as a team name, and can sort photos by content, such as pictures of beaches, animals or food.
    • Language Understanding Intelligent Service (LUIS): This enables applications to understand what users mean when they say or type something using natural, everyday language. Using machine learning, in which systems get better at predicting what the user wants based on experience, it then figures out what people want the app to do. For example, in an exercise app the system might learn that when the user says “I want to start my run,” “begin a run” or even “go for a run,” it all means that it should begin tracking the person’s distance, and that the type of activity is a “run”.

    If you have basic development skills, or you can team with somebody who has, then the Project Oxford website is the place to start.

    So far, so good. But what about real uses of this technology? And what about the simplification angle?

    How-old.net - sparking a viral use of machine learning

    image

    A couple of months ago something called the #HowOldRobot went viral globally. It was the work of 3 Microsoft engineers who were applying machine learning systems to the challenge of working out how old somebody looks. And they made it very simple. You go to http://how-old.net and either upload your own photo, or find one on the internet, and it will estimate how old everybody in the photo looks.

    Of course, it’s not 100% accurate, but it’s a powerful demonstration of simplifying the use of machine learning - for a start, it’s trying to guess how old you look, not how old you are!

    Users are simply posing a problem, and letting the technology start solving it.

    I found it disturbingly accurate. For example, a month before my 50th birthday, it tagged me as 50, and then got me as 48 for a photo taken when I was 48.

    imageimage

    But, then again, when I tried it with a photograph of me in my 30’s, it completely messed up, and pronounced me as 15 years old (better than prematurely ageing me)

    image

    You can read a lot more about the #HowOldRobot, and the technology behind it, here, and there is also an excellent behind the scenes look at the viral growth of the #HowOldRobot here (imagine building something you thought would be used by 50 users, and getting 35,000+ people using it within three hours).

    Inspired by this project, another Microsoft engineer Mat Velloso built a service in just a few hours to compare two photos of people and rate their similarity, with a ‘twin rating’. With TwinsOrNot.net, it’s the same simple user interface, and process of hiding the complexity of machine learning - with just a few hours coding. Again it uses the Project Oxford work.

    You should try it for yourself, but here’s the result for the two most twin-like members of our Australian education team.image

    How can simplifying machine learning in education help?

    If we can use machine learning algorithms for (arguably) trivial things, and make it very simple to use, where can it be applied in education?

    Carnegie Mellon University are already using it work out how to cut campus energy usage by 20%.

    Helping student retention in universities

    One of the examples that is easy to see (and currently very difficult to solve) is the problem of students dropping out of universities. In Australia, one in five students drops out of their course in the first year, with the majority dropping out of the university altogether. In some universities, this is as high as one in three students.

    And yet, there is a strong bank of research from different universities which identifies the key factors that are associated with students dropping out (across six different studies, there are four factors which feature in the top five of over half the studies). Some projects have identified over 30 factors to monitor and analyse. It is the perfect scenario to use machine learning, because instead of spending a year or two analysing the factors, you can analyse the data every night for every student, and help identify the students at risk of dropping out. And plan your proactive intervention and support in response to what you can predict will happen, rather than reacting once you’ve discovered something has happened.

    The beauty of using machine learning to do this is that the system can manage the model itself - it learns as it goes along, rather than you having to keep using an out-dated idea of what the causes of drop-out are. That’s just one of a few key reasons why you find telco’s using it to forecast customer churn, and online retailers using it to suggest additional products to buy.

    What to do next

    If you’ve made it this far, hopefully you can see that there’s some value in keeping an eye on machine learning. So what can you do next? Here’s three resources and ideas for next steps:

    1. Find a Microsoft partner with the Data Analytics competency, and experience in education, and see what ideas they have to help
      > Go to Pinpoint, the Microsoft website for finding partner solutions
    2. Learn more about Azure Machine Learning yourself, or talk to your analytics team internally to try an idea out
      > Machine Learning documentation and tutorials are here

    3. Read the Microsoft Machine Learning blog
      > You’ll find it on TechNet here
    4. Keep an eye out for events which include Azure Machine Learning, and especially use cases in education
      > There’s the first ever Cortana Analytics workshop in Seattle in September
      > Closer to home, S1 Consulting are running a workshop to launch their Student Retention module, which uses Azure Machine Learning, in Brisbane on 10th August
    5. Or share this article with friends and colleagues, and see what they have to add!
      > Share on Twitter
      > Share on LinkedIn
      > Share on Facebook
  • Education

    Two ways to use Azure Machine Learning in education

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    You can't read anything about technology trends these days without reading about Big Data and the power of algorithms. It pops up in education with lots of discussions of education analytics/learning analytics and a pile of other acronyms.  I think that the discussion is so intense in education because it’s one of the key sectors that could tap into the power of data to improve business processes – whether that’s improving administration or improving teaching and learning. And it links directly to work our teams are doing with analytics and cloud services. I’m going to share ideas for using Azure Machine Learning in education that will help illustrate what’s possible.

    The education sector is awash with data, although it’s often locked away, and it is also full of powerful cases for using algorithms to improve learning and administration. But one challenge is that the skills you need to analyse and use education data are exactly the same skills that are in demand in the rest of the business world – whether that’s because a bank wants to use algorithms to reduce credit card fraud or make more profit trading shares; or a retailer wants to use algorithms to recommend the next product you should buy from their website; or an advertiser wants to put exactly the right advert into your eyeline at exactly the right time. So if you want to use Azure Machine Learning in education you’re going to be competing for the experts with banks, retailers and marketing companies!

    There are two ways to solve this problem:

    1. Data scientists build and share education algorithms that make it easier to analyse data to produce answers
    2. Make it easier for every day users to be able to apply their expertise to analyse their own data

    In this blog post, I’m going to cover the first way – building and sharing algorithms – and then next week, I’ll look at what we’re doing to make it easier for every day users to be able to use intelligent analytics and machine learning.

    Part One: Building and sharing algorithms

    One of the services in the Microsoft Azure cloud is Machine Learning – a way of using the power of a cloud data centre to do complex analyses without having to build your own room of whirring high performance servers to crunch numbers.

    Azure Machine Learning Algorithms

    Machine Learning allows you to build the algorithms (“based on these twenty things, it looks like this is going to happen”) and then run them to interpret your own data. Some examples of how this is used today include:

    • Estimating demand for a service/product
      eg forecasting how many ice creams will get sold next week
    • Turn speech into text
      eg creating captions for TV programmes
    • Tell you what a picture is
      eg identifying an animal in a photo
    • Identify a person in a photograph, or generic information 
      eg tell you whether they are male or female, or their age
    • Recommend products based on what you’ve just bought
      eg customers that bought this game, also bought…
    • Identify which customers are likely to change suppliers
      eg who’s going to change phone company
    • Detect anomalies in data
      eg creating an alert when somebody logs on to their social media account from the other side of the globe

    In the past, that would have been a massive task, with massive teams of very highly specialised experts and loads of technology – and a long time between having the idea and getting a working system.

    imageBut today it’s like many other IT projects – it’s quicker and easier to just get on and build something as a prototype, than to get people together to sit in a room and decide what should be done. And then, of course, you just keep improving what you built in iterations.

    And that’s the power of algorithms and machine learning – you keep improving the algorithm as you go along, and through machine learning, so does the system. You don’t need to work out all the rules in advance, but learn as you go.

    imageThere is a marketplace emerging for these algorithms – the Azure Machine Learning marketplace has a growing bank of them that include many of the scenarios above, as well as standard statistical models (see the Machine Learning API projects here). Some of these are being created as research projects, others are being created by businesses who will license or sell them to other organisations (eg advanced product recommendation or customer churn algorithms have high commercial value).

    So what does this mean for education?

    Many of the current experimental projects in other industries have a direct parallel in education. For example, a customer churn prediction algorithm has direct relevance to the student attrition problem in Australian universities (even down to the actual churn rates, where the churn rate for an Australian telco matches the student drop out rate for Australian universities).

    Experimental projects are being published constantly – the list today includes over 400 experiments in hundreds of areas :

    Azure ML Experiment How could that be used in education?

    More detailed information

    Social media sentiment analysis What are people saying about my university/school?

    Link

    Movie recommendation What supplementary course materials match this lecture recording?

    Link

    Flight delay prediction What is the likely lecture room capacity needed to optimise campus use?

    Link

    Predictive maintenance What does the facilities team focus on to minimise campus disruption?

    Link

    Fraud detection Which students are getting somebody else to submit their assignments?

    Student problem solving Will a student eventually be able to solve the problem, based on their first attempt?

    Link

    Customer Segmentation How do we divide our 100,000 prospective student into groups for marketing purposes?

    Link

    Buyer propensity model How many of our student applicants are likely to start the course?

    Link

    Student performance – Mathematics Predict a student’s performance in future tests

    Link

    You can find the full list of experimental projects on the Azure Machine Learning Gallery

    How can this be used in education?

    Today, there are groups of data scientists and specialists using this technology to build algorithms, and also converting algorithms from previous ways of doing the analysis. Some of that work is happening in universities, and some is happening in specialist suppliers to education.

    image

    If you’re a budding data scientist, or have access to a data scientist team, then there’s plenty of information (and training materials) on our Azure Machine Learning Studio website if you want to do-it-yourself.

    The alternative is to work with one of our advanced analytics partners. For example, S1 Consulting in Sydney and Neal Analytics in Seattle are collaborating on building an advanced student retention system, using big data analytics to predict which TAFE and university students are likely to need help and interventions to keep them on track. This being launched at a briefing event in August in Brisbane on 10th August. They’ve created, in a few months, the kind of analytics system that previously would have cost an individual university millions of dollars in technology and staff time to develop.

    Part Two: Making it easier for every day user to use intelligent analytics and machine learning

    For the majority of people reading this blog post, the real problem is knowing how to use the information above! (Congratulations and thanks for sticking with it). It’s interesting, but to make use of it, you’ve got to go and find the person in your organisation, or an external partner, who has the technical skills to use the technology. And your critical input is to help them identify the real problems that the data can help solve, and the value to the organisation from solving them.

    Next week, in Part Two, I’ll look at our work to try and solve this part of the challenge – to bring the power of machine learning and advanced analytics without needing to be a rocket scientist/brain surgeon.

  • Education

    Ways for schools and universities to manage inappropriate web content

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    For six years, the Microsoft Digital Crimes Unit has been working on PhotoDNA technology – a way of detecting illegal online child sexual abuse photos. It is used by a wide range of social media and photo sharing companies, like Facebook, Twitter and Flipboard, to scan user-generated images as they are uploaded onto their web services. Any organisation that hosts user-generated content – video, images, text – carries a risk of users uploading offensive or illegal materials. PhotoDNA provides a way to deal with the most extreme examples, and there are associated services that provide ways for schools, TAFEs and universities to manage inappropriate web content being posted on their own services.

    Often there’s a hard choice between allowing users to post content freely, or ensuring that every piece of content is approved before posting. Depending on your users, that can create a horrible balance between risk or overwhelming workload!

    Examples of where this challenge exists are:

    • Services that provide facilities for students to comment on each other’s work
    • Enquiry forms that allow users to send requests or messages through to teachers/staff
    • Web portals built for parents or students to interact with teachers
    • Competition websites where people upload photos or videos

    If you’re developing a web service or app that includes user generated content or provides discussion capabilities, especially in scenarios where they are anonymous or not easily traceable, here’s a couple of services to take a look at – Content Moderator and PhotoDNA .


    Ways for schools to manage inappropraite web content

    Content Moderator

    First, let’s look at a service to manage inappropriate or offensive (rather than just illegal) content. Microsoft Content Moderator is a suite of intelligent screening tools to provide automated content moderation in the cloud, that enhances the safety of your user engagement and communication. Image, text, and video moderation can be configured to support your policy requirements by alerting you to potential issues such as pornography, racism, profanity, violence, and more. This is a cloud service running in Microsoft Azure, and can be used by any organisation, including education customers and independent software developers.

    It provides three core services:

     

    • Image Services: Fuzzy image matching against custom and shared blacklists even when file types are changed or images are otherwise altered. Also includes optical character recognition (OCR), face detection, and adult image detection.
    • Text Services: Detect profanity in more than 100 languages and match against custom and shared blacklists. The text service will also integrate with Azure Machine Learning Text Analytics for sentiment analysis.
    • Video Services: Video hashing technology matches video clips against both custom and shared blacklists. The video service will also soon integrate with Azure Media Services for closed-caption text generation.

    Because this is a cloud service, it is much simpler to implement:

    1. Sign up and start playing with the sample code and live API on the portal
    2. Create your custom match list, that you want alerts on, or use others’ lists
    3. Call an API method with your content to invoke a check
    4. The Content Moderator service processes your content, and generates labels to describe it (without every storing your data)
    5. Your service receives API-based alerts for each content item matched
    6. You then use the alerts as signals to make content decisions – eg remove content; send it to a human checker; put it on hold for moderation etc

    And it does all of this in real-time – as your user hits ‘submit’ or ‘send’.

    Learn MoreRead more about Content Moderator, and find out how you can use it

     


    image

    PhotoDNA

    Although PhotoDNA has been in use for over 6 years, and is now used by more than 80 significant organisations, like Facebook, it has historically been time consuming to implement, as organisations required time, money and technical expertise to get it up and running in their own systems. Recently, we have built a new cloud service for PhotoDNA, using Microsoft Azure, that allows you to use the service through simple API calls. Here’s an example of how it’s used:

    imageKik, a chat network that’s popular among teens and young adults around the world, recently became the first company in Canada to deploy the PhotoDNA Cloud Service. Kik uses it to detect exploitive profile photos as they’re being uploaded, so the company can immediately remove them, report them to law enforcement and remove the user’s account.

    “It is allowing us to identify and remove illegal content, so it’s been a huge plus from our perspective in helping keep our users safe,” says Heather Galt, Kik’s head of privacy.

    The company does manually review some images, but with more than 200 million users globally, automation is a must. PhotoDNA allows Kik to identify known illegal images among a much greater number of photos, while in many cases letting human moderators avoid the disturbing task of identifying them.

    Another crucial advantage for Kik is that it doesn’t cause any delay for users sharing content.

    It’s “so fast and does its work so efficiently that it’s been implemented with no negative impact whatsoever on the experience for users,” Galt says.

    Learn More

    Read the Microsoft News story about how PhotoDNA is protecting children and businesses

    Find out who can use PhotoDNA Cloud service, how it works, and how to apply to use the service (PhotoDNA Cloud Services are free to qualifying organisations that are approved through an independent vetting service)

  • Education

    Event Invitation: Keep Students. Save Millions

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    One of our specialist partners, S1 Consulting, work with universities and TAFE institutes across the country to help improve the value of student management systems, and the use ability for individual institutions to manage the student lifecycle and student experience.

    Recently, they have been working closely with Microsoft, and international specialists in big data to apply the latest cloud analytics to the challenge of student retention and attrition management, and next month they are going to be launching their new Student Retention system at an event at our offices in Brisbane on 10th August.

    I'll let Blake Burningham, the CRM Practice Lead for S1 Consulting, take over the story:


    Education providers are losing millions of dollars from student drop outs every year. So we developed a solution....


    S1 Consulting has partnered with Microsoft Australia to produce a revolutionary piece of CRM software. Based on a complex algorithm, our Student Retention module identifies at risk students, to limit drop outs, and the dramatic effect this has on the bottom line. Here’s an interesting statistic:

    The average Australian University has 25,383 students enrolled, with a contribution of $12,500 per student, per year.  Research by Microsoft has found an average dropout rate of 13.5%, equating to $43 million of lost revenue every year.
    Reducing this rate by as little as 1% will earn universities an extra $3 million dollars every year.

    To be part of the unveiling of our Student Retention module, please join us for our one day CRM Forum in Brisbane on the 10th of August. We'll also be demonstrating how our CRM technology can help you Recruit, Retain and better Maintain your customers. 

    We encourage you to register as early as possible by clicking here or calling 02 9887 3980.



  • Education

    Hololens in education - case study video

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    The team working on the Microsoft Hololens are quietly working on creating ambitious new ways of achieving new things - you've hopefully seen some of the demonstrations that they've given at global events like Build and the Windows 10 announcement earlier in the year (if not, jump here). And that includes working on uses for Hololens in education, and imagining some of the ways that we can enhance teaching with Hololens.

    Although much of their work is being done behind firmly closed doors, when they do give us a sneak peek of what's to come, it's thought provoking stuff. And this week is no exception, as they have just released a video case study of Hololens in Education, exploring the work they are doing with Case Western Reserve University in the US, who are working on new ways of teaching using Hololens. In their case, it's looking at human anatomy, and Barbara R Snyder, the President of Case Western Reserve University explains why:

      We've been teaching human anatomy the same way for a hundred years. Students get a cadaver and then they look at medical illustrations, and it's completely two-dimensional. And the human body isn't.  


    But when you add the interactivity of a see-through holographic computer, it enables high-definition holograms to come to life in your world, seamlessly integrating with your physical places, spaces, and things. We call this experience mixed reality. And it looks like this:

     Watch the video case study to see what Case Western Reserve University are doing with Hololens:

     Bonus info:

  • Education

    Wired: Pens are making a high-tech comeback

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    Last week, Wired ran a story on the research team working on next generation of digital pens and software experiences at Microsoft. It's a great read, because it raises some thoughtful questions about the way that we use computers, and the impact on learning and retention of information in our brains.

    We've had a stubborn focus on pen interfaces for computers for decades - my first pen-equipped tablet was a PC running Windows that our family took on a one year backpacking trip around the world with our 3 & 7 year-old daughters. And we chose a pen-equipped tablet because it was exactly the right thing to help our kids continue their learning whilst travelling, in as many ways as possible.

    The Wired article says:

    Study after study shows we remember things better when we write them—our brain stores the letter-writing motion, which is much more memorable than just the mashing of a key that feels like every other key. We think in fragments, too, in shapes and colors and ideas that just don’t come through on a keyboard. “Think about how many things that are built start as a drawing,” Bathiche says. “Most things, right? Everything you’re wearing probably started as a drawing.”

    You can’t type out the folds of a dress, or the gentle curves of a skyscraper. Drawing with your stubby finger on a touchscreen isn’t much better. Humans are tool-based creatures: Our fingers can do amazingly intricate things with a pen, a brush, or a scalpel, that we can’t replicate with a mouse or the pads of our fingers. Our computers are giving back that kind of detailed control. In turn, the pen is opening up new ways of digital expression, new tools for communication, new ways to interact with our tech.

    As well as talking about recording and recalling information, and the visual aspects of idea creation, the article also covers the research that's going into ideas like being able to search the web by drawing what you're looking for, and also the need to create a digital pen experience that is as simple and authentic as holding an actual every day pen - although you might well be writing on an 84" digital display, as well as on your personal tablet screen.

    Read the full article here: Wired - Pens are making a high-tech comeback

  • Education

    Uses for Hololens in education

    • 2 Comments

    Less than 100 days ago, we revealed Hololens during the Windows 10 announcement, and since then we’ve all been waiting for a second chance to see it…

    Hololens in Education

    Well, last night, that finally happened at the \\Build\ conference as the team revealed what they have been working on, and especially focused on Hololens in education, with teaching and learning scenarios.

    imageThey started with Prof Mark Griswold from Case Western Reserve University, talking about, and demonstrating live, the way that Hololens could be used to study anatomy, something traditionally done with a combination of textbooks, models and cadavers.

    image

    After that demo, they switched gears to demonstrate the use of Hololens to interact with, and programme, a Maker Kit based on the Raspberry Pi 2. That was a fascinating demonstration, as it showed how an object in real life – in this case a Maker kit robot – could be paired with an associated hologram to create a single object.

    image

    During the broadcast of the keynote, what the team effectively setup was a camera with a Hololens on, so that you see through the video what a user would see wearing a Hololens.

    Without a shadow of doubt, there are going to be some amazing things done with Hololens in education – classrooms, learning spaces, lecture theatres and research labs - over the next few years, helping students to learn by doing as much as by watching.

    You can watch the keynote, and download it (eg as a teaching resource) from the Channel 9 website.

    Learn MoreWatch the keynote on the Channel 9 website, and fast forward to 2 1/2 hours for the Hololens section!
    Bonus info:

  • Education

    Uses for Office 365 Video in education

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    A few months ago we announced the Office 365 Video service, which is an internal video publishing service on Office 365, and we recently confirmed that it is now rolling out to all of our Office 365 Enterprise customers (that automatically includes Office 365 Education services, with plan E1 and E3). What the service allows you to do is create a video portal within your Office 365 Education service, and create channels (eg for specific curriculum subjects or special interest groups) for users to watch.

    Office 365 Video begins worldwide rollout and gets mobile 2

    The Office 365 Video service uses a group of cloud services in Azure Media Services, to make it easier for your users to publish their videos in easily accessible formats, so that they can watch on a PC, Mac, tablet or phone. We’ve also announced an Office 365 Video for iPhone app, so that staff or students can record and watch videos on their iPhone. And because the service runs on top of Office 365 and Azure cloud services, it means that the security that applies to all of your other information also applies to the videos – for example, the videos are stored and transmitted with secure encryption, to keep them private.

    It’s also running as a service within your Office 365 setup, so there are no additional charges for the service (for example, the media transcoding is included within the Office 365 Video service, and the video storage uses your existing SharePoint team allocation in Office 365)

    There are two key ways that you could use this service in a school, TAFE or university:

    • Simply start to publish videos using the standard Office 365 Video services and mobile apps, and start to create channels for your different content. Your teaching staff could also upload recordings of lessons, presentations, screen recordings etc directly from Office Mix into your Office 365 Video portal. The process is easy for users – they can just drag and drop an existing video onto the portal (recorded in heaps of formats), and it handles all of the transcoding needed to make the video available on different devices. And you can setup multiple channels, and select different users as admins for the channels.
    • You could use develop a customised service for your users using the developer APIs available for Office 365 Video. For example, if you wanted to use this to deliver a lecture capture and streaming service, a developer can build a service to upload from your lecture capture hardware into your Office 365 Video portal and publish automatically in the correct channels. All the documentation for the Office 365 Video APIs are previewed here.

    Learn MoreVisit the Office 365 Video website


    I’d also recommend taking a look at the
    Office 365 Video Uservoice site – this is where the team are collecting feedback and requests for future features – so if there’s something you’d like to see added to the service, this is where you can go and vote for it, or suggest it!

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