Category Archives: machine learning

Australia Microsoft Ignite 2017–special pricing for education customers


Over 4 days next February, more than 2,500 Australian Professional Developers and IT Pros converge on the Gold Coast to preview new technology, build industry skills and be inspired by innovation at Australia’s Microsoft Ignite 2017. It’s an event for the techies, and people obsessed with what they can do with technology. And it’s the chance to meet other people with a similar inquisitive nature. And at the bottom of this post, you’ll find a Microsoft Ignite 2017 promo code for education customers.… Visit the author's original post

Learning Data Science through a free online course

Last year I shared information on the Machine Learning course I took on edX, which taught me how to implement Azure Machine Learning services, and allowed me to build predictive data models. I’ve subsequently spoken with people who took the course on my recommendation, and have built interesting predictive models for student behaviour. One person created a model that used the data in their Student Information System to predict which students would fail a course – and which is 90% accurate!… Visit the author's original post

Artificial Intelligence and Machine Learning in Education – a glimpse of what that might mean

Last year, when I wrote “Two ways to use Azure Machine Learning in education” and “Making machine learning in education easier for every day users‎”, I imagined a future where the power of the Project Oxford services could help education institutions with scenarios like face recognition (Who’s in my classroom today? Is anybody smiling in this picture?), speech processing (Who’s speaking in the lecture now?), visual analysis (Does this image contain inappropriate material?Visit the author's original post

IT Heroes: How to stop parents or students block your parking bays?

imageI’ve been talking about machine learning a lot recently - the idea that we can use a bit of computing brain power to look at data, and make predictions, spot patterns and improve the value we can get from data in education. As an example I often talk about, that estimates how old you look from simply looking at a picture of you - it’s a good example because it was built very quickly because of the work we’ve been doing to simplify the process of using machine learning services.… Visit the author's original post

Student drop out in Australian schools

Yesterday media around the country reported that one in four Australian school students drop out without completing Year 12. The stories were based on a report from the Mitchell Institute, who published the report “Educational opportunity in Australia 2015: Who succeeds and who misses out”, and a detailed factsheet about completion in senior schools. There were some key demographic factors that drove the differences in drop out rates - location, indigenous status, language background and socio economic status, and they also identified a number of key other factors - “poor grades in core subjects, low attendance, and disengagement in the classroom, including behavioural problems”.… Visit the author's original post

Partner Training – Building a Learning Analytics business

One of the key things that I’ve been focusing on over the last few months has been helping Microsoft’s partners to develop solutions that help education customers use data better - whether that’s for learning analytics, or looking at their management data in new ways. There are two key focus areas - one is making it more accessible to more people using better and simpler visualisation with Power BI; and the other is the power of the cloud to perform advanced analytics easily - for example, to deal with high volumes of data and provide easily interpreted answers to critical questions.… Visit the author's original post

Learn Data Science and Machine Learning Essentials on edX

At one stage in my career, working for fifteen years in marketing, I realised that it wasn't just enough to understand what the data told me about my customers, but to also understand how to extract stories from the data. And to do that, I had to understand the techniques for analysing data - not just look at reports that other people gave me. I’m definitely not a data scientist, and my knowledge of statistical techniques is wafer-thin. But I knew enough to be able to understand the meaning of the data.… Visit the author's original post

Free Azure Machine Learning in education – trials and credits

A couple of weeks ago I wrote a couple of articles about using Azure Machine Learning in education. Two ways to use Azure Machine Learning in education and Making machine learning in education easier for every day users. It covered scenarios like using Azure Machine Learning for education problems like student retention models, as well as things like face recognition to catch exam cheats, or text analysis of documents and research.

But I should also have covered one get thing - that for education customers there are some ways to use Azure Machine Learning that aren’t available to everybody else Smile

Azure Machine Learning Banner

There are three easy ways you can get started with Azure ML for education:

  • There is a free tier that includes 10GB of Azure storage for our datasets, and the ability to build Azure ML experiments for an hour with up to 100 modules.
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Stopping exam cheating – plagiarism checking is not enough

I read in today’s Sydney Morning Herald the continuing story of universities in Australia fighting a constant battle with cheats in exams and assessments. Today’s story reveals that there’s not just a problem with plagiarism in essays, but also students paying impersonators to sit their exams for them:


University students are increasingly paying impersonators to sit their exams or smuggling in technology to help them cheat, while other students cannot be trusted to sit in sloping auditoriums because of their willingness to copy answers in multiple choice tests, a new report reveals.

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

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.… Visit the author's original post

Making machine learning in education easier for every day users

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.… Visit the author's original post

Two ways to use Azure Machine Learning in education

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 is 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.… Visit the author's original post

Slimme auto’s door de vierde industriële revolutie

Technologie speelt een almaar grotere rol in onze levens. Door de opkomst van het internet of things (IoT) zijn steeds meer zaken verbonden met het internet. Auto’s vormen daarop geen uitzondering meer. Ook in de automotive-branche wordt de opmars van IoT nu al de vierde industriële revolutie genoemd, of industry 4.0.

Met die kennis is het dan ook niet heel erg verrassend dat automotive afgelopen week een belangrijke rol speelde op technologiebeurs Hannover Messe 2015, en dat het op de AutoRAI dit jaar meer dan ooit draait om ICT.… Visit the author's original post

Het is 2015. Zijn onze huizen klaar voor Back to the Future?

Dertig jaar geleden alweer stapte Marty McFly in zijn DeLorean om een bezoek te brengen aan de toekomst. In het tweede deel van Back to the Future werd ons een wondere wereld beloofd, waarin vooral het huis van de toekomst indruk maakte. Met nog iets meer dan een half jaar te gaan tot 21 oktober 2015 lijkt het erop dat die belofte is in te lossen. De technologie is er al, nu moeten we er alleen nog mee leren omgaan. Of beter gezegd: de technologie moet beter met ons leren omgaan.Visit the author's original post