Let the good times roll: March student visa statistics indicate continued growth in Australian international education.

Just last week, it happened that I was sitting at home in front of my laptop with a glass of red wine in my hand when I noted the Department of Immigration and Border Protection had released their latest visa statistics on international students.

This sounds a tad depressing in retrospect, but the mood did strike. I decided to boot up my trusty excel models to quickly cut up some of the statistics to see what insights might be developed from the visa numbers.

Given that international student visa grant rates have a strong correlation with international student commencements, these figures are a good proxy measurement for future international student demand.

While I don’t usually delve too deep into the visa data every single month, March is usually a pretty important month in international education, particularly in higher education, as generally most new international student cohorts have commenced for the first half of the year.

While I still await the Department of Education data on commencements and enrolments) (which should arrive any day or week now), what follows is a slice and dice of some interesting metrics giving an idea of how the entire industry (and its sub-sectors) are faring in terms of visa grant rates. But first, some assumptions!

Data assumptions

  • I’ve only examined primary visa applicants.
  • In most cases, I’ve also set the ‘month’ filter to ‘March’, so I can compare year on year data to get an idea of how the visa grant rates have changed over time.
  • In certain cases, I’ve also limited data to the last five periods, covering July to March of each relevant financial year (DIBP generally publish visa grant data in terms of financial years). In others, I’ve simply looked at the last two financial years: 2015 /16 and 2016/17 only covering the visa grant data between July and March of these periods.
  • I look at cumulative visa grants from July to March of each period, rather than visa grants in a month.

Of course, the full data sets are available online and for free at the below website:

That data set does have its own usage guidelines which may be of interest.


Too long; Didn’t read? Here’s the take away headlines

  • International student visa grants are up 14% year on year as at March 2017.
  • International students being granted visas are primarily looking to enroll in higher education, which has about 53% of the market. VET and ELICOS have increased their proportions of all visa grants by 0.5 percentage points each, indicating increasing popularity.
  • China and India have continue to dominate student visa grant recipients. Source countries like Brazil and Nepal have made surprisingly good year on year gains.
  • Queensland has performed well in the latest data, with 18% year on year growth in applicant grant rates. New South Wales continues as the most popular institution destination of all students granted a visa in 2016/17 with approximately 37.7% of the market.
  • Not a huge amount of change in the proportion of onshore/offshore international student visa grant recipients, with tow thirds being offshore in 2016/17.

Read on for a bit further details and some explanatory charts.

How is 2017 shaping up in terms of international student visa grants?

Figure 1 below shows a year on year and month by month comparison of the number of student visas granted by DIBP to primary visa holders. And as you can see, it’s looking pretty good for the 2016/2017 financial year.

Figure 1: Cumulative international student visa applicant grant rates – 2012 to 2017 (July to March of each financial year)


  • We can see that in the period 2016-March 2017, cumulative international student visa grant rates are 14% up from at the same time last year. Since 2012/2013, cumulative visa grant rates to international students has grown on average 8% per reporting period.
  • On the basis of above, we could reasonably conclude that, on a whole, visa grant data indicates continued growth for international higher education in Australia throughout 2017.

A glance at industry sub-sectors

Figure 2 below shows the breakdown of cumulative visa grants at March of the relevant period. The percentage indicates the share of the total industry each sector occupies for the particular period (for example, higher education attracted 53.1% of all visa grants to international students in total for the period 2016-2017 (July to March).

Figure 2: International student visa grant applicants by sector – 2015/16 versus 2016/17 (with % proportion of entire industry)


  • As at March 31 2017, more than 50% of all visas granted during 2016/2017 period have been for higher education. This proportion has remained relatively unchanged compared to 2015/16, with a slight change of 0.2 percentage points.
  • The chart shows a slight bump of 0.5% percentage for the VET sector, whereas a healthy bump for ELICOS of half a percent also. Non-award is less popular compared to last financial year, dropping 0.9% percentage points.
  • We can also see from the height of the bars that Higher Ed, VET and ELICOS have all seen promising increases in total cumulative visa grants in the 2016 to March 2017 period.

Where are the students generally from?

Figure 3 below shows the top five nationalities across all sectors of student visa grants for primary applicants. Year on year change is indicated as a percentage.

Figure 3: International student visa grant applicants by top five nationalities- 2015/16 versus 2016/17 (with % year on year change)


  • Analysis of the top five source nationalities for international student visa grants from July 2016 to March 2017 reveals a familiar story: China once again dominates visa grants. As at March 2017, primary applicant student visa grants for China were 59,121, an almost 16% growth compared to March 2016.
  • India provided the second highest source of visa grants with 21,856 (+18.3%) across all sectors.
  • Considering the recent fate of the Brazilian Science without Borders scheme, it was somewhat surprising to see Brazil’s not only retain but increase its total number of visa grants year on year, with growth at almost 31% year on year. The primary reason for this is due to seemingly increasing demand from Brazil for VET and ELICOS student visas.
  • Nepalese student visa grants grew a staggering 90.8% year on year to 10,265 student visa granted between July and March of the current financial year. This was due to strong demand for higher education visas, which grew 76% year on year to 8,245 visas granted financial year to date.

Where the students are likely headed to?

Figure 4 below shows the aggregate data on the likely institution location of the student visa recipient. This is broken up by Australia State/Territory.

Figure 4: International student visa grant applicants by institution location – 2015/16 versus 2016/17 (with % year on year change)


  • The visa grants data has some good news for Queensland, who showed the sharpest year on year growth at 18.1% between March 2016 and March 2017. Queensland improved its proportion of visas grants from 17.5% to 18.2% – a difference of 0.7% – between March 2016 and March 2017, the best of any state or territory during this financial year.
  • New South Wales continues to attract the largest proportion of international student visas grant recipients with approximately 37.7% of all international student visas granted in the current financial year nominating to study in New South Wales .Victoria follows behind with roughly 27.1%.
  • Western Australia visa grant rate dropped 5.7% year on year, its proportion of all visa grants falling from 7.7% to 6.4%.
  • Note large proportion of ‘not available’ visa grants, which may alter the above chart and analysis further down the line.

Are those who are being granted visas already here in Australia?

And finally, figure 5 below shows the location of the applicant when the visa application was made, giving us an idea of the relative importance of onshore and offshore recruitment in international education.

Figure 5: International student visa grant applicants by applicant location – 2015/16 versus 2016/17 (with % year on year change)


  • There hasn’t been much change in the proportion of onshore versus offshore applicants between 2015/16 and 2016/17, with more or less two thirds of all applicants being offshore students.
  • In total 76,446 applicants were onshore when they applied for their international student visa versus 150,649 offshore.
  • Saying that onshore applicant volume grew 14.2% versus offshore 13.3%

And that’s it…

There is plenty more one is able to do with these data sets, and heaps of value at looking into nationality level data by sector to get an idea of the changing demands for Australian education throughout the world. I would fully expect this data to indicate healthy overall commencement numbers in the Department of Educaiton (“DET”) March census data.

Get in touch

As I said a the top, I was having a glass of wine when putting these together (one glass only), so if you spot an error or have a question, feel free to drop me a line, via LinkedIn or Twitter or simply by commenting here.

The Data Game: Building Analytics Capability in International Education

[Originally published by IEAA’s Vista Magazine (Summer 2016/17) — I’d strongly recommend following them online and reading their publications if you’re interested in Australian international education!]

Embracing an analytic mindset and capitalising on the technologies in the era of big data are key to reaching Australia’s strategic international education goals, writes Darragh Murray.

A tale of prediction and teenage pregnancy

In 2012, journalist Charles Duhigg came across a fascinating story concerning the power of prediction and teenage pregnancy. Writing for the New York Times, Duhigg told how an irate man confronted the manager of a Target department store in the United States, demanding to know why the retailer kept sending his teenage daughter coupons for baby clothes and lotions.

“Are you trying to encourage my daughter to get pregnant?!” the angry father complained, presenting the unfortunate manager with bundles of baby-related paraphernalia. The manager had little idea how this had occurred and promised to follow up. However, investigations were cut short when the father rang back days later to apologise. His teenage daughter was indeed pregnant and somehow Target knew before her family did.

How could Target possibly know this? Well, the answer is through the precise use of data and analytics. Target had been heavily investing in analytics capability — a specialty that places data at the centre of knowledge discovery and communication. Through the use of predictive models, the store could precisely identify potentially pregnant customers based on historical shopping patterns.

While this anecdote is both fascinating and creepy, it reminds us how modern industries are leveraging vast amounts of data to pursue strategic business objectives. Whether it be targeting customers who are expecting, or using data to evaluate the potential of international student markets, skilled use of data is quickly becoming a resource on which businesses and organisations compete.

The data revolution

Using data to solve problems isn’t a recent development. What we now call data science has been widely used in the fields of science and engineering since the 1970s, typically for risk management and workplace health and safety. The field gathered further steam during the 1990s when banking and finance increasingly used data monitoring for combating fraud and credit card theft.

The recent convergence of massive computational power, inexpensive data storage and the development of modern data mining and machine learning technologies has led to the mainstreaming of data as a valuable everyday business resource. It all culminates in the emergence of ‘big data’ as the latest buzzword across the land.

This data and analytics revolution is now seen as critical to the ongoing development of the modern global economy. In their excellent work Competing on Analytics, researchers Davenport and Harris argue that data is now the key resource organisations must use to discover the distinctive capabilities that keep them competitive (see also this HBR article written by Davenport in 2006 for a summary of this great book).

As shown in Figure 1 (p.18), Davenport and Harris conceptualised a scale of organisational analytics capability, ranging from basic standard reporting to advanced predictive models that permit data-driven forecasting and risk management optimisation. If your organisation is still monitoring key metrics using simple standard reports, you may already be lagging behind.

How then does the analytics revolution intersect with the Australian international education sector? Given the growing number of internationally mobile students — as well as increasing interest from modern economies with advanced education systems in teaching these students — the idea of competing on analytics and data is highly relevant. Knowing more about potential international students before competitors do makes sense if Australia wants to continue to attract the highest quality international students.

Education providers who can compete best in terms of data and analytics will reap the future benefits. Australia’s international education sector is fortunate to have one comparative advantage: we have a large amount of good quality student data that other markets seemingly do not.

Australia’s comparative data advantage

Australia has world class data on its international students. Government agencies such as the Department of Immigration and Border Protection(DIBP) regularly publish detailed and timely statistics on student visa application and grant rates that permit analysis of future demand. Similarly, the Department of Education and Training (DET) provides valuable information on international student enrolments and commencements that can be sliced and diced by numerous metrics across all sectors within international education.

Online data portals such as the uCube allow detailed local competitor analysis and benchmarking. Australia’s Market Information Package (MIP) is a global leader in international student data visualisation, providing an integrated business intelligence platform (see Figure 2) that allows institutions and business the ability to analyse the Australian international student market without large scale IT infrastructure investment.

These examples don’t even take into account the countless other sources of private organisational information on Australia’s international student cohort that can be integrated into these robust public sources.

Such up-to-date and integrated sources of data are not exactly evident in other competing markets for international students. For example, the United States relies on Open Doors published by the Institute of International Education, whereas the Higher Education Statistic Agency (HESA) in the United Kingdom provides some wide-ranging details on the entire student population.

While these services are undoubtedly handy, they don’t seem to have the specialist, integrated or flexible platforms for data analysis that the Australian sector enjoys. They can also suffer from lack of timely updates or data that is difficult to extract and analyse.

It’s not unreasonable to claim that Australia is a market leader in international student data. The question is, how can we use these datasets to further Australia’s international education sector?

Building analytics capability: the data-driven mindset

Good business decisions are supported by robust data and comprehensive analysis. As Davenport and Harris assert, organisations that are successful in certain markets where competitors flail are nearly certainly winning by driving their strategic business decisions using analytics and data.

Given Australia’s enviable international student data resources, a change of mindset and some creativity may be all that’s needed to start making large competitive gains. Let’s examine a few examples. Assume you’re trying to decide whether to enter an international student market. The ‘gut-feel’ response may be to justify decisions based on what you’ve read in the media, the recommendation of a trusted colleague or on the basis of what your organisation has done before.

The analytical, data-driven mindset demands much more. A good place to start would be to test for key influential variables in a relevant dataset. Can you identify factors in other mature markets that have historically influenced growth based on data alone? Are variables like gross domestic product or scholarship availability influential and relevant in this case? Finding the answers to such questions in the available data can assist in both firming up confidence in a recommendation as well as result in better strategic planning.

Furthermore, data familiarisation is paramount in building analytics capability. Data mining and visualisation tools such as IBM SPSS, Tableau or TIBCO Spotfire, can be helpful aids in understanding the natural relationships underpinning datasets. Clustering, a technique by which to organise data into distinct groups based on their natural attributes, can be very useful in uncovering insight.

This advanced level of analytics capabilities means moving into the territory of predictive models. This involves examining historical patterns in datasets to help make informed forecasts about the future. Predictive modelling leverages machine learning techniques such as classification, neural networks and logistical regression.

Use of these techniques could afford international education organisations the ability to calculate international application outcome probabilities, or even whether a current student will pass or fail their first year. Predictive modelling has incredible value and countless uses in the context of Australia’s international education sector.

The take away message here is that pressing business problems should be tackled by moving from the intuitive to the analytic. Embracing an analytic mindset and capitalising on the technologies in the age of big data could be the key for furthering Australia’s strategic international education goals.

Signal and noise

Australia has set out a bold, three pillar agenda in its ‘National Strategy for International Education 2025’. Many of the goals set out in the strategy, particularly in pillar three ‘Competing Globally’, can be furthered simply by improving our collective analytical capability and embracing data-driven decision making mindset. Competitive modern day organisations invest in advanced analytical capability, using technologies and methods such as data mining, clustering and predictive models to better understand and tackle key strategic problems.

The Australian international education sector is not immune to these developments and there will come a time where we will need to rely on our comparative data advantage to keep ahead of competing international education hubs. We have the raw materials, it’s just a case of building on these to advance the industry’s collective analytics capability and stay ahead of the competition.


Enhancing organisational analytics and building data knowledge isn’t simply a case of grabbing a dataset and hoping for the best. Here are three titbits of advice about how someone in an analytics position can help their organisation do more with data.

Focus on process and the end objective

Doing data correctly requires time, precision and purpose. Colleagues may not be as aware of how complicated organising and manipulating data may be and aren’t forthcoming with all of their business requirements when requesting the data they need to make decisions. The more regimented you are with gathering requirements ahead of any analytics-based project, the better it will be for you and your organisation. If you’re asked to do data analysis without a solid strategic reason, you’re simply wasting your time.

Some core skills can go a long way

Basic statistical skills are very helpful for understanding the shape of data. Learn how to compile a five-number statistical summary, know how different measures of averages such as median and mean work and come to grips with the concept of outliers. These are all key skills to being able to understand your data. It takes time to turn data into meaningful insight and requires good skills in data manipulation. Being able to organise information using relational databases or even good spreadsheet skills can take you a long way in the data game.

Communication is key

Even if you’re the greatest statistician or data scientist known to humankind, it’s worth peanuts if you cannot communicate insight correctly. Being able to write about data succinctly and with purpose — alongside the skillful use of meaningful data visualisation — will do a lot more for increasing executive support and increasing organisation analytics capability. Often, when it comes to communicating data, less is more.