Casino Games Bets Perform At Tribal

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Step by Step Guide to Install Windows 8 Consumer Preview To Go on a USB

Note: These instructions apply to the Consumer Preview of Windows 8 and technically isn’t actually Windows To Go, it is just Windows 8 running from a USB stick (which is slightly different).  From my discussions with Microsoft at TechEd Europe 2012 Windows To Go is only available with Windows 8 Enterprise Edition and only on the final release (the Consumer Preview wasn’t an Enterprise Edition).

Also worth noting that when creating a Windows To Go on USB then the USB drive is best to be one that is certified by Microsoft for that usage as that will ensure it is capable of handling the type of read/write access required for this type of usage from both speed and longevity perspectives.  Check out the Hardware Considerations on Wiki for more details.

If you are still wanting to give it a go knowing this then by all means go ahead but still bear in mind the hardware suggestions.

Following the hype surrounding Windows To Go (which allows you to run an instance of Windows 8 from a USB drive on any Windows 7 capable hardware, even on a Mac, without affecting any installation on that machine) I was surprised to see how little information there is on how to go about getting it installed (various descriptions for the Developer Preview) for the newly available Consumer Preview.  Also it was interesting to see a response on TechNet from a Microsoft employee that “The capability to build Windows To Go drive is not available widely in this Consumer Preview release, we are working on options to possibly make this available in the future. Thanks very much for your interest”.

Well, it turns out it is actually fairly easy (albeit time consuming) to do and very similar to the Developer Preview. Instructions on the flip…

UPDATE: some stats on boot time comparisons included at the end.

Prerequisites

  • A Windows 7 machine (or Windows 8 )
  • A 32GB (or larger) USB Flash drive.  I believe you can also use an external HDD instead but I’ve not tried that.

Downloads

  1. Start downloading the Windows 8 Consumer Preview ISO image of your choice as at 3.3 GB for the 64-bit download it might take a while.  Note: if you intend to run your Windows To Go on hardware that doesn’t support 64 bit then you’ll need the 32-bit ISO file.
  2. If you don’t already have it then start downloading The Windows Automated Installation Kit (AIK) for Windows 7 ISO file. You actually only seem to need a single file from this install (imagex.exe) but unfortunately this 1.7 GB download is the only official way to get it.
  3. You’ll also need some software to mount these ISO files unless you want to burn them to DVD (if you can remember what those things are).  I’ve used Virtual CloneDrive.  UPDATE: or you can use winzip or 7-zip (thanks to Gary P in comments)

It doesn’t matter in which order you do the following 3 sections, just do whichever you can based on download times.

Set up your USB

Almost the same as this description from tweaks.com but with a small tweak mentioned by ed810 in the aforementioned TechNet thread. See those links for more detailed explanation if necessary. Hit ENTER key after each typing step.

  1. Plug in USB drive.
  2. Open administrative level command prompt.
  3. Type “diskpart”.
  4. Type “list disk”.
  5. Type “select disk #” replacing # with the number shown for your USB drive.
  6. Type “clean”.
  7. Type “create partition primary”.
  8. Type “format fs=ntfs quick”.
  9. Type “active”.
  10. Type “assign”.
  11. Type “exit”.

Get imagex.exe from Windows AIK

  1. Mount your Windows AIK ISO file (or use your ZIP utility).
  2. If Autorun doesn’t want to work for you (as it didn’t for me) run “StartCD.exe”.
  3. Install the kit.
  4. In Windows Explorer navigate to “C:\Program Files\Windows AIK\Tools\amd64″ and copy the file “imagex.exe”.
  5. If you haven’t already done so in the steps for getting the “install.wim” below, create a handy, local folder and paste the “imagex.exe” file into it.

Get install.wim from Windows 8 ISO

  1. Mount your Windows 8 ISO file (or use your ZIP utility).
  2. In windows explorer open the “sources” folder and copy the “install.wim” file (2.9GB).
  3. If you haven’t already done so in the steps for getting the “imagex.exe” above, create a handy local folder and paste the “install.wim” file into it.

Note: If you can’t find the “install.wim” file, just an “install.esd” file then chances are you’ve installed the Consumer Preview from the main “Download Windows 8 Consumer Preview” link on the main Windows 8 Download page rather than just downloading the ISO file (the link to which is in small text below that main link, but just use the one above).  To get the “install.wim” file you won’t have to install Windows 8 anywhere beforehand, just get the ISO file and follow the above instructions.

And Finally – Install Windows 8 Consumer Preview on your USB

So you should now have both the “imagex.exe file and “install.wim” file sitting next to each other in a new directory somewhere.

Make sure your USB drive is still plugged in and where you see # in the following steps replace it with your drive letter and press ENTER after each typing step.

  1. Open administrative level command prompt or if it’s still open use the one from before.
  2. Navigate to your new folder containing both the “imagex.exe” and “install.wim” files.
  3. Type “imagex.exe /apply install.wim 1 #:\”.
  4. Wait a long time.
  5. If using a Windows 7 machine type “bcdboot.exe #:\windows /s #:”, if Windows 8 then type “bcdboot.exe #:\windows /s #: /f ALL”.

Step 3 can take a fair bit of time so make sure you aren’t planning on shutting your machine down for the next few hours!

The first time you boot from the USB drive you’ll need to go through the initial set up process and each time you boot using different hardware then it may have to download/install any necessary drivers.

Hands on Statistics

Using my old Dell Latitude D630 which has the Windows 8 Consumer Preview installed as its only OS we get the following timings (all taken from pressing the power button which includes 7 seconds of initial BIOS time):

  • Time to login screen: 14 seconds.
  • Time to Desktop (via Metro Start Menu): 31 seconds.
  • Time to shut down: 11 seconds.

On the same machine booting from my USB2 Windows To Go:

  • Time to login screen: 1 minute 20 seconds.
  • Time to Desktop (via Metro Start Menu): 2 minutes 16 seconds.
  • Time to shut down: 2 minutes 10 seconds.

The start up time is a little misleading as despite being able to get into the operating system fairly quickly (relatively speaking), everything feels rather sluggish for a good few more minutes.  More of a worry is that the Metro apps more often than not fail to load: splash screen for app displays and then after between 1 and 15 seconds it flicks back to the start screen with no error.  Maybe this is why Microsoft aren’t wanting many people trying it out at this stage?

Potentially this is down to the read/write speed from/to the USB drive but I can’t confirm that.  If I’m feeling brave I’ll install Visual Studio 2011 on there and debug some test apps and see if they suffer from the same thing.

I will also try and get hold of a USB3 drive and track down a device that can actually support it and see what difference that makes to the timings and the reliability of the Metro Apps.

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Learning Analytics – Business Intelligence in Education and Learning

The umbrella term “business intelligence” has been widely used for many years to refer to the tools, techniques and concepts of analysing raw data and turning it into useful information which can help users and businesses alike gain important insight.

I have been working in the education technology field for about 10 years now and in the past 7 years have been a strong advocate of using business intelligence in education. It is well known that collecting good data on learner progress and performance is invaluable to supporting those learners. By using business intelligence tools and techniques, such as data analytics, education organisations can use the data they have about learners and their organisation to help drive improvement and support individuals who may be struggling.

However, the term “learning analytics” is relatively new to me, which I came across whilst I was researching a project on student retention analytics.

In his Educause article “Penetrating the Fog: Analytics in Learning and Education” George Siemens has a good definition of learning analytics, and how it differs from “organisational BI”:

“Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. Academic analytics, in contrast, is the application of business intelligence in education and emphasizes analytics at institutional, regional, and international levels.”

I like the distinction that is being made here. Learning analytics is focused on the learning process – for example, identifying learners who are struggling. Whilst academic analytics is focused on using institutional level data to understand the institution. The latter might include benchmarking an institutions academic performance against other institutions, or using attendance data to find which classes have poor attendance. In any case, academic analysis is the way I have seen business intelligence used in education so far.

George goes on to include a table which clarifies these distinctions further:

 

Learning Analytics and Knowledge 2012 Course

If you are interested in finding out more about learning analytics, the Society for Learning Analytics Research is offering a free open online course, Learning Analytics and Knowledge 2012.

The course is offered as a “Massive Open Online Course” (MOOC) which I think is a really cool concept. It essentially a distributed course, which links to content which can be found all over the web. It encourages collaboration through the use of social tools such as blogs, twitter and Diigo to share course links and generate your own content based on your understanding of the material. This generates a knowledge network from all the people who have taken part in the course. They’ve posted a video which explains more about what a MOOC is.

What is a MOOC?

I’m really excited about the course as its an opportunity to find out about a field that is rapidly advancing and has the potential to impact education significantly over the next few years. The course has been going a few days, but you can still register. I’ll be posting links to the materials I’ve read and thoughts about learning analytics as I progress on the course.

Useful Resources

I’ll be posting links to learning analytics resources that I find on the course and in my own research on my diigo list.

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Learning from OnStar on Mobile Platforms and Ecosystems

As with many mobile events, there was much discussion at the recent at MEF Americas conference over which devices and platforms firms should develop mobile applications for. It’s a challenge that many firms face – Tribal included – but this was the first event at which these conversations had a greater sense of relevance to me… The normal pre-occupation with ‘how to attract new customers’ (often the focus for conferences attended by start-up businesses) had been replaced with ‘how to address an existing user community through mobile’.

It’s a challenge many established businesses face and changes the dynamics of the decision-making process. When considering a pre-existing group of customers (or students, or employees…) smartphone ownership (both actual ownership and types owned) varies widely and, however important our service is to that group, it will only be one factor amongst many in an individual’s choice of device. We may choose just to offer the extra benefits of an app – for example – to iPhone owners (as many banks did initially), but if we need to address all (or most) of a pre-existing group we will need to factor this variety into our solution accordingly.

This was a theme I discussed with Steve Schwinke from OnStar – a long-established provider of in-car safety and navigation technology for US markets.  See highlights of our conversation in the video below:

During our conversation Steve highlighted 2 key points:

1. The Impact of Ecosystems

The move from ‘platforms’ to ‘ecosystems’ is now clear to see in consumer electronics. Originally MP3 players would just synchronise content from a PC… Now they are replaced by smartphones which include ways to purchase media and stream content to / from other devices. Consumer electronic devices no-longer exist in isolation and device manufacturers, media owners and network providers find themselves co-operating and competing in unexpected ways.

Vehicles are no different. Integration started with an ‘iPod connector’ for audio then added hands-free phone calling and now smartphone apps can utilise in-vehicle displays. OnStar – who built a business on providing in-vehicle hardware for safety and navigation – must now adapt to both operate with others’ apps and to extend OnStar’s features to mobile devices.

It’s quite a challenge, but it’s not unique to in-vehicle technology. At Tribal we’re already thinking about the impact of students using their own connected devices to learn in and away from the classroom, ensuring educators retain the same level of information about progress as if face-to-face.

2. Choosing a technology platform

In my last post I praised SMS’s ubiquity across all mobile phones. However, systems often require more complex interaction than SMS offers so an app (and choosing a platform for that app) is unavoidable. ‘Native’ applications offer speed and flexibility whilst web-based solutions are portable but more limited – it’s often a close call on a pure technical cost / benefit comparison.

However, in OnStar’s case the decision needed to consider wider factors… Without an established developer community existing knowledge, transferable skills, availability of tools and best practice all needed to be factored into creating what is effectively a new platform. For Steve Schwinke this was a simple choice, “HTML5 was a no-brainer”. The skills and technologies already exist and – crucially – as it matures many believe HTML5 and its successors will ultimately replace native code.

It’s a similar decision we’ve made at Tribal Labs recently… Developing cross-platform mobile apps for clients with diverse user-bases across many demographics and geographies, we have to factor in far more than just development cost and performance so are increasingly turning to mobile web (and hybrids). Unlike OnStar we don’t get to control the end user hardware, but in building tools to fit existing eco-systems many of the considerations are the same.

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SMS: The ultimate cross-platform mobile app

There’s a lot to consider when designing a mobile product or service. Users can be anywhere, at any time, on or off a network and – crucially – on many different types of device. In fact, how (or even if) to target users on different platforms has become a hot debate – there are advocates for mobile web, native applications, multi-platform solutions and hybrids of all. In truth none are perfect yet with technologies (and user adoption) moving at such a rate that today’s good decision may not stand-up next year.

Discussions of this sort where much in evidence at the MEF Americas conference I recently attended too. The big players seemed to be hedging their bets – treating all their current efforts as ‘research’ and ready to switch back and forth. This ‘watching and monitoring approach is much the same as the one we’ve adopted with Tribal’s mobile products.

There was, however, another strand to this conversation – SMS. It’s multi-platform, completely ubiquitous and hugely popular (one in three US teens send over 100 SMS messages per day according to Pew) and often overlooked… Whilst person-to-person SMS revenues for mobile network operators are being eroded by social networking and ‘over the top’ services like Apple’s iMessage, several participants were keen to point out that the ‘long tail’ for SMS would actually be ‘application to person’ messaging – especially as location context can now widely be applied.

At the event I spoke to John Orlando from Sixth Sense Media in a short interview summarising a panel session which covered this topic:

In the video John focuses on marketing, but I believe the point is valid far more widely. Monitoring my own usage, in the last month I’ve received SMS alerts:

  • that a ferry I was due to travel on was fully-booked so extra check-in time was required;
  •  that a package was due for delivery that day in a specific hour time-slot (with the option to reply to re-schedule);
  • of large transactions taking place on my bank account (planned ones thankfully!);
  • that an opticians appointment (booked weeks ago) is due; and
  • that the credit on a parking meter was about to expire.

In each case the organisation in question has a mobile ‘presence’ (mobile web or app) offering more complex features but I don’t have it installed / bookmarked – mostly because I’m not a frequent enough user. However, text-based alerting through SMS vastly improved my customer experience, got my repeat business or ensured I stayed a customer. Simple but effective.

Next time we’re discussing a new mobile product for Tribal I’ll be considering if some or all of its user interaction could be done in this way…

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A view of the mobile landscape for Nokia UK

I was recently invited to present to a group of Nokia partners meeting at the National Space Centre in preparation for the launch of the firm’s first Windows Phone – the Lumia 800. Although not strictly within the Labs team’s ‘innovation’ brief, co-operating with firms like Nokia is a great way to stay up-to-date on the rapidly changing mobile industry and to demonstrate our ‘thought leadership’ credentials.

As part of my presentation the Nokia UK team wanted an independent perspective on the mobile industry and  – as broad a topic as it is – I took the opportunity to highlight five points specific to the UK:

  1. The importance of using local market data for decision making.
  2. The dominance of smart-phones.
  3. The delay in 4G services and likely network capacity issues resulting.
  4. The increasing trend to consumerisation and the relevance to businesses.
  5. The  shift in importance from technical platforms to ecosystems.

The UK is not in North America

The UK is not North America

This raised a chuckle around the room, but behind the apparent absurdity of pointing this out is a serious point…

It seems an obvious thing to say, but too many commentators inappropriately offer global data when they really are only addressing one geography. Worse, too often that data ignores the differences between those markets – especially between the UK and the US which are superficially similar.

Here are a few example differences:

  • The US has two mobile standards (CDMA and GSM) versus the UK’s one (GSM).
  • Number porting between mobile operators is long-established in the UK and widely understood / used.
  • Mobile penetration in the UK stands at well over 100% (some estimates place it around  120%) whilst the US is at around 80%.
  • 4G (high speed wireless data) technology has launched in North America.

Key Point: None of this makes the US or the UK better or worse – it’s just important to remember they are different. Decision makers – including Tribal’s partners and customers – need to make their decisions with the right information. Global trends are useful for investors and analysts, but we need to take care to use regional data for our own planning when relating to real end users, their preferences and priorities.

The UK has reached a smartphone ‘tipping point’

 

Just under 50% of the UK mobile-phone owing population now have a ‘smartphone’. The 50% line will be crossed any day and will definitely be true by early 2012 – one firm of analysts reports this figure moving at 2.5% per week, although that is difficult to verify. The fact that 70% of devices sold in UK are smart-phones is certainly not contentious and all of these measures are well ahead of where they had been predicted to be just a few years ago as adoption accelerates.

The T-Mobile G1 was the first Android device to be available in the UK. It launched 3 years ago.

Google’s Android platform is powering half of ‘smart-phones’ sold, followed by RIM’s BlackBerry models with 23% and Apple’s iPhone at 19% according to Kantar Worldpanel ComTech. Windows Phone has yet to make an impact on those figures, but with little marketing of the platform or specific devices prior to November 2011 that’s unsurprising.

Key Point: Android only arrived into the UK as the ‘G1′ with T-Mobile in September 2008 – the rate of change is huge and significantly faster than many firms’ planning and product cycles. If addressing the market now Android should be a key consideration – despite the media attention Apple’s iPhone is still in third place. However, the speed of change suggests this market is still one that can be significantly disrupted in a short space of time so a focus on multi-platform solutions and building skills in transferable technologies will be investments more likely to give long-term returns.

The UK is behind on 4G

4G is a technical standard with many different benefits, but the key elements for UK users will be that it moves all services onto an IP-based network and delivers greater speeds – up to about 100Mbit.

In the UK 4G spectrum auctions will also go some way to un-clogging the imminent ‘capacity crunch’ that is going to affect all mobile network operators as our demand for wireless data soars – offering more a significant capacity improvement, even over existing spectrum.

However, the UK isn’t now scheduled to have its first 4G spectrum auctions until the end of 2012, putting it 4 years behind the earliest adopters and concerning many over the business impact of the delays. Even Lithuania and Uzbekistan are more advanced in their roll-outs!

The upside – limited as it is – is that 4G handset technology is still immature and the normal challenge of smart-phone battery life is even greater with these devices. Some of these problems will have been resolved by the time manufacturers ship devices to UK consumers.

Key Point: Allocating new wireless spectrum is complex technically and commercially, but the extension of OFCOM’s (necessary) consultation process has resulted in delays to the UK 4G spectrum auctions. The impact to mobile users in the UK is that over-the-air services are going to have to exist in an increasingly crowded space. Smartphone-users may see service-levels reduce in the short-term and businesses planning to use wireless data will need to ensure that their services are tolerant of  low-bandwidth / higher latency connectivity and ‘offloading’ to WiFi networks – a measure which many network operators are turning to as a short-term fix.

Tribal customers such as Higher Education establishments may notice these effects sooner than the general population as students turn to campus networks when 3G networks become overloaded by the relatively high density of mobile users in those areas.

Consumerisation

Consumerisation is often described as the flow of ideas, technology and – most importantly to me – user expectations into the workplace. Sometimes it’s characterised as people’s home computers being more powerful to use than their work-supplied ones, but crucially its also about it being more pleasant to use and more capable. This is especially true for smart-phones and tablets where enterprise workers may choose to use their own devices in preference to a work-supplied computer to give them greater flexibility where to work.

Consumerisation is also a huge driver for ‘bring your own device’ (BYOD) strategies – the attempt to make staff more productive (and happier) by letting them select their own IT tools (typically using ones they already own) and actively putting in place ways to secure and manage this to enable it. Users’ enthusiasm for this is often driven by usability – consumer services have to be pleasant to use and can’t require hours of ‘training’ or people won’t use them. No one needs ‘Facebook training’ despite it being a hugely complex social network, media sharing and application platform.

All those entering the workplace for at least the last 5 (and probably 10) years have grown up with high-speed Internet, mobile phones and games consoles and this ubiquity, ease of use and speed of development have become the benchmark by which they assess the tools provided in the workplace.

Key Point: Producers of mobile services should look to the consumer market for standards of user experience, performance and capability. The apparent contradiction of device manufacturers like Blackberry and Microsoft adding both entertainment and enterprise features to their products is driven by a recognition increasingly users move between personal and business use. Accordingly businesses should develop a BYOD strategy (even if it is only to clearly disallow it) and recognise that tacit permission is not a strategy – this requires measures (and typically tools) to enable it in a managed way.

It’s all about Ecosystems

 

The quote above is from Stephen Elop, Nokia CEO at a press conference with Microsoft CEO Steve Balmer. I suppose you could argue that ‘they would say that wouldn’t they’ – it suits the ‘two Steves‘ to talk about ecosystems because they’re both backing a new one (Microsoft’s Windows Phone).

But I think they’re right and it shows elsewhere… Kantar Worldpanel ComTech note that there’s a big gap between the number of people planning to stay with Android for their next handset upgrade and those planning to stay with the same device manufacturer.

Ecosystems consist of far more than just a mobile device’s operating system though… All the large players – Android, Apple, Blackberry, Windows Phone and even Symbian have:

  • 3rd party developer programmes
  • Application stores
  • Music and digital content stores
  • Online services such as email, mapping, media sharing and social networking.
  • Device recovery, management, ‘remote kill’ and tracking features.

This provides a huge barrier to entry by new players – witness Palm and then HP’s failed attempt with WebOS – and has required many established firms (previously building their own OS) to consolidate around other’s ecosystems – SonyEricsson and Motorola adopting Android and Nokia adopting Windows Phone.

Key Point: For consumers, handset manufacturer is becoming less relevant than the ecosystem it belongs to, but this (entirely intentional) ‘lock in’ effect users may also have the undesired consequence of ‘locking out’ potential ‘switchers’ due to the inconvenience. Businesses users and app producers – like Tribal – will also need to adapt as commercial arrangements, access to customer through app stores, support and innovation shifts to the ecosystem provider who’s focus is global. As mobile ecosystems increasingly overlap with other businesses (music, online services, entertainment etc) external – and less predictable – influences too will also have a greater impact.

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Using Data Mining to help with Student Retention in Further and Higher Education

Student retention is an important and high profile issue in Further and Higher Education. Students may drop out of their course for a number of reasons and their withdrawal has implications for both the institution and the student themselves. For the institution the main impacts are reduced funding and the need to maintain expensive processes to identify students at risk of withdrawal.

Whilst being widely used by large private sector companies, my impression is that data mining has not been extensively used in the education sector, with the possible exception of educational research. Although I have an interest in data mining generally, I am really interested in seeing how data mining and predictive analytics can be used in the education sector. One area where I feel it has particular relevance is in student retention. As a result, I have started to investigate whether data mining could be used to help institutions understand student retention, and crucially whether it can help them identify students most at risk of withdrawal.

Student Retention

The Higher Education Academy EvidenceNet report on retention, describes retention in the following way:

Student retention refers to the extent to which learners remain within a higher education institution, and complete a programme of study in a pre-determined time-period.

A wide range of terms is used in both the UK and internationally to describe retention and its opposite. Some tend to emphasise what might be termed the student dimension, e.g. ‘persistence’, ‘withdrawal’ and ‘student success’. By contrast, others focus on the place (e.g. retained within an institution) or the system (e.g. graduation rates) and then the responsibility shifts to either the institution or government.

Measuring Student Retention

In the UK, there are two agreed measures of retention defined by The Higher Education Funding Council for England (HEFCE).

  • Completion rate – the proportion of starters in a year who continue their studies until they obtain their qualification, with no more than one consecutive year out of higher education.
  • Continuation rate – the proportion of an institution’s intake which is enrolled in higher education in the year following their first entry to higher education.

These measures give us ways to measure student retention, and see whether retention rates are improving or getting worse. However, both measures are largely historic. They allow us to see what retention rates were like, but they don’t enable us to be proactive in managing student withdrawal.

What would be really useful then is to be able to understand the factors that influence student retention, and use this understanding to help predict where we are likely to have issues. If we could predict which courses or groups of students are likely to have retention issues, or, perhaps, identify which individual students are most likely to withdraw, this would help us to be much more proactive in managing retention. With restricted public finances squeezing the further and higher education sector, this insight would enable institutions to maximise funding as well as ensure that pastoral support is being given to the right students.

Factors Influencing Student Retention

There has been a large body of research carried out into the issue of student retention and the research generally agrees that poor student retention is linked to low levels of academic and social integration in students. Expanding this in some more detail, the following are examples which have been shown to link to these areas. (Source Higher Education Academy EvidenceNet report – Student retention and success: a synthesis of research)

  • Students are not adequately prepared for higher education, especially academically
  • Students who leave higher education often find that the programme they have enrolled in does not meet their expectations or they are simply on the wrong course.
  • Students do not feel integrated into the social environment of the institution – i.e. the extent to which students feel that they “fit in”
  • From an academic perspective, performance, personal development, academic self-esteem, enjoyment of subjects and identification with one’s role as a student all contribute to a student’s overall sense of integration into the university.

To a lesser extent, the following issues can affect retention:

  • Lack of money and concern about debt both adversely affect retention. Most studies however, confirm that finance is not the main reason why students withdraw.
  • Studies show that personal circumstances (such as mental and physical health problems, whether the student has to care for a relative or dependant) are relevant factors for some students, but they are not as significant as is sometimes assumed.

Whilst it is recognised that students who are highly integrated academically and socially are more likely to persist and complete their degrees, these concepts are abstract and difficult to measure. Many institutions carry out surveys to try and measure different facets of academic and social integration, however these are expensive and time consuming to carry out.

Application of Data Mining to the Student Retention Problem

Data mining uses algorithms with predictive capabilities which can be used to find patterns and correlations in underlying data sets. Data mining is already widely used across the private sector and customer churn analysis is the activity which is most closely related to the problem of student retention, i.e. identification of customers who are at risk of leaving the company. This is important since the cost of retaining a customer is far less than acquiring a new one. There has also been a large amount of research looking at the applicability of using data mining to identify students at risk of withdrawal, and this research shows that data mining can be applied successfully to this problem.

The key benefit of applying data mining to this problem is that often there are multiple complex factors which influence a student’s likelihood to withdraw. Data mining enables us to analyse historical data sets at an institution, identify the combination of factors which are most closely correlated with student withdrawal and build a model which allows us to predict the likelihood of individual student withdrawal in the future. In addition, it allows us to use data which changes over time (the so called “activity” data) to see whether there may be indications of increased risk of withdrawal. Together these give us a really powerful way to understand retention and a proactive way to manage retention issues.

Benefits to HE Institutions and Individual Students

Use of such a mining model would provide a higher education institution with the following benefits:

  • An understanding of the factors which influence student retention (courses, periods of time, students from a certain background etc.)
  • Understand how data which changes over time may influence a student’s risk of withdrawal (for example, their VLE usage)
  • Generate a prediction of the risk associated that an individual student will withdraw from their course
  • Enable them to intervene earlier with high risk students, design and implement appropriate intervention programmes
  • Assess which intervention programmes make a positive difference to student retention
  • Understand the impact of student withdrawal on funding

Providing individual students with an indication of their risk may also have a number of benefits:

  • Allowing students to recognise earlier whether there are patterns in their learning that indicate that they have a problem and help them to be responsible for and be more proactive in resolving the issues.
  • Enabling them to chart their academic engagement and see what impact it has on their success on their course

The proof is in the pudding

My colleagues will tell you I have a particular fondness for cakes and puddings (which is puddingtrue!).

You don’t know that something tastes good until you try it. So, over the next few months, I’ll be working on putting together a prototype to test whether data mining can be used to support student retention. In particular, I’ll be looking to try and build a solution which can be used to aggregate multiple indicators of risk and present them in a way which can help institutions make the right decisions about risk of withdrawal. Also on my radar is how we can build a solution which adds value to institutions over the standard data mining tools, such as SPSS, which are available. Presently, data mining tools require that users have a deep understanding of the different types of data mining algorithm, how to prepare data for mining and how to interpret the mining results. These present significant barriers to institutions looking to use data mining. My research will look at how we can package up flexible data mining processes which are geared towards the education sector, but simplify their adoption and implementation.

I’ll post further updates as I progress.

In the meantime, if you work for an institution in the Further or Higher Education sector or are interested in what we will be doing, I’d love to hear your thoughts – so please, drop me a line!

Sources

Higher Education Academy EvidenceNet Report: Student retention and success: a synthesis of research.

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Using Learning Analytics to improve the student experience

Can Learning Analytics help to improve the student experience?

Yesterday I was asked to speak at the Higher Education Show in London about how higher education can engage with students through improved ICT. This is obviously a broad area, and there are many areas of technology which are changing the way that students are engaging with their university.

I decided to focus on an area which I think has the potential to transform the way that institutions support students: Learning Analytics.

8 out of 10 cats

Ask 100 students what they think student experience is, and you’re likely to get 100 different answers. However, there is likely to be commonality between the answers, and top responses are likely to mention good student union, social opportunities, good academic and personal support and the quality of their course and teaching.

Never before have prospective students been able to find out so much about what past student’s have thought about their experience at university. In the UK, the Key Information Set (KIS) and the recently launched Unistats website have made available data collected from the National Student Survey and the university to allow student’s to compare institutions and courses.

Most students have always wanted to do well in their course – they want to be successful academically and be given the opportunity to be the best that they can be. However, the changes to tuition fees and funding have changed the game. Many students will invest significant amounts of money in their education which they will repay over many years. As a result, the expectations they have of their university are much higher– they have become customers and they expect the university to provide support and services to enable them to be successful academically.

What is Academic Success?

What does it mean to be successful academically? Its about about the university enabling each and every student to reach their potential. The institution helps them to recognise what they could achieve and help them to reach their aspirations. Its about the student getting the support they need to help them achieve success. Its about recognising when things could go wrong, when they have gone wrong and offering the student help and support to resolve problems. So the support, services and systems which help a student be successful academically are at the heart of the student experience.

So, identifying problems early which may mean that a student is not going to be successful is important. It means we need to find a way to measure how likely a student is going to be successful and understand how their likelihood of success is impacted by their learning style, patterns and interactions with the university.

Learning Analytics at its heart is about using data you collect about students and their interactions to help you understand the progress that a student is making and make predictions about where they might end up. By collecting data about the student such as who they are and how they interact with the university and applying analytics to that data we can use it to

  • Identify problems to help steer students toward academic success and ultimately help students reach their potential
  • Provide personalised support, by providing staff and students with the right information much earlier
  • Provide a more personalised experience for the student

Data, Data, everywhere…

I love this quote from Eric Schmidt (Google CEO):

“Every two days we create as much information as we did from the dawn of civilisation up until the end of 2003” (Eric Schmidt – Google CEO)

Users of web sites, social networks and systems generate huge amounts data about their interactions. Analytics is increasingly being used to help provide better services to customers and make predictions to help target limited resources more efficiently. An excellent example of this that I gave in my talk is PredPol, which uses crime data which has always been collected by police forces to make predictions about where, what and when crime may occur.

In the same way, students leave a trail of data from their interactions with university services. Interactions with the library and VLE, attendance at lectures and workshops, social interactions on forums and social media is being held separately in information silos by most universities. If this data is combined it can be used to understand how patterns of engagement, preparation for higher education and social interaction are likely to impact on the student’s likelihood of academic success.

The Student at the centre

By using data and Learning Analytics we can tailor support to the student by identifying issues much earlier before they become entrenched. We can also provide information to students and staff which enable them to understand in what areas the student may be struggling.

Using Learning Analytics is not about replacing existing support staff or advisors. As one speaker in the session noted, students should be partners in their learning with the university. To provide effective support to students, staff need to get to know the student and develop a relationship with them. Learning Analytics is NOT about replacing that relationship with cold hard facts about how often they have logged into the VLE. Its about providing both students and support staff with additional information which can enrich the relationship and prompt support staff to ask the right questions. Its about helping to uncover areas of need which might otherwise not have been apparent. Ultimately, this should help institutions to provide personalised support to students which enable students to be successful academically and reach their full potential.

Here’s the full presentation I gave during the session.

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SoLAR Storm webinar – Learning Analytics at Tribal

I had the pleasure of being asked by SoLAR (Society for Learning Analytics Research) tosolar do a talk for the first “SoLAR Storm” open webinar on the work we are doing with Learning Analytics.

Storm is SoLAR’s distributed research lab which aims to “facilitate discourse around learning analytics and advance the quality of research in the field”. They hold open webinars on Learning Analytics on the 1st Thursday of every month with invited speakers from industry and research. They are a great way for those of us who work in the education technology business to engage with those involved with Learning Analytics from a more academic perspective.

I really enjoyed being part of the first webinar and thanks to all of you who took the time out to join the session. There was lots of discussion and debate and I received some really useful feedback.

One of the most interesting areas we discussed was what do we mean by student “success”? It is a term which gets used an increasing amount, and a number of Learning Analytics tools have been developed around the notion of predicting the likelihood of success. In order to build a model which can be used to predict success, it requires a definition of success which is quantitative. However, by its very nature “success” is a very qualitative term. I think we need a better definition of success as currently they are focused around academic success – whether the student is likely to achieve a pass or the average grade. We need to view success as a sliding scale ranging from course completion, whether the student passes the course, through to whether the student exceeds the expectation which has been set for them. Indeed I think we need to develop a view of success which takes into account other factors such as course satisfaction.

Have a look at the webinar replay if you want to find out more about what we are doing and have a sneak peak at some screenshots for our Learning Analytics prototype. Note you will need Java installed to replay the webinar. Click on the Playback > Play menu to start the session playback.

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