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Demographics in Higher Education are important. They can be used to identify disparity in academic achievement, support targeted provision for those less privileged and track demographic changes in student populations – aiding institutional planning. But what place do they have in Learning Analytics?

Demographics feed into a number of key strategy issues – like recruitment and resource planning, Higher Education Statistics Agency reporting and access and participation targets. It’s therefore no surprise that in our work supporting sector planning teams, demographic splits are regarded as crucial data points for supporting university decision making.

Learning analytics

With the rise of data-led machine learning techniques, it is now possible to use data to enhance our understanding of learners and their learning environments – resulting in the rapid emergence of ‘Learning Analytics’ within the sector – data based on algorithms that support decision making and offer new areas of insight. These developments mean that we are now able to measure and codify students to support progress, attainment and retention at institutional and individual student levels – often through targeted, real time interactions that are specific to an individual student need.

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Learning Analytics enable universities to use algorithms to support decision making. But what affect does demographic data have on science behind the algorithm?

Demographic data undoubtedly plays a major part in Higher Education. We understand that some student groups do less well than others and strategies to support these groups should be put in place to improve their likelihood of success. However, in adding demographic filters to Learning Analytics projects, institutions run the risk of demotivating their students and missing valuable opportunities to provide assistance to those in need of academic development or wellbeing support.

Demographics in Higher Education are important as analysis of them can:

  1. Identify disparity in academic achievement
  2. Support targeted provision of inclusive and positive action for those less privileged
  3. Track demographic changes in student populations, aiding institutional planning

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Mental health conditions within the student community are rising, yet just under half of those affected still choose not to disclose it to their university.

In September last year, the Institute for Public Policy Research (IPPR) published their findings from a student wellbeing project in the Improving Student Mental Health in the UK’s Universities report. Almost 50% of students with a mental health condition are choosing not to disclose this information to their University and less than one third of Higher Education Institutions have designed an explicit Mental health and wellbeing strategy.

Almost 50% of students with a mental health condition are choosing not to disclose this information to their University and less than one third of Higher Education Institutions have designed an explicit Mental health and wellbeing strategy.

In September last year, the Institute for Public Policy Research (IPPR) published their findings from a student wellbeing project in the Improving Student Mental Health in the UK’s Universities report.

Over the past 10 years, there has been a fivefold increase in the number of students who disclose a mental health condition to their institution (IPPR, 2017).

The research, which was funded by Universities UK and the Mental Health and Wellbeing In Higher Education (MHWBHE) Group, acknowledges that recent increases in mental health and wellbeing levels amongst UK Higher Education students are high in comparison to their wider peer group. The IPPR, a progressive policy think tank, suggests that this due to a combination of academic and financial factors, and social pressures.
Just under half of students who report experiencing a mental health condition still choose not to disclose it to their university.

The report highlighted that in 2015/16, 15,395 first year students in the UK disclosed a mental health condition – a figure five times greater than in 2006/07. It also highlighted that almost 50% of students with a mental health condition are choosing not to disclose this to their university, preventing them from accessing the help they need at a time when they could be at their most vulnerable. Poor mental health and wellbeing can affect students’ academic performance and desire to remain in higher education. In the most severe and tragic circumstances, it can contribute to death by suicide – levels of which have also increased among students in recent years.

In 2015, the number of students who experienced mental health problems and dropped-out of university increased by 210 per cent in comparison to data from 2010.

It is generally accepted that poor mental wellbeing can affect academic performance and the numbers of students leaving Higher Education early, and in the most extreme and tragic circumstances, contributes to death by suicide. Findings extracted from the report support this claim – in 2015, the number of students who experienced mental health problems and dropped-out of university increased by 210 per cent in comparison to data from 2010. In the same time period, the number of student suicides also increased by 79 per cent.
So, what can universities do to meet the challenge? And what more can be done?

The final chapters of the report sets out a number of recommendations for the sector to consider.

The IPPR (2017) found that 71 per cent of UK universities do not have an explicit mental health and wellbeing strategy.

The first of which advises the student mental health and wellbeing issue should become a strategic priority for the sector. Variation exists across the board when it comes to the delivery of a strategic response to wellbeing. No two institutions are alike in their approach to mental health challenges and there is little in terms of guidance around sector best practise on how best to support the student community. The IPPR (2017) found that 71 per cent of UK universities do not have an explicit mental health and wellbeing strategy.

Many of our customers cite organisational culture and processes as the main barriers to a successful engagement project. An approved strategy could seek to address challenges around University-wide buy in for the adoption of a digital software solution.

The second point for consideration is to focus on early intervention, risk management and specialist care referrals through use of a University-wide digital platform. The IPPR claim the use of software to monitor attendance promotes self-determined learners and can be invaluable in the identification of disengaged students – in light of this, the study found that only 29 per cent of UK institutions do not monitor the attendance of all students.

It is widely recognised that institutions embark upon Learning Analytics projects for a variety of reasons; often to enhance the student experience, to support student retention or to improve academic attainment – or sometimes, to achieve a blend of objectives. However, for some, the benefits that engagement analytics can bring to the wellbeing agenda have yet to be considered.

Many of our customers have found that their digital engagement platform is well positioned to spot behavioural changes in individual students. It is this information that enables the university to initiate the pastoral care conversation, and offer advice and guidance to support the student during their time at the institution.

Irrespective of the direction a university chooses to take, mental health and wellbeing is attracting considerable interest within the Higher Education sector. Get in touch to learn more.

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In recent years researchers and educators have begun to explore how we can use new sources of data on students and their learning, together with predictive analytics techniques, to improve many aspects of the educational experience.

Learning Analytics is a hot topic in educational technology, with principals, vice chancellors and government education departments taking an increasing interest in the insight that can be gained from student data. But what exactly is Learning Analytics?

In recent years researchers and educators have begun to explore how we can use new sources of data on students and their learning, together with predictive analytics techniques, to improve many aspects of the educational experience.

Learning Analytics is a hot topic in educational technology, with principals, vice chancellors and government education departments taking an increasing interest in the insight that can be gained from student data. But what exactly is Learning Analytics?

I’ve been immersed in this area for the past few years. In an attempt to understand the many aspects and potential benefits of this fascinating and rapidly evolving field, and to explain them to others, I’ve spent much of the last year writing a book about it.

Learning Analytics uses data about students and their learning to help understand and improve educational processes, and – crucially – to assist the learners themselves. Students are increasingly carrying out their learning online, using devices such as laptops, tablets and smartphones. This leaves a “digital footprint”, which can be automatically analysed, and combined with data about their backgrounds, past academic performance or their career aspirations. Educational content, activities or processes can then be adapted to provide an enhanced or more personalised experience for the learner. This promises to be better for students than the “one size fits all” approach to education, which has been deployed traditionally across much of higher and further education.

I’ve found four main uses of learning analytics in my discussions with experts in the field, and in the more than 600 articles and academic papers which I reviewed for my book:

 

Early alert and student success

All universities, colleges and schools have an interest in ensuring that their learners are learning effectively. Many institutions have problems with high rates of student attrition, and this has been responsible for much of the interest in learning analytics, particularly in the United States.

Early alert systems enable the automated identification of learners at risk of failure or withdrawal, sometimes very early on in their studies. Dashboards and automated alerts sent to teachers and personal tutors allow them to view the students who could most benefit from their input. Interventions at an early stage can result in students being retained who might otherwise have dropped out. Better retention has financial and reputational benefits for institutions. There can also, of course, be a huge impact on the self-esteem and career prospects of those individuals who ultimately succeed in their studies.

Other institutions have much less of a problem with students withdrawing. However, they’re aware that many of their students could perform better: with a clearer idea of how they’re learning, good students could become excellent. Are you on target for the degree classification you want to achieve? Models can be produced from historic data about previous students, and mapped onto your own data to show you what aspects of your engagement you need to improve on.

Giving learners data about their own learning through dashboards and apps, is an increasingly promising area for learning analytics. Building on the popularity of fitness apps, the software can help to motivate students, help them feel less isolated, and make them aware of patterns of activity in their learning which could be enhanced.

 

Course recommendation

A second use for learning analytics is recommending to students what courses or modules they should study next. We’ve become used to recommender systems when we buy items online: products are suggested to us by vendors such as Amazon, based on our previous purchases. Companies are increasingly using data about other customers “like you”, as well, to recommend items you might want to buy.

Similar underlying technologies are increasingly being deployed in many institutions, particularly universities in the United States, where students can be faced with a huge choice of courses. Recommender systems predict the ones where they’re most likely to succeed or which may help meet their career aspirations.

 

Adaptive learning

Also increasingly set to have a big impact across education are adaptive learning systems, which tailor the material presented to students, based on how they interact with it. Thus a student who is struggling with a particular topic can be directed to additional materials automatically, before moving onto the next topic.

Some of the experts I’ve interviewed see the future as being a much more personalised approach to education, where data on learners and their activity is constantly used to update what is presented to and expected of them. This doesn’t need to be a solitary learning experience, where the student simply interacts with an intelligent system. The algorithms can also help them to connect with others who are encountering similar issues, or who are prepared to offer their expertise.

 

Curriculum design

There is a final significant area in which data on learners’ activities is increasingly being used. This is in the design and enhancement of the learning and assessment content and activities that are provided to students. The effectiveness of different aspects of the curriculum can be analysed using data to enable “on the fly” enhancements or more significant alterations which can benefit future cohorts.

For instance, the data might demonstrate that a key piece of learning content is not being accessed by your students. You might then try to discover why not. Was it because the content was too difficult, not easy to find, sequenced at the wrong time, or you didn’t effectively communicate to the students the importance of accessing it?

There are also examples of institutions discovering from the analytics that a particular minority group is underperforming in an aspect of the curriculum. This can then lead the institution to attempt to identify whether this is due to linguistic or cultural issues, or perhaps a lack of prerequisite knowledge. Additional support can then be targeted at the group to bring their performance up to the standards of the rest of the cohort.

 

Conclusion

Anyone thinking about deploying learning analytics at their institution is likely to encounter ethical objections from some of their colleagues, and perhaps some of their students too. There is no doubt that there are many possibilities for the misuse of student data: I’ve identified 86 separate ethical, legal and logistical issues which occur in the growing number of articles and research papers written about learning analytics.

Predictions can of course be wrong, and students are complex individuals, not simply labels such as “at risk” or “not at risk”. Analytics can’t tell us whether they haven’t turned up to lectures because they’re struggling academically, because they find our lectures boring, or because they’re having to look after a sick relative.

I worked with the UK higher and further education communities and the National Union of Students to capture such issues in Jisc’s Code of Practice for Learning Analytics, which can help institutions to develop their analytics capabilities legally and ethically.

One of the key ethical issues that crops up is the “obligation of knowing”. As institutions assemble ever greater quantities of data, is there not a moral requirement on us to use the insight that can be provided from it to help our students? If the analytics suggest there is a strong likelihood that a student is at risk of dropping out, for example, shouldn’t we be trying to find out if we can help that student? Is it justified, as students incur increasing amounts of debt to fund their studies, for us to continue to provide learning content and activities without doing everything we can to assess whether they’re proving effective?

As industry and government increasingly use “big data” to analyse their customers’ and citizens’ behaviours to enhance the effectiveness of their operations, education is in danger of being left behind. Learning Analytics, carried out strategically across an institution, with attention to the ethical issues and the needs of users, promises to enable a much better understanding of a wide range of educational processes. This should lead to decision making based on evidence rather than intuition, with multiple benefits for institutions, and – most importantly – for our students.

 

About the author

Niall Sclater is consultant and director of Sclater Digital, an educational technology consultancy. As Director of Learning and Teaching at the Open University, he led its institutional learning analytics project. More recently, he has recently written a book “Learning Analytics Explained”.

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Some people have asked if Learning Analytics breeches the privacy of the student. The simple answer is no, but let me explain why…

The internet can be a terrible echo chamber, especially on perceived contentious points, so I write this with some trepidation. However, I would like to state from the outset that my opinion has been formed by students and users of Learning Analytics solutions and not formed by those that sit on the outside looking in.

Scaremongering of a Big Brother state, poorly conceived initiatives and fool hardy experiments by corporations has made many in society sceptical about the use of data.

Some people have asked if Learning Analytics breeches the privacy of the student. The simple answer is no, but let me explain why…

Our Learning Analytics tool StREAM only uses the information and data already owned by a university. By using existing data, whether historical or as the data as it is created, the software uses algorithms to map a successful student, and a student who may be at risk.

 

Intelligent information

Using centralised data points to collect behavioural information (attendance, system logs, door access records, library use, VLE/LMS data), StREAM analyses the individual’s digital interactions with the University. Similarly, it then performs the same process with a student who has failed or left the university early.

StREAM then builds intelligence around its findings and calculates the likelihood of success and failure, modifying the students risk status with each interaction. By monitoring these patterns of behaviour and making existing data more readily accessible to the organisation, StREAM identifies these student behaviours that may lead an individual to terminate their time at University much earlier than current methods and so giving institutions the time to react and modify student behaviours.

And the information StREAM exposes is purely objective, not subjective, based on a single persons view, based on a few interactions. Inaccurate judgements about an individual or demographical biased information such as gender, ethnicity, family income etc becomes a thing of the past and since rational and forecast are applied, the learning process is enhanced. Essentially, StREAM uses data to make a positive, not negative, changes.

 

Opinions matter

From the perspective of the student, most people in the Internet age have a wildly different view on the value of data and are prepared to share it more easily as they often disclose information for access to services or resources.

A recent survey posed the following question to a group of learners; “If there was a problem, would you want to know?” A staggering 93% said yes they would.

Many students simply don’t know what a good learner looks like and providing them with a tool that allows them to track their own daily progress is invaluable in making them independent self-determined learners. Through access to the StREAM app, students gain an understanding of their development and a demonstration of negative activity is often the motivator needed to trigger a positive change in their behaviour.

 

Enhanced delivery of education

From an internal personnel point of view, in many cases the technology chimes with tutor intuition and presents them with the opportunity to improve learning outcomes and student engagement. If a learner ceases to participate with the University, the tutor is notified and can then begin to foster a positive conversation, offering support, advice and guidance. Moreover, this objective ‘evidence’ ensures that tutors can be very specific with their guidance on how a student could improve making the value of every tutor/student interaction valuable, whether saving the tutor time or by ensuring the time is spent on the conversation and not the research of ‘how are they doing’.

Ultimately, it’s not about trying to find out how long someone has spent in the Student Union bar; it’s a tool to recognise when a student needs help.

 

Support when it’s needed the most

If negative activity is exposed, it’s up to the University to decide if a support intervention needs to be made. And interventions aren’t designed to penalise, they’re designed to offer encouragement and reassurance -making the experience better, not punitive.

The beauty of StREAM is that it doesn’t just focus on the students who stand out at either end of the success spectrum. Instead, it seeks to help all, including those in the middle, who are often overlooked, achieve the best they can. For that reason, if you take into account the positive value StREAM can deliver, the privacy argument almost becomes obsolete.

 

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