Personalisation is everywhere. From your morning Starbucks to the supermarket loyalty cards in your purse and the app on your mobile which helps you to plan your business travel and recommends hotels based on your travel history. Yet while education has traditionally taken the one-size fits all approach; students follow the same curriculum and are given the same reading lists in order to prepare for examinations, thanks to predictive analytics we can now select what we want to learn, at our own pace and with online learning.
The proposed Teaching Excellence Framework (TEF), sees the government monitoring and assessing the quality of teaching in England’s universities, moving towards the rebalancing of university priorities towards teaching, enhancing the student experience and driving up teaching quality. While many would argue that ‘personalised learning’ is what good teachers do as a matter of course to accommodate the different ways pupils learn, things have moved on from face-to-face classroom interaction.
This January, The Higher Education Commission launched its fourth inquiry report, From Bricks to Clicks – The Potential of Data and Analytics in Higher Education. This highlighted how data analytics has the potential to transform the higher education sector; the opportunities that greater engagement with data and analytics offer; and how HE students stand to benefit. Similarly, research carried out by Jisc found evidence that using learning analytics to take a more data-driven approach to higher education could help assess differing outcomes amongst the student population, boost retention rates and enable the development of adaptive learning.
Around the same time, Facebook founder and CEO Mark Zuckerberg and his wife, paediatrician Priscilla Chan, announced that they’d eventually give 99 percent of their Facebook shares—an estimated $45 billion—to reshape public education with technology with their Personalised Learning Platform. Although not part of Facebook, the underlying principles are the same as how the site’s news feed works: algorithms provide users with content based on an analysis of their past behaviour and demonstrated interests.
How would this work in practice? Each time a student interacts with its university – by going to the library, logging into their virtual learning environment or submitting assessments online – they leave behind a digital footprint. Learning analytics is the process of using this data to improve learning and teaching. Students are directed to learning materials on the basis of their previous interactions with, and understanding of, related content and tasks. The more a student interacts with the course material, the more the software adapts to the individual student’s learning strengths and weaknesses — modifying the teaching method accordingly.
Adaptive learning provides students with access to more individually tailored tutoring and with it, arguably greater success. What sets adaptive learning apart from other learning technologies is that it analyses the learning history of the student using it and provides interactive adjustments based on their understanding and ability to learn. Rather than one size fits all, every individual can enjoy their own tailored experience; a bit like having a private tutor but without a massive cost outlay. The main issue that adaptive learning has highlighted is the need for colleges to access student data often and ensure that the actual learning has taken place. How can this be achieved without increased face-to-face interaction?
Dashboard technology makes the extraction of learner data easy; instead of gauging responses in the classroom or marking papers, a single click of the mouse can provide accurate, real-time monitoring and reporting of key performance indicators, delivering not only organisation-wide visibility of student achievement but the ability to drill down to a very granular level as well. By highlighting any issues as and when they occur, tutors can review teaching areas and methods, look at performance to identify key trends and track learners. This in turn delivers continuous feedback from students, instructors and course designers, helping to improve teaching and learning outcomes and improve cost efficiency.
The use of dashboard is intrinsic to implementing adaptive and data driven learning, as they ensure students and teachers are aware of the progress through multiple courses, highlighting cumulative progress across the entire curriculum, as well as presenting a learning calendar to plan for trainings as per individual schedule, every time they log on to the system. By delivering a streamlined interface that connects students with the functions used most frequently, they can help simplify the learning experience and put student progress, achievement and satisfaction first.
It’s early days, but if personalised learning is seen as the means to adapt and customise a pupil’s learning according to their needs, dashboards can deliver the effective insights on key metrics to help colleges and universities pinpoint exactly where they need to focus their attention.