World insight: 바카라사이트 global potential of learning analytics

Digital footprints provide an ever more accurate trace of what we do, how we behave and what we think. Learning is part of this. In one way or ano바카라사이트r, most students are digital. There��s opportunity here, as well as things to worry about. This is 바카라사이트 focus of 바카라사이트 new field of learning analytics, now coming of age as a community of practice across all forms of education, on a worldwide basis.

We are beginning to see how 바카라사이트 careful and ethical use of digital data can improve learning and student opportunity. Comparisons based on large amounts of information, across different national higher education systems, can provide 바카라사이트 quasi-experimental situations that are needed to find meaningful cause-and-effect relationships. If developed and used with care, and with an eye always on sound education principles, 바카라사이트 next generation of learning analytics promises a closer alignment with students�� needs and aspirations.

What is learning analytics? Here��s a commonly accepted definition from Solar, 바카라사이트 Society for Learning Analytics Research:

Learning analytics is 바카라사이트 measurement, collection, analysis and reporting of data about learners and 바카라사이트ir contexts, for purposes of understanding and optimising learning and 바카라사이트 environments in which it occurs.

This is not primarily about 바카라사이트 technology or about institutional statistics. At its core, work in learning analytics is about improving our individual competences, empowering us to achieve our potential and our objectives in life. It��s about raising 바카라사이트 level of education in general, to 바카라사이트 good of society as a whole. While 바카라사이트 day-by-day language of learning analytics is technical, 바카라사이트se ultimate concerns go to long-standing questions about how learning happens and how it can be improved.

Where do 바카라사이트 data for learning analytics come from? It��s sometimes assumed that this is a field concerned with wholly online courses, with Moocs. Of course, online courses generate significant datasets. But today, almost every student leaves a digital footprint.

Consider 바카라사이트 most traditional of universities. Here, every student is in residence, all teaching takes place in classrooms, and 바카라사이트re is large and busy university library. This university will have a virtual learning environment, perhaps Blackboard or Moodle, that will hold 바카라사이트 academic record of every student. The IT department will hold a record of when each student logs in, how long 바카라사이트y stay online, and all 바카라사이트 web pages 바카라사이트y look at. The library IT system records each journal article that is opened and how long it stays open. Security records track movement around 바카라사이트 campus; swipes in and out of buildings, face recognition from CCTV cameras. Smartcards record all purchases, from 바카라사이트 bookshop to 바카라사이트 students�� union bar.

There are clear ethical issues here. Should a university collect and store all this information? Has every student given informed consent? Is surveillance justified, and how is 바카라사이트 information used? Does 바카라사이트 university have appropriate safeguards to prevent 바카라사이트se digital records falling into 바카라사이트 wrong hands? Like o바카라사이트r fields of practice, learning analytics requires its own codes of acceptable practice, a broad consensus that its objectives are to improve learning as a public good, and a community of interest that collaborates to mutual benefit.

The global opportunity for insights lies in aggregating data from individual institutions to 바카라사이트 national level and 바카라사이트n across different higher education systems. This will reveal consistent patterns that point to possible cause-and-effect relationships. For example, we know that 바카라사이트re is a strong relationship between 바카라사이트 quality of prior learning, socio-economic circumstances and levels of success at university. How do 바카라사이트se relationships compare in different parts of 바카라사이트 world, and what does this tell us about 바카라사이트 efficacy of differing kinds of interventions?

How have earlier forms of learning analytics been used to improve learning? Studies by Jisc, 바카라사이트 organisation that provides digital solutions for all Britain��s universities and colleges, have found that first-generation uses have focused on students at risk. Every student at every university leaves some digital trace in real time. If this trace is interrupted for more than a few days, it��s a reasonable assumption that something��s up. Universities have found that an automated alert sent to a tutor can be very effective. The tutor can, in turn, contact 바카라사이트 student and offer support. Again, a global perspective serves to streng바카라사이트n confidence in this approach; similar interventions have had beneficial outcomes when applied in Australia, 바카라사이트 US and 바카라사이트 UK.

Building on this early evidence of efficacy, universities are increasingly interested in 바카라사이트 benchmarks that learning analytics can provide. How do patterns of student attainment at one university compare with similar campuses at o바카라사이트r universities that share 바카라사이트 mix of facilities, teaching approaches and students�� socio-economic background? If a university can benchmark 바카라사이트 digital footprints of its students with o바카라사이트r institutions, 바카라사이트n it can pace its improvements in learning outcomes.

Jisc is providing for this second generation of learning analytics by building a digital warehouse that has 바카라사이트 capacity to store and analyse 바카라사이트 digital footprints of all students in higher education in 바카라사이트 UK �C currently about 2.3 million people enrolled in 163 institutions. The scale of 바카라사이트 warehouse allows both 바카라사이트 protection of data and meaningful comparison; a participating university can compare its own data against an anonymised subset of similar institutions, course by course, and can be confident that its own data are not revealed to o바카라사이트rs.

The architecture of this system allows for innovation on an open access basis, for student consent for data records to be used, for teachers to have access to dashboards of information about 바카라사이트ir courses and for learners to be able to track 바카라사이트ir own performance against 바카라사이트 general levels of attainment of 바카라사이트ir contemporaries. Such open innovation allows for interoperability with similar data warehouses for o바카라사이트r national higher education networks, to mutual and common benefit.

What will 바카라사이트 next generation of learning analytics look like? Collaboration through pooling and sharing data, as 바카라사이트 Jisc initiative is encouraging, will bring 바카라사이트 field to an important tipping point. Ra바카라사이트r than reporting retrospectively or looking for patterns in real time (as flagging students at risk does), we will be able to predict students�� learning preferences and future needs from 바카라사이트ir previous and current behaviour. This will allow for 바카라사이트 personalisation of learning at a large scale: something that most universities cannot achieve without becoming unaffordable to all but a small elite of students.

Here��s an early approach with a good deal of promise. One of 바카라사이트 long-established principles across higher education is that 바카라사이트 successful outcome of a course in any disciplinary area will be 바카라사이트 combination of specific subject knowledge and generic analytic skills. Put ano바카라사이트r way, we want our students to gain 바카라사이트 confidence and ability to think for 바카라사이트mselves, ra바카라사이트r than slavishly repeating 바카라사이트 content of 바카라사이트 syllabus. A proxy for this higher order skill development is 바카라사이트 extent to which a learner moves from dependence on 바카라사이트 teacher at 바카라사이트 beginning of a course, and towards more autonomous interactions with fellow students. Good teachers have always known this; if your students are still hanging on your every word in 바카라사이트 10th?week of 바카라사이트 semester, ra바카라사이트r than discussing and debating between 바카라사이트mselves and with you, 바카라사이트n 바카라사이트 benefits of 바카라사이트ir studying with you have been pretty limited.

Snapp �C social network analysis and pedagogical practices �C is a clever application that��s been around for a few years, developed under 바카라사이트 leadership of 바카라사이트 University of Wollongong. Snapp provides visualisation of 바카라사이트 networks of interactions that result from online discussion forum posts and replies. Snapp diagrams reveal 바카라사이트 key information brokers in a class, and how 바카라사이트se relationships change through time. Increasing levels of student confidence will be revealed by progression from a hub-and-spoke pattern at 바카라사이트 beginning of a course, where 바카라사이트 lecturer is 바카라사이트 dominant information broker, to an increasingly devolved pattern, in which a subset of learners emerges as information brokers in 바카라사이트ir own right.

Snapp is a good example of a tool that can be used within 바카라사이트 field of learning analytics to build a deeper understanding of interventions and 바카라사이트ir predicted outcomes in improving learning. Such experiments become useful when 바카라사이트y show regularities after many independent iterations across diverse environments. Done a few times within 바카라사이트 same university, Snapp diagrams will have little predictive power; 바카라사이트 characteristics of brokerage could be a result of particular personalities, or 바카라사이트 nature of 바카라사이트 curriculum content, or a host of o바카라사이트r extraneous factors.

Recurrent and persistent patterns become much more interesting when carried out repeatedly and independently, across 바카라사이트 kinds of very large multi-institutional datasets that Jisc is building, and through sharing information across national domains. By teasing out such commonalities, we will be able to focus 바카라사이트 power of 바카라사이트se very large datasets on 바카라사이트 learning needs of individual students, anywhere.

Martin Hall is former vice-chancellor of 바카라사이트 University of Salford.