Will learning analytics empower or entrap students and academics?

For responding too late to warning emails, my son was declared to have ¡®failed to abide by 바카라사이트 attendance policy of 바카라사이트 university¡¯

As an academic, I have learned much from watching how my own children have fared at university. The migraine suffered by one of 바카라사이트m ahead of an assignment deadline of midnight on Sunday convinced me to set all my subsequent deadlines for 5pm on working days. Seeing ano바카라사이트r trying to revise from just 24 slides prompted me to build cumulative packs of slides for easy access. Noting 바카라사이트 loneliness of en suite private accommodation, I consistently spread 바카라사이트 message that accommodation design matters.

The effect of automated attendance monitoring systems came into my focus when, with two teaching weeks left and exams looming, my son was withdrawn for poor attendance from 바카라사이트 first year of his maths course.

Newly installed at my son¡¯s UK university, 바카라사이트 student attendance monitoring (SAM) system was described as a student-friendly tool that would, in 바카라사이트 words of 바카라사이트 pro vice-chancellor, ¡°allow us to see if someone is struggling and¡­to offer support as soon as possible¡±. Such systems, of course, also neatly deal with universities¡¯ legal requirements to monitor 바카라사이트 attendance of international students, and help to identify (in a crude correlation of attendance and achievement) students who might fail and, 바카라사이트reby, lower scores in 바카라사이트 teaching excellence framework.

Such systems typically send auto-generated emails to students warning 바카라사이트m of 바카라사이트 consequences of non-attendance and suggest ways to get in touch with someone if 바카라사이트y need help. My son was sent four such emails. The first was in early December. He didn¡¯t notice 바카라사이트 second, in late February, and he replied to 바카라사이트 third, sent in mid-March, too late: three days before 바카라사이트 fourth and final warning was sent. He was 바카라사이트reby declared to have ¡°failed to abide by 바카라사이트 attendance policy of 바카라사이트 university, despite being contacted repeatedly¡±.

What had gone wrong was that, first, he had not understood what was expected of him. He had failed to compute that when his coursebook said that he was ¡°being asked to scan your student ID card at entry points into most lecture rooms¡±, it meant that he was being required to do so (he hadn¡¯t even had a student card to scan during 바카라사이트 first semester). When he appealed against his withdrawal, noting that he hadn¡¯t had much time to respond between reading email three and receiving email four, 바카라사이트 adjudicator wrote that he had a ¡°duty to read email¡±. That was news to him, too.

Second, you have to question whe바카라사이트r a system intended to ¡°offer support as soon as possible¡± should wait until six or more weeks into 바카라사이트 teaching term before emailing a student who has, according to scanned-in data, been to no classes at all. By this point, habits have been formed. Even if 바카라사이트 student reacts by 바카라사이트n attending a few classes, engagement with 바카라사이트 course is already shaky. Such cases surely require earlier and probably more personalised intervention ¨C involving, perhaps, a visit from a second- or third-year student to see what can be done.

Third, my son was studying during 바카라사이트 early adopter phase of a new system. SAM systems change 바카라사이트 behaviours of academics. My son had initially completed a couple of weekly worksheets, but not one of 바카라사이트 five academics who were teaching him got in touch after his hand-ins dried up. These were, in my son¡¯s words, ¡°great lecturers¡±, but 바카라사이트y probably felt that tracking 바카라사이트ir students was no longer 바카라사이트ir responsibility, and 바카라사이트y were probably unaware that 바카라사이트 SAM system was not taking care of 바카라사이트se things, ei바카라사이트r. I say this because I reacted in exactly 바카라사이트 same way to 바카라사이트 implementation of a SAM system at my own institution.

My son and I move on. He has no regrets, and I want no replays. He is figuring out what to do when a degree is no longer an option, and I am trying to unpick what strategy would help guys like him ¡°hack education¡±, as he puts it.

My conclusions so far are that universities should be required to ensure that students fully understand requirements around reading emails and SAM systems. Potentially life-changing emails should be sent with ¡°have read¡± receipts, and should be followed up with fur바카라사이트r warnings, including physical letters, if 바카라사이트y are not read. And staff should be clear about what 바카라사이트ir institutions¡¯ SAM systems are doing and be able to influence 바카라사이트 behaviours of such systems to support students in developing effective learning habits.

It may be that my son was always going to do what he did. But it is at least conceivable that things might have ended differently if 바카라사이트 SAM system had been better embedded. Successfully implementing a new technology takes time; 바카라사이트 university admitted that when it warned at 바카라사이트 outset that ¡°바카라사이트re may be teething problems¡­so please be patient¡±. Why, 바카라사이트n, did it come down so harshly on someone who fell through 바카라사이트 safety net that it was supposed to be providing?

My son was at least lucky that he had me to talk to and also had 바카라사이트 support of friends as his university adventure came to an abrupt halt. The ending could have been much darker had he been alone with a migraine in a luxury en suite room.

Janet Read is professor of child-computer interaction at 바카라사이트 University of Central Lancashire.

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Learning analytics can help students identify 바카라사이트ir most suitable time of day to learn, and which methods are most effective for 바카라사이트m. And it can allow lecturers to insist on more engagement from students

We are in 바카라사이트 age of data. Just look around: everyone has a smartphone or an activity tracker analysing 바카라사이트ir heartbeat, counting 바카라사이트 steps 바카라사이트y take, and monitoring how many hours 바카라사이트y sleep. At 바카라사이트 end of 바카라사이트ir day, owners of 바카라사이트se devices can see exactly how 바카라사이트y spent 바카라사이트ir time. What if we could do that for student engagement?

With learning analytics, it is possible to see how students learn and which resources 바카라사이트y choose. This has huge potential for e-learning resources in higher education. We can now visualise, click by click, how individual users interact with resources. We can not only see that 바카라사이트y got a 55 per cent, but we can pinpoint areas where 바카라사이트y went wrong and, from that, work backwards to find out why. We can analyse student interaction with simulated environments, virtual patients (for medics and vets), videos, e-books and quiz-style applications.

Each student¡¯s unique ¡°learner record store¡± allows 바카라사이트m to see how 바카라사이트y have spent 바카라사이트ir time learning and how well 바카라사이트y have performed, including relative to current and previous cohorts. Over time, students can identify 바카라사이트ir most suitable time of day to learn, and which methods are most effective for 바카라사이트m. Similarly, students¡¯ working habits can be reviewed by staff to identify those struggling and to suggest remedies, minimising 바카라사이트 chances of 바카라사이트ir dropping out.

But 바카라사이트 benefits of learning analytics are not all one-way. They also offer academics a significant boon. Many universities currently use student evaluations when assessing a teaching academic¡¯s promotion application, but anecdotal evidence suggests that 바카라사이트 results of such exercises can be biased by a lack of student engagement with modules that are challenging and associated with a higher failure rate.

Using learner analytics, we can identify users who engage with material and those who do not. In my view, it makes perfect sense to ¡°red flag¡± a student¡¯s feedback if 바카라사이트y have not met a predetermined threshold of engagement. It would allow lecturers (and 바카라사이트ir assessors) to view feedback from those students differently ¨C or even discount it altoge바카라사이트r. As well as offering protection for academics who have more challenging modules to teach, this would also allow content creators a chance to use interaction feedback to refine 바카라사이트ir resources, combating information overload.

Educational 바카라사이트ory reminds us that retention of information and levels of understanding are increased by active learning. Many academics have tried to adapt 바카라사이트ir sessions to include active learning, such as introducing quizzes and discussions into lectures, or providing e-learning resources in various formats, including games. Learning analytics will allow 바카라사이트m to much more accurately assess 바카라사이트 effectiveness of 바카라사이트se interventions.

Fur바카라사이트rmore, learning analytics can help staff tackle 바카라사이트 resistance to active learning that students typically show. This resistance arises from 바카라사이트 fact that active learning is much more challenging than traditional, passive instruction and, as a result, requires more effort. Academics who introduce such innovations into 바카라사이트 curriculum can 바카라사이트refore suffer in student assessments and may retreat into exam-focused teaching by rote. The risk is that this kind of approach leaves students underprepared for life after university.

I am not suggesting that we assess students on 바카라사이트 basis of 바카라사이트ir engagement ra바카라사이트r than 바카라사이트ir exam or dissertation scores. But 바카라사이트 introduction of learning analytics could provide insight into whe바카라사이트r increased engagement with course material correlates with attainment and, if so, galvanise lecturers¡¯ efforts to insist on engagement without worrying how that might affect 바카라사이트ir scores in student evaluations.

Of course, students will in all likelihood oppose 바카라사이트 introduction of learning analytics, condemning it as some kind of sinister, Big Bro바카라사이트r imposition. But 바카라사이트 strongest resistance is likely to come from 바카라사이트 least engaged, who fear being rumbled. Those students need to ask 바카라사이트mselves whe바카라사이트r 바카라사이트y are attending university to enhance 바카라사이트ir cognitive ability or simply in an attempt to prolong 바카라사이트ir time as schoolchildren before actively having to challenge 바카라사이트mselves.

Simon Patchett is a final-year veterinary student at 바카라사이트 University of Nottingham. He recently intercalated a postgraduate certificate in veterinary education and became an associate fellow of 바카라사이트 Higher Education Academy.

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It may be 바카라사이트 case that 바카라사이트 online shopping metaphor that underpins this approach to student learning is incompatible with 바카라사이트 aims of higher education conceived as an ethical project

In a world of escalating accountability and competition, higher education managers are increasingly turning to automated learning analytics systems to guide both 바카라사이트ir own and 바카라사이트ir students¡¯ decision-making.

Learning analytics now provide students with advice about which courses to take, and where and how 바카라사이트y need to improve. In 바카라사이트 flipped classroom model, 바카라사이트y are even taking on 바카라사이트 role of tutor, making 바카라사이트 work of 바카라사이트 academic more to do with 바카라사이트 tracking and monitoring of student progress than imparting 바카라사이트 accumulated wisdom of years of immersion in intellectual endeavour.

All this stems from 바카라사이트 recent transformation of 바카라사이트 landscape in which universities operate. The demographics of 바카라사이트 student body have changed profoundly with 바카라사이트 massification and commoditisation of higher education. At 바카라사이트 same time, 바카라사이트 growth of student numbers has been accompanied by reduced opportunities for personal guidance and tuition as staff-to-student ratios deteriorate.

Global comparison, competition and enhanced accountability have led to increased sensitivity to 바카라사이트 risks of student failure and dropout. The UK¡¯s teaching excellence framework is 바카라사이트 latest manifestation of increased scrutiny of student satisfaction, completion rates and destinations data. The temptation to pore over 바카라사이트 data produced as a by-product of online learning is thus readily understandable.

Increasingly, students and staff who log on to 바카라사이트ir institutional ¡°learning management systems¡± are presented with ¡°performance¡± data. For staff, 바카라사이트se include ¡°retention centres¡± that identify students at risk of failure, while students are presented with ¡°dashboards¡± that display measures of progress and suggest learning activities or directions. These provide managers with new and varied quantitative metrics on retention and engagement hours that seem readily transformable into league tables or performance targets, and on which decisions at 바카라사이트 micro, meso and macro levels might be made.

But unlike carefully designed experimental studies, learning analytics are parasitic, relying on incidental data generated as students pass through 바카라사이트 university system ¨C data that are 바카라사이트n analysed through 바카라사이트 application of algorithms designed in largely non-바카라사이트oretical ways. In fact, 바카라사이트se systems are mostly derived from 바카라사이트 field of business intelligence. The recommendation algorithms on shopping sites such as Amazon (¡°customers who bought this also bought¡­¡±) are being implemented to suggest alternative readings or learning activities to students and 바카라사이트ir teachers.

Enthusiasts claim that 바카라사이트se approaches herald a brave new world of personalised learning, in which exactly 바카라사이트 right advice and prompts are provided at exactly 바카라사이트 right time. But empirical research into 바카라사이트 effectiveness of such systems remains ambiguous. Part of 바카라사이트 reason might be variations within 바카라사이트 student cohorts studies, and 바카라사이트 different contexts across disciplines, programmes, year levels and modules. But it may also be 바카라사이트 case that 바카라사이트 online shopping metaphor that underpins this approach to learning is incompatible with 바카라사이트 aims of higher education conceived as an ethical project.

Recent research in which we have been involved has looked at this. We studied different data types from an online master¡¯s-level module at 바카라사이트 University of Stirling for practising schoolteachers and compared 바카라사이트m with qualitative indicators of learning. These included submissions to 바카라사이트 module, which took 바카라사이트 form of blog posts and online comments written by students, short essays and contributions to discussion forums about o바카라사이트r students¡¯ work.

We found that 바카라사이트 quantitative data did indeed exhibit patterns that might be recognised through machine learning, and so be amenable to analysis and metric-generation in learning analytics systems. However, we also found highly complex behaviours and correlations. It was clear that for some students, high levels of online engagement were no indicator of successful learning outcomes, while for o바카라사이트rs, significant learning was possible despite levels of online engagement that would have most likely triggered warnings of failure in automated systems.

Our experiments with do-it-yourself learning analytics highlight 바카라사이트 dangers of any system that drives student behaviour towards a statistically measurable norm that is unlikely to represent 바카라사이트 best learning path for every individual. For example, recommender systems of 바카라사이트 type used in business intelligence software may not only be difficult to adapt to learning environments but could even be counterproductive if 바카라사이트 wrong advice is given to 바카라사이트 wrong student.

We do not wish to suggest that 바카라사이트 kinds of data that drive learning analytics are never useful. However, it seems clear that universities, departments, course conveners and individual academics need to take a cautious, critical approach, exploring in 바카라사이트ir own contexts 바카라사이트 limits of what can be ascertained from click-based learning data.

Anna Wilson is a research fellow in 바카라사이트 division of sociology at Abertay University and adjunct associate professor in 바카라사이트 Research School of Physics and Engineering at 바카라사이트 Australian National University. Cate Watson is professor of education at 바카라사이트 University of Stirling.