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So, did universities grasp the opportunity to mitigate student drop-outs by increased adoption of learning analytics?
It’s not yet clear if institution’s responses to the pandemic included increased use of learning analytics to monitor their students’ progress given the increase in remote learning. Those that did are much more likely to identify those at risk, and consequently to make positive interventions and potentially reduce drop-outs.
The UK’s Higher Education Statistics Agency
reports a total of 6.7% of full-time first year degree students dropping out in the academic year 2018/19. In Australia and the US drop-out rates are typically higher, for 2018, Australia Higher Education Department reported 18% and the US National Center for Education Statistics reporting 24% for 2018.
Consequential impacts on the students and their families can be significant and these must be our first concern, but the financial impacts on the universities are substantial. In the case of the UK, if a student drops out early in their first year, at worst, the university loses three years of around £9,000 fee revenue. With almost 327,000 first year students, the national drop-out level of 6.7% translates into an almost £200M hit to total university budgets.
We won’t know the aggregate national statistics for university student drop-out rates for academic years 2019/20 or perhaps more interestingly those for 2020/21 for months, but it may be that administrations should be preparing for the worst if they haven’t yet taken learning analytics initiatives.
Why? Because a glance at the established research shows that historically the very factors having the most significant effects on drop-outs are those that are most likely to be the ones exacerbated by Covid. Atif et al.’s 2015 paper “Student Preferences and Attitudes to the use of Early Alerts”
described research showing that in the schools of Computing, Physics and Mathematics at Macquarie University, Sydney, Australia, the highest three factors affecting student performance were Emotional Health (48%) and Family Responsibility / Commitments (28%), followed by Financial Issues (21%). These would appear to be three of the highest components of people’s lives likely to be directly affected by the pandemic.
This research, supported by similar research focussing upon Open University student performance (Castles, 2004, “Persistence and the adult learner: Factors affecting persistence in Open University students”
) indicates that academic issues such as subject knowledge or difficulties of understanding topics are much less important factors in poor performance and hence drop-outs. Castles’ research corroborates Atif’s 2015 survey of the factors affecting student performance, albeit with a different categorisation of factors.
Worryingly, the OU’s research is of course directly relevant to on-line/remote learning – and it is on-line/remote learning that has very significantly increased by Covid restrictions on universities.
The Open University has world class learning analytics, identification of students at risk and positive intervention systems and processes, so they have the tried and tested capabilities to take care of their students - as do the other institutions who already had learning analytics in place before the pandemic.
It could be that the a proportion of the increase in on-line/remote learning driven by the pandemic sticks when it’s over and institutions go back to normal, whatever “normal” is. The trend had already been for increasing on-line availability of teaching material, providing flexibility for students to match the demands of their lives, and with the consequent benefits to academic staff and institutional infrastructure. This shift makes the implementation of learning analytics a necessity, not a nice to have.
Those institutions that post the effects of the pandemic have recognised the value of implementing learning analytics systems deserve the benefits that the implementations will give their students and themselves. For those yet to take the first steps, now is the time. Remember – “The best time to plant a tree was 20 years ago. The second best time is now”. I did try to point the way.
(If you’d like to learn more, check out The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes
, if you’d like to learn a lot more, and you are somewhere where falling asleep would not be hazardous to your health, then The Application of Data Mining Techniques to Learning Analytics and Its Implications for Interventions with Small Class Sizes).
By Dr Ed Wakelam
Keywords: Analytics, Big Data, COVID19