An interesting proposition to contemplate – what would you do differently if you could turn back the clocks?
Would we all choose the winning lottery numbers so our time was our own? Or take a completely different fork in the road?
There are many reflections when you start playing with the time continuum ….
Andrew's story can be likened to time travel – the question that plagued him was...
“Could I have made a difference to my students if I had known this information at the start of the presentation?”
How many of us understand how Predictive Learning Analytics (PLA) are used in higher education?
At The Open University our in house designed PLA is the Early Alerts Indicators Dashboard (EAID). Associate Lecturers (ALs) are now able to easily access current student information in one place to inform their support of their students. Dashboards such as EAID have been shown to increase student retention. (Herodotou et al. 2019).
Andrew, an AL on an FBL module I manage, came to me worried about student retention on his module. By the February - 5 months into the presentation - he had lost 8 students out of his 20 in the group. He had never experienced such poor retention during his many years of being an AL. He had done the same as he had always done in previous presentations, such as generic emails to his group. At that time, I was new in post and decided to do some research to help and support him.
My investigation introduced me to The OUs Predictive Learning Analytics. In Andrew’s case the long-term predications showed that over 50% of his group had low probabilities of completing or passing the module. Armed with this new information I met with Andrew, and we discussed strategies to reach out to his students. Drawing on the work of Tomlinson’s (2014) who advocates differentiated learning, which values tailoring learning methods to individual student needs, Andrew identified how he would now change the timing, quantity, and content of his interventions. For those remaining students he was able to target individuals with timely, tailored emails relevant to their progress. The EAID also provided him with an indication as to when he should be alerting Student Support Team (SST), a team of study advisers who are the experts in supporting individual students with the non-academic life events.
This experience led me to undertake scholarship into the adoption and use of PLA.
My findings included each AL has a different experience of using PLA (Herodotou C. Maguire C. et al 2021). A common factor for our proactive ALs is that they want to know their students, respond to them as and when needed and are happy to spend time checking out each student’s progress. For them the EAID will save time. For many it provides a short cut to understanding student activity our dashboard being linked to the VLE, captures attendance at tutorials among other key indicators, provides that weekly visual update for the whole tutorial group in one place.
With regular use of the dashboard ALs are effectively targeting those students who may be starting to struggle with their studies. When they start to disengage is captured but the why is not. This is where the human touch become so important. ALs can promptly reach out to those students identified, and help the student get back on track. By sending an email to students this alone may be the contact needed to get the student re-engaged and submitting. Or it may be the start of further tailored support.
A recent study has demonstrated the intersection between some of the students identified by the dashboard and our more disadvantaged students, so by using the dashboard ALs in the study proactively helped to close the awarding gap. (Hlosta M. et al 2021)
Over the last five years the Dashboard has been developed with feedback from our AL who have found it a useful tool in their active support of students. The research indicates that by regular ALs visits (the timelier the better) to the dashboard students can gain the benefits of having support at the time they most need it. Each small action we take today will result in a more positive outcome for our students in the future.
Once you know you can access timely information at your fingertips, what would you do differently?
Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development, 67(5), 1273-1306.
Herodotou, C., Maguire, C., McDowell, N., Hlosta, M., & Boroowa, A. (2021). The engagement of university teachers with predictive learning analytics. Computers & Education, 173, 104285.
Hlosta, M., Herodotou, C., Fernandez, M. and Bayer, V. (2021). Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Lecture Notes in Artificial Intelligence, Springer. http://oro.open.ac.uk/76042/ Tomlinson, C.A. (2014) The differentiated classroom: responding to the needs of all learners. Second edition.
Claire is currently an Assistant Head of Student Experience and Student Experience Manager (SEM) in the Faculty of Business and Law.
She was previously employed as a Career Coach for the MBA programme at Cranfield University and previously as a National Lead for a range of talent management programmes in the NHS.
Claire’s career has always been focused on the development of people and organisations.
Claire enjoys her scholarship work which includes using Predictive Learning Analytics (PLA) to enable Associate Lecturers to effectively support students.
Other scholarship is focused on how Co- tutoring can enhance colleagues’ skills and competence which is congruent with her passion for developing others.