By Tom Lowe
Education in a university or college does not take place solely on campus. Even prior to the technological innovations we have today, students were always learning off campus, learning through conversations with their peers, visiting museums, libraries, archives, websites, and of course through reading. Adding to this, in the modern-day university, we can make learning opportunities even more accessible off-campus, through offering access to thousands of online resources (books and journals etc.), curating bespoke learning resources to be engaged with a-synchronously, and, particularly increased since the global pandemic, studying an entire course by distance. When engagement moves into the digital realm, students have so many options to support their learning, and the reliance on in-person engagement decreases. This creates positive opportunities for students, such as learning on an individual timeframe at their pace, being able to hold employment alongside their studies, and to support family in caring responsibilities. Contemporary higher education is has become increasingly flexible and therefore, as my discussions in this series have already highlighted, students no longer feel the need to, nor have to, attend in-person as much.
However, we still care about student engagement and attendance feels the best, or at least the most familiar, way to measure this. Perhaps we “cannot see the wood for the trees” in this respect. With so many options available, as highlighted above, the old assumption of student in-person attendance alone does not give us a full picture of their engagement. A student may not be attending classes but may still be passing as they are engaging with a vast array of online resources. While true, there are also students who are not attending and are also not engaging in online resources, and who may be in need of support. It is difficult to know which non-attendee a student may be. Responding to this in the digital age, learning analytics technologies have become in growing demand, as the different online pathways of engagement data offer opportunities to un-mask the hidden students’ engagements in the flexible university and understand more holistically who may be the students that are not engaging in any course-related activities.
Student or learning analytics systems hold their origins in commercial sectors, often relating to online customer purchases influencing data for marketing purposes, or resource management insights to understand demand for supply. Concerning attendance in class, most readers will have seen the transition in the 2010s, from hand-written registers to card scanning, individual code submissions or as Simac use, the Chippo app, or QR/Challenge codes in class. These systems are complex, with high amount of service and development needed on the back end to link upwards of 500 classes a day in different rooms to often upward of 10,000 different student records. The attractions if the technology can function can be great, where from a management level, whole university populations and provisions of academic study can be assessed, for student support and enhancement purposes. However, those of us working in universities and colleges know too well the technology limitations, where we have seen one student scanning multiple cards, someone signing in their friend in, or at worse, the arm appearing through the crack in the door to scan and quickly disappear again. Like all systems in society, people will always get to know, then try and ‘hack the system’, but there is still great merit for learning analytics in contemporary education.
Traditionally aged students in higher education are transitioning from often mandatory education, where legally, students have to be enrolled on and attend some form of study or apprenticeship. In a university, other than those courses with professional body requirements, students are able to choose to engage as little or as much as they wish. Higher education is purely voluntary – an aspect that is often forgotten in the discussion on attendance. We become concerned, often with good reason with mental health and outcomes in mind, when students are not engaging in the forms of in-person class and support services. Learning analytics systems offer this opportunity to assess the unseen engagements for wider sources of data, such as online reading, watching catch up videos and engagement the library. If a students’ engagement drops both physically and digitally, we can quickly reach out with support, often far timelier then manually drawing the spreadsheets together. Students may have fallen into poor mental health, financial troubles, or even habits of not engaging, where reaching out with support can offer great value to both the student and the university.
There are some considerations though. First of all, how we reach out to offer support – how do we phrase it, how frequently, through what means - is still a space for development for many universities. Further, questions persist around who should reach out (should it be an academic or student services)? At what level of engagement do the reach outs begin (where does engagement end and disengagement begin)? And finally, what data sets to include in the analysis? Attendance is an obvious data set to include as we have explored previously in this series, but there are ample other options, such as engagement in the Virtual Learning Environment (access, clicks, time spent on site), Library Access, Door Access on Campus, meeting with a Personal Tutor, and engagement in Professional Services. All of these are of course promising indicators, but across different disciplines and modes of study, there are context-specific student experiences and expectations of engagement and, therefore, university-wide rules based on a number of engagement hours (or percentages) can lead to reaching out and contacting a student at the wrong time – or, in the reverse, reaching out too late. Either way, the cause of learning analytics outweighs to potential negative cost of not doing anything to support students when the data is readily available.
Finally, as highlighted in my first blog, it is important to remember that behavioural engagement (whether digital or physical) is only part of the student engagement picture. We cannot measure through these systems the cognitive or emotional engagement of students – perhaps two thirds of the engagement pictures – let alone two students chatting about the course in a bar/café/online forum, or a student reading a physical book (not checked out of a university library). I believe learning analytics should be the start of the conversation, rather than the conclusion or judgement of a students’ engagement. We should continually revisit what we are analysing and even engage students in discussions of how the ‘reach outs’ and calculations for engagement decisions are reached. While student engagement remains hyper flexible, the place and potential of such data sources cannot be ignored, but they do require plenty of critical reflection.
Tom Lowe has researched and innovated in student engagement across diverse settings for over ten years, in areas such as student voice, retention, employability and student-staff partnership. Tom works at the University of Westminster as Assistant Head of School (Student Experience) in Finance and Accounting where he leads on student experience, outcomes and belonging. Tom is also the Chair of RAISE, a network for all stakeholders in higher education for researching, innovating and sharing best practice in student engagement. Prior to Westminster, Tom was a Senior Lecturer in Higher Education at the University of Portsmouth, and previously held leadership positions for engagement and employability at the University of Winchester. Tom has published two books on student engagement with Routledge; ‘A Handbook for Student Engagement in Higher Education: Theory into Practice’ in 2020 and ‘Advancing Student Engagement in Higher Education: Reflection, Critique and Challenge’ in 2023, and has supported over 40 institutions in consultancy and advisory roles internationally.
Recommended further readings on learner analytics systems:
Beig, Z., 2023. Learning analytics in higher education: The ethics, the future, the students. In: Lowe, T. (ed) Advancing Student Engagement in Higher Education: Reflection, Critique and Challenge. Abingdon: Routledge.
Foster, E. and Siddle, R., 2020. The effectiveness of learning analytics for identifying at-risk students in higher education. Assessment and Evaluation in Higher Education, 45(6), pp.842–854.
Jones, K.M., Asher, A., Goben, A., Perry, M.R., Salo, D., Briney, K.A. and Robertshaw, M.B., 2020. “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology, 71(9), pp. 1044–1059.
