The late 19th and early 20th centuries hailed the dawn of production line manufacturing – and with it the idea of scientific management. Sometimes known as ‘Taylorism’, this theory views organisations like machines, with a series of inputs, processes and outputs that can be fine-tuned to deliver maximum benefit to owners and shareholders.
Times change and so do ideas. Organisations – especially universities and colleges – are rarely viewed in these terms these days. In fact, the broadly accepted reality is that they are social; run, operated and experienced by humans who engage according to a complex mix of information and motivations. Some of this can be quantified by hard data – such as statistics and TEF scores. Others are more subjective, difficult to identify and hard to measure.
A balancing act
The psychologist Jerome Bruner advocated for a balance of empirical evidence, logic and rationality with interpretations of lived experience. If data underpins one side of the scale, then stories are fundamental to the other – and we need both to bring structure and meaning to our messy daily experience.
How might this look in education? As students interact with systems and services during their education, data will be generated. This can be incredibly detailed, as Manchester Metropolitan University’s head of learning and research technologies Mark Stubbs, illustrates with his ‘data map (below).
Source: Mark Stubbs/Manchester Metropolitan University
Collecting, collating and analysing all this data to identify patterns is powerful – and it’s essential in planning university and college services, allocating resources and responding to problems. But according to Bruner, no matter how detailed, this will only ever be half the picture.
The role of technology
Technology is often proposed as a way of measuring or improving students’ engagement with learning. A headband using ‘electroencephalography sensors’ to monitor and record students’ brain activity, for example, claims to identify when someone is “switched on” to learning, enabling a teacher or student to gather actionable evidence to address any shortcomings. Similar technologies have analysed facial expression and body motion capture. But such technology gathers relatively simple data on ‘attention’ in seeking to analyse the far more complex idea of ‘engagement’. Attention can be measured. For how long did a student look at something? How accurately did they answer a question? Engagement, on the other hand, is open to interpretation. If a student doesn’t appear to be logging onto their VLE on a regular basis, are they disengaged? What does engagement even look like – and who decides?
It’s also worth noting that data, machine learning and algorithms aren’t immune from subjectivity and human influence. Humans make decisions about what data to look at, what to ignore, how to present it and how to act on it – and this is based, partly, on a narrative understanding of the world.
Stories can also be valuable in addressing imbalances of power and opportunity. For example, knowing that people from under-represented social and economic backgrounds can struggle to get their needs addressed in large organisations, Dr Liz Austen at Sheffield Hallam University collected stories of students from traditionally hard-to-reach groups. Being listened to, she demonstrates, is different to being surveyed, or surveilled.
In a world of ubiquitous data and evolving theories on how organisations function, let’s not forget the power of storytelling. Data on its own is rarely persuasive – but when supported by narrative, it enables us to build more a complex, layered and informed understanding, influencing change and encouraging new behaviours.
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‘Never forget the power of stories’
Chris Thomson
The late 19th and early 20th centuries hailed the dawn of production line manufacturing – and with it the idea of scientific management. Sometimes known as ‘Taylorism’, this theory views organisations like machines, with a series of inputs, processes and outputs that can be fine-tuned to deliver maximum benefit to owners and shareholders.
Times change and so do ideas. Organisations – especially universities and colleges – are rarely viewed in these terms these days. In fact, the broadly accepted reality is that they are social; run, operated and experienced by humans who engage according to a complex mix of information and motivations. Some of this can be quantified by hard data – such as statistics and TEF scores. Others are more subjective, difficult to identify and hard to measure.
A balancing act
The psychologist Jerome Bruner advocated for a balance of empirical evidence, logic and rationality with interpretations of lived experience. If data underpins one side of the scale, then stories are fundamental to the other – and we need both to bring structure and meaning to our messy daily experience.
How might this look in education? As students interact with systems and services during their education, data will be generated. This can be incredibly detailed, as Manchester Metropolitan University’s head of learning and research technologies Mark Stubbs, illustrates with his ‘data map (below).
Collecting, collating and analysing all this data to identify patterns is powerful – and it’s essential in planning university and college services, allocating resources and responding to problems. But according to Bruner, no matter how detailed, this will only ever be half the picture.
The role of technology
Technology is often proposed as a way of measuring or improving students’ engagement with learning. A headband using ‘electroencephalography sensors’ to monitor and record students’ brain activity, for example, claims to identify when someone is “switched on” to learning, enabling a teacher or student to gather actionable evidence to address any shortcomings. Similar technologies have analysed facial expression and body motion capture. But such technology gathers relatively simple data on ‘attention’ in seeking to analyse the far more complex idea of ‘engagement’. Attention can be measured. For how long did a student look at something? How accurately did they answer a question? Engagement, on the other hand, is open to interpretation. If a student doesn’t appear to be logging onto their VLE on a regular basis, are they disengaged? What does engagement even look like – and who decides?
Listening and learning
I think we can better understand engagement by listening to students’ experiences and reflections – their stories and narratives – and entering into a dialogue with them. Anthropologist Donna Lanclos has done this, exploring the complex behaviours of people engaging with learning via digital tools and platforms – and the University of Gloucestershire’s approach to data analytics and students’ wellbeing shows how modes of sense-making, data and narrative can be brought together to improve the student experience.
It’s also worth noting that data, machine learning and algorithms aren’t immune from subjectivity and human influence. Humans make decisions about what data to look at, what to ignore, how to present it and how to act on it – and this is based, partly, on a narrative understanding of the world.
Stories can also be valuable in addressing imbalances of power and opportunity. For example, knowing that people from under-represented social and economic backgrounds can struggle to get their needs addressed in large organisations, Dr Liz Austen at Sheffield Hallam University collected stories of students from traditionally hard-to-reach groups. Being listened to, she demonstrates, is different to being surveyed, or surveilled.
In a world of ubiquitous data and evolving theories on how organisations function, let’s not forget the power of storytelling. Data on its own is rarely persuasive – but when supported by narrative, it enables us to build more a complex, layered and informed understanding, influencing change and encouraging new behaviours.
You might also like: ‘Teachers need support to find and make better use of digital resources’
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