We are becoming accustomed to big data and analytics. Colleges and
universities around the world are using big data analytics to help
market their programs and services. Others are going further, using
analytic models to predict when students are going to be in difficulty
or are likely to drop out. Analytic and statistical models have been
developed to predict students’ interest levels, travel and mobility
ranges, likelihood for successfully passing courses, financial need
levels, and likelihood of persisting to graduation. Big data has become
big business.
Its growing use will continue in higher education as new models emerge, based on much bigger data sets.
Speaking of these developments, Michael King, IBM’s Vice President for Global Education Industry, said recently:
"The right set of information is everything. I think
that, looking at lifelong learning and using data to help provide
clearer pathways to students for a multi-institutional education plan,
using tools like Watson, is an important goal. We want to show how to
put more tools in their hands for broad data. We can give prescriptive
data to save time intervening for individual students."
Big data has limitations
Big data is here to say. But it has limitations. While it may help
provide insights as to who may be struggling with a component of a
course or to complete an assignment, it does not suggest ways these
students can best be helped. This is why we need small data.
The idea of small data is not new – it is how professions gain
insights into practices that make a difference to outcomes. What is
happening now is that there is an emerging discipline for small data
capture – using observation, insights and clues to identify
opportunities to change or improve practice. Learning is about
relationships – the relationship between a student and their instructor,
between a student and their peers, and the student and the body of
knowledge or practice. Small data collection focuses on the ways in
which these relationships develop, fail or succeed and seeks to
understand patterns of activity within these relationships.
Small data “connects people with timely, meaningful insights
organized and packaged – often visually – to be accessible,
understandable, and actionable for everyday tasks”. This is how Allan
Bonde of Actuate Corporation, which uses Small Data, sees it. He and his
colleagues observe how people do things, store things, analyze things
and share things, and use their observations to improve products and
services. For example, many aspects of the physical design of tools we
use each day are based not on big data analytics, but on small data. The
gardening tool where the handle is shaped to reduce stress on the wrist
is based on hours of observation of how gardeners use tools. The
specially weighted knives, forks and spoons for those with Parkinson’s
disease came from a similar set of detailed observations. Just as a
forensic scientist investigates a crime scene looking for small clues,
so those of us interested in improving learning outcomes, student
success or course design need to do the same.
Small Data is Making a Big Difference
Here are five examples where small data have made a big difference:
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How changing language can change behaviour
A college used to use the language of “students at risk” and
encouraged faculty to identify students who were at risk. Few faculty
did so. But when they changed this ask to “identify students with
promise whose promise is “unfilled”, not only did they get a much larger
response, they also received a great many suggestions from faculty
about what an appropriate response to the student’s need might be.
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How changing context can change outcomes
When an instructor, either in an online course or in a classroom,
gives a context to some task or challenge, then students see the task or
challenge in that context. Changing the context – for example, rather
than being a health care context it becomes the context for a new video
game – can change how the learner approaches this task. A legal
education colleague did this – asked his class to create the rules for a
new battle game between conflicting parties and then showed them how
their rules for the battle were like the rules for a legal process.
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How observing peer groups can produce better group activities
A faculty member sent three groups different versions of the same
online task – each group had a different component of the same problem
and the “solution” required all three groups to realize they needed to
share information between each other if any one of them was to be
successful in solving the problem. Though she did not intend to formally
teach problem-solving skills, she used the experience of the group work
to do so with the result that the next time she undertook group
activities all her groups performed better, faster, and smarter.
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How watching an individual student try to master a complex problem can help identify problem solving skills which can be share
The faculty member sat with a student who was struggling with some
basic chemistry. Rather than explain the chemistry, the faculty member
explored how the student was thinking about the work and what kind of
processes they were using to “solve” the problem. This generated several
insights into why this and other students were struggling with thinking
like a chemist and led to significant changes in the design and
delivery of these courses.
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How close observation leads to innovation
In designing a new approach to the design of a graduate degree, a
small team spent time discussing the hopes and ambitions or potential of
students and realized two things: (a) the students were looking for
greater flexibility and choice than existed in any other program
available to them – they were looking to “mix and match” their own
degree; and (b) they wanted the opportunity to be flexible in how they
studied (some in-class, some online, some through intense but short
courses, some through projects). This led to a unique design for a
graduate Masters degree which, now that the team can observe student
behaviours within it, changes frequently to meet the faculty’s emerging
understanding of what matters most to students.
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How close review of instructor online participation can change instructor behaviour
In one course, each instructor is assigned to a mentor. The mentor
reviews not just the frequency of interactions with groups of students
and individual students, but also the quality of these interactions.
Careful observation can frequently lead to improvements in instructor
interaction – more frequent, more varied, more humorous – so that the
quality of relationship between instructor and students is both more
authentic and more valuable.
These examples did not choose between small and big data. Where
appropriate, big data helped confirm or suggest new avenues for small
data exploration. But in these examples, it was the small data which led
to the identification of patterns that then led to change.
Why Small Data?
Small data is what faculty and program level leaders use to make most
decisions in post-secondary institutions. While some see the examples
given above as anecdotes, repeated stories that combine to suggest
patterns of distinct behaviours or opportunities lead to insight which
in turn leads to change.
For example, Lego observed that one of the most frustrating things
for parents is stepping on Lego blocks in bare feet. This led them to
develop a slipper (link is external)
which is “Lego proof”. More importantly, Lego’s turnaround was
triggered not by big data but by ethnography – the chance observation in
the home of a German 11-year-old transformed their thinking about their
market and their products (for this story, see
here (link is external)).
Observations can change some organizations’ thinking in the same way a
small clue at a murder scene can change how the investigators understand
the crime.
What is so interesting about small data?
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Big data is hard
Doing it at scale and waiting for trickle down benefits can take time.
Many in the college or university do not understand the analytic models
and the complexities of the data and they are also suspicious of
correlational data being used to suggest causation – a common mistake.
Small data, in contrast, is a story or understanding which can be shared
quickly and effectively and is easily related to
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Small data is all around us
Social channels are rich with small data that is ready to be collected
to inform learning design and educational decisions. At a personal
level, we are constantly creating this small data each time we
teach a class, mark assignments, log in, browse, post etc. Understanding
patterns from these observations can trigger change.
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Small data is at the center of the new understanding of student behaviour
Small data is the key to building rich profiles of our students. Not
just who they are, but how they think, work, share, engage and work with
others. Understanding these behaviours and ways of thinking should lead
to significant improvements in pedagogy and the design of learning,
especially online learning.
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Data-driven learning is the next wave
Big and small data-driven learning design has the potential to
revolutionize the way faculty interact with students and knowledge,
transforming how students interact with each other and how students
utilize knowledge resources for learning. This work will also transform
assessment.
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Platform and Tool vendors are starting to pay attention
New tools are emerging which enable faculty, students and others to
capture and share small data so we can identify patterns quickly.
Collaborative software with pre-designed capture tools are growing in
use and several small data supports (Kanban systems, quick video
capture) are emerging for this purpose.
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Small data in education is about the learner
Small data is about the learner: what they need, and how they can act,
what supports work best for them, how they use learning resources, what
they know and don’t know about the technology they use. Focus on the
learner first, and a lot of our decisions about teaching, learning
design, technology supports will soon become clearer.
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Simple
Small data is the right data. Some small data will start life
as two or three faculty members making the same observations about how
their students approach a learning challenge. Soon big data can be used
to look at the behaviour on a larger scale, but you shouldn't need to be
a data scientist to understand or apply small data for everyday tasks.
Less is more and simple and small is good.
Three Keys for Small Data Collection and Use
Underlying the work with small data is the idea of collaborative
professional autonomy. While each faculty member has a degree of
autonomy in both what they teach and how they do so, outcomes from this
work can be greatly improved through a deepening understanding of who
learners are, how they learn and what other faculty members are doing
which is making a difference to learning outcomes. Collaborative sharing
of insights, observations and practices – sharing small data – can lead
to major changes in what we teach and how we do so. This is true
whether the program or course is in-class, online or blended. This is
the first key to the effective use of small data: collaboration is key
to understanding and interpreting small data.
The second key is to move beyond anecdotes and look for patterns.
Anthropologists do this well – they study the behaviour of groups and
then discern patterns in this behaviour – rituals and routines - and
then observe deviance. This is what faculty members need to do to better
analyze and interpret their small data observations. How do we
interpret the thirty examples we have from a single classroom or online
course in such a way as to discern a pattern which could change the way
we design and deliver that course?
The final key is to do what kindergarten children do when given a new
challenge. They work collaboratively, problem solving as they go, do
lots of prototyping until they find a workable solution and then go for
that solution with gusto. This is how young children use small data to
discern patterns and proto-type solutions, each of which yields its own
small data, so they continuously improve their understanding and
approach and get it right. Adults, faced with the same task, often plan
in depth the “winner take-all, one shot” solution and it either works or
it doesn’t. They generally do not use small data to prototype and find
solutions through trial and error and small data observations; they go
for the big bang. We could learn a lot from watching how very young
children solve complex problems (see
here (link is external) for an example).
Big + Small Data = More Opportunities for Teaching and Learning
There is no suggestion here that big data is not helpful. It can be.
But it can also be misleading. Big and Small data will reveal far more
opportunities for change which will reach into classrooms and affect
teaching and learning. Small data can identify the nuances which will
make all the difference to learning outcomes.
Suggested Reading
Lindstrom, M. and Heath, C. (2016)
Small Data: The Tiny Clues That Uncover Huge Trends. New York: St. Martin's Press
Boje, D. M. (2014)
Storytelling Organizational Practices: Managing in the Quantum Age. London: Routledge.
Reference
TEACHONLINE.CA, Big Data vs Small Data asic
Computer Skills Curriculum. Retrieved 02 May, 2017, from https://teachonline.ca/tools-trends/exploring-future-education/big-data-vs-small-data available under a
Creative Commons Attribution-Sharealike 4.0 International License.
