Page 116 - Looking_after_school

Looking after school: a critical analysis of personalisation in education

completely lost without it. We already pointed out the more extreme
version of 360° feedback. The ideal is finding a perfect match between
how you evaluate yourself, and how others evaluate you. This often
comes down to looking at and evaluating yourself through the eyes
of the other, and thus a desire for recognition and acknowledgment
or, in extreme cases, a desire to be popular and to be applauded. The
risk is of course that students (and this also counts for teachers) are
‘nobody’ without feedback. Even more, while feedback may have had
the intention to give the students more confidence and certainty, when
pushed further, feedback loops may lead to insecure students who
only dare to act when they know for sure what the specific outcomes,
gains, or criteria of evaluation are. When the feedback circle closes,
students risk becoming helpless and obsessed with feedback. A step
into the unknown - and thus without knowing on beforehand what
will be the gain or outcome - is then uncomfortable and unsettling
and even becomes something to be avoided at all costs. A step into the
unknown is paramount to learning in freedom and equality: ‘try this’,
or becoming exposed to a world or subjects that you, as a student, had
no knowledge of before, and of which you could not imagine, is exactly
what should arouse interest. In this respect, feedback is at odds with
scholastic learning.

The calculating student and being calculated

In a learning environment that emphasises learning gain, there is a
possibility of calculating learning time and learning outcomes. It is first
the student, themselves, who makes their own balance, and should
also calculate what should be learned at what time, at what speed, and
when and how the subsequent outcomes should be evaluated. But the
student, especially in a digital learning environment, also leaves traces.
These are the traces which allow - after analytical operations – for the
personalisation and adjustment of the learning path, when needed.
But these traces also deliver (big) data to profile students, or make pro-
files of effective and efficient learning paths, to perfect these learning
environments (Williamson, 2015). In other words, forms of learning
analytics deliver the input for (algorithmically) modelling learning
environments and for the creation of adaptive learning environments
which work almost automatically. Through these systems, the student

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