Edited by Henry James Robinson, Sunday, 2 Aug 2020, 10:42
image: by pxfuel
Learning analytics in learning and teaching
Huh? For your average overworked underpaid member of the
teaching staff, who isn't a statistics buff, (but ought by now to be realizing
the huge part computers can play and have played in our careers, whether in the
foreground or background) learning analytics is something you may hope stays in
the background. Something for the middle and senior academic staff and
administrators, all the way up to education authorities and grants bodies to be
involved in - not you. Think again.
Learning analytics just hit me with a sock (with a cold bar of soap in
it)! It is time to wake up to the fact
that COVID-19 has changed things. In an era when you don't have the expressions
of your students' faces or the way your co-ordinator is stirring his coffee
while he remotely observes your class to go on, every advantage that a new laptop
and a TEL course can give you is worth it. And education has been screaming
about learning analytics for ages. It's
time to retool if you are going to keep your finger on the pulse of your
teaching effectiveness, the satisfaction levels of your clients, and your fast -becoming-obsolete
career. And I know very little about LA and the different forms of data
analytics relevant to education out there. So, we need a definition. However,
we probably need to first acknowledge how it is becoming such a prominent force
in education and why teachers need to get a handle on it.
Why is it? The Internet and also every type of technological device in
our everyday lives have now become constant, explicit, and quite unstoppable
transmitters of our every action, thought and utterance because we have taken
it for granted and implicitly accepted that our personal information is open
for use by commercial, governmental and other entities unless we purposely and
systematically opt-out in most cases. All digital devices leave a trail, a
digital footprint. Education is one of the few areas where that open data can
have the least malign effect on our lives, however. Few people doubt the value of data that is
used to enhance our educational experiences, though it is wise to remember that
in the current global economic environment, education institutions are much
more commercialized. Nevertheless, for the teacher, 'these learner-produced
data trails can provide ...valuable insight into the learning process' (Long
and Siemens, 2011). Teachers who find
ways to get access to it, and gain permission to use it to make improvements
are at the very least impressing learners and all other stakeholders of their
investment in the idea of improving educational outcomes.
What is it? Now for some definitions. As recently ago as 2010,
non-specialists in data analytics were going onto various sites and platforms
such as Wikipedia with simple definitions of learning analytics, such as 'the use of data and models to predict
student progress and performance, and the ability to act on that information'.
(Blackall, 2010). Which sounds a bit
naive. What if they do not know how to
'act on it', never mind analyze it? Does it stop being learning analytics? A criticism of this definition posited by Siemens
(2010) was its implied limitation to extrapolating trends. Could it also be used to transform learning
outside of the 'box'? Of course, this is
when in education earning analytic was still note being utilized to anywhere
near its potential, which changed as software and hardware and theories
developed.
The current Wikipedia definition, one which stands back
from any assumed use of analytics and the analytical capabilities of its
collectors, is: 'Learning analytics is the measurement, collection, analysis
and reporting of data about learners and their contexts, for purposes of understanding
and optimizing learning and the environments in which it occurs.' Since 2010, theorists have separated areas of education
data analysis into several areas: educational data mining, academic analytics
and learning analytics. All of these areas utilize similar or the same data -
just for different purposes and/or from different perspectives.
The area of most concern for the teacher is learning
analytics because it focuses on the learning process and the relationship
between the learner and educator, what is being learned, and the learner's
perception of the institution in which they learn. Adopting the teacher's perspective, then, I've simply
adapted learner focussed definitions that I've read into the following:
Learning analytics is digital data research, aimed at
enhancing the student learning experience, looked at from both the teaching and
learning effectiveness perspectives.
Long and Seimen (2011, p.36) put forward the following
cycle, which is useful for focusing on the levels of learning that goes on in
that three-way relationship:
1. Course-level: learning trails, social network analysis,
discourse analysis 2. Educational data mining: predictive modelling,
clustering, pattern mining 3. Intelligent curriculum: the development of semantically
defined curricular resources 4. Adaptive content: adaptive sequence of content based on
learner behaviour, recommender systems 5. Adaptive learning: the adaptive learning process (social
interactions, learning activity, learner support, not only content).
Whatever tool is used for learning, a data trial is left
and many tools (e.g. learning management systems, virtual learning
environments; communication collaborative software have analytics facilities
built-in for use by their owners but are seldom exploited.
Back to why in teaching/learning Learning analytics is only really in its early stages of
implementation and experimentation by teachers and learners. There are concerns
about its use by teachers: at what age can learners give consent. What about issues
of privacy, the teacher's undue influence on students' decisions concerning
their privacy and profiling, data security and (yes) the ability of the teacher
to exploit in a positive way the potential value of the data without succumbing
to 'deterministic modelling'. There are
dangers, but whatever happened to the classroom as a place to experiment and
carry out action research? Should big
data only be the sole remit of business managers concerned with profitability,
long term financial sustainability and meeting so-called performance
benchmarks? When used for those purposes learning analytics often becomes just
a means of predicting market behaviours and where economies can be made.
Learning is multi-dimensional and that's why data has to be utilized at every
level and sphere of the education process, where different types of data, not
simply behavioural, are examined. Sharing that information with learners, where
they are able to gain another form of feedback on their performance and
comparison of different learning methods can be highly motivational and improve
teacher and learner efficacy.
References
1st International Conference on Learning
Analytics and Knowledge, Banff, Alberta,
February 27–March 1, 2011. [Online]. Available at: https://dl.acm.org/doi/proceedings/10.1145/2090116 (Accessed 05 July 2020).
Siemens, G. and Long, P., 2011. Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), p.30. [Online]. Available at: https://eric.ed.gov/?id=EJ950794 (Accessed 05 July 2020).
Learning analytics for teachers
image: by pxfuel
Learning analytics in learning and teaching
Huh?
For your average overworked underpaid member of the teaching staff, who isn't a statistics buff, (but ought by now to be realizing the huge part computers can play and have played in our careers, whether in the foreground or background) learning analytics is something you may hope stays in the background. Something for the middle and senior academic staff and administrators, all the way up to education authorities and grants bodies to be involved in - not you.
Think again. Learning analytics just hit me with a sock (with a cold bar of soap in it)! It is time to wake up to the fact that COVID-19 has changed things. In an era when you don't have the expressions of your students' faces or the way your co-ordinator is stirring his coffee while he remotely observes your class to go on, every advantage that a new laptop and a TEL course can give you is worth it. And education has been screaming about learning analytics for ages. It's time to retool if you are going to keep your finger on the pulse of your teaching effectiveness, the satisfaction levels of your clients, and your fast -becoming-obsolete career. And I know very little about LA and the different forms of data analytics relevant to education out there. So, we need a definition. However, we probably need to first acknowledge how it is becoming such a prominent force in education and why teachers need to get a handle on it.
Why is it?
The Internet and also every type of technological device in our everyday lives have now become constant, explicit, and quite unstoppable transmitters of our every action, thought and utterance because we have taken it for granted and implicitly accepted that our personal information is open for use by commercial, governmental and other entities unless we purposely and systematically opt-out in most cases. All digital devices leave a trail, a digital footprint. Education is one of the few areas where that open data can have the least malign effect on our lives, however. Few people doubt the value of data that is used to enhance our educational experiences, though it is wise to remember that in the current global economic environment, education institutions are much more commercialized. Nevertheless, for the teacher, 'these learner-produced data trails can provide ...valuable insight into the learning process' (Long and Siemens, 2011). Teachers who find ways to get access to it, and gain permission to use it to make improvements are at the very least impressing learners and all other stakeholders of their investment in the idea of improving educational outcomes.
What is it?
Now for some definitions. As recently ago as 2010, non-specialists in data analytics were going onto various sites and platforms such as Wikipedia with simple definitions of learning analytics, such as 'the use of data and models to predict student progress and performance, and the ability to act on that information'. (Blackall, 2010). Which sounds a bit naive. What if they do not know how to 'act on it', never mind analyze it? Does it stop being learning analytics? A criticism of this definition posited by Siemens (2010) was its implied limitation to extrapolating trends. Could it also be used to transform learning outside of the 'box'? Of course, this is when in education earning analytic was still note being utilized to anywhere near its potential, which changed as software and hardware and theories developed.
The current Wikipedia definition, one which stands back from any assumed use of analytics and the analytical capabilities of its collectors, is: 'Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.'
Since 2010, theorists have separated areas of education data analysis into several areas: educational data mining, academic analytics and learning analytics. All of these areas utilize similar or the same data - just for different purposes and/or from different perspectives.
The area of most concern for the teacher is learning analytics because it focuses on the learning process and the relationship between the learner and educator, what is being learned, and the learner's perception of the institution in which they learn.
Adopting the teacher's perspective, then, I've simply adapted learner focussed definitions that I've read into the following:
Learning analytics is digital data research, aimed at enhancing the student learning experience, looked at from both the teaching and learning effectiveness perspectives.
Long and Seimen (2011, p.36) put forward the following cycle, which is useful for focusing on the levels of learning that goes on in that three-way relationship:
1. Course-level: learning trails, social network analysis, discourse analysis
2. Educational data mining: predictive modelling, clustering, pattern mining
3. Intelligent curriculum: the development of semantically defined curricular resources
4. Adaptive content: adaptive sequence of content based on learner behaviour, recommender systems
5. Adaptive learning: the adaptive learning process (social interactions, learning activity, learner support, not only content).
Whatever tool is used for learning, a data trial is left and many tools (e.g. learning management systems, virtual learning environments; communication collaborative software have analytics facilities built-in for use by their owners but are seldom exploited.
Back to why in teaching/learning
Learning analytics is only really in its early stages of implementation and experimentation by teachers and learners. There are concerns about its use by teachers: at what age can learners give consent. What about issues of privacy, the teacher's undue influence on students' decisions concerning their privacy and profiling, data security and (yes) the ability of the teacher to exploit in a positive way the potential value of the data without succumbing to 'deterministic modelling'. There are dangers, but whatever happened to the classroom as a place to experiment and carry out action research? Should big data only be the sole remit of business managers concerned with profitability, long term financial sustainability and meeting so-called performance benchmarks? When used for those purposes learning analytics often becomes just a means of predicting market behaviours and where economies can be made. Learning is multi-dimensional and that's why data has to be utilized at every level and sphere of the education process, where different types of data, not simply behavioural, are examined. Sharing that information with learners, where they are able to gain another form of feedback on their performance and comparison of different learning methods can be highly motivational and improve teacher and learner efficacy.
References
1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, February 27–March 1, 2011. [Online]. Available at: https://dl.acm.org/doi/proceedings/10.1145/2090116 (Accessed 05 July 2020).
pxfuel (2020) 'person typing' [Online]. Available at: https://www.pxfuel.com/en/free-photo-jrtyv (Accessed 5 July, 2020).
Siemens, G. and Long, P., 2011. Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), p.30. [Online]. Available at: https://eric.ed.gov/?id=EJ950794 (Accessed 05 July 2020).