I’ve just received the call for papers for the
1st International Symposium Learning Analytics
I’ve no doubt about the good intentions of the organizers, that emerge also from the description of the symposium which I report (partially) below:
Learning Analytics refers to the measurement, collection, analysis and reporting of data on the progress of learners and the micro and macro contexts of learning environment. It uses the data collected from the digital footprint of the learner and applies bigdata concepts to understand the influencing factors that affects learners whether it is educational policy and strategy, or it relates to the micro level module contents and its delivery. This has potential to support designing data and fact-based policy both for campus based and online delivery.
Through Learning Analytics, it is possible to provide tailor-made learning and teaching for a specific group. It can lead to reduce disparity of standard within different systems and optimise the resources required to achieve required standards. Learning Analytics can support the prediction of outcome and guide early intervention to boosting retention rates. It will make significant contribution for quality assurance.
When combined with refined visualization tools and suitable interfaces in an e-learning system, the use of Learning Analytics fosters a student-centred approach, supports self-regulated learning, and eventually helps learners (and teachers) along the road to educational success. This research area is witnessing swift developments since several years, and this Symposium aims to gather contributions to allow for exchange and further advancements. Technology Enhanced Learning advocates are invited to submit their original research work involving the use of Learning Analytics in education.
However, I have to express strong concern about what could be additional uses (or abuses!) of “Learning Analytics”,
and in particular about using it as a tool for potential discrimination. I think it is worth citing here what
Ben Buchanan Taylor Miller of the Belfer Center for Science and International Affairs of the Harvard Kennedy School wrote
with reference to (big-data and) machine learning in their report of 2017 titled “Machine Learning for Policymakers. What It Is and Why It Matters”:
"On one hand, machine learning has the exciting potential to diminish many of the effects of bias on the day-to-day lives […]. Neutral
algorithms could make hiring decisions, approve people for loans, and recommend criminal sentences without the implicit preconceptions that
humans bring to the table. Properly implemented, this could lead to a more just society.
On the other hand, machine learning is sometimes critiqued as “money laundering for bias.” At its very worst, machine learning can cloak inequity
with the imprimatur of science.” [emphasis added by me]
Buchanan e Miller then continue describing a few well known cases of discrimination based on AI tools.
I hope that the researchers in Learning Analytics will do their best for avoiding that the results of their scientific work,
and the related technology, will never be used for discriminatory purposes.
Diego
Dott. Diego Latella - Senior Researcher CNR-ISTI, Via Moruzzi 1, 56124 Pisa, Italy (http:
www.isti.cnr.it)
===================
The quest for a war-free world has a basic purpose: survival. But if in the process we learn how to achieve it by love rather than by fear, by kindness rather than compulsion; if in the process we learn how to combine the essential with the enjoyable, the expedient with the benevolent, the practical with the beautiful, this will be an extra incentive to embark on this great task.
Above all, remember your humanity.
-- Sir Joseph Rotblat