New forms of (digital) inequality

27 February 2018 0


In the network society, the code is the new law.
The intensive use of extensive volumes of data, retrieved from different sources, open new forms for understanding learners’ behavior. For instance, learning analytics can develop tools to identify common patterns of those students who are at risk of dropping out.

If an educational system has access to the students’ devices or to the network that they use at school, they could explore some of the following questions (assuming the skills, experience, tools, and permissions required are available): How socially intensive is their online learning experience? What can be learned from those individuals who are learning in a specific style (i.e. learning patterns)? What are the most effective technologies? What are the most effective pedagogical methods?

Developing regions are in an early stage regarding the proficient use of large-scale data to serve education. Although students and teachers from developing countries produce large-scale information they are not in conditions to exploit it for their own benefit. In the best scenario, they will need an external expert, in most cases from overseas, who can help analyze that information for their benefit. The challenges are not only technical but legal and ethical.

To avoid the creation of new gaps (informational and technical inequities), developing new capacities will be of utmost relevance. Since many of these challenges are not sufficiently addressed in the educational sector’s agenda there is a critical need to raise awareness and expand the levels of data literacy and transparency. It will be crucial to demand higher levels of accountability to the algorithms used to assess online learners or teachers’ performance. Creating committees and sub-regional agencies are needed to ensure that massive information is being used in a transparent and reliable manner but also used in ways that protect the learners, their privacy, and integrity.

by Cristobal Cobo – Hugo Pardo Kuklinski – Cecilia Aguerrebere

 

Recommended readings:

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor.

O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.