Big Data In Education: Literacy
Your education attainment does not tell whether or not you are literate.
Reading, listening, speaking, and writing is not sufficient either to define
that one is literate. Yet data are being collected to gauge the literacy rate
in the world. According to the UNESCO Institute for Statistics (UIS) literacy
could be defined as: “Can [Name] read and write a simple sentence in
[Language(s)]” which quote is also part of the questionnaire used for data
collection to assess literacy. Being literate or illiterate definitely does not
mean belonging to a specific community of language-speakers or being able to
speak a particular language. What does it mean then to be literate? And how
could big data in education contribute to literacy?
Up to date literacy is still one of the most pressing problems on our planet. Recent statistics evaluate literacy rates among youth (15-24) and adults, as positive (UNICEF, 2019). However, the number of illiterate is still alarming with millions of children and youth still out of school and more than half of those in school not meeting minimum proficiency standards in reading and numeracy at the end of 2019 (UN, 2020). And yet, we are living in a digital era of big data.
Big
data in Education
Due
to the new technologies’ advances, education has been revolutionized
tremendously. The last decade has witnessed the usage of big data in education.
In particular, big data storage technologies and analytics tools have helped to
collect online data which are capable of producing the following insights on
the education system for the benefits of both main actors (teachers and
students):
➧ Improved
instructions: Learning is not a one size fits all approach and human beings
learn differently.
➧ Matching students to programs: With the boom of new technologies we now have enormous data about different university curriculum systems.
➧ Matching students to employment: Like with matching students to study programs, big data analytics solutions help map students’ suitability for employment.
➧ Transparent education financing: If financing one’s education was a problem, nowadays with the data acquired from various university curriculums it’s no big deal that you could have at no cost the amount range to finance your education using for instance an online calculator.
➧ Efficient system administration: An education system includes human resources (teachers and students) and study resources (books, library, furniture, etc.).
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Literacy
in the technological era
While
last year’s International Literacy Day (ILD) celebrations
focused on Literacy and Multilingualism highlighting the need to preserve the
global world in which we are all living, it’s becoming clearer for us that no
dialect should be left behind. UNESCO
2005 report on the importance of mother tongue-based schooling for educational
quality clearly states that the common belief that bilingualism causes
confusion and that the first language must be pushed aside so that the second
language can be learned is a myth. Our challenge then becomes to unlock the
potential of our education regardless of the language or dialect and reduce the
illiteracy rate. This is probably among some of the drives for the ILD 2019
theme on the global inclusion of languages. And the attainment of such goals
falls within the scope of research.
Natural Language Processing (NLP) for big data in education could help research reduce the burden on the literacy aspect of our education. However, NLP’s current state of the art is really biased toward high resource languages such as English, Spanish, Chinese, Arabic, French, among others. This inequality which is due to the digital language divide, causes the impossibility of digitization in some indigenous languages hence the need to resort to a second language in some countries. As a result, from the approximately seven thousand languages existing on the globe, about thousands are dying (SIL, 2020). READ MORE ➤➤
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