SWOT Analysis of innovative technologies applied to educational sector (Strengths, Weaknesses, Opportunities, and Threats)8 June, 2018
Some voices are talking about the so-called “4rth Industrial Revolution” as we have seen in several articles, books and keynotes. In 2017 some of the latest digital technologies kept on revolutionizing the education landscape and promised to be on top of the cutting-edge trends.
We have carefully selected and analysed three innovative technologies applied to the educational sector in a SWOT matrix.
1. Blockchain in the educational system. Cryptoblocks on student data storage and delivery
At first, it seems a bit difficult to link blockchain technologies to education, accustomed as we were to relate it to cryptocurrencies. Nonetheless, this Distributed Database is being implemented in many fields. One of the advantages of Blockchain applied to education is the possibility to bring a global digital system which would allow institutions to share validated documents and to recover academic records under will. Despite this, some issues would come by the hand of the lack of standards and the possibility of security failures.
2. AI and Machine Learning applied to education. Chatbots and automated processes
With proper training and a good amount of data, Artificial Intelligence and Machine Learning applied to education can help in analyzing student needs. Artificial Intelligence helps to boost automated processes, improving user experience and solving problems anytime. These technologies can help to economize operations and aid the teacher in some boring or repetitive tasks although they still have some issues to solve, such as information leaks or programming errors. They must, first of all, gain the confidence of users, not accustomed yet to interact with bots.
3. Learning Analytics and Big Data inside education. Student process analysis, dashboards and learning optimizing
Learning Analytics are critical in the process of mapping student behavior and predicting situations such as disengagement. Educational data mining helps building adapted learning, eases the process of admissions to the HEI and facilitates student course completion. It can also be a tool for professors to adjust their teaching when necessary. On the negative side, there are dangers such as depersonalization during the process due to automation in the data mining and a shock between the procedure and how society understands the control of significant amount of behavioral data by automatic digital means.