In recent years, there has been a huge increase in new educational methods that are based primarily on the tools that technology offer. In this framework, we need to first create the assumption that technological tools and instruments not only assist didactic methods and educational norms but, due to their growth, that they have also generated their own learning methodology.
In this sense, it is fundamental to begin by focusing on the phenomenon of ‘learning’. In general terms, the concept of ‘learning’ can be defined as the means by which humans acquire different skills, abilities or knowledge through their study, research or experience, or through being taught by others.
In more systematic words, learning is a cognitive process by which a person’s experience may alter their future observations and activities permanently and in ways that are usually beneficial in terms of productivity or efficiency.
Taking all of this into account, we might summarise learning as a cognitive process of acquiring knowledge through the use of experience, with the subsequent learning causing long-lasting or permanent changes to a person’s level of knowledge and insights about the world. With this definition, learning would be the central purpose of any form of education.
In our modern world, both digitisation (including how we map our observations into digital and computational formats and structures) and digitalisation (how we construct digit-based digital systems) stem from data.
This means that a person’s data is the most important and valuable building block of all digitised and digitalised systems. Data expresses how an individual sees the world and how they make sense of their world based on his/her observations and insights. In such a framework, a person’s learning processes and, consequently, their education in tomorrow’s world will rest heavily on ‘data’ and, subsequently, we will have data-driven education (DDE).
DDE focuses on the conceptual, logical and pragmatical identification, description, classification, interpretation and organisation of a person’s data in various conditions and contexts.
The systematic development of DDE will, therefore, be interrelated with how learners:
In the framework of DDE, learners’ knowledge of the world will emerge out of collections of their data relating to their education-driven learning activities. In DDE, we will therefore need to carefully observe and interpret how learners will need to see the world to understand their own education-driven activities and collect more data – be it textual, numerical, or pictorial and so on – based on how learners have behaved in these activities.
Using quantitative and qualitative data analytics, DDE will also be able to offer descriptive, prescriptive and predictive models for the analysis of learners’ behaviour. In this way, we can typify and categorise learners’ data and organise various classified and clustered data, respectively.
The ultimate goal is to be able to analyse learners’ various descriptions, explanations, justifications and argumentations in different contexts and conditions. The DDE framework allows us to look at learners’ creative and innovative learning activities based on an understanding of how they might interact with these activities.
We can now be sure that information technology has changed all aspects of human life, including education. However, technology can never offer a substitute for human wit and all the other qualities and characteristics that define us as humans. As such, we must have the wisdom to use the evolution of technology in a collaborative way and learn from it. This is why we call the process ‘human and machine learning’.
Kyriakos Kouveliotis is provost and chief academic officer at Berlin School of Business and Innovation. Maryam Mansuri and Farshad Badie are head of postgraduate studies, and vice-dean of the faculty of computer science and informatics, respectively, at Berlin School of Business and Innovation.
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