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’.