Learners engagement with digital knowledge artefacts, such as conceptual models, data profiles and visualizations
as well as software scripts is emerging in multi-disciplines like data science, and exasperated by the diversity
of learners backgrounds entering such areas of study. Adaptive learning platforms have been developed to assist
in such scenarios, however, current research into adaptive learning does not provide sufficient guidance on
how a learner's engagement can be systematically measured to allow evidence of identifying knowledge gaps and
facilititating knowledge transfer.
Neuro-physiological methods and tools provide the capability of continuous and real time observation of
a learner revealing a learner's cognitive and emotional processes while engaging
in a learning task.Measurements of learner's cognitive load such as using eye tracking technologies or heart rate variability can provide
insights that unveil the challenges a learner faces in a much more fine grained and timely manner than existing learning platforms.The usage of neuro-physiological
and psycho-physiological methods and tools has the potential to measure a learner's emotional processes and to adapt before a learner's motivation and engagement
has dropped. Knowing both a learner's knowledge gaps and cognitive as well as emotional state will help to identify adaptation points
and to refine and focus the learning pathways in the learning platform which according to the theory of deliberate practise has the potential
for improving learning outcomes