Learning Analytics, Probabilistic Graphical Models, Bayesian Networks, Probability, Metacognition, Self-Reflection, Personalised Feedback, Data Literacy, Ethics
The emerging field of learning analytics is showing promise as a
light to shine into the dark corners of individual student
experience. By making the richness of the learning process more
visible, learners and teachers can access deeper insights into their
shared experience. Data and models can provide a mirror for selfreflection
and metacognition [1]. As Gašević [2] reminds us,
Learning Analytics are about learning. However, too little
attention has been paid to the student’s role in data-rich learning
environments [3].
This research will use probabilistic machine learning techniques
in conjunction with other learning model approaches to produce
interactive learning models [4] that can be integrated in existing
learning analytics systems. One such system will be shared with
students in a module of a BSc in Computing degree course and a
mixed-methods study of their experience conducted – with
students having full control of their data.