Conference Publication Details
Mandatory Fields
Mary Loftus & Michael G Madden
ACM WomENcourage
Ways of Seeing Student Learning – With Machine Learning and Learner Models
Optional Fields
Learning Analytics, Probabilistic Graphical Models, Bayesian Networks, Probability, Metacognition, Self-Reflection, Personalised Feedback, Data Literacy, Ethics
Barcelona, Spain
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.
ACM WomENcourage
Grant Details