Machine Learning: The Future of Sustainable Teacher Education is Here

Abstract

This paper explains machine learning and demonstrates its potential to improve teacher education. Machine learning, a branch of artificial intelligence, is a set of algorithms with ability to learn, act and adapt autonomously without being explicitly programmed. Machine learning provides systems with the ability to learn and act like humans, while also improving their learning over time through observations and real world interactions without being explicitly programmed. Machine learning can be applied in day-to-day lives. Industries are adopting it to improve their business models. Unfortunately the teacher education sector is notoriously slow in adopting change, including incorporation of machine learning in its programming. Yet machine learning has potential to support pedagogy, andragogy, heutagogy and adaptive learning by fabricating patterns from data and fashioning educational insights useful for personalizing students’ learning paths. Such an approach ensures learning takes place based on the learner’s pace of grasping concepts. In this paper we discuss implications of using machine learning to offer differentiated teacher education pedagogy and andragogy. We showcase its potential for heutagogy and self-paced learning, for identifying teachers’ knowledge, skills and abilities in learning and for personalizing/customizing programmes based on teachers’ unique needs, eye-balling students in need of remediation and for collecting assessment data on learning and professional development.