Developing a knowledge-based system for identifying and mapping student learning in mathematics using rich student assessment data
The Team: Dr Lihua Xu, Dr Wei Luo, A/Prof Jianxin Li
This interdisciplinary collaboration aims to explore and develop novel approaches to analyse rich student assessment data in mathematics, to provide crucial evidence for innovations in teaching and learning in schools. It draws on the great potential of the latest technology breakthroughs in Artificial Intelligence, particularly in the machine learning approaches suitable for learning analytics, to identify and map student learning in mathematics using a wide range of student assessment data that has not been dealt with extensively in learning analytics, including audio/visual records of student classroom interactions, student artefacts, and other student assessment data types collected regularly by their classroom teachers. Two major activities are proposed: 1) interdisciplinary workshops to exchange ideas and methods in each discipline and to discuss scoping and study designs of potential research projects; and 2) a literature review focusing on learning analytics and assessment data in the context of school mathematics.
The Team: Dr Lihua Xu (lead CI, pictured), Dr Wei Luo, A/Prof Jianxin Li
A collaboration between the School of Education and the School of Information Technology