Due to the COVID19 pandemic, more higher-level education programmes have moved to online channels, raising issues in monitoring students’ learning progress. Thanks to advances in online learning systems, however, student data can be automatically collected and used for the investigation and prediction of the students’ learning performance. In this article, we present a novel approach to analyse students’ learning behaviour, as well as the relationship between these behaviours and learning assessment results, in the context of programming education. A bespoke method has been built based on a combination of Random Matrix Theory, a Community Detection algorithm and statistical hypothesis tests. The datasets contain fine-grained information about students’ learning behaviours in two programming courses over two academic years with about 400 first-year students in a Medium-sized Metropolitan University in Dublin. The proposed method is a noval approach to data preprocessing which can improve the analysis and prediction based on learning behavioural datasets. The proposed approach deals with the issues of noise and trend effect in the data and has shown its success in detecting groups of students who have similar learning behaviours and outcomes. The higher performing groups have been found to be more active in practical-related activities throughout the course. Conversely, we found that the lower performing groups engage more with lecture notes instead of doing programming tasks. The learning behaviours data can also be used to predict students’ outcomes (i.e. Pass or Fail the terminal exams) at the early stages of the study, using popular machine learning classification techniques.