Research has shown online learners’ performance to have a strong association with their demographic characteristics, such as regional belonging, socio-economic standing, education level, age, gender, and disability status. Despite a growing number of studies exploring factors for successful online learning outcomes, most researchers have utilised one or a combination of very few learner characteristics. Moreover, a limited number of studies scrutinised the impact of individual characteristics on learning outcomes as learners progress in a course. The current research aims to explore the dynamic impact of demographic characteristics on academic outcomes in the online learning environment. We investigated and compared the dynamic influence of six demographic characteristics on online learning outcomes using a sample of 8581 UK based learners across four Open University online courses from four different disciplines. We found region, neighborhood poverty level, and prior education respectively, to be strong predictors of overall learning outcomes. However, at a fine-grain level, such influence varied temporally as the course progressed, as well as between different courses. To conclude with, we discussed the implications for institutional support on adopting a tailored approach towards a more personalised student support system. •In Decision trees, some predictive variables were more contributing than others.•Region, multiple deprivation, and education mostly contributed in tree induction.•Linkage between learning outcome and gender or disability was trivial.•Precision and recall for each class calculated separately (accuracy upto 83.14%).•Evidently, predictive models were biased towards majority class (Pass in this case).