As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of educational data mining (EDM) to understand students' learning experiences is a critical step forward. The adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to exploit the untapped data generated by various student information systems (SIS) and learning management systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live video streaming (LVS) students' online learning behaviours and their performance in their courses. Students' participation and login frequency, as well as the number of chat messages and questions that they submit to their instructors, were analysed, along with students' final grades. Results of the study show a considerable variability in students' questions and chat messages. Unlike previous studies, this study suggests no correlation between students' number of questions / chat messages / login times and students' success. However, our case study reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical framework capable of enabling a deeper and richer understanding of students' learning behaviours and experiences. (Contains 5 tables and 3 figures.)