Analysis of the factors affecting successful completion of asynchronous online learning programs


  • Buddhika Karunarathne University of Moratuwa
  • Vishaka Nanayakkara University of Moratuwa
  • Eshana Ranasinghe University of Moratuwa
  • Malik Ranasinghe University of Moratuwa
  • Supunmali Ahangama University of Moratuwa
  • Sandareka Wickramanayake University of Moratuwa
  • Chathuranga Hettiarachchi University of Moratuwa



e-learning, online asynchronous learning, student performance


Within a year of its launch, the University of Moratuwa's open learning platform ( has over 180,000 registered participants, providing free and open access to a series of asynchronous online courses in software development. Nearly 17,000 people have completed the two foundation courses: Python for Beginners and Web Development for Beginners. Considering the low overall completion rate compared to the total number of registered users, it is important to study the factors contributing to successful completion while promoting meaningful learning. To support the learning process, there are multiple activities like lecture videos, lecture notes, coding playgrounds, lecture slides, assessments, and discussion forums. Thus, this study investigates student behaviour to understand activities that are high in cognitive load and promote active engagement. The study is carried out using learning management system (LMS) user logs and feedback from completed and ongoing students. The elaborative logs with user activities and time stamps help identify the pace of completion by students with different capabilities and learning patterns. The study's findings will be useful to educators because they will be able to design similar LMS platforms that optimise student performance and promote effective learning outcomes. Furthermore, it would be useful to evaluate how students would manage the load in asynchronous online learning programs. Understanding how the learning and assessment activities are related to the completion of the course would enable predicting completion rates and times to properly plan for the employment opportunities for the successful learners.