Are participants who completed the assessment tasks designed for the course and passed the course with an overall course grade of > 50%.
Published in Chapter:
Using Learning Analytics to Improve Engagement, Learning, and Design of Massive Open Online Courses
David Santandreu Calonge (Canadian University Dubai, UAE), Karina M. Riggs (The University of Adelaide, Australia), Mariam Aman Shah (University of South Australia, Australia), and Tim A. Cavanagh (University of Adelaide, Australia)
Copyright: © 2019
|Pages: 32
DOI: 10.4018/978-1-5225-7470-5.ch004
Abstract
Academic research in the past decade has indicated that using data and analyzing learning in curriculum design decisions can lead to improved student performance and student success. As learning in many instances has evolved into the flexible format online, anywhere at any time, learning analytics could potentially provide impactful insights into student engagement in massive open online courses (MOOCs). These may contribute to early identification of “at risk” participants and provide MOOC facilitators, educators, and learning designers with insights on how to provide effective interventions to ensure participants meet the course learning outcomes and encourage retention and completion of a MOOC. This chapter uses the essential human biology MOOC within the Australian AdelaideX initiative to implement learning analytics to investigate and compare demographics of participants, patterns of navigation including participation and engagement for passers and non-passers in two iterations of the MOOC, one instructor-led, and second self-paced.