By employing learning analytics methodology and big data in
Learning Management Systems (LMSs), this volume conducts
data-driven research to identify and compare learner interaction
patterns in Massive Private Online Courses (MPOCs). The
uncertainties about the temporal and sequential patterns of online
interaction, and the lack of specific knowledge and methods to
investigate details of LMSs' dynamic interaction traces have
affected the improvement of online learning effectiveness. While
most research focuses on Massive Open Online Courses (MOOCs),
little is investigating the learners' interaction behaviors in
MPOCs. This book attempts to fill in the gaps by including research
in the past decades, big data in education presenting micro-level
interaction traces, analytics-based learner interaction in massive
private open courses, and a case study. Aiming to bring greater
efficiency and deeper engagement to individual learners,
instructors, and administrators, the title provides a reference to
those who need to evaluate their learning and teaching strategies
in online learning. It will be particularly useful to students and
researchers in the field of Education.
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