  Effective adoption of online platforms for teaching, learning, and skill
development is essential to both academic institutions and workplaces. Adoption
of online learning has been abruptly accelerated by COVID19 pandemic, drawing
attention to research on pedagogy and practice for effective online
instruction. Online learning requires a multitude of skills and resources
spanning from learning management platforms to interactive assessment tools,
combined with multimedia content, presenting challenges to instructors and
organizations. This study focuses on ways that learning sciences and visual
learning analytics can be used to design, and to improve, online workforce
training in advanced manufacturing. Scholars and industry experts, educational
researchers, and specialists in data analysis and visualization collaborated to
study the performance of a cohort of 900 professionals enrolled in an online
training course focused on additive manufacturing. The course was offered
through MITxPro, MIT Open Learning is a professional learning organization
which hosts in a dedicated instance of the edX platform. This study combines
learning objective analysis and visual learning analytics to examine the
relationships among learning trajectories, engagement, and performance. The
results demonstrate how visual learning analytics was used for targeted course
modification, and interpretation of learner engagement and performance, such as
by more direct mapping of assessments to learning objectives, and to expected
and actual time needed to complete each segment of the course. The study also
emphasizes broader strategies for course designers and instructors to align
course assignments, learning objectives, and assessment measures with learner
needs and interests, and argues for a synchronized data infrastructure to
facilitate effective just in time learning and continuous improvement of online
courses.
