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Effective adoption of online platforms for teaching, learning, and skilldevelopment is essential to both academic institutions and workplaces. Adoptionof online learning has been abruptly accelerated by COVID19 pandemic, drawingattention to research on pedagogy and practice for effective onlineinstruction. Online learning requires a multitude of skills and resourcesspanning from learning management platforms to interactive assessment tools,combined with multimedia content, presenting challenges to instructors andorganizations. This study focuses on ways that learning sciences and visuallearning analytics can be used to design, and to improve, online workforcetraining in advanced manufacturing. Scholars and industry experts, educationalresearchers, and specialists in data analysis and visualization collaborated tostudy the performance of a cohort of 900 professionals enrolled in an onlinetraining course focused on additive manufacturing. The course was offeredthrough MITxPro, MIT Open Learning is a professional learning organizationwhich hosts in a dedicated instance of the edX platform. This study combineslearning objective analysis and visual learning analytics to examine therelationships among learning trajectories, engagement, and performance. Theresults demonstrate how visual learning analytics was used for targeted coursemodification, and interpretation of learner engagement and performance, such asby more direct mapping of assessments to learning objectives, and to expectedand actual time needed to complete each segment of the course. The study alsoemphasizes broader strategies for course designers and instructors to aligncourse assignments, learning objectives, and assessment measures with learnerneeds and interests, and argues for a synchronized data infrastructure tofacilitate effective just in time learning and continuous improvement of onlinecourses.

Key principles for workforce upskilling via online learning: a learning analytics study of a professional course in additive manufacturing