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

TL;DR

This study explores how visual learning analytics can enhance online workforce training in additive manufacturing by analyzing learner performance, engagement, and course design improvements in a professional course with 900 participants.

Contribution

It introduces a novel application of visual learning analytics to improve online course design and learner engagement in advanced manufacturing training.

Findings

Visual analytics helped identify learner engagement patterns.

Course modifications improved assessment alignment.

Data-driven strategies enhanced online training effectiveness.

Abstract

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.