The effects of individual abilities and training strategy on learning: an empirical investigation in assembly tasks

In current human-centric systems, assembly tasks have become increasingly complex due to the transition to mass customisation, necessitating a deeper understanding of the relationship between human abilities and learning phenomena. Unlike previous studies, this work introduces a novel approach by explicitly quantifying the impact of minimum repetition training strategy and individual abilities on learning. Laboratory experiments were carried out with 98 subjects. Planning and problem-solving skills of participants were assessed using the Tower of London test, while manual dexterity was measured via the Purdue Pegboard Test. Subjects were asked to assemble two Lego models of different complexity during three single-repetition training sessions spaced four to five weeks apart. Each session was conducted using either paper-based procedures or assistive technology as support. A mixed-effects analysis was carried out to model learning effect. Results show that regression models perform better when observed data on individual abilities are considered. Findings reveal a significant learning effect during training even if only a single repetition is performed once every month for each product complexity investigated. These insights would help companies, engineers and managers in designing effective training strategies tailored to groups of subjects with specific abilities, thus reducing time and economics efforts.