A gamification training system designed according to a mental model structure: A case study of universal robots.

Area of Science:

• Robotics and Automation
• Educational Technology
• Human-Computer Interaction

Background:

• Collaborative robots (cobots) are crucial for Industry 5.0, but a skills gap hinders adoption.
• Effective e-learning is needed to address knowledge and skill deficiencies in cobot operation.
• Integrating innovative learning strategies can overcome training barriers.

Purpose of the Study:

• To design and evaluate a cobot training system integrating gamification and a mental model learning structure.
• To compare the effectiveness of gamified e-learning with a mental model approach against traditional e-learning.
• To assess the impact on cobot task performance and user learning outcomes.

Main Methods:

• Experimental design with three conditions: traditional e-learning, gamified e-learning, and gamified e-learning with a mental model structure.
• Training participants on basic cobot pick-and-place tasks.
• Measuring success rates, task completion time, and error frequency.
• Utilizing regression analysis based on the Technology Acceptance Model.

Main Results:

• Success rates for cobot use were 15% (traditional e-learning), 53% (gamified e-learning), and 74% (gamified e-learning with mental model).
• The mental model gamification group demonstrated significantly higher success rates, reduced task time, and fewer errors.
• Technology Acceptance Model analysis confirmed the influence of Attitude Towards and Perceived Usefulness on Behavioral Intention.

Conclusions:

• Gamification combined with a mental model learning structure is highly effective for cobot training.
• This approach significantly improves user performance and learning outcomes in cobot operation.
• The findings support the integration of advanced pedagogical strategies for industrial skills development.