Leesa Joyce
Head of Research Implementation at Hololight
Training methodologies significantly impact skill acquisition and retention, especially in fields requiring psychomotor skills like machining or other technical tasks. Open-ended and close-ended training represent two distinct pedagogical frameworks, each with unique advantages and limitations.
Close-Ended Training
Traditional close-ended training is characterized by its structured and prescriptive approach. Tasks are typically designed with a single correct pathway, focusing on minimizing errors and ensuring compliance with predefined standards. This method is effective for teaching specific skills that require strict adherence to safety protocols or operational sequences, such as in high-stakes environments like aviation or surgery (Abich et al., 2021).
However, close-ended systems often limit learners’ creativity and adaptability by discouraging exploration. They can result in a rigid learning experience, where trainees may struggle to apply knowledge flexibly in unstructured real-world scenarios (Studer et al., 2024). Additionally, such systems may reduce engagement, as they often emphasize repetition and discourage deviation from the expected path.
Open-Ended Training
Open-ended training, in contrast, fosters exploration and self-directed learning. It is rooted in constructivist principles, emphasizing active engagement and the development of problem-solving skills through exploration. This approach allows multiple pathways to achieve the same goal, encouraging learners to experiment and understand the underlying principles of tasks (Land & Hannafin, 1996).
In the context of psychomotor skills, open-ended training enables learners to adapt to different tools, approaches, and constraints. For example, an open-ended VR system for machining skills, as demonstrated by Studer et al. (2024), allows trainees to achieve objectives using various methods while enforcing critical protocols where necessary. This flexibility mirrors real-world scenarios where tasks rarely follow a single blueprint, enhancing learners’ readiness for practical challenges.
Benefits and Challenges
Open-ended training excels in promoting creativity, adaptability, and deeper conceptual understanding. Studies have shown that learners trained in open-ended environments often exhibit better problem-solving abilities and higher engagement levels (Ianovici & Weissblueth, 2016). For instance, in manufacturing industry, the employees may encounter situations requiring innovative approaches to meet production goals. An open-ended framework better equips them for such challenges.
However, this method may be less suitable for beginners who require a clear framework to build foundational skills. Research suggests that novices benefit from close-ended approaches to develop initial competence before transitioning to more exploratory methods (Ianovici & Weissblueth, 2016).
Applications in Modern Training
The integration of technologies such as Virtual Reality (VR) and Augmented Reality (AR) into learning processes has amplified the potential of open-ended training. Extended Reality (XR) platforms can simulate diverse scenarios, offering real-time feedback and dynamic task adjustments to accommodate different learning styles and levels of expertise (Abich et al., 2021). In contrast, close-ended modules provide step-by-step instructions for specific tasks, ensuring accuracy and consistency.
For example, the open-ended XR training system for machining tasks by Studer et al. (2024) combines guided tutorials with open-ended practice. This hybrid approach balances structure and flexibility, addressing the limitations of both methods.
The choice between open-ended and close-ended training should align with the learners’ needs, the complexity of the skills being taught, and the desired outcomes. While close-ended training ensures compliance and foundational competence, open-ended training prepares learners for the dynamic and unpredictable nature of real-world challenges. Leveraging both approaches in a complementary manner, particularly through advanced technologies like XR, offers a comprehensive framework for effective skill development.
Hybrid Learning in Industrial Assembly Lines: VOXReality’s Transformative Approach
The VOXReality project revolutionizes hybrid learning in industrial assembly lines by integrating cutting-edge AI-driven natural language processing and speech recognition modules. This approach addresses the key challenges of open-ended training, such as a lack of familiarity with machinery, uncertainty about assembly protocols, safety concerns, and insufficient guidance. By offering real-time interaction and support, VOXReality fosters an environment where workers can learn dynamically and creatively without feeling overwhelmed. The system enables users to receive immediate feedback and contextual instructions, paving the way for more efficient and engaging open-ended training scenarios. VOXReality not only enhances workforce competence but also ensures a safer and more intuitive learning process in industrial settings.
References
- Abich, J., Parker, J., Murphy, J. S., & Eudy, M. (2021). A review of the evidence for training effectiveness with virtual reality technology. Virtual Reality, 25(4), 919–933.
- Ianovici, E., & Weissblueth, E. (2016). Effects of learning strategies, styles, and skill level on motor skills acquisition. Journal of Physical Education and Sport, 16(4), 1169.
- Land, S. M., & Hannafin, M. J. (1996). A conceptual framework for theories-in-action with open-ended learning environments. Educational Technology Research and Development, 44(3), 37–53.
- Studer, K., Lie, H., Zhao, Z., Thomson, B., & Turakhia, D. (2024). An Open-Ended System in Virtual Reality for Training Machining Skills. CHI EA ’24.
