Meaningful Training With VizMove PRISM Virtual Simulation Room
September 12, 2021
Bryce Armstrong & Andy Beall
MEANINGFUL TRAINING WITH VizMove PRISM
In the world of training, there's a huge divide between rote learning and meaningful learning. Too often, instructional methods promote rote learning and we fall short of achieving meaningful learning outcomes. VizMove PRISM provides the tools to create powerful situational awareness through immersive simulation.
To be blunt, rote learning leads to a training regimen that leaves a trainee at best with an ability to recall information. Beyond that, this trainee will have no ability to use what has been learned to solve new problems. Without meaningful learning, a trainee simply lacks the ability to transfer his or her knowledge into future situations.
According to Mayer (2002), modern learning theory includes six categories of learning processes. Rote learning drives just one of these 6 categories, namely Remember. The 5 remaining categories of meaningful learning include: Understand, Apply, Analyze, Evaluate, and Create. It's clear we should endeavor to achieve more than just Remember.
So, how is VizMove PRISM used to engage trainees in meaningful learning? It does so by providing a platform for learning by doing, evoking presence and a sense of place, providing arousing and emotional experiences, and combining rich visualizations with 3D sound.
WorldViz has nearly two decades of experience designing and building virtual experiences for training and research. Our experts help programs implement protocols to meet training objectives and demonstrate the results of meaningful learning.
LEARNING - MEDICAL SIMULATION CONTEXT
Simulation scenario training is common in hospital and prehospital programs. Effective Simulation Operators are frequently educators, actors, and clinicians all at once. They use the tools at their disposal to present clinical scenarios to students that challenge and prepare them for their careers ahead. Thankfully, the medical simulation industry is flourishing with task trainers and patient simulators available to practice most clinical tasks. Using these tools, many programs have the ability to support repetition practice and elevate students' clinical abilities beyond the limits of rote learning. That said, the context in which skills training occurs is an extremely important external factor crucial to meaningful learning.
Undoubtedly, simulation centers are charged to push students toward deep learning. Simulation training scenarios are built to exercise both clinical skills as well as cognitive skills. Task trainers and patient simulators present wonderful opportunities to develop procedural and intervention expertise. VizMove PRISM offers programs an opportunity to exercise students’ skills in a relevant context that engages cognitive skills.
According to Mayer (2002), the learning category “Apply” can be further broken down to 2 cognitive processes: executing— when the task is an exercise (i.e., familiar to the learner), and implementing—when the task is a problem (i.e., unfamiliar to the learner). In medical simulation, this can be compared to executing a procedure vs addressing a patient in need. Patients present in context, and therefore the environment is a critical part of assessing and then addressing a patient’s needs.
LEARNING - SPEED & ACCURACY
Time to completion is one important measure of task performance, and when controlled against an accuracy trade-off, it provides a simple measurement of learned skills over time. VizMove PRISM is ideally suited as a scenario environment because of its tight control, while an instructor can easily measure students’ speed and accuracy.
The learning process can be revealed by a learning curve that exposes how time to completion (of a task or set of tasks) decreases over time. See the figure below. With VizMove PRISM supplying control over the scenario context, it is possible to capture and record different phases of task completion, thus allowing a fine-grained analysis of different learning components and identifying areas of difficulty. By recording this information across all users, the trainer can easily collect a large corpus of data that characterizes the overall learning performance.
LEARNING: TIME-TO-COMPLETION DECREASES OVER TIME
EXAMPLE METHODS MADE EASY WITH VizMove PRISM:
- Setting the training context creates an opportunity for students to engage in the five categories of meaningful learning. VizMove PRISM is a training context where students can Apply clinical skills in an environment with distractions, hazards, and influences of the real world.
- Trainees practice clinical skills in many relevant scenarios. VizMove PRISM can alternate between hospital and/or pre-hospital settings at the push of a button. Programs customize and localize content by capturing their own local neighborhoods, clinics, hospitals, etc, with a 360 camera.
- Learning time-to-completion from start to finish, or of tasks within a simulation can be measured in a meaningful way because of the controlled training context.
- Over time, it's expected that time-to-completion decreases along a predictable curve. By monitoring and ensuring that accuracy rates remain relatively constant (i.e., trainees don't try to game the system and get low times but low accuracy), performance across trainees can be effectively compared.
MASTERY - RETENTION, UNDERSTANDING AND CREATION
Once a task has been trained, it is critical to understand the depth of training, and this can be determined through the measurement of retention. After an individual experiences an initial training regimen, it is typical for performance to decay over time unless the individual has an opportunity to retrain or refresh their knowledge. With a single training exposure, the "forgetting curve" is steep and nearly all training knowledge can quickly be lost (Finkenbinder, 1913). With temporally spaced review exposures, the forgetting function flattens and knowledge can become more permanently retained.
With VizMove PRISM-based training, programs quickly and efficiently create or recreate training scenarios for intermittent or recurring training. During these intervals, by measuring time-to-completion one can determine where the trainee is on the forgetting curve. The trainer can quickly judge the effectiveness of training for groups or particular individuals and modify their program effectively.
Beyond retention, VizMove PRISM can be used to train students to adapt to conditions, which is difficult to cover in more traditional training formats. An individual's ability to adapt and perform reasonably varies. VizMove PRISM is an ideal testbed for examining these perturbations and their effects on performance. Perturbations can be the sudden change or omission of expected environmental conditions or manipulatable objects, or the sudden addition of unexpected environmental factors, or the rearrangement of manipulable objects. Similar to measuring the learning or forgetting curves, VizMove PRISM can monitor time to completion and accuracy when a trainee is suddenly presented with a novel stimulus condition. Besides measuring a trainee's ability to ignore irrelevant stimuli, the process can be used to train coping strategies and inform the trainee how to prioritize tasks in spite of non-optimal conditions.
FORGETTING: REVIEW HELPS RETAIN KNOWLEDGE OVER TIME
Please check out our VizMove PRISM page to learn more about WorldViz VR training solutions.
Contact us at email@example.com for a free consultation call. Let’s discover together what VR training solution suits you best!
Mayer, R. E. (2002). Rote versus meaningful learning. Theory into Practice, 41, 226–232.
Finkenbinder, E.O. (1913) The curve of forgetting. The American Journal of Psychology 24: 8–32
Some relevant quotes from: http://web.mit.edu/jrankin/www/teach_transfer/rote_v_meaning.pdf
Meaningful learning is recognized as an important educational goal. It requires that instruction go beyond simple presentation of Factual Knowledge and that assessment tasks require more of students than simply recalling or recognizing Factual Knowledge (Bransford, Brown, & Cocking, 1999; Lambert & McCombs, 1998).