The psychology of learning and VR training

In this blog post, DR DAVID HARRIS, from the University of Exeter, discusses how psychological theories and learning principles underpin what we do at Cineon to develop meaningful, useful, virtual, training tools that work to maximise skill learning.

The training tools that we develop at Cineon are underpinned by several learning principles. By designing VR training tools based on established psychological theories of learning, we aim to maximise the benefits of our VR training. The development of these guiding principles has been part of our ongoing relationship with University of Exeter academics (VITAL research group) who have developed questionnaire tools for measuring user experience and frameworks for testing the validity of virtual environments. In this blog post, we discuss some of these general principles that can be used to maximise learning.

Summary of training principles:

– Make learning more active
– Design the learning activities to provide the optimal level of difficulty
– Provide lots of repetition, with some variance
– Give intermittent feedback which is also timely, specific, task-related, and individualised

Explorative practice

When implementing VR training, an important pedagogical principle is the value of active practice. Allowing learners to actively engage with the material and learn through discovery – as opposed to receiving information more passively – is beneficial for achieving deeper learning.

An approach known as explorative practice (Trninic, 2018) proposes that guided discovery, where the student is shown how to practice, is the optimal approach. Explorative practice emphasises a high degree of guidance, but a minimal explanation. In other words, the instructor (or in this case the VR tool) leads the learner towards the discovery – the moment of realisation when they produce the desired outcome – but avoids providing explicit knowledge about why it is right or what is right about it.

Virtual reality training is ideally suited to exploit the benefits of explorative practice and active learning. VR can often enable explorative practice for tasks that were previously taught through instructive methods in the classroom, and VR tools can be easily furnished with guiding information that leads the learner to discover the solution.

The value of repetition without repetition

It is well established that to develop a high level of motor skill, extensive repetition, and practice is needed (Fitts & Posner, 1967). While the oft-quoted requirement of 10,000 hours of practice to develop expertise has been largely debunked as a myth (Ericsson et al., 1993), it remains true that repetition is needed to embed and refine new learning.

A well-known perspective on practice in the field of motor learning comes from the Russian neurophysiologist Nikolai Bernstein. Bernstein argued that skill development follows the process of “repetition without repetition”, by which he meant that practice was not a case of beating down the same path, but was an exploration of the optimal ways in which to solve a motor problem.

Providing varying challenges or constraints within the learning activity can support the learner in exploring new solutions. The ability of VR to provide a large number of repetitions, but with slight variations, affords an excellent opportunity for “repetition without repetition”.

Balancing load

A feature that is common across most computer games, and can be capitalised upon in VR training, is the matching of task demands to the player’s skill level. Computer games naturally get harder as the player improves and learns, which serves to support engagement, enjoyment, and flow (Csikszentmihalyi, 2014; Hamari et al., 2016). If this doesn’t happen, the lack of demand makes a task boring and leads to disengagement (Chanel et al., 2008). But in addition to maximising enjoyment and engagement, a number of educational principles suggest that an optimal level of demand or difficulty is crucial for learning as well.

Firstly, well-established work on Cognitive Load Theory (CLT; Sweller, 1988) has shown that high demands on information processing can impair learning and that tasks should be scaled so as not to overload the learner. Returning to a motor learning focus, the Challenge Point Framework (Guadagnoli & Lee, 2004) outlines the importance of the relationship between the difficulty of the task and the skill of the performer. This means that when the task is too easy there is little information available and no learning will occur, but when the task is too challenging there is too much information for learning to occur. Consequently, the optimal amount of information differs as a function of the skill level of the individual and the difficulty of the task.

An autonomous system, such as a VR training tool, can make use of these principles to enhance learning through data-driven feedback. A VR tool can output performance metrics or indicators of the user’s state (e.g. eye-tracking) which can then feedback and determine changes in the simulation that provide a balanced load for the performer. This method can be referred to as adaptive (or self-adaptive) VR, a method we are currently developing.

Implementing feedback

Feedback is information provided by an agent about aspects of one’s performance, and may have as large an impact on learning as prior cognitive ability (Hattie, 1999). Feedback is known to be more effective when it provides information on correct rather than incorrect responses (Kluger & DeNisi, 1996). Feedback needs to be delivered in a timely, specific, and individualised manner (Gibbs & Simpson, 2005) and should preferably be task-related, focusing on the quality of student performance, as opposed to focusing on the personal characteristics of the trainee (Shute, 2008).

An important source of feedback available for guiding learning comes from the learner. A point that links nicely with the importance of active learning, where practice provides its own feedback.

While feedback is necessary for learning, overly-frequent knowledge of results can lead to feedback dependency and hinder learning (Salmoni et al., 1984). Partial feedback can help the learner to develop their own error-detection ability, which will be needed for ongoing and self-directed learning. Therefore, some feedback is useful, but allowing the learner to engage in discovery learning, learn what right feels like (Vygotsky, 1997) and develop their own error detection ability is important.

The flexibility of VR training tools means that performance can be supplemented with external feedback at any point. But the real strength of VR in this area might be the potential for active learning which can allow learners to get things wrong in a safe environment and therefore provide their own feedback.

In summary, it is important to bear in mind these educational principles, and others, when designing VR training. VR is well suited to exploit these learning principles, by providing an active learning environment, with just the right amount of guidance, but also providing opportunities for learners to make mistakes and discover solutions.

References
Chanel, G., Rebetez, C., Bétrancourt, M., & Pun, T. (2008). Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. Proceedings of the 12th International Conference on Entertainment and Media in the Ubiquitous Era, 13–17. https://doi.org/10.1145/1457199.1457203
Csikszentmihalyi, M. (2014). Toward a Psychology of Optimal Experience. In Flow and the Foundations of Positive Psychology (pp. 209–226). Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9088-8_14
Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The Role of Deliberate Practice in the Acquisition of Expert Performance. Psychological Review, 100(3), 363–406.
Fitts, P. M., & Posner, M. I. (1967). Human performance. Brooks/Cole.
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
Gibbs, G., & Simpson, C. (2005). Conditions Under Which Assessment Supports Students’ Learning. Learning and Teaching in Higher Education, 1, 3–31.
Guadagnoli, M. A., & Lee, T. D. (2004). Challenge Point: A framework for conceptualizing the effects of various practice conditions in motor learning. Journal of Motor Behavior, 36(2), 212–224. https://doi.org/10.3200/JMBR.36.2.212-224
Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179. https://doi.org/10.1016/j.chb.2015.07.045
Hattie, J. A. (1999). Influences on student learning. (Inaugural professorial address, University of Auckland, New Zealand). Retrieved from http://www.arts.auckland.ac.nz/staff/index.cfm?P=8650.
Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Kapur, M. (2016). Examining Productive Failure, Productive Success, Unproductive Failure, and Unproductive Success in Learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457
Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284. https://doi.org/10.1037/0033-2909.119.2.254
Macnamara, B. N., Moreau, D., & Hambrick, D. Z. (2016). The Relationship Between Deliberate Practice and Performance in Sports: A Meta-Analysis. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 11(3), 333–350. https://doi.org/10.1177/1745691616635591
Masters, R., & Maxwell, J. (2008). The theory of reinvestment. International Review of Sport and Exercise Psychology, 1(2), 160–183. https://doi.org/10.1080/17509840802287218
Salmoni, A. W., Schmidt, R. A., & Walter, C. B. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 95(3), 355–386.
Schmidt, R. A., & Bjork, R. A. (1992). New Conceptualizations of Practice: Common Principles in Three Paradigms Suggest New Concepts for Training. Psychological Science, 3(4), 207–218. https://doi.org/10.1111/j.1467-9280.1992.tb00029.x
Shea, J. B., & Morgan, R. L. (1979). Contextual Interference Effects on the Acquisition, Retention, and Transfer of a Motor Skill. Journal of Experimental Psychology: Human Learning and Memory, 5(2), 179–187.
Shute, V. J. (2008). Focus on Formative Feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7
Trninic, D. (2018). Instruction, repetition, discovery: Restoring the historical educational role of practice. Instructional Science, 46(1), 133–153. https://doi.org/10.1007/s11251-017-9443-z
Vygotsky, L. S. (1997). Educational psychology. (R. H. Silverman, Translator). Boca Raton, FL: CRC Press LLC.
Wolpert, D. M., & Flanagan, J. R. (2001). Motor prediction. Current Biology, 11(18), R729–R732. https://doi.org/10.1016/S0960-9822(01)00432-8
Wulf, G., & Schmidt, R. A. (1997). Variability of practice and implicit motor learning. Journal of Experimental Psychology. Learning, Memory, and Cognition, 23(4), 987–1006.