Abstract
Educational immersive virtual reality is often tasked with minimising distractions for learners and maintaining or signalling their focus to the right areas. Managing location, density and relevancy of visual information in the virtual environment pertain to this. Essentially this problem could be defined as the need of management of cognitive load from the visual information. To aid in the automated handling of this problem, this study investigates the use of automated state-space planning to model the current “state” of the virtual environment, and determine from a given pool of steps or “actions”, a sequence that prioritise minimising cognitive load from visual information through planning the location and density of objects. This study also investigates modelling the state of what a learner has been informed of and applied. This enables planning to determine when to have the learner relate concepts to existing knowledge for deeper knowledge; planning their generative learning. These states are planned in conjunction with the virtual environment states. The planning is also responsive to identified changes in the learner’s deviated attention, or performance with the task. Together it has the potential to minimise the cognitive load from being taught intrinsic information, and minimising extraneous information from the virtual environment. What was produced currently does not yield many results beyond the method of planning hel** the virtual reality applications manage where information appears, but it at least also established a framework for future testing, and improvements to the used methods. This paper provides in more detail, the background for this topic in immersive virtual reality, its significance, the methods used and an evaluation of the method and how further investigations will be continued.
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Notes
- 1.
Serious games: Games created with the aim of educating or informing the player.
- 2.
Such as lecture powerpoint presentations [22].
- 3.
Like optimising immersion, visualisation or fidelity of interaction in virtual reality applications [5].
- 4.
Planning is a known field, which - when phrased broadly - consists of the use of algorithms to identify a series of needed steps, to reach an identified goal.
- 5.
Interactive media is often tasked with minimising the distractions from its visualisation if it needs the user to focus on a specific area.
- 6.
Like in a study by Kyritsis [17].
- 7.
- 8.
Personalised learning learns from the learner as they learn, which occurs both before and during learning. Preferences on the other hand are strictly identified before learning begins, and changed and re-planned when their preferences have been identified to be different from what was expected, such as when the user states their preferences have changed.
- 9.
Knowledge that is not involved in any action precondition or effect.
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All authors and contributors are a part of Teesside University. The research is funded by Teesside University.
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Ladeinde, O., Razzaque, M.A., Han, T.A. (2020). Using Automated State Space Planning for Effective Management of Visual Information and Learner’s Attention in Virtual Reality. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_3
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