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Machine Learning Enabled Game Design to Improve Children's Cognitive Performance


   School of Computing

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  Dr Shufan Yang, Dr M Wimmer  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

About the Project:  An equitable society is for creating conditions that allow all to reach their full potential. However, studies found However, research has found a relation between socio-economic background and cognitive ability that extends to performance in STEM subjects during years of schooling, which prevents them from reaching potential as their counterparts. In this project we propose to set a series of experiments to understand the development of hand and eye coordination and other cognitive skills in children and explore the fundamental trade-off between nature and nurture.

Based on those understandings, we will develop a machine learning based multimedia game to help children boost their cognitive development through playing. Thus, targeted learning through play may provide a fruitful individual training tool for working towards a more equitable society. 

Academic qualifications:

A 2:1 degree or above in a relevant scientific discipline, such as computer science, engineering, mathematics, physics, or cognitive neuroscientist. Desirable skills include mathematics, statistics, machine learning, computer vision, deep learning, natural language processing, human-computer interfaces, and software engineering.

English language requirement:

IELTS score must be at least 6.5. Other equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

Candidates who are interested this research topic need to have a problem-solving skill with a strong passion and commitment.

Edinburgh Napier University is committed to promoting equality and diversity in our staff and student community

https://www.napier.ac.uk/about-us/university-governance/equality-and-diversity-information.


Funding Notes

This is an unfunded position.

References

[1] Yang, S., Wong-Lin, K., Andrew, J., Mak, T. and McGinnity, T. M. (2018) A Neuro-inspired Visual Tracking Method Based on Programmable System-on-chip Platform. Neural Computing and Applications, 30(9), pp. 2697-2708. (doi:10.1007/s00521-017-2847-5)
[2] Yang, S., Wong-Lin, K., Rano, I. and Lindsay, A. (2018) A Single Chip System for Sensor Data Fusion Based on a Drift-diffusion Model. In: Intelligent Systems Conference (IntelliSys) 2018, London, UK, 7-8 Sept 2018, pp. 198-201. ISBN 9781509064359 (doi:10.1109/IntelliSys.2017.8324291)
[3] Khine M.S. (2017) Spatial Cognition: Key to STEM Success. In: Khine M. (eds) Visual-spatial Ability in STEM Education. Springer, Cham. https://doi.org/10.1007/978-3-319-44385-0_1
[4] Wimmer, M. C., Maras, K. L., Robinson, E. J., Doherty, M. J., & Pugeault, N. (2015). How Visuo-Spatial Mental Imagery Develops: Image Generation and Maintenance. PLOS ONE, 10, e0142566--e0142566. https://doi.org/10.1371/journal.pone.0142566
[5] Wimmer, M. C., Maras, K. L., Robinson, E. J., & Thomas, C. (2016). The format of children's mental images: Evidence from mental scanning. Cognition, 154, 49-54. https://doi.org/10.1016/j.cognition.2016.05.017
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