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UCL are currently advertising two fully-funded Bloomsbury PhD studentships.
Apply for one of the below:
1. Project title: Mathematical abilities in Williams syndrome across development: A longitudinal study
Closing date for applications is: 22 February 2022
Principal Supervisor: Dr Jo Van Herwegen (IOE, UCL’s Faculty of Education and Society)
Co-Supervisor: Prof Michael Thomas (Birkbeck University College)
Project Description
The proposed studentship project will involve secondary data analysis and collection of new data to examine longitudinal trajectories of mathematical development and relationships to executive functioning, visuo-spatial and language abilities in individuals with Williams syndrome. It will use data from the WISDOM project (See https://blogs.ucl.ac.uk/wisdom/wisdom-database/) as well as involve new data collection. The project will take place in the context of the Centre for Educational Neuroscience, a cross-institution research centre spanning IOE and BBK, which has an active research programme in linking the cognitive neuroscience of neurodevelopmental disorders with SEN pedagogy.
Background
Good mathematical skills are important for everyday life and independence. Good mathematical abilities rely on domain-specific abilities related to maths as well as a wide range of domain-general abilities such as visuo-spatial skills, language and executive functioning abilities.
Many children with the genetic disorder Williams syndrome (WS) are delayed in their mathematical abilities and low mathematical abilities may impact on their level of independence. Variations in developmental trajectories in genetic disorders shed light on the constraints that shape typical development, and may point towards methods or timing of best interventions for low performance in neurotypical children. However, most studies so far have relied on cross-sectional data, rather than longitudinal data. In addition, nothing is currently known about the individual differences and how different developmental trajectories for mathematical abilities are driven by variations in domain general abilities.
Aims
The current PhD studentship will examine longitudinal developmental trajectories of mathematical abilities in WS from infancy onwards, how these are influenced by domain-general abilities, how cross-sectional trajectories compare to longitudinal ones, as well as what factors (e.g., SES, type of education) influence individual differences in longitudinal trajectories for mathematical development.
Outcomes
A better understanding of development in WS will allow us to predict particular educational needs earlier, leading to more effective teaching and learning systems and develop a better understanding of how better to support people with WS across the whole lifespan. Understanding of these developmental trajectories will also provide further theoretical insight into the foundations of mathematical abilities in general, and sources of variation in developmental trajectories.
Subject Areas/Keywords
Mathematical development, Williams syndrome, SEND, education, Longitudinal.
Key References:
1Van Herwegen, J., Purser, H., & Thomas, M.S.C. (2019). Development in Williams syndrome: Progress, prospects and challenges. Advances in Neurodevelopmental Disorders, 3, 343-346. https://doi.org/10.1007/s41252-019-00109-x.
3Van Herwegen, J. & Simms, V. (2020). Mathematical development in Williams syndrome: A systematic review. Research in Developmental Disabilities. Special Issue
Further details about the project may be obtained from:
Principal Supervisor: j.vanherwegen@ucl.ac.uk
Co-Supervisor: m.thomas@bbk.ac.uk
Further information about PhDs at UCL Institute of Education is available from: https://www.ucl.ac.uk/ioe/courses/graduate-research/psychology-human-dev...
Candidates can apply via: https://ucl.fluidreview.com/prog/
Please ensure the correct project title is selected.
After a successful interview, the successful candidate will be required to submit a proposal and apply to the IOE: https://www.ucl.ac.uk/prospective-students/graduate/apply.
2. Project title: Improving Children’s Executive Function Skills Through Virtual Reality Neuromonitoring and Feedback
Closing date for applications is: 7 March 2022
Principal Supervisor: Prof Mavrikis Manolis (IOE, UCL’s Faculty of Education and Society)
Co-Supervisor: Prof Tim J. Smith (Birkbeck University College)
Project Description
Executive function (EF) and self-regulation skills are critical predictors of school success (Blair & Razza, 2007), wellbeing and general life outcomes (Moffitt et al., 2011). While there are clear indications of the benefits of training EF in early developmental stages (Blair, 2016) and of helping children think explicitly about their own learning (Hattie, 2012), schools and parents are not equipped to support learners with this fundamental skill. This is partly because little is known about the exact mechanisms involved in EF from a cognitive psychology perspective, how to support self-reflection in young children from a pedagogical perspective (Larkin, 2010) and critically how EF operates during real-world problem solving.
Neuromonitoring provides a methodological platform that has the potential to offer both research insights into our collective understanding of EF, but also real-time support through feedback for reflection and self-regulation.
This project will design and empirically evaluate an intervention using real-time neuromonitoring and feedback that supports the training of executive function in a real-world scenario. This is possible due to the highly unique facilities provided by BBK’s Wellcome-funded ToddlerLab Neuroimaging CAVE (Cave Automatic Virtual Environment), a world-first facility allowing children to interact with augmented virtual/real environments (e.g. AR/VR) whilst wearable technology tracks their movements (via motion capture), attention (via an eye tracker), and brain activity (via functional Near Infra-Red Spectroscopy; fNIRS).
By integrating techniques from the field of Artificial Intelligence in Education (AIED) such as User Modelling (UM) a computational architecture could be developed to enable the provision of real-time feedback (e.g. pre-emptive attention prompts) and generation of reflection opportunities (e.g. recordings of critical moments) that have been shown to improve children’s EF. Previous work has demonstrated the potential of such approaches in supporting learning in real-time e.g., when students interact with digital problem-solving environments such as games and simulations (Grawemeyer, Mavrikis et al. 2016). Additionally, learning analytics (LA) and open learner modelling (OLM), while usually targeted to teachers (Mavrikis et al. 2019) have also been used as tools for reflection and metacognition to support self-regulation even for primary school children (Bull, McKay, 2004; Jones et al., 2018). The challenge is leveraging the insights of learner modelling from screen-based interactions and applying them to realistic everyday 3D scenarios.
Requirements
We are looking for a highly-motivated candidate with strong quantitative, analytical and/or programming skills and a desire to make an impact in the intersection of education with developmental psychology.
The candidate must have evidence of outstanding undergraduate academic performance in either cognitive science, psychology or computer science, artificial intelligence, 3D modelling/games design and ideally have (or be predicted to obtain) a strong Master’s degree in Cognitive/Developmental Neuroscience, Artificial Intelligence in Education, computational data science, or any cognate field (candidates will be asked to demonstrate how their background provides solid foundations to allow them to focus on the core aspects of this studentship).
Candidate must also demonstrate solid foundations in academic writing and presenting, in independently organising aspects of their research (e.g. through a previous dissertation if not publications) and experience of working with young children.
Subject Areas/Keywords
Artificial Intelligence in Education, Developmental Science, Cognitive Psychology, Executive function, Neuroscience
Key References:
Jones, A., Bull, S. & Castellano, G. “I Know That Now, I’m Going to Learn This Next” Promoting Self-regulated Learning with a Robotic Tutor. Int J of Soc Robotics 10, 439–454 (2018).
Hendry, A., Agyapong, M. A., D'Souza, H., Frick, M. A., Portugal, A. M., Konke, L. A., Bedford, R., Smith, T.J., Jones, E., Charman, T. and Brocki, K. (2018, December 5). The Problem-Solving Box: A Novel Task for Assessing Executive Functions in 1.5- to 4-year-olds. https://doi.org/10.31219/osf.io/srafv
Harrivel AR, Weissman DH, Noll DC and Peltier SJ (2013) Monitoring attentional state with fNIRS. Frontiers in Human Neuroscience. 7:861.
Grawemeyer, B., Mavrikis, M., Holmes, W. et al. (2017) Affective learning: improving engagement and enhancing learning with affect-aware feedback. User Modeling and User-Adapted Interaction 27, 1, pp.119–158.
Further details about the project may be obtained from:
Principal Supervisor: Prof Manolis Mavrikis (m.mavrikis@ucl.ac.uk)
Co-Supervisor: Prof. Tim J. Smith (tj.smith@bbk.ac.uk) and https://www.cinelabresearch.com
Further information about PhDs at IOE and BBK is available from:
Birkbeck Psychological Sciences: https://www.bbk.ac.uk/study/2022/phd/programmes/RMPPSYCH
and
https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/cul...
Candidates can apply via: https://ucl.fluidreview.com/prog/
After a successful interview, the successful candidate will be required to submit a proposal and apply to the IOE: https://www.ucl.ac.uk/prospective-students/graduate/apply.