Mathematical achievement is predictive of academic success, future employment, economic productivity1 and health outcomes2. Global comparison studies such as PISA3 have emphasised the impact of the mathematical skills of a country on economic growth and well-being4. The cost of poor numeracy to the UK public sector has been estimated at £2.4bn per year5. On a national level, a substantial proportion of children (e.g. 13% in England) do not meet expected outcome levels in mathematics in primary school at Key Stage Two.6 Poor early numeracy skills have been acknowledged as contributing to a “viscous cycle of disadvantage and a poverty of opportunity”7.
A number of general cognitive and mathematics-specific skills have been identified as being predictive of mathematical achievement in primary school aged children such as working memory8, inhibitory control9, counting skills10 and flexible strategy use11. Although there has been a recent increase in the focus on mathematical development research there is still paucity in our understanding of mathematical achievement when compared to reading. Specifically, the identification of children who may struggle with mathematics has been problematic.
Therefore, there is a current debate in the academic literature, and with practitioners, focused on identification and intervention. This is mainly due to the lack of diagnostic tools. Eye tracking provides a unique opportunity for identifying children who may struggle with learning mathematics. Specifically, a case study12 and a small scale study13 using eye tracking data has indicated that there may be differences in strategy use between children who do and do not struggle with mathematical processing. However, these studies are far from conclusive, mainly as they use very small samples. Thus, by using eye tracking methodology in more adequately powered studies this project will aim to gain insight into children’s processing and, using computational models of eye tracking data, will attempt to identify children who find mathematical processing difficult.
Primary objective of the research:
To investigate the visual processing strategies children use when completing mathematical tasks using eye tracking technology;
to utilise eye tracking data to identify children who may struggle with mathematical processing.
Methods: This study will assess children’s strategy use when completing a number line estimation task, a non-symbolic comparison task and a basic arithmetic task. Standardised mathematical tasks will be administered to assess mathematical achievement. Children’s eye movements will be recorded using a TOBII eye tracker (new, state of the art equipment in the Psychological Science Laboratory at Ulster University). These data will be used to establish developmental patterns in strengths and difficulties in strategy use when completing mathematics tasks.
This study will contribute important data to inform diagnoses and intervention. Ethics Eye tracking technology is non-intrusive (the eye tracker is positioned along the bottom of a computer screen). Ethical approval will be sought through Ulster University Ethics Panel before commencing the project. Parents/guardians of participating children will provide written informed consent for their child to take part in the study. Children will provide assent.
1. Williams, J. Clements, S.,Oleinikova, K. & Tarvin, K. (2003) The Skills for Life survey: A national needs and impact survey of literacy, numeracy and ICT skills. United Kingdom: Department for Education and Skills.
2. Every Child a Chance Trust and KPMG. (2008). The long term cost of numeracy difficulties. United Kingdom: Every Child a Chance Trust.
3. Organisation of Economic Co-operation and Development (2014). Education at a glance: OECD indicators. Source: http://www.oecd.org/edu/Education-at-a-Glance-2014.pdf Last Accessed: 1st January 2016.
4. Organisation of Economic Co-operation and Development (2010). Education at a glance. Source: http://www.oecd.org/education/skills-beyond-school/45926093.pdf Last Accessed: 1st January 2016.
5. Goss, J., Hudson, C. & Price, D. (2009). The Long Term Costs of Numeracy Difficulties. Every Child a Chance Trust: London.
6. Department for Education. (2015). Source: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/483897/SFR47_2015_text.pdf Last Accessed: 1st January 2016.
7. Northern Ireland Audit Office. (2013). Improving literacy and numeracy achievement in schools. United Kingdom: Northern Ireland Audit Office.
8. Raghubar, K.P., Barnes, M.A., & Hecht, S.A. (2010). Working memory and mathematics: A review of developmental, individual difference, and cognitive approaches. Learning and Individivual Differences, 20, 110-122.
9. Cragg, L. & Gilmore, C. (2014). Skills underlying mathematics: The role of executive function in the development of mathematics proficiency. Trends in Neuroscience and Education, 3, 63-68.
10. Cowan, R., Donlan, C., Shepherd, D.L., Cole-Fletcher, R., Saxton, M., & Hurry, J. (2011). Basic calculation proficiency and mathematics achievement in elementary school children. Journal of Educational Psychology, 103, 786-803.
11. Geary, D., & Brown, S. (1991). Cognitive addition: Strategy choice and speed-of-processing differences in gifted, normal, and mathematically disabled children. Developmental Psychology, 27, 398-406.
12. van Viersen, S., Slot, E., Kroesbergen, E., van’t Noordende, J., & Leseman, P. (2013). The value of eye-tracking in diagnosing dyscalculia; a case study. Frontiers in Psychology, 4, 679. Doi: 10.3389/fpsyg.2013.00679
13. van’t Noordende, J., van Hoogmoed, A., Schot, W., & Kroesbergen, E. (2016). Number line estimation strategies in children with mathematical learning difficulties measured by eye tracking. Psychological Research, 80, 368-378.
This is a self-funded MRes opportunity.
Completing the MRes provided me with a lot of different skills, particularly in research methods and lab skills.
Michelle Clements Clements - MRes - Life and Health SciencesWatch Video
Friday 31 May 2019
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