Rationale / background
Rationale / background (summary of essential literature contributing to the rationale for your study, and if possible, a statement on why the research is worthwhile)
Alpha power and education are seen to be important in measuring the success rate in children’s educational behaviour. The underpinnings of resting state alpha in relation to learning disabilities in children is that it measures resting state alpha EEG (Jäncke & Alahmadi, 2015). Engagement and attention are fundamental for the students in learning. In this case, EEG can be suitable method of assessing educational success because resting state alpha EEG is different amongst children with Appropriate Developmental Milestone and children with learning disabilities.
Children with learning disorders tend to exhibit higher power and coherence in slow frequencies such as delta as well as theta bands while lower power in alpha band compared to healthy children where power for all bands exhibit similar results. Hence, EEG is considered as promising approach for assessing level of learning disability and success level in education.
Roca-Stappung et al. (2016), conducted experimental study regarding Electroencephalographic characterization for children with learning disability. The findings reported that alpha power is lower in terms of writing, reading and understanding for the children with learning disability, especially they score lower than average. The children with significant deficiency such as dysphonetic, dyskinetic tend to exhibit a decrease in mean amplitude and absolute power. They usually have higher level of delta and theta that indicate lag in brain development which reflected in deficiencies in arithmetic and calculation process compared to children with minor disabilities (Alahmadi 2017). On the other hand, Alahmadi, N. (2015), also conducted experimental study regarding Electroencephalographic characterization for children with learning disability. The findings reported that EEG abnormalities exhibit for learning disability especially low score in neural activities indicate they have low reading, writing and understanding capabilities. Hence, teachers can assess EEG data for assessing the learning disability based on resting alpha and provide additional support to improve reading, writing and understanding.
Majority of the method of assessing attention are unrealistic or intrusive that limit the identification of severity of learning disability and impact on educational surface. Therefore, this study is suitable for the research professionals because traditional approaches measure engagement through assessing behaviour, self-reporting or manual annotation. From educational perspective, such quantitative measure enable may enable researchers to identify the mechanism that make learning more efficient. It will also enable researchers to it enable researchers to align better educational services and monitor critical task performance, summery feedback for the teachers and develop motivational strategies for the student (Poulsen et al., 2017).
Research question / aims / hypotheses (including rationale for how these can be derived from the relevant literature)
The research question in mind is how does alpha power correlate with children’s educational behaviour?
The study aims to assess alpha power correlate with children’s educational behaviour
Null hypothesis: Alpha power of EEG is not correlate with the educational behaviour of the children.
Alternative hypothesis: : Alpha power of EEG is directly correlate with the educational behaviour of the children.
Alpha power and education in measuring success in children’s behaviour.
The relevant literature discussed above used EEG for assessing level of Engagement and attention amongst children. In this case, the emerging research suggested that children with learning disability exhibit high power in slow frequencies such as delta and theta bands and lower power in alpha band. Hence, it also has impact on educational behaviour because they tend to have low reading and writing capabilities.
Participants and ethical considerations/concerns (e.g. vulnerable groups?; DBS approval?; NHS Trust ethical approval?)
The participants can be obtained from primary school, aged between 2 to 5 years. The inclusion criteria for participant selection are 1) children aged 2 to 5 years 2) children having issues in learning and exhibited poor academic performance. The children will be assessed with Wechsler Intelligence Scale for Children–IV or e Child Neuropsychological Assessment to assess the issues in learning. The children will be subjected to Full Scale Intelligence Quotient (FSIQ). Full Scale Intelligence Quotient (FSIQ) is most suitable way of suggesting that learning disability because children with learning disability usually have IQ lower than 70 (Roca-Stappung et al., 2016). Hence, children having IQ higher than 70 will be excluded from the study. Their parents will be complete the consent form ( outlining purpose, aim , procedure and expected outcome) while children will be involved after taking verbal permission from the children.
DBS approval will be obtained for conducting research on vulnerable population using Deep brain stimulation (Kumar et al., 2018). It will crucial to gain DBS approval because DBS usually assess any criminal convictions are in place while conducting research. It will enable researchers to maintain autonomy , beneficence without causing harm to the human subject. After obtaining DBS research, the research design and careful assessment will be done.
Proposed methodology/design/analyses/ (tick as many as appropriate and describe the method/design/analyses/ and how these will address the hypotheses)
Spearman’s rank correlation will be used in relation to resting state alpha EEG and children’s educational behaviour. It is nonparametric version of the Pearson product-moment correlation (Makowski et al., 2020). 0.95 co-efficient of correlation will indicate a strong correlation between alpha EEG and children’s educational behaviour and hence, alternative hypothesis will be accepted.
Quantitative Yes
Experimental
Lab-based Yes
Field-based
Observational
Questionnaire
Interview
Archival
Multi-method
Proposed work plan to indicate your study timeline (ethics/recruitment/data collection/data analysis/write-up)
The topic selection will be done for the research so that the conducted research can contribute to future research. For example, in this case, researchers aim to assess how does alpha power correlate with children’s educational behaviour. This will be considered
Summary of essential literature will be conducted and literature gap will be discussed that will further provide idea of the future research and instrumentation
Apart from Summary of essential literature, methodology selection will be done for assessing appropriate method that will support the research result and improve relevance of the research.
After selecting the methodology, the data will be collected from primary school, children aged between 2 to 5 years. The venue, place and setting will be selected and informed consent will be obtained.
The data assessment will be done for 1 month and it will be done using Spearman’s rank correlation.
The report will be written and first draft will be shared to the professors for correction.
Dissemination to the participants
References
Jäncke, L., & Alahmadi, N. (2015). Resting State EEG in Children With Learning Disabilities. Clinical EEG And Neuroscience, 47(1), 24-36. doi: 10.1177/1550059415612622 Alahmadi, N. (2015). Classifying children with learning disabilities on the basis of resting state EEG measures using a linear discriminant analysis. Zeitschrift für Neuropsychologie. https://econtent.hogrefe.com/doi/abs/10.1024/1016-264X/a000161?journalCode=znp
Alahmadi, N., 2017. New approaches to the diagnosis and treatment of learning disabilities in an international context. Zeitschrift für Neuropsychologie. https://econtent.hogrefe.com/doi/abs/10.1024/1016-264X/a000180?journalCode=znp
Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27-30. https://search.informit.org/doi/abs/10.3316/INFORMIT.738299924514584
Kumar, V., Machado, A. G., Ramirez-Zamora, A., & Pilitsis, J. G. (2018). 12 DBS Innovations in the Near Future?. Surgery for Parkinson’s Disease, 159. https://books.google.co.in/books?hl=en&lr=&id=etSBDwAAQBAJ&oi=fnd&pg=PA159&dq=DBS+approval+&ots=FVzOzsS84t&sig=2oGeqaT9qnyw5ml7kLIocc69kkE&redir_esc=y#v=onepage&q=DBS%20approval&f=false
Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Methods and algorithms for correlation analysis in R. Journal of Open Source Software, 5(51), 2306. https://joss.theoj.org/papers/10.21105/joss.02306.pdf
Poulsen, A.T., Kamronn, S., Dmochowski, J., Parra, L.C. and Hansen, L.K., 2017. EEG in the classroom: Synchronised neural recordings during video presentation. Scientific reports, 7(1), pp.1-9. https://www.nature.com/articles/srep43916
Roca-Stappung, M., Fernández, T., Bosch-Bayard, J., Harmony, T., & Ricardo-Garcell, J. (2017). Electroencephalographic characterization of subgroups of children with learning disorders. PloS one, 12(7), e0179556. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179556
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