Neural Correlates and Educational Performance Analysis by Rapid Assignment Help
Comparing education-related differences and neural correlates is helpful for understanding how the neural basis underlies performance practices. It has been reported that electroencephalography (EEG) in resting state can record different cognitive conditions. Therefore, by investigating the relationships between various EEG waves, such as alpha and beta, and academic performance in mathematics and English, researchers can evaluate how cognitive processing interacts with education. This study concerns identifying such correlations among primary school children to elaborate patterns that could improve children’s educational assessment and possibly inform interventions (Klimesch, 2012). Out of these, alpha suppression has been studied in relation to cognitive engagement, especially when performing tasks. Such analyses are often supported by academic resources, including those offering Online Assignment Help in UK, which assist students in interpreting and presenting complex neurological concepts effectively.
Alpha waves with a peak frequency between 8 to 12 Hz are observed during wakefulness with ideation, dozing and during eyes close condition. Beta, gamma, delta, theta, and other cognitive- and task-related frequencies all take turns depending on what kind of area the brain is working on, with one notable exception: during periods of focused attention, such as reading or solving a problem, alpha wave activity suppresses (Jensen & Mazaheri, 2010). Such a reduction indicates that the brain is doing its work, or from a resting, non-hypnosis to hypnosis mode of operation. Knowledge about these changes in neural activity especially in children is important in assessing how academic work affects cognitive functions.
Studies prove that EEG is effective in revealing dynamism of the activity linked to mental tasks such as attention, memory, and even information processing (Klimesch, 2012; Bazanova & Vernon, 2014). Such EEG-based observations have been used to capture and analyse the brainwave activity in real time, and therefore determine how students interact with academic content. The present study extends previous research by analysing the relationship between EEG signals, namely, alpha suppression and achievement in mathematics and English amongst primary school going children.
Alpha Suppression and Cognitive Engagement
Alpha suppression is a well-established measure of mental workload during the tests which require the use of specific part of the brain. As a result of changing states, from the rest with dominant alpha waves, active thinking causes a decrease in alpha waves. This means that the brain has to invest more resources to handle and perform the analysis of the received data, known as desynchronization (Klimesch, 2012). These last changes reflects decreased alpha activity, which, at a frequency <9 Hz, particularly in the occipital and parietal areas, indicates shift toward attention and effort (Jensen & Mazaheri, 2010).
As it has been mentioned, a number of researches have been devoted to the application of alpha suppression in environments. For example, Bazanova and Vernon (2014) when studying qualitatively marked children higher alpha suppression in children during reading comprehension tasks resulted in enhanced academic performances. Alpha suppression, the ability of the brain to filter out irrelevant stimuli and focus on stimuli related to, means it is the most important characteristic of how students interact with content (Jensen & Mazaheri, 2010). Preliminary momentum and alpha suppression dependably quantify the extent to which the students paying attention in tasks that require long concentration such as solving arithmetic problems (Bazanova & Vernon, 2014).
As elaborate by Klimesch in a study, participants displayed comparatively lower alpha amplitude during complex tasks compared to simple tasks reiterating the suppression of alpha wave in respect to mental effort. In particular, the authors found that alpha wave desynchronization increases with the load on working memory and is a direct measure of data processing difficulty in a given task. For instance, while performing reading comprehension tasks, those people with enhanced alpha suppression encoded more facts proving the critical part played by alpha desynchronization in information processing. There was also evidence that alpha suppression is linked with superior academic achievement in Mathematics and English. As the children perform activities involving critical thinking the level of alpha waves reduces while the level of beta waves increases.
Alpha suppression is linked with mental effort as well as working memory and information processing. Whenever one is doing a short-term task, such as in a math problem or when recalling something to state during reading, alpha suppression reflects the efficiency with which the brain retrieves and processes that information (Klimesch, 2012). Studies have demonstrated that the more significant alpha suppression in children during these activities, the better students perform on memory-based assessments (Soltanlou et al., 2018). This relationship between alpha suppression and performance on cognitive tasks highlights the significance of EEG to understand how students process academic material.
While in the classroom, measures through EEG can happen in real time on how learners respond to instructional material (Jensen & Mazaheri, 2010). Alpha wave activity can be monitored, and changes in alpha waves may relate to the degree of activation in tasks such as reading; this has large implications for educational interventions, since knowing when and how students are most engaged might be used to time and adjust instructional strategies accordingly to improve outcomes (Bazanova & Vernon, 2014). For instance, if the EEG data show poor suppression of alpha in a task, instructors can modify the level of difficulty in that task or provide additional support to elicit more engagement by students.
Do EEG signals correlate with maths and English attainment in primary school children?
Null Hypothesis (H₀):
Analysis of the findings of the study explains that there is no relationship between EEG signals and mathematics and English attainment in the primary school children.
Alternative Hypothesis (H₁):
The present research study has established a rather strong relationship between the EEG signals and mathematics and English achievement among the primary school learners.
It can also be said that, in understanding how EEG changes associated with these activities might relate to child outcomes, specifically regarding mathematics and English performances, EEG data must be captured both when the cognitive states are active and when they are at rest. This would allow for a comprehensive investigation into changes in neural activity concerning stimuli both internal and external to the body. Significant differences in EEG results, if studied as obtained from cylinder with eyes open and with cylinder closed help the researcher to isolate changes in neural processes accompanying certain tasks.
This work will include both time-domain and frequency-domain analysis of the EEG signals with both periodic and aperiodic methods of analysis, hence giving a comprehensive analysis of the EEG measures. Event-related synchronization will measure the brain's response to particular stimuli, and event-related desynchronization will offer indications of fluctuations in ongoing neural activity. This combined strategy is rather unique because it allows exploring the interrelation of cognitive operations and neural processes in more detail.
It can also be said that there is less engagement during closed eyes, and therefore, the need to capture EEG signals in both states. In such an approach of the research, the set objectives of improving the understanding of how EEG signals connect with the performance of children in school are realized.
In terms of the basis of academic achievement in universities has emerged as an important avenue of research in educational neuroscience. This broad research area seeks to determine how brain function, especially as assessed by measures such as EEG, can be linked to cognition and education. Potential advantages of identifying neural correlates consist in using the neurological findings, enhancing educational approaches, early, interventions, catering for multiple needs. For instance, associating neural patterns with specific skills like mathematics, and literacy may help understand how children learn and who among them needs special attention.
On this basis, the necessity to specify antecedents of academic achievement in neural activities and how this research can boost the knowledge of cognitive functions relevant to academic outcomes. After this, the reader is introduced with EEG, or electroencephalography, as the beneficial method to analyse the activity. It should then expand to some possibilities as to what may be seen from EEG linked to academic performance (alpha suppression), then lead to the formal research question associated with the aspects of the neural correlates under study in this research.
EEG is a non-invasive functional brain imaging technique with a high temporal resolution, which is obligatory for this kind of studies, as dynamic changes in the brain’s activity should be observed. EEG records electrical activity across multiple regions in the brain and can show specific patterns associated with specific cognitive functions including attention, memory and problem solving. Such patterns may be called event-related potentials or oscillatory components and have been examined in terms of cognitive development and learning. In the aspect of learner achievement, analysing the characteristics of EEG signals can establish relationship between neuroscience and education which connects neural data to achievement. This approach is in consonance with the current heightened global sentiment on research-based education.
The present study extends this by determining whether ‘EEG signals are related to maths and English achievement. This research question corresponds to the overall research question of the present study, which aims at adding to the existing literature on the neural markers that would predict academic performance. The subsequent sections provide a more refined look at some of the secondary analysis measures related to EEG incorporating alpha suppression and advancing to consider educational implications. This type of narrative structure has the intention of relating the more general imperative for neural indexes of learning back to the tailored subject of the research.
According to, Riddle et al. (2020), Alpha suppression or alpha desynchronization: Synchronized alpha waves usually recorded in the 8 to 12 Hz frequency range decrease in amplitude due to mental activity, especially tasks requiring focused attention, and cognitive function. Alpha activity is usually seen in relaxed states with no focused attention, or in other words, a state of rest. When people do tasks that have a requirement for cognitive activity, like reading, or focused attention on certain activities, alpha waves decrease, and that decrease is termed alpha suppression.
Alpha waves are mainly observed under a condition of wakeful rest, including their eyes being closed or resting without active processing. In this state, the sensory inputs to the brain are relatively minimal and with reduced activation of neural networks, thus alpha rhythms are what will dominate the electrical activity within the brain. As soon as the subject begins to actively engage in mental tasks, the brain recruits the required resources to take care of the incoming information. In this respect, a suppression or reduction of alpha wave activity indicates that it has shifted to a more cognitively engaged state. Alpha waves typically arise in the occipital and parietal areas of the brain, but they predominate when attention is directed inward and sensory processing is diminished. The shift from alpha waves to beta waves, which have higher frequencies, has been interpreted as the neural mechanism for lifting a person from a relaxed state of engagement and then engaging in active mental processing.
According to Duan et al.(2021), Alpha suppression is typically found to occur with cognitive tasks of EEG studies where the subjects are undertaking activities that pertain to sustained attention, such as solving mathematical problems, reading, or even complex reasoning. It has been shown that the alpha wave suppression varies directly with the intensity and complexity of the cognitive demands; this means that the higher the suppression, the more challenging the task is. For example, Klimesch (2012) performed an experiment where the subjects who carried out activities that demanded more focused attention exhibited the most alpha desynchronization. This could be an indication of alpha suppression being a sign of focused attention and involvement in the cognitive process.
Alpha suppression is one of the areas most documented. Previous studies have shown that alpha waves are suppressed whenever there is presentation of visual stimuli or spatial tasks; further, the suppression is in regions of the parietal cortex involved with visual and spatial information processing (Duan et al. 2021). Suppression of alpha waves would thus be a manifestation of the brain's need to focus attention on the stimulus itself, reducing the background noise of neurons to facilitate better sensory processing and performance in most tasks. Another connection of alpha suppression is in academic performance, as cognitive engagement is a critical component of and memory consolidation. Many studies have shown that better academic performance, especially in reading comprehension and mathematics, is found in students whose alpha suppression levels are higher in the process of tasks. Such a relationship would enable the consideration of alpha desynchronization as a neural marker of effective processes.
For instance, in a study by Bazanova and Vernon (2014) to witness the phenomenon, it was found that those students who revealed more alpha suppression while reading proved to understand and remember the text better than those with lesser suppression. Therefore, it was inferred that alpha suppression may develop into a potent indicator of cognitive effort and engagement, which are the biggest assets in something new.
According to Liu & Gu, (2020), Alpha suppression is what has been referred to describe in EEG research as the reduced power of alpha waves after cognitive stimuli. EEG machines measure the activity of the brains at various bands of frequency, and of course, the alpha band forms part of the most important bands which relate an average individual to a relatively relaxed and wakeful state. During alpha suppression, there is a noticeable change in electrical activity emanating from the brain, with grossly reduced alpha power and increased power in higher-frequency bands such as beta - 13-30 Hz, often associated with active thinking.
For instance, alpha suppression is measured with many experiments through a reading or arithmetic task and comparing the brain waves that are observed during resting states to those of active cognitive states. The level of alpha suppression that occurs during that experiment is inferred to represent the participant's level of engagement or the participant's ability to focus at that time. In many instances, an enhanced level of alpha suppression is related to an enhanced level of cognitive effort.
According to Cainelli et al.( 2023), EEG is painless, and research on the human brain and and academic achievements using EEG has been ongoing for more than fifty years. It records electrical activity in the head and researches how different patterns of a wave can affect one and other cognitive functions including attention, memory– which are aspects that define academic achievement. Knowledge of the EEG markers of academic performance can reveal new aspects of the relationship between the neural processes that occur during and the corresponding effects on the improvement of students’ academic performance for educators to be able to enhance the potential of enhancing the achievements of the beneficiaries.
The EEG records electrical activity from the brain which is dispersed across different frequencies, all of which are associated with a given level of awareness as well as cognitive processes. Some of the most prominent brain wave frequencies are as follows:
Delta waves (0.5-4 Hz): These are generally coupled with sleeping and tissue repair mechanisms but can also be coupled with in children below two years of age.
Theta waves: 4-8 Hz: Creativity, relaxation, and the processing of memories are all observed during this frequency. Highly reported, theta waves commonly occur in relaxed wakefulness and when the individual is fully immersed in their cognitive task - such as daydreaming or recalling memories.
EEG Correlates of Reading Ability
According to Rasheed et al. (2021), numerous studies have emerged that examine how EEG measures correlate with reading ability in children and adolescents. One conclusion drawn consistently in this literature review is that greater beta and gamma activity is associated with better reading abilities. For instance, children with higher reading abilities demonstrated increased beta and gamma power for tasks that involved reading; thus, these frequencies of the brainwaves are associated with neural processes in language comprehension, phonological processing, and the integration of written text.
On the other hand, higher levels of theta activity correlated with the comprehension of written information have been associated with inefficient processing. It has also been posited that higher levels of theta activity may reflect a reliance on the use of less adequate or inefficient cognitive strategies and/or processes relying more on memory, which can ensure the hindrance of fluent reading. Klimesch (2012) further explains how excessive mental effort in reading is indicated by theta power during cognitive activities. However, an overload or disengagement leading to high levels of theta activity is indicative of struggling readers.
EEG Correlates of Mathematical Ability
According to Mualem et al.(2021), In keeping with their overall conceptual definition, mathematical ability, in so far as it is considered part of cognitive psychology, is often assigned an important role in assessing cognition and mental abilities. However, research studies conducted on EEG note that several factors and numerical reasoning that occur during mathematical tasks are correlated with certain brainwave frequencies. Especially beta and gamma waves relate to high-order mathematical skills. High-scoring students in tests associated with mathematics-related activities were found to have stronger beta and gamma activities when performing tasks related to logical reasoning and arithmetic calculations, according to Soltanlou et al. (2018). This, therefore, indicates that these frequencies play part in the neural mechanisms that underlie processes such as attention, working memory, and strategies applied when performing mathematical tasks. Theta waves are associated with various mathematical outcomes both positively and negatively, depending on conditions. Theta power tends to peak at the early stages of of novel mathematical material in relation to how much brain memory and cognitive resources depend on its use in processing such novel information.
General Academic Performance and EEG Correlates
Alpha desynchronization has been found to relate with general academic performance too, since there is a larger alpha desynchronization with more attentive, as well as cognitively active, engagement with the process of. For example, while Bazanova and Vernon (2014) worked with students who achieved better academic performance with higher grade scores, students whose alpha activity was suppressed tended to be those who also achieve better grades, which indicates that such desynchronization is a valid marker of cognitive effort and efficiency during.
The ability of electroencephalography to measure electrical signals from the brain has been very powerful in understanding the measures. The signals recorded by EEG are typically divided into five different frequency bands, each of which corresponds to a different level of cognitive and physiological functioning. These include alpha, beta, and delta, theta, and gamma waves of the brain. They aid in knowing what is going on in the level of neural processes during different mental states like relaxation, concentration, sleep.
Alpha Waves (8-12 Hz)
According to Velnath et al. (2021), Alpha waves are usually recorded and described during states of relaxed wakefulness and meditation. They are most commonly recorded from subjects who are awake but are not performing some task that calls for 'effortful' mental processing. Alpha activity is most prominently represented in the occipital lobe, although activity may be noted in other parts of the brain as well. Alpha waves have two very significant phenomena within the process of: alpha synchronization, where alpha waves increase; that is, whenever a person can be allowed to relax from any external tasks, for instance, at rest or idle moments (Kalra et al.2020). On the other hand, alpha suppression, or alpha desynchronization, which is the phenomenon in which alpha activity decreases as an individual focuses attentional activities to cognitive tasks. This repression suggests an increased cognitive involvement, as shown in tasks such as, reading, or working memory tasks.
It is the case in educational settings that the phenomenon of alpha suppression manifests itself in the behavior of students at a time when they engage in deep activities, for example, reading for meaning or mathematics. Other researches have already established that alpha suppression has been linked with superior performance in since it is indicative of the state transition of the brain from the relaxation state to an active and involved state required to process and remember information.
Beta Waves (13-30 Hz)
According to Attar, (2022), Beta waves are believed to represent active mental concentration, and decision-making. These waves are faster and more frequency compared with alpha waves, normally dominating at times of sustained attention, logic-based reasoning, and critical thinking. The most crucial point is that beta activity is dominant over the frontal and parietal areas of the brain that are responsible for the performance of functions and sensory-motor coordination. Increased beta wave activity has been associated with enhancements in performance in the class of tasks that demand an attention to details, such as reading comprehension, and new ideas. Generalization of this aspect reveals that those with stronger beta activity during the process tend to exhibit better performance on other academic tasks that demand critical thinking and focused attention.
Delta Waves (0.5-4 Hz)
According to Babiloni et al.(2021), Delta waves are the slowest of all brainwave frequencies and typically occur in states of deep, restorative sleep. They most frequently occur during the non-rapid eye movement, or non-REM, sleep stages and are most pronounced in and young children, who tend to decrease as age advances. Though delta waves are most associated with sleep, they are also a part of the memory consolidation process. During deep sleep, that is, the stage of slow brain waves, the brain procedures and consolidates information learned during the day. Information, which was previously stored in the short-term memory, is now transferred to the long-term store. Delta waves have been proved to contribute toward developing brain function that enables holding and more systematically organizing new information. Some studies have indicated that children are more efficient when showing higher levels of delta wave activity during sleep for better cognitive development.
Theta Waves (4-8 Hz)
Theta waves are the most common type of wave observed during activities like creativity, relaxation, and the deeper stages of meditation or daydreaming. Typically, they manifest when people have tasks that require deep attention but with a relaxed posture, like during meditation or automatic repetition of tasks. Theta activity is most associated with and memory in education. For instance, theta activity is generally increased in tasks requiring retrieval of information from memory or finding divergent solutions to creative thinking problems. For instance, when students recall facts from memory or work on complex problems involving divergent thinking, the theta activity is prominent.
Gamma Waves (30-100 Hz)
The fastest waves are the gamma waves, which are classically linked to high-level cognitive functions such as information processing, perception, and consciousness. Gamma waves are less commonly represented in routine EEG recordings because they have a very high frequency (Nordin & Alias, 2022). However, they are important for complex cognitive tasks and the integration of sensory information. In educational contexts, gamma wave activity has been linked to task processing that requires attention, and how to assimilate new information with any prior knowledge. For example, gamma waves are activated by a flash of insight or when a student, suddenly, gains an understanding of something new. Higher gamma wave activity has been associated with better performance in tasks that require integrating a lot of information or novel.
Aperiodic and Periodic Components of EEG and Their Relation to Cognitive Functions
Electroencephalography (EEG) signals are complex and can be classified into two primary components: Cyclic and non-cyclic behaviour respectively. Both of these components offer rich measures for cognition and learning, especially for children performing tasks associated with mathematics and language.
2.3.1 Periodic Components
Periodic components of EEG are characterized by regular, rhythmic oscillations of activity that occur at consistent frequencies. These rhythmic oscillations correspond to the traditional frequency bands of EEG: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (30–100 Hz). Each band range is linked to certain mental processes showing the brain’ activity frequency range.
Brain waves refer to the electrical activity in the brain, and they have been categorised into five bands which include delta (0.5–4 Hz For instance, alpha is usually related to wakefulness without drowsiness and beta is related to active thinking. Even when there is periodic activity like increased alpha suppression during reading or solving math problems, has been associated with increased ability and higher academic performance.
2.3.2 Aperiodic Components
Here, non-rhythmic or less rhythmic circuit activity in the brain is described, which is different from the periodic oscillations. Such changes are irregular, and their occurrence is not limited by specific frequency parameters. Periodic activity is related to basic processing and output, while aperiodic activity is related to higher order processing, integration, and multi-source analysis.
According to the research done by He et al. 2020 the aperiodic components of EEG, specifically, 1/f noise or ‘scale-free’ activity best characterises flexibility of thinking and amount of cognitive resources available. These irregular patterns are consistent with the fact that the human brain can perform certain tasks efficiently and in different ways depending on task difficulty and time or a change in environment. This variability is very important when it comes to activity that involves, constant attentiveness and systematic problem solving as they are in mathematics and language.
Aperiodic activity has been identified as one of the critical markers of brain flexibility, thought to indicate how the brain maintains adaptability in response to novel stimuli and unstable, unpredictable changes in task demands. While the distinct patterns of periodic brainwave activity indicate more organised cognitive processes, much can be learned about the ability of the brain to perform higher-order cognitive tasks, such as decision making, learning, or problem solving from the components of the aperiodic activity (He et al., 2020).
Neuropsychological patients with aperiodic activity demonstrated better performances in cognitive flexibility, which was reflected in the children’s ability to accomplish mathematical problems using the integration of several pieces of information. In the context of problem solving during mathematics computation for instance, flexibility of neural networks in switching between several networks and rapidly and consistently changing corresponding mental models is driven by aperiodic activity; this activity assists in processing new information appropriately and adapting subsequent cognitive strategies (Voytek et al., 2015). One of them also revealed that higher aperiodic activity is beneficial for learning performance because it indicates the adaptable and dynamic process of the brain’s working ability.
Aperiodic components also determine cognitive control. Studies have established that individuals with higher aperiodic levels of activity exhibit enhanced ability to modulate the utilization of their cognitive resources, which is critical for holding attention and focus in a task (He et al., 2020). In this regard, high-order cognitive control seems to be a critical requisite for tasks where an individual needs to adapt learning conditions, accomplish multiple tasks, or address events that are not expected in an academic condition.
Periodic EEG activity is associated with academic performance in school contexts, particularly for math and language activities. Inhibition of alpha rhythms during cognitive tasks is associated with attention and increased processing speed. For instance, those tasks require solving arithmetic problems or reading with a good understanding. On the other hand, the higher theta and alpha power can indicate a greater engagement, or cognitive load when completing more complex tasks at school.
EEG and Memory Processing
It is for this reason that EEG is used widely in research on memory processes during through its capacity to monitor theta and gamma wave activity, of which the former is believed to stimulate memory formation and recall, and the latter is monitored when combining new information to previous knowledge. Evidence shows that increased theta activity during the student-coding process is associated with the higher retention levels of that information (Ismail & Karwowski, 2020). Theta waves are well fitted to tasks that involve recall of facts or that depends upon previously learned material. For instance, several studies using EEG established that people who evince an increased level of theta wave activity in tasks tend to perform much better on memory-based tasks, such as vocabulary recall or fact-based exams.
Gamma waves are important in tasks that also involve the creation of new information. For instance, the engagement of gamma activity is usually increased while performing exercises that require integration of several sources of information. This therefore shows that gamma waves are important when it comes to complex cognitive processing such as understanding abstract concepts or creating new ideas through learned information.
Using EEG to Assess Cognitive Load
According to Iqbal et al.(2021), Cognitive load is the amount of mental work required to process information when performing a activity. When it reaches too high an intensity for students, is ineffective because the system capacity of the brain for working memory overloads. Cognitive load has been successfully measured through EEG, specifically when studying changes in patterns of brain waves. High beta activity indicates a greater cognitive load while performing a task that requires stronger concentration. High beta can be an indicator of students dealing with cognitive overload, thus reducing their efficiency. On the other hand, higher alpha wave activity may imply a low level of cognitive load. This could be an indication that the task is too simple, or that the learner is not stimulated.
Interplay between Aperiodic and Periodic Components
There is always a connexion between the aperiodic and the periodic components.
The ongoing research shows that the interactions of periodic and aperiodic components should develop as a new parameter for the study of cognitive performance. The alpha and beta rhythms have been classified as periodic; they normally relate to a given task and the processing related to it, while the remaining components reflect the general network in the brain and, therefore, the brain’s capacity to manage varying cognitive demand. These two enable fluent work fully Cherokee engagement connexions, due to the capacity for periodic work and the aperiodic work, which is very crucial in the success of the learner’s academic performance.
Langer et al, 2016 also pointed out the regularity, as well as the irregularity is crucial for the optimal brain functioning. For instance, for activities that require the subject to focus like reading or doing arithmetic, alpha wave suppression paradigm (a periodic component) is used while the increased aperiodic activity is useful when the subject needs to accommodate into new information or knowledge such as in a problem solving endeavour. This periodic and aperiod activity arrange may further facilitate requisite neuro-cognitive frequencies’ optimization for efficient mathematical and linguistic processing, resulting in learning and better academic performance in children. Both the periodic and aperiodic EEG facets are associated with cognition and academic performance.
Periodic functions are related to more structured cognitive tasks requiring held attention; whereas, aperiodic functions represent the brain's adaptive ability to accommodate new and constantly changing cognitive demands. Together, they give a complete picture of how the brain acts as a support for acquisition learning and, by extension, academic attainment in children. Further research into the dynamic interaction between these components will therefore be critical to understanding the full spectrum of cognitive processes underpinning academic success.
EEG in Measuring Emotional Engagement
Emotional engagement is one of the areas where EEG can be used to measure what happens during. States of emotions, such as frustration or excitement have been established to influence results of (Zhu et al.2021). Frontal alpha asymmetry, or the difference in alpha wave activity between left and right hemispheres of the brain, is typically how EEG measures of emotional engagement are obtained. Research has indicated that left-sided frontal activity is associated with positive effects and that right-sided frontal activity is associated with negative effects such as nervousness or frustration. Now for the, measurement of the emotional engagement, this periodic and aperiodic graph makes a significant role in the detection of the brain waves in the EEG. Here Periodic part makes the detection clear rather than the aperiodic part. Now, in the present this aperiodic part makes pattern very repetitively. So this will be an outburst of that mood swing in the difficult situation.
The literature review suggests that the role of EEG is basically critical in understanding measures and its relation to processes. Alpha suppression findings describe how alpha wave activity reduces while doing cognitive tasks, signifying engagement and attention. EEG correlates of academic attainment studies establish a relation between brain-wave patterns, mainly associating them with theta, beta, and gamma waves, along with cognitive functions such as attention, memory, and, stressing their educational dimension. Measures by alpha, beta, delta, theta, and gamma waves in EEG work as indicators of various states of cognition during : from complete relaxation up to the considerably higher state of focus leading to further consolidation of memory. Last but not least, the practical application of EEG in cognitive process measurement during tasks explains why EEG is a valuable tool for monitoring attention and cognitive load and adding emotional dimensions to processes. These studies clearly point out the potential that EEG has to provide real-time data for the optimization of environments, enhancement of student engagement, and improvement of academic performance. In the EEG process, there are a combination of aperiodic and periodic components in the basics. Hence all the aperiodic component makes the irregular pattern, in the EEG part graphical representation. So this will be an undetectable part in the EEG detection of a human being. On the contrary, this periodic part makes the good band in the detection of that EEG in the human being. Hence, this aperiodic part will consume the power law to solve this pattern in that electroencephalogram detection of a victim or patients. So, in this part of this dissertation, this aperiodic and periodic part will be used in that analyzing deployment of the EEG in mathematics. In ultimatum of that literature review, this application of that EEG is used with the periodic and aperiodic part in the graphical output for mathematical operation.
The following section describes the technique and methodology that will be applied to the data to determine whether or not there is a correlation between early childhood brain development and academic achievement. The study's objectives and the strategy employed to pursue them are interrelated. Apart from that the research is to determine whether or not the brains of children act differently when switching between reading and math.
To achieve the objective of this study, the research design employed is the correlational research design. Instead of controlling variables as it is done within experimental research, this study aims at finding correlation between the resting-state EEG recorded with eyes open and closed and standard academic scores. In the context of the presented experimental tasks, the interest lies in the identification of correlations between increased beta and theta activity and academic performance.
The rationale for using the quantitative approach is provided since the collection of numerical data is possible and analysis of statistics will highlight the trends and correlations. This design enables researchers to measure brainwave activity in conjunction with academic performance, as well as literacy and math subscores. That is why the attempt of the study is to utilise periodic and aperiodic frequency analysis of the EEG signals, in order to make reasonable conclusion regarding the effect of cognitive activity on the results.
Children from a particular class in the selected primary schools aged 11-12 years were selected to eliminate variability arising from educational input. Children and parents provided informed consent to participate in the study and the children reciprocated by providing assent to participate in the study. The participants were selected from similar academic background hence minimising variability that results from differences in education. The selection ensured that observed correlations are less likely to be tainted by variability in the quality of instruction. If n is sufficiently large, the central limit theorem (Islaqm, 2018) states that sample results can be extrapolated to the population as a whole. There were 34 participants, 15 females and 19 males. Participants were selected from the same class and ranged in age from 11-12 years (class 6) to assure homogeneity. The majority of the children should have been attending education.
Resting state EEG data was collected while the subject was in a quiet relaxed condition; his eyes were either open or closed to eliminate muscular and ocular artefacts using a non-invasive EEG machine. This protocol was useful in achieving the initial status of activity while minimising the impact of task execution, which is useful in recording native rhythms. Alpha, beta, delta, and theta waves were recorded from each participant during resting EEG, over a fixed time for comparison with each subject’s academic performance results in Math and English. Data is collected through the use of specific instruments to assess brain wave activities such as Beta; Alpha; Theta; Delta together with the participants ‘Academic performance’ indicant. During the time children wore EEG net, both dry and wet method, they looked at fixation cross on the screen for 4 minutes with eyes open. To capture EEG data of the participants’ brains, the study uses two types of EEG machines; the Enobio dry electrode system and the ant moist electrode system. In addition, prior to the main experiment, a familiarisation session was performed to cheque participants’ comfort with EEG equipment. The purpose of the study and the specific type of dry electrodes used, the Enobio, were explained to the participants and everyone agreed to participate and in case of minor’s consent of parents was received. Any time they had their eyes shut, points were to be deducted to emphasise on attentiveness. After this, participants received further instruction to relax and close their eyes to enable the researchers to capture the mental status and EEG while in the open and close eyes state. Frequency centres, peak power, bandwidths and coefficients of determination R² captured from the EEG were valuable parameters for determining the measures. The collected data was then keyed into Excel for initial sorting and then exported for analysis with Statistical Package for the Social Sciences (SPSS).
To get reasonable and comprehensive understanding about processes in children based on the neurological factors, this experiment is also designed to intermittently take measurements of centre frequencies, bandwidths, peak power and error fits of the measures of brain. Such an approach promotes efficient examination of the connexion between working memory, neural activation, and academic achievement, and generates important insights for educational neuroscience.
Several signals were tested for periodic measurement to understand the characteristics and behavior of periodic movements. For instance, Center Frequencies to determine the central frequency, Bandwidth measurements assess the range of frequencies within which the periodic movement occurs, Peak power measurements to determine the maximum power level reached during the periodic movement, R^2, or the coefficient of determination, is a statistical measure that represents the proportion of variability in the periodic movement data that the chosen model or function can explain. . It helps assess the model's accuracy and the error level in representing the periodic movement.
The actual EEG data were then analysed by means of correlational statistics so that the relations between different types of the brainwaves and academic performance could be observed. In particular, it concerned the investigation of major relationships between the values of the basic characteristics of the EEG and the results of the cognitive test in math skills and English. The data were initially transferred to an Excel file before being imported into SPSS for analysis. Statistical methods, like measurements of means, stand deviation, correlations, autocorrelations, were used to measure periodic measurements, aperiodic measurements. The average frequencies of theta, delta, and beta waves were used to determine the strength of the correlation between activity of brain and intelligence quotient (IQ). In addition, using periodic and aperiodic data, the stimulus and neural mood represented by neural transmissions were evaluated.
The consent of the student, guardian, and school administrator was required prior to data collection. In order to compute the average score for reading-scaled grades, and math-scaled grades, the research team obtained the school's permission to use the provided data. Participants could exit the study if they experienced distress during the signal identification procedure. This includes any discomfort caused by damp electrodes and any general anxiety. The parent were permitted to withdraw from the study at any time, and neither was required.
When determining the level of various activities of brain, it is essential to obtain readings of the subject's offset values while awake and asleep. Memories that were extraneous to the stimulus aid in forming long-term memory. By research by Medvedeva et al. (2021), the termination of a stimulus reactivates a previously experienced event, which modifies the episode's binding. The discrepancies discovered with the eyes open and closed were compared in the current investigation. The following table compares the offset with the eyes open versus the offset with the eyes closed.
Table 1: Aperiodic Offset mean when eyes are opened and when closed
|
Variable |
N |
Mean |
Std. Deviation |
|
Offset (Eyes Closed) |
34 |
12.47 |
0.54 |
|
Offset (Eyes Open) |
34 |
12.96 |
0.72 |
Source: (Author, 2023)
According to Table 1, the average difference between the offsets acquired with the eyes open and closed is insignificant. This may suggest that the offset stimulus is not activated more powerfully, regardless of whether visual stimulation is present or absent. Furthermore, wet electrode EEG and dry electrode EEG, the two distinct varieties of EEG testing, cannot be distinguished from one another, as shown in the output that follow:
Table 2: T-Test for mean equality, Enobio vs. ANT (wet method)
|
Variable |
Systems |
N |
Mean |
SD |
F |
Sig. |
t |
Mean Difference |
|
Offset (Eyes Closed) |
Enobio |
17 |
-12.42 |
0.66 |
3.11 |
0.09 |
0.55 |
0.19 |
|
ANT |
17 |
-12.52 |
0.39 |
3.11 |
0.09 |
0.55 |
0.19 |
|
|
Offset (Eyes Open) |
Enobio |
17 |
-12.92 |
0.94 |
7.07 |
0.01 |
0.27 |
0.07 |
|
ANT |
17 |
-12.99 |
0.44 |
7.07 |
0.01 |
0.27 |
0.07 |
Source: (Author, 2023)
As can be seen from Table 2, which represents the offset values between two EEG systems, Enobio and ANT, were compared under two conditions: eyes closed and eyes open. The number of participants (N), mean, standard deviation (SD), F-statistic, significance level (Sig.), t-value, and mean difference were included for both systems. Here, the statistical results are given for the differences between the systems across the two conditions, highlighting their performances.
Table 3: Aperiodic Correlation coefficients between offset and the student scores
|
Variable |
N |
Correlation |
Sig. (Significant at 0.01) |
||
|
Offset (Eyes Closed) |
Offset (Eyes Open) |
Offset (Eyes Closed) |
Offset (Eyes Open) |
||
|
GPS Scaled Score |
34 |
-1.54 |
-0.01 |
0.39 |
0.96 |
|
Reading Scaled Score |
34 |
-0.13 |
-0.08 |
0.49 |
0.65 |
|
Math Scaled Score |
34 |
-1.33 |
-1.49 |
0.46 |
0.41 |
Source: (Author, 2023)
The correlation calculations of offset values (eyes closed and eyes open) with the students’ GPS, reading, and math scaled scores are generally low and insignificant. In GPS scaled scores, the correlation was -1.54 for eyes closed, and -0.01 for eyes open, with the significance values of 0.39 and 0.96 respectively, implying no correlation. Reading scaled scores had slightly lower correlations, -0.13 (eyes closed) and -0.08 (eyes open) with significance levels of 0.49 and 0.65 respectively. The correlations between offsets and math scaled scores were equally low (-1.33 and -1.49) without even statistical significance which means that as with lesson completion, there is no strong positive or negative correlation between offsets and academic performance.
Table 4: Descriptive statistics for the Aperiodic Exponent
|
Variable |
N |
Mean |
SD |
Skewness |
|
Exponent (Eyes Closed) |
34 |
1.97 |
0.54 |
0.71 |
|
Exponent (Eyes Open) |
34 |
1.91 |
0.53 |
0.23 |
Source: (Author, 2023)
Table 4 presents the descriptive statistics for the Aperiodic Exponent measured under two conditions: eyes closed and eyes open. The mean exponent for participants with eyes closed was 1.97 (SD = 0.54), indicating a slight positive skewness of 0.71, suggesting that most scores are concentrated toward the lower end. In contrast, the mean exponent for eyes open was 1.91 (SD = 0.53) with a skewness of 0.23, reflecting a more symmetrical distribution.
Table 5
Correlation between Aperiodic exponent and the scores
|
Variable |
N |
Correlation |
Sig. (Significant at 0.01) |
||
|
Exponent (Eyes Closed) |
Exponent (Eyes Open) |
Exponent (Eyes Closed) |
Exponent (Eyes Open) |
||
|
GPS Scaled Score |
34 |
-0.14 |
-0.13 |
0.45 |
0.46 |
|
Reading Scaled Score |
34 |
-0.53 |
-0.14 |
0.77 |
0.45 |
|
Math Scaled Score |
34 |
-0.21 |
-0.26 |
0.25 |
0.15 |
Source: (Author, 2023)
Table 5 shows the correlations between the Aperiodic Exponent and academic scores. Notably, a moderate negative correlation was found between the Exponent (Eyes Closed) and Reading Scaled Score (-0.53), suggesting that higher exponent values are associated with lower reading performance. Other correlations were weak and non-significant, indicating that the Aperiodic Exponent does not significantly relate to GPS and Math scores in either condition.
When investigating periodic movements, the following signals were tested; Center Frequencies, Bandwidth, Peak Powers, R^2 and Error Fit. See table 6 and 7.
Table 6
Descriptive Statistics Periodic Measurements
|
Variable |
N |
Mean (Eyes Closed) |
Mean (Eyes Open) |
SD (Eyes Closed) |
SD (Eyes Open) |
|
Centre Frequencies |
33 |
12.16 |
13.15 |
4.31 |
5.03 |
|
Peak Powers |
33 |
0.90 |
0.70 |
0.28 |
0.23 |
|
Band Widths |
33 |
4.51 |
4.56 |
2.44 |
3.59 |
|
R^2 |
34 |
0.93 |
0.92 |
0.05 |
0.06 |
|
Error Fit |
34 |
0.14 |
0.14 |
0.04 |
0.06 |
Source: (Author, 2023)
Below Is the summary of the correlation results.
Table 7
Correlations Periodic Measurements
|
Variable |
Correlation & Sig. |
N |
Centre Frequencies |
Peak Powers |
Band Widths |
R^2 |
Error Fit |
|||||
|
Eyes Closed |
Eyes Open |
Eyes Closed |
Eyes Open |
Eyes Closed |
Eyes Open |
Eyes Closed |
Eyes Open |
Eyes Closed |
Eyes Open |
|||
|
GPS Scaled Score |
C |
34 |
0.15 |
0.42 |
-0.13 |
-0.36 |
0.11 |
-0.27 |
0.15 |
0.13 |
0.29 |
0.88 |
|
P |
34 |
0.43 |
0.20 |
0.49 |
0.05 |
0.54 |
0.14 |
0.03 |
0.43 |
0.48 |
0.11 |
|
|
Reading Scaled Score |
C |
34 |
0.13 |
0.29 |
-0.16 |
-0.33 |
0.27 |
-0.30 |
0.02 |
0.42 |
0.29 |
0.36 |
|
P |
34 |
0.48 |
0.11 |
0.37 |
0.07 |
0.13 |
0.11 |
0.46 |
0.02 |
011 |
0.05 |
|
|
Math Scaled Score |
C |
34 |
0.29 |
0.36 |
-0.30 |
-0.38 |
-0.01 |
-0.27 |
0.13 |
0.72 |
0.62 |
0.36 |
|
P |
34 |
0.11 |
0.05 |
0.87 |
0.03 |
0.98 |
0.15 |
0.05 |
0.00 |
0.01 |
0.11 |
Source: (Author, 2023)
Center Frequencies
The mean of the brain's center frequencies is marginally higher when the eyes are open than when they are closed, as demonstrated by the previous finding. The center frequencies of the brain can be used to determine whether the brain is engaged during an activity or at leisure. It is generally accepted that when people close their eyes while their intellect remains active, they rest. The amount of visual processing that occurs in the brain of an individual increases when they open their eyes. Consequently, it has been discovered that the average center frequency increases when the eyes are wide. The correlation between center frequencies and GPS-scaled scores, as well as reading and math scores, which further demonstrates the veracity of the previously stated concept.
As shown in Table 7 above, a positive correlation exists between center frequencies measured with the eyes open and GPS-scaled scores at the 5% significance level. The findings of this study indicate that children with greater brain center frequency activity are more likely to have a high general intellectual capacity. Upon opening their eyes, one will also observe a strong correlation between the arithmetic scaled score and the center frequencies. According to Soltanlou et al. (2018), greater hippocampus activity is associated with superior arithmetic ability. According to Kersey and James (2013), it is also possible to struggle with reading despite having a strong mathematical aptitude. This study reveals that the correlation between reading and GPS scores is considerably stronger (0.829) than between math and GPS scores (0.721). Students with high reading scores have a greater chance of increasing their GPS scores than students with high arithmetic scores, although students with high arithmetic scores have greater cerebral activity.
Peak Powers
According to the results, peak strengths are much higher when someone's eyes are closed (mean = 0.9) than when they are open (mean = 0.7). The mind is likened to the highest power level when unforced and untroubled. The brain is either at rest or functioning at a lower capacity than usual when peak powers are used. The collected data show that the powers are at their highest point when the youngster is resting, as expected. The relationship between these factors is demonstrated in Table 7.
The results of a comparison between the maximal powers and the acquired scores are shown in Table 6. As shown in the data, there is a statistically significant relationship between the peak powers and the GPS-scaled scores. A strong negative correlation exists between peak powers and math-related scores at the 5% significance level. This relationship is extremely secure. This indicates that an increase in the amount of idle time children's brains expend causes a significant decline in their arithmetic abilities.
Bandwidths
There is no correlation between the bandwidths of brain impulses and the grades children receive on literacy and mathematics exams. See Table 7. The correlation are shown in Table 7. Except for the score-relationship, all other correlations have a p-value of less than 5%. The bandwidth's specs are laid forth in the table 7.
The above output (table 7) demonstrates that the bandwidth is slightly higher when the eyes are open (4.56 Hz) than when they are closed (4.51 Hz). Mental bandwidth refers to the total amount of information the human brain can process concurrently. Larger bandwidth has almost certainly improved performance, translating to higher grades. On the other hand, GPS performance is inversely proportional to bandwidth. Both literacy and math performance improved when bandwidths were reduced.
Error Fits and R^2
In the table 6 output, the means for average error fits and R^2. The results indicated that the average r2 for both experiments were significantly greater than 90%. Strong R2 values indicate that the observed brain signals affect the described alterations. The high value of r2 indicates that the study's conclusions provide accurate estimates of activity of brain. A suit error less than 15% is also displayed in the table at the top. As the fit errors decrease, the predicted model begins to look more and more like it should compared to the actual data. This indicates that the accuracy of the outputs when used for prediction is 15% or less.
This research section will analyze the data through the prism of the beta, delta, and alpha signal means. When the pupils are opened, the brain produces beta waves, which are high-frequency pulses. Beta waves are essential for human logic and reasoning because they stimulate the brain. (Klimesch, 2018) Beta waves indicate that a person is vigilant and aware of their surroundings. Since the means of delta, theta, alpha, and beta waves were obtained from the study, the present data were compared with a normative sample and previous literature. The delta waves, normal for deep sleep and rest, were down slightly from the norm. The slow and often associated with creativity and relaxation theta waves were present in the normal limits. Alpha waves, associated with relaxation and concentration, have a moderate correlation with the prior research. Beta waves associated with active thinking and concentration was slightly increased, an implication of the activity of brain during a particular task. These findings confirm previous knowledge and the consistency of EEG data that was obtained during the experiment. In order to provide reliable averages and comparisons, the data was factored 10 trillion times before being analyzed. It is not easy to deduce comparisons and correlations from the data without treating 0. Consequently, it is essential to determine whether beta values are higher when a child is awake or asleep.
Table 8
Descriptive Statistics delta, theta, alpha, and beta
|
Variable |
N |
Mean (Eyes Closed) |
Mean (Eyes Open) |
SD (Eyes Closed) |
SD (Eyes Open) |
|
Beta Mean |
34 |
0.03 |
0.01 |
0.03 |
0.01 |
|
Alpha Mean |
34 |
0.67 |
1.00 |
1.11 |
0.12 |
|
Delta Mean |
34 |
0.99 |
0.40 |
1.50 |
0.50 |
|
Theta Mean |
34 |
0.65 |
0.16 |
1.11 |
0.15 |
Source: (Author, 2023)
Below is the corelation results;
Table 9
Correlations Periodic Measurements
|
Variable |
Correlation & Sig. |
N |
Beta Means |
Alpha Means |
Delta means |
Theta Means |
||||
|
Eyes Closed |
Eyes Open |
Eyes Closed |
Eyes Open |
Eyes Closed |
Eyes Open |
Eyes Closed |
Eyes Open |
|||
|
GPS Scaled Score |
C |
34 |
0.16 |
0.00 |
0.13 |
-0.07 |
-0.12 |
0.04 |
-0.32 |
-0.02 |
|
P |
34 |
0.94 |
1.00 |
0.48 |
0.71 |
0.51 |
0.84 |
0.07 |
0.92 |
|
|
Reading Scaled Score |
C |
34 |
0.10 |
0.06 |
0.13 |
0.06 |
-0.01 |
0.13 |
-0.15 |
0.01 |
|
P |
34 |
0.58 |
0.76 |
0.47 |
0.76 |
0.96 |
0.48 |
0.41 |
0.95 |
|
|
Math Scaled Score |
C |
34 |
-0.05 |
-0.10 |
0.12 |
-0.22 |
-0.04 |
0.04 |
-0.23 |
-0.20 |
|
P |
34 |
0.78 |
0.58 |
0.52 |
0.21 |
0.83 |
0.84 |
0.21 |
0.26 |
Source: (Author, 2023)
8 Years | MSc
Beta Waves
According to the data in the table above, beta is marginally higher when the eyes are closed than when they are open. The following is the outcome of an investigation into the relationship between betas and scores. The table output above shows the correlations between the beta means and scores. The table shows no significant correlation between the beta signals and the scores. Neither reading, math, nor GPS scaled scores bear a significant relationship with the beta values, implying that beta measurements cannot be used as significant predictors of performance among children. Before conducting the study, one would expect that high beta values are related to good performance because beta values increase concentration and focus. However, as can be seen from the study findings, this is not true. Perhaps the stimulating effects of betas and increased focus and concentration caused by betas do not explain the performance, or their effect is so small compared to the other signals that they become insignificant.
Alpha Waves
According to Klimesch (2018), a child's brain emits alpha impulses when it is not focusing on anything in particular. Since alpha waves are associated with tranquil states of consciousness, it is reasonable to anticipate high levels of them when at ease. According to the findings of numerous studies, alpha oscillations in the brain are associated with creative thought. Both attentiveness and meditation are associated with alpha wave activity. According to studies (Klimesch, 2018), naturally inquisitive individuals are predicted to have higher alpha wave activity. There is a possibility that higher scores are associated with greater alpha activity. This activity should be present when participants' eyes are open, but absent when their eyes are closed. However, as shown in the table 9, there is a significant increase in alpha wave activity when the eyes are closed.
When one's eyes are closed, 0.6654 alpha waves are typically present; however, when one's eyes are open, 0.09598 alpha waves are typically current. When applied to alpha waves, the accurate mean is divided by 10(trillion), bringing the value to zero. The correlation study's results are shown in the table 9. The table 9 demonstrates no statistically significant correlation between alpha brain waves and academic achievement in minors. Alpha brain waves have a marginally negative effect on math scores but a positive effect on reading and GPS-scaled scores. Insufficient evidence exists to conclude that alpha waves have an impact on children’s.
Delta Waves
The delta wave is one of the slower brain wave variants. Delta waves have been shown to have calming and therapeutic effects and are frequently associated with brain injuries (Klimesch, 2018). Both adults and children, as well as neonates, experience delta waves during sleep. The data in the table 9 illustrate the anticipated trend: with eyes closed, delta waves are more prevalent in juveniles than with eyes open.
When a person closes their eyes, the brain enters a state of rest and exhibits minimal activity. Therefore, when one closes their eyelids, their delta wave frequency increases. According to Klimesch's (2018) study, delta waves tend to decrease as an individual's performance improves. According to Klimesch (2018), delta waves increase when brain measures is minimal. Given this information, it is logical to assume that low delta waves are associated with proficiency in reading comprehension, mathematics, and the Global Positioning System. The following table illustrates the relationship between delta waves and the levels of demonstrated on tests.
The correlation table just displayed shows no correlation between the delta values and the scaled scores for reading, mathematics, or GPS. Neonates generally have the strongest delta waves, while adolescents have the weakest. Therefore, it is probable that delta waves have a pervasive effect on children, which would have the stabilizing effect of causing most children to have approximately the same amount of delta waves. It is conceivable that the results are not solely attributable to the strength of the delta waves but also involve other factors.
Theta Waves
Exposure to theta waves has been shown to improve memory and cognitive performance. According to research by Klimesch (2018), each of these actions may cause the production of theta waves. Both the limbic system and the hippocampus region of the brain display activity that has also been linked to the occurrence of theta waves. According to the reviewed study (Klimesch, 2018), the hippocampus is essential for developing children's ability to perform arithmetic. Theta has been found and linked with relaxation, creativity and other cognitive functions such as memory. Although it has been posited in other studies that theta activity is associated with and memory most especially under or relaxing conditions (Klimesch, 2018), the outcome of the present study does not record a direct relationship between children’s theta wave, their performance in class. Table 9 reveals that there is no significant relationship between theta waves and GPS, reading or math scores. Furthermore, no augmentation of cognitive performance was observed following theta activity as the statistical correlation indicated weak or negative values. Further, it was revealed that in children with their eyes shut the degree of theta wave is elevated and this is favourable for the child’s relaxation. Contrary to the prevalent theories suggesting theta activity enhancement and its contribution to and memory, the results obtained require the investigation of the role and changes in theta wave characteristics. Children's theta brainwave activity does not correlate with their scores, as shown in the table 9 of correlations above. When the significance level is 10% or less, the correlation between GPS-scaled scores and theta waves is weak to nonexistent. The correlation between theta brain wave activity and academic achievement in children is not statistically significant.
In this particular study, the goal was to understand the link between and activity that takes place inside the children’s brain and this analysis was going to focus on theta, alpha, beta, and delta brain waves and afterwards compare the result with tonometry, reading, and GPS scaled scores. More work has underlined that activity in the brain is directly correlated with cognitive growth in children. Certain forms of the EEG were found to have a positive correlation with some academic achievement in mathematics and English. For instance, alpha frequency band connectivity was positively related to the number scores in math, while the beta band connectivity to English results. From this study, it can be concluded that resting state oscillatory activity is linked with differences in subsequent academic performance on academics. The results offer the first hypothesis of the extent to which aspects of EEG can map cognitive states associated with education achievement while no causal conclusions are made from such correlations. Certain forms of the EEG were found to have a positive correlation with some academic achievement in mathematics and English. For instance, alpha frequency band connectivity was positively related to the number scores in math, while the beta band connectivity to English results. From this study, it can be concluded that resting state oscillatory activity is linked with differences in subsequent academic performance on academics. The results offer the first hypothesis of the extent to which aspects of EEG can map cognitive states associated with education achievement while no causal conclusions are made from such correlations.
The detected correlations of residual EEG with academic scores indicate possible correlation between resting state activity and cognitive academic abilities. A correlation between alpha waves and quantitative ability scores, and between beta waves and literacy scores suggests that specific cognitive architectures may be representative of subject-specific aptitudes. Thus, although this study does not establish causality, the patterns can be used to inform further research on how resting-state EEG measures may eventuate as indexes of the cognitive capacities arrayed to educational accomplishment. This paper may inform future uses of EEG in educational assessments.
There are specific parts of the brain which are more active at moments especially literacy and numeracy. Klimesch, & Soltanlou, et al. 2018 works have also supported this. However, the outcome above found in the previous study did not align with the present finding, and critical differences arose in those studies. Interestingly, none of the different wave frequencies demonstrated any good correlations with the children’s outcomes. Theta waves, often considered one of the fundamental wave measures associated with memory and cognitive performance in both children and adults, did not correlate significantly with any of the academic scores measured. Other studies have thus suggested that theta activity is associated with better especially in states of relaxation or meditation. In this study no statistically significant relation was found between the test performances of the children and their occurrence of theta waves thus negating part of the assumed claims in literature.
The research also could not find any significant correlation between delta and beta wave activity with scholastic performance. Beta waves had historically been linked with active thinking; thus, it was assumed that beta activity was going to be positively related to the performance on tasks such as arithmetic and literacy. This proved to be the case as beta wave activity was found to be unable to correlate significantly with GPS, reading, or math scores. Indeed, there was some suggestion that higher beta wave activity was associated with lower scores, although this result did not achieve statistical significance at the 5% level.
Alpha wave activity was marginally correlated with math performance, but again this was not significant statistically at the conventional threshold. Alpha waves are associated with a relaxed state of mind that is alert or attentive and have previously been associated with creative and information processing. Alpha wave measures had a weak positive correlation with mathematics performance, but the effect size was not very strong to allow for any solid conclusion. Further studies using more extensive samples and even more polished methods may be required to study this relationship much more intensely.
Another important methodological consideration from this study is that of practice and neural efficiency in children's cognitive development. This means training is found to require repeated presentations of the tasks, which may enhance the transmission efficiency in the brain, hence leading to faster recall time in children. This result is in line with previous studies that have found that the parietal areas of the brain appear to be more active in older compared to younger individuals, hence enhancing the speed. However, the current study was not designed with the objective of exploring developmental trajectory from early childhood to adolescence. Future work must explore this area.
It also needs to be mentioned that even though the individual brain waves, such as theta, beta, alpha, and delta, did not correlate to any significant levels to suggest any relation with the outcomes of the students. Brain activities are a very complicated matter, and interaction among various types of brainwaves along with other neural mechanisms might be the obvious thesis. The results from this research indicate that the results of cannot be explained by focusing on certain types of brainwaves and that a more holistic approach to understanding brain activities is necessary.
Where research has described brainwaves as a potential contributor to cognitive performance, the results of this study represent the opposite: clearer evidence that brainwave patterns contribute to cognitive performance was not seen in academic scores. This means that merely having brainwave patterns is not necessarily indicative of success. Further research should then be targeted towards the integration of more holistic models of brain functions perhaps both the neurophysiological data and the behavioral measures to better understand the complex nature of among children.
The current study revealed that beta waves enhance test scores, one of its primary aims. This theory was disproven when it was discovered that beta waves did not affect performance on the GPS, reading, and arithmetic tests (P values of 5%). According to findings from the parietal lobe, concentrated practice can reduce children's recall times. Researchers discovered that a child's signal for information transmission becomes stronger with practice. On the other hand, there was insufficient evidence to support the notion that adult retrieval systems improve over time. This demonstrates the child's ability to retain information quickly. There is evidence that, as children mature into adolescents, the parietal regions of the brain become more active, which could accelerate the process. The optimal time to teach mathematics is during adolescence, according to the research of Soltanlou et al. (2018), who also discovered that the putamen is more active during this time. Even though the authors of the study were unable to explain why the putamen regions of adolescents were more active than those of adults, they discovered that regular application of lessons enhanced retention (Soltanlou et al., 2018). Approximately 100 billion brain cells are present in an adult at birth. This is also the maximum number of brain cells believed to exist in a typical human. More than a trillion synapses, or connections between brain cells, are formed at birth. The child's actions are the primary factor that determines the total number of connections. The more activities a child participates in, the more neural pathways are formed in his or her brain, which enhances performance. The research findings imply that the null hypothesis, which designates that there is no significant relation between EEG signals including alpha, beta, theta, delta waves and academic achievement in mathematics and English among the primary school children, is valid with most efforts. These correlated findings were not statistically significant at the conventional level of ASR via p > 0.05 even though certain weak positive correlations were noted. Therefore no causal relationship evidenced was found to reject the null hypothesis in this research. The extensive and complex network of connections between neurons in the brain permits them to communicate and share information. The ability of the child's brain to continually create new connections is essential to their development.
Get assistance from our PROFESSIONAL ASSIGNMENT WRITERS to receive 100% assured AI-free and high-quality documents on time, ensuring an A+ grade in all subjects.
Current research is rife with errors and deficiencies. The first factor that must be considered is the inaccuracy of the sample size. However, according to the central limit theorem, a sample must contain at least 30 observations to be considered valid and representative. The sample size of 34 respondents may not be sufficient to account for all factors influencing how well children learn. In addition, the children's academic performance was evaluated using their classroom grades as a surrogate for their general intelligence. is a broad concept that extends beyond academic aptitude. Walking, creating works of art, and speaking are all abilities that can be acquired through practice. There are other organs besides the intellect that can be educated. Due to the short scope of the study, the current research cannot adequately account for the breadth of non-cognitive categories.
Using both secondary and primary data concurrently is an additional significant limitation of the study that is currently being conducted. The average results of the students accurately reflect their overall performance, not just during the study period. Nobody knows what factors may have influenced the students' test scores, what they thought or felt during the exams, or even where they were when the assignment was complete. This study incorrectly implies that time and events have a linear relationship, which would require keeping all relevant variables constant to predict future brain measures in accurately. This assumption is inaccurate because it requires researchers to conduct the study while holding all relevant variables constant. This presumption is false because external factors, such as emotions, experiences, and traumatic events, can alter brain measures and impair a person's ability to concentrate. This demonstrates that even exceptionally talented students may struggle in certain subjects.
It is, therefore, impossible to assert with certainty that the findings of this study will continue to hold in the future. The activity of the brain can be affected by a variety of circumstances. Excitement, one's surroundings, and fear can interfere with the brain's normal functioning. This suggests that it may be challenging to implement the study's findings in the real world. The outcomes obtained in a laboratory setting may not always accurately reflect what occurs in the real world. Unless the children wore electrodes on their heads all day and did nothing to alter the measurements, the results may not accurately represent measures of brain across the board when it comes to different types of scenarios. Another limitation of the study is that it may be difficult to use the same criteria considered in this study in future research.
5.3 Conclusion
This investigation's results provide insight into young children's brain plasticity. In order to provide factual and empirical evidence, the conclusions of previous studies are contrasted with the current investigation. It was once believed that the developing hippocampus was essential for the development of mathematical abilities in children. There was a correlation between enhanced performance and increased hippocampal activation. Using GPS-calculated peak powers has a negative effect on ratings. Several metrics, including math, literacy, and GPS performance, can be substituted with peak power frequencies because these metrics negatively correlate with peak power frequencies. Predicting the effects of a child's educational experience based on brain wave frequency (beta, alpha, delta, or theta) is impossible. When alpha waves are present, children's performance is slightly enhanced; when beta waves are present, it is slightly diminished.
On the other hand, there is a correlation, albeit a faint one, between delta waves and test scores in mathematics, reading, and global positioning system. Nevertheless, the p-value indicates that the significance threshold of 5% is not met. A 10% significance level indicates that theta waves significantly negatively impact the scaled ratings produced by the GPS. Theta waves with higher frequencies likely reduce brain measures because they tend to induce feelings of calm and comfort.
This study has the potential to lay the groundwork for future research into the connection between brains and how learn. In order to simulate real-world situations more accurately, scientists may attempt to reproduce the experiment in future studies under different conditions, such as a different time of day or a different environment. Future research can overcome the limitations of the current study by investigating the different levels of inaccuracy and utilizing a larger sample size. A novel set of signal measurement techniques would be necessary to accomplish this. The applicability of the results can be improved by conducting additional research that concentrates on comprehending the impact of confounding factors or correcting for their influence. Additional variables, such as the effect of age, will likely be included in future studies. In addition, it can improve evaluation methodologies for cognitive and locomotor skills as indicators. If practicable, this enhancement would be feasible. If this type of research were conducted, the results would increase our understanding of how the brains of young children function.
5.5 Recommendations
Based on the study's findings, the following recommendations can be made to parents and educators to support children's academic performance, reading comprehension, and cognitive development. First, encourage ongoing mental activity. Motivate children to engage in continuous mental stimulation, such as reading, , puzzles, and critical thinking exercises. This ongoing mental activity can improve academic performance, particularly in reading comprehension and GPS-based tests. Also, parents and educators should actively participate in children’s journey. They must stay involved, provide support, and offer guidance to meet children's academic needs. Regularly communicate with teachers and maintain a collaborative approach to education. Limit prolonged inactivity. Discourage prolonged periods of inactivity, such as excessive screen time or sedentary behavior. Extended periods of inactivity can negatively impact peak cognitive performance. Encourage children to engage in physical activity and maintain an active lifestyle. Promote a positive attitude and resilience by highlighting the importance of maintaining a positive attitude towards and academic challenges. Teach children the value of perseverance and resilience in overcoming difficulties. This positive mindset improves academic performance and helps children resist the urge to give up. Lastly, teachers must avoid teaching negative concepts that hinder and discourage academic success. Provide a supportive and nurturing environment that fosters positive experiences, as traumatic events and harsh remarks can impede cognitive development and encourage children to engage in interactive activities and play, which have been found to support academic success. Interactive activities provide cognitive, social, and emotional development opportunities, enhancing overall outcomes.
References
1.0 Introduction – ECN102 Written Assignment Sample The “Human Capital Development Project for Burundi”...View and Download
Introduction Get free samples written by our Top-Notch subject experts for taking online assignment services. Part A...View and Download
INTRODUCTION Learn how to manage library resources and stay focused with expert Assignment Help UK, including tips on goal...View and Download
Introduction Struggling with commercial law tasks? Get Assignment Help Online for expert guidance, accurate legal analysis, and...View and Download
Introduction - SHN4013: Psychological Perspectives On Health & Wellbeing Struggling with case studies involving...View and Download
Introduction: Exploring Ethics, Collaborative work,and Values in Teamwork Enhance your academic work with Assignment Help UK by...View and Download
Copyright 2025 @ Rapid Assignment Help Services
