TEAM COOPERATION TO FIGHT EARLY SCHOOL LEAVING

Neuroscientific findings concerning education and what they imply for teaching and learning

Thursday 23 July 2015, by Tina Rutar Leban

Evidence shows that findings in the area of neuroscience can help teachers develop new ways to improve the learning process. Improving the learning process by adapting it to meet students’ neurological characteristics may help them attain higher academic achievements and self-efficiency, factors that counter ESL.

Keywords : early school leaving 


The emerging field of neuroeducation, also known as educational neuroscience, explores how children learn and which practices promote and sustain the learning process. Neuroeducation is an interdisciplinary field combining neuroscience, psychology and education to help improve teaching methods and curricula so as make them better support students’ individual learning needs (Rich & Goldberg, 2009). Meeting students’ individual learning needs may be seen as a protective factor against ESL.

Recent reviews of neuroeducation highlight the growing body of scientific research that is clearly relevant to education (Ansari, Smedt, & Grabner, 2012; Goswami, 2006; Twardosz, & Bell, 2012) and may support teachers’ efforts to develop more efficient learning methods. For example, brain imaging enables researchers to map the anatomy of reading and mathematics and correlate individual differences in how these skills are acquired with differences in brain structure and function (OECD, 2002; Dehaene, 2009; Nieder & Dehaene, 2009).

This paper explores selected neuroscientific findings and considers their possible impacts on the learning process. It stresses the importance of such advances in neuroscientific knowledge for teachers’ professional development and how this knowledge impacts students, their academic achievements and self-efficiency. These factors are established in the literature (Dumcius, R., Peeters, J., Hayes, N., Van Landeghem, G., Siarova, H., Peciukonyte, L. et al., 2014) as negatively correlating with ESL and thus work as protective factors countering it.

Introduction

Neuroscience is a relatively new area of research that brings neurophysiology, neuropharmacology, neurology, psychology and neuro-imaging together (Purpura, 1992; Kandel & Squire, 2000; Gill, 2005). The development of modern techniques for recording the brain’s physiological activity (functional magnetic resonance imaging – fMRI) while children, adolescents and adults perform a certain cognitive activity has allowed scientists to localise neuronal circuits or areas that synchronously activate within the brain (Gazzaniga & Ivry, 2002; Blakemore & Frith, 2005; Willingham & Lloyd, 2007). Neuroscientific studies provide fresh new insights into certain aspects of attention, memory, language, reading and writing, mathematics teaching, sleep and emotion that are useful in the area of education (Berninger & Corina, 1998; Byrnes & Fox, 1998; Stanovich, 1998; Brown & Bjorklund, 1998; Geake & Cooper, 2003; Geake, 2004). Moreover, neuroscientific methods enable a better understanding of learning, the early diagnosis of learning disabilities and development of alternative methods for children in need of special education (Markram & Markram, 2010).

Beyond specific neuroscientific domains, general knowledge about how the brain develops might be very beneficial for all teachers. For example, discoveries about structural and functional changes in the adolescent brain (Gogtay et al., 2004) can deepen teachers’ understanding of adolescent behaviour. Similarly, knowledge of the prolonged period in which the neural systems implicated in cognitive control and attention develop (Luna et al., 2001) can lead to the appreciation of what constrains learning, from learning to play an instrument (Bengtsson et al., 2005) to the development of arithmetic skills (Rivera, Reiss, Eckert & Menon, 2005), which cannot solely be attributed to immature knowledge or aptitude. Findings indicating that different aspects of memory are activated in various emotional contexts (Erk et al., 2003) support the links between emotion and cognition and give teachers a better understanding of the important role played by emotions in the learning process. The results of cognitive neuroscience research can also provide a better understanding of the roles of sleep (Maquet, 2000) and nutrition (Ivanovic et al., 2004) in brain development and learning, thereby assisting educators when deciding if and how to integrate such variables into their curricula. Further, neuroscientific findings point to structural (Rotzer et al., 2008) and functional particularities (Kaufmann & Vogel, 2009) in how the brains of children with specific learning difficulties function, for example in mathematics (dyscalculia) or reading (dyslexia). Such research is important for educational work and planning forms of assistance (interventions) for these children and for the early detection of learning difficulties.

This article explores some neuroscientific findings about changes in the brain occurring in adolescence. It presents what happens in the brain during learning and learning difficulties and suggests some strategies and implications for teachers’ practice. It focuses on adolescence because this is the period in which changes to the brain have the biggest effect on students’ school work and school behaviour. These structural changes in the brain affect adolescents’ cognitive and socio-emotional functioning in different ways. Some outcomes of the brain maturing hold implications for the brain’s functioning, which may be seen in lower levels of attention, self-discipline, task completion, and emotion regulation. All of these variables influence academic achievement and school engagement which are themselves important preventive factors for ESL (Lan & Lanthier, 2003). Such structural changes occurring in the brain can exacerbate difficulties with school work, affect school achievement and be especially hard for lower achievers and students at high risk for ESL. Understanding that some of the difficulties they encounter at school may come from development of the brain might make the latter students decide to remain in school and thereby prevent ESL. Further, neuroscientific findings also explain how the brains of students with different learning difficulties function. Given that students’ learning difficulties often result in lower levels of achievement, motivation and engagement in school work and that all of these factors are well recognised as being ESL risk factors (e.g. Lan & Lanthier, 2003; European Commission, 2011; PPMI, 2014), monitoring developments in neuroscientific research that touches on learning may indirectly help efforts to prevent ESL.

Methodology

The article is based on a review of literature entailing a search conducted in the PsycINFO, ERIC, Proquest, Science Direct and Google Scholar, Proquest Dissertation & Theses Global databases. Keywords used in the literature search were: neuroscience in education, neuroeducation, educational neuroscience, early school leaving, dyslexia, brain changes in adolescence, brain functioning etc. For the purposes of this article, we mainly took scientific papers and some online scientific books into consideration.

Changes in adolescents’ brain and the implications for teacher practice

Several MRI-facilitated studies conducted in the past years have investigated the way the structure of the brain develops during childhood and adolescence (e.g. Paus, 2005; Casey, Tottenham, Liston, & Durston, 2005). Studies carried out on large groups of subjects show there is increased white matter and decreased grey matter density in the frontal and parietal cortices throughout adolescence (e.g. Barnea-Goraly et al., 2005; Giedd et al., 1999; Reiss, Abrams, Singer, Ross, & Denckla, 1996; Sowell, Thompson, Tessner, & Toga, 2001; Sowell, Peterson, Thompson, Welcome, Henkenius, & Toga, 2003). This increase in white matter seems to be linear across all brain areas, with changes in grey matter density appearing first in the brain’s sensory and motor regions followed by the rest of the cortex, and finally the temporal cortex (Gogtay et al., 2004). Two brain regions consistently shown to undergo continued development during adolescence are the prefrontal cortex and parietal cortex. The continuing structural changes occurring in these brain regions during adolescence (negatively) affect the cognitive abilities – executive functions – that rely on how such regions function, regions like selective attention, decision-making, voluntary response inhibition and working memory (Jensen & Nutt, 2014). Each of these executive functions has a role in cognitive control, for example inhibiting impulses, filtering unimportant information, holding in mind a plan to carry out in the future etc. Prospective memory (the ability to hold in mind an intention to conduct an action at a future time) is also associated with frontal lobe activity (Burgess, Veitch, Costello, & Shallice, 2000) and has been shown to develop through childhood as we develop our future-oriented thought and action (Ellis & Kvavilashvili, 2000). The ability to multitask is known to be a test of prospective memory because it requires a person to remember to perform several different tasks, mirroring everyday life. In a study of the development of prospective memory from childhood to adulthood (Mackinlay, Charman, & Karmiloff-Smith, 2003), a significant improvement in both the efficiency and quality of strategies was found between the ages of 6 and 10. However, between the ages of 10 and 14, there was no significant change in performance. The adult group (mean age 25), on the other hand, significantly outperformed the children. The authors argued that prospective memory continues to develop during adolescence and that it is possible that the lack of improvement in performance between the 10- and 14-year-olds is related to their pubertal period that sees structural changes occur in the brain (Mackinlay et al., 2003). Similar results were found in a study (McGivern, Andersen, Byrd, Mutter, & Reilly, 2002) that investigated the development of working memory and decision-making (functions of the frontal lobe) during childhood through to adolescence and adulthood. The results reveal that at the age of puberty, at 11–12 years, there was a decline in performance compared with the younger group of children. The results suggest there is a dip in performance for such functions in the period of puberty. After puberty, from 13–14 years, performance improved again until it reached the pre-pubescent level at around the age of 16–17. The authors explain this pubertal dip in performance with the proliferation of synapses that results in perturbation of cognitive performance during adolescence. Only later, after puberty [1], do the excess synapses become refined into specialised, efficient networks, thus a post-pubescent further improvement (McGivern et al., 2002).

It is thus important for teachers to remember that even though teenage brains are learning at their peak efficiency, another considerable part of them is performing inefficiently (in some cases even less so than in childhood), including attention, self-discipline, task completion, and emotions. Accordingly, it is perhaps more reasonable to not overwhelm teenage students with too much instruction at any one time. Although they may look like they can multitask, their brain is not yet ‘wired’ enough for this activity and they are not very good at it. Encouraging them to stop and think about what they need to do and when they need to do it will help increase blood flow to those areas of the brain involved in multitasking and slowly strengthen them (Jensen & Nutt, 2014). Writing instructions and directions down, in addition to giving them orally, and limiting instructions to one or two points can help adolescent students focus more easily. Teachers can also help adolescent students better manage their time and organise their tasks by advising them to use calendars and suggesting they write down their daily schedules.

Apart from executive functions, evidence shows the prefrontal cortex is involved in several other high-level cognitive functions and capacities, including self-awareness (Ochsner, 2004) and theory of mind (Frith & Frith, 2003), that is, the ability to understand other people by attributing mental states such as beliefs, desires and intentions to them (Frith, 2001). On top of neural development, major hormonal changes are occurring during puberty. While it is impossible to dissect every important influence on adolescents’ social and emotional behaviour, both neural development and hormonal changes probably influence their social cognition. In one study (Choudhury, Blakemore, & Charman, 2006), the development of perspective taking was investigated before, during and after puberty. The results show that the development of social perspective taking undergoes perturbation during puberty parallel to the discontinuous processes of brain maturation. This means that adolescents may find it difficult to understand the perspective of others.

Understanding that these difficulties may also be due to a student’s neurological and hormonal background is important for teachers. It is easier to work with possible misbehaviour (deriving from not respecting another person’s perspective) when appreciating that such behaviour could be due to neurological and hormonal changes rather than simple egoism and deliberate disrespect. Talking to students openly, explaining the changes occurring in their brains to them helps them to become more aware of these situations and focus more intentionally on other people’s perspectives.

What occurs in the brain during learning and learning difficulties and what this implies for teacher practice

Attention and learning do not occur in isolated brain structures but by way of various layers of neuronal nets that are interconnected via complex and unstable links (Edelman, 2006). A further understanding of how information is ‘translated’ by the sense organs, turned into perception and later stored in long-term memory might assist teachers when preparing instruction strategies to improve students’ learning success. Neuro-imaging pinpoints areas of the brain involved in the visual-spatial processing functions that are active during mathematics and science problem-solving. This knowledge suggests that visual-spatial skills should be integrated into mathematics education as a means to develop more efficient methods for teaching mathematics (Dehaene, 1997; Simon, 2006; O´Boyle et al., 2005). This means the teacher can support the learning of maths and science by including activities that encompass visual-spatial skills (such as following directions on a map in space, executing dance moves etc.) in mathematics and science instruction. In addition, future studies in neuro-genetics and neuro-imaging could help understand if the visual and phonological processing occurring in certain areas of the brain are the roots of dyslexia and other learning problems (Fisher & Francks, 2006; Plomin, Kovas, & Haworth, 2007).

What is more, neuroscience provides scientific clues about whether some educational approaches might be more effective than others. For example, different teaching strategies are available to help children with dyslexia. Typical public school and special education interventions often stabilise the degree of reading failure rather than remediate (normalise) the reading-skill level (Torgesen, 2006). Using a neuroscientific approach, researchers can identify changes in the brain that may determine the effectiveness of teaching strategies to reduce dyslexia problems. In fact, functional neuroimaging studies show the brain plasticity associated with effective intervention for dyslexia (e.g. Temple et al., 2003; Shaywitz et al., 2004; Aylward et al., 2003; Eden et al., 2004). Studies reveal the most efficient strategy for dyslexia intervention is special intensive (for instance, 100 min. per day for 8 weeks) instruction provided in small groups (1 or 2 students per teacher) and including explicit and systematic instruction in phonological awareness and decoding strategies.

Having said this, there is great potential to harness neuroscience to help design programmes to train neurocognitive functions, such as working memory, that are expected to have effects on overall brain function. Neuroscientific research may be able to enrich our understanding of how academic skills are generally acquired. Further, modern brain-imaging methods hold considerable potential to serve as diagnostic tools as well as measures of the effects of educational interventions like those concerning dyslexia (Ansari, De Smedt, & Grabner, 2012). Moreover, while working with adolescent students who have learning difficulties such as dyslexia, dyscalculia etc. neuroscientific findings may be helpful in explaining to them details of what is going on in their brains and how the educational intervention (special learning strategies, additional work etc.) is designed to alter the way their brain functions so they can understand why they need to do additional work. Showing them brain images can help them to better understand this.

Implications for ESL

Neuroscientific findings can also be used to help prevent ESL. For example, different neuroscientific studies show that the learning, behavioral and emotional difficulties (such as difficulties with selective attention, decision-making, voluntary response inhibition, working memory etc.) some students encounter during adolescence are to some extent consequences of brain changes in the prefrontal cortex and parietal cortex (e.g. Burgess, Veitch, Costello, & Shallice, 2000; Ellis & Kvavilashvili, 2000). These difficulties can produce lower self-efficacy feelings, a lower academic self-image, more negative school-related attitudes, lower achievements and poorer engagement in school (Luna, 2009). All of these factors have been established as important predictors of ESL (e.g. Battin-Pearson, et al., 2000; Lan & Lanthier, 2003). For example, ESLers and underachievers are identified as having lower self-esteem, a lower academic self-concept and a lower perception of self-efficiency than other students (PPMI, 2014). Other studies show that the self-concept plays a significant role in enhancing students’ intrinsic motivation, positive emotion, and performance, all shown in different studies to be important preventive factors for ESL (e.g. Battin-Pearson, et al., 2000; Lan & Lanthier, 2003).

Helping students at risk for ESL understand the reasons behind some difficulties they are experiencing are also due to natural developmental changes in their brain (which will pass in time) may lessen the impact of these difficulties on their self-image and attitudes to school. Consequently, this strategy may assist in preventing ESL. Moreover, showing students at risk for ESL the use of scientific findings and newly developed technologies (e.g. MRI brain images) in their own life may help them find a reason and motivation to stay at school. This could also be an opportunity for science teachers to go more in depth while addressing the subject of brain development and to include the latest neuroscientific findings on adolescent brain development in their lessons (Jensen & Nutt, 2014).

Although neuro-education is developing very rapidly and we can see its potential in aiding the development of teaching and learning processes, its concrete implications remain limited. Therefore, the strategy presented above is just one of the possibilities revealing how neuroscientific findings can help in improving educational practices and prevent ESL.

Conclusions

Neuroscientific research of the brain’s development during adolescence shows that, while the brain’s learning capability is at its peak in adolescence, the continued structural changes in the brain negatively affect teenage cognitive and social-emotional functioning. The results of brain maturation hold implications for functioning of the brain, showing as lower attention, self-discipline, task completion, and lower emotion regulation. Further, neuroscientific research methods, especially brain imaging, provide a deeper understanding of brain functioning in dyslexia, dyscalculia and other learning difficulties.

Education researchers are very optimistic that neuroscience findings can effectively contribute to improving educational practices. But the neuroscientific research itself does not introduce any new educational strategies. This is still the domain of educators. Being familiar with the latest neuroscientific findings on adolescents’ brain changes can assist teachers in understanding adolescents’ behaviour and adapting their teaching strategies to suit their students’ maturing brain functions.

Moreover, teachers can use the neuroscientific knowledge to develop educational interventions for low achieving students, students at risk for ESL and students with dyslexia or other learning difficulties. Becoming familiar with the neuroscientific findings on adolescent brain maturation and functioning might also be very interesting to students, especially those struggling with learning and emotional difficulties. By knowing what is happening in their brains, students might better accept the consequences they experience in everyday life and also be more willing to train their brain with regard to the scope of its accelerated development.

This is even more important for students with learning difficulties and others at greater risk of ESL. Neuroscientific findings on adolescent brain changes can at least to some extent help students at risk for ESL appreciate that some difficulties they are experiencing in their cognitive functioning are also due to natural developmental changes occurring in their brain. Knowing that these changes are part of the normal brain development process and that they will pass can at least partially reduce the impact of these difficulties on adolescents’ self-image and attitudes to school and thus help students remain at school through to the end, thereby preventing ESL.

Footnotes

[1Puberty is the process of physical changes by which adolescents reach sexual maturity, i.e. become capable of reproduction. Puberty refers to these bodily changes, while adolescence is the period of psychological and social transition between childhood and adulthood (that also includes puberty).

, , & (2012). Neuroeducation – a critical overview of an emerging field. Neuroethics, 5(2), 105–117.

, , , , , , … & (2003). Instructional treatment associated with changes in brain activation in children with dyslexia. Neurology, 61(2), 212–219.

, , , , , , … & (2005). White matter development during childhood and adolescence: A cross-sectional diffusion tensor imaging study. Cerebral Cortex, 15(12), 1848–1854.

, , , , , & (2000). Predictors of early high school dropout: A test of five theories. Journal of Educational Psychology, 92(3), 568–582.

, , , , , & (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience, 8(9), 1148.

, & (1998). Making cognitive neuroscience educationally relevant: Creating bidirectional collaborations between educational psychology and cognitive neuroscience. Educational Psychology Review, 10(3), 343–354.

, & (2005). The learning brain: Lessons for education. Blackwell Publishing.

, & (1998). The biologizing of cognition, development, and education: Approach with cautious enthusiasm. Educational Psychology Review, 10(3), 355–373.

, & (1998). The educational relevance of research in cognitive neuroscience. Educational Psychology Review, 10(3), 297–342.

, , , & (2000). The cognitive and neuroanatomical correlates of multitasking. Neuropsychologia, 38(6), 848–863.

, , , & (2005). Imaging the developing brain: What have we learned about cognitive development? Trends in Cognitive Sciences, 9(3), 104–110.

, , & (2006). Social cognitive development during adolescence. Social Cognitive and Affective Neuroscience, 1(3), 165–174.

(1997). The number sense. Oxford: Oxford University Press

(2009). Reading in the brain: The new science of how we read. Penguin.

, , , , , , … & (2014). Study on the effective use of early childhood education and care in preventing early school leaving. Luxembourg: Publications Office of the European Union.

(2006). Second nature: Brain science and human knowledge. Yale University Press.

, , , , , , … & (2004). Neural changes following remediation in adult developmental dyslexia. Neuron, 44(3), 411–422.

, & (2000). Prospective memory in 2000: Past, present, and future directions. Applied Cognitive Psychology, 14(7).

, , , , , & (2003). Emotional context modulates subsequent memory effect. Neuroimage, 18(2), 439–447.

, & (2006). Genes, cognition and dyslexia: Learning to read the genome. Trends in Cognitive Sciences, 10(6), 250–257.

(2001). Mind blindness and the brain in autism. Neuron, 32(6), 969–979.

, & (2003). Development and neurophysiology of mentalizing. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 358(1431), 459–473.

, , & (2002). Cognitive neuroscience. New York: W. W.

(2008). Neuromythologies in education. Educational Research, 50(2), 123–133.

, & (2003). Cognitive neuroscience: Implications for education? Westminster Studies in Education, 26(1), 7–20.

, , , , , , … & (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2(10), 861–863.

(2005). Principles and practice of behavioral neurology and neuropsychology. Neurology, 64(4), 769–769.

, , , , , , … & (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America, 101(21), 8174–8179.

(2006). Neuroscience and education: From research to practice? Nature Reviews Neuroscience, 7(5), 406–413.

, , , , , , … & (2004). Head size and intelligence, learning, nutritional status and brain development: Head, IQ, learning, nutrition and brain. Neuropsychologia, 42(8), 1118–1131.

& (2014). The teenage brain. A neuroscientist’s survival guide to raising adolescents and young adults. Harper Collins Publishers.

, & (2000). Neuroscience: Breaking down scientific barriers to the study of brain and mind. Science, 290(5494), 1113–1120.

, , , , , & (2009). Developmental dyscalculia: Compensatory mechanisms in left intraparietal regions in response to nonsymbolic magnitudes. Behavioral and Brain Functions, 5(1), 35.

, & (2003). Changes in students’ academic performance and perceptions of school and self before dropping out of schools. Journal of Education for Students Placed at Risk, 8(3), 309–332.

, , , , , , … & (2001). Maturation of widely distributed brain function subserves cognitive development. Neuroimage, 13(5), 786–793.

(2009). Developmental changes in cognitive control through adolescence. Advances in Child Development and Behavior, 37, 233–278.

, , & (2003, April). Remembering to remember: A developmental study of prospective memory in a multitasking paradigm. Poster presented at the Society for Research in Child Development, Biennial Meeting, Tampa, Florida (Vol. 24).

(2000). Sleep on it. Nature Neuroscience, 3, 1235–1235.

, & (2010). The intense world theory – a unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience, 4.

, , , , & (2002). Cognitive efficiency on a match to sample task decreases at the onset of puberty in children. Brain and Cognition, 50(1), 73–89.

, & (2009). Representation of number in the brain. Annual Review of Neuroscience, 32, 185–208.

. (2002). OECD Annual Report 2002, OECD Publishing, Paris.

, , , , , , & (2005). Mathematically gifted male adolescents activate a unique brain network during mental rotation. Cognitive Brain Research, 25(2), 583–587.

(2004). Current directions in social cognitive neuroscience. Current Opinion in Neurobiology, 14(2), 254–258.

(2005). Mapping brain maturation and cognitive development during adolescence. Trends in Cognitive Sciences, 9(2), 60–68.

, , & (2007). Generalist genes: Genetic links between brain, mind, and education. Mind, Brain, and Education, 1(1), 11–19.

(1992). A neuroscience curriculum. Commission on Medical Education: The Sciences of Medical Practice. New Jersey: The Robert Wood Johnson Foundation. Princeton.

, , , , & (1996). Brain development, gender and IQ in children: A volumetric imaging study. Brain, 119(5), 1763–1774.

, , , & (2005). Developmental changes in mental arithmetic: Evidence for increased functional specialization in the left inferior parietal cortex. Cerebral Cortex, 15(11), 1779–1790.

, , , , , & (2008). Optimized voxel-based morphometry in children with developmental dyscalculia. Neuroimage, 39(1), 417–422.

, , , , , , … & (2004). Development of left occipitotemporal systems for skilled reading in children after a phonologically-based intervention. Biological Psychiatry, 55(9), 926–933.

(2006). Key developmental understandings in mathematics: A direction for investigating and establishing learning goals. Mathematical Thinking and Learning, 8(4), 359–371.

, , , , , & (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6(3), 309.

, , , & (2001). Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation. Journal of Neuroscience, 21(22), 8819–8829.

(1998). Cognitive neuroscience and educational psychology: What season is it? Educational Psychology Review, 10(4), 419–426.

, , , , , , & (2003). Neural deficits in children with dyslexia ameliorated by behavioral remediation: Evidence from functional MRI. Proceedings of the National Academy of Sciences, 100(5), 2860–2865.

(2006). Recent discoveries from research on remedial interventions for children with dyslexia. In Snowling, M. & Hulme, C. (Eds.). The Science of Reading: A Handbook, chap. 27. Blackwell, Malden, MA.

, & (2012). Introduction to the special issue on neuroscience perspectives on early development and education. Early Education & Development, 23(1), 1–3.

, & (2007). How educational theories can use neuroscientific data. Mind, Brain, and Education, 1(3), 140–149.