Impact of Climate on Working Memory Capacity of Students in Learning Mathematics at Elementary Level.
Description
ABSTRACT:
Understanding is a central goal for all education. Research over many decades has shown
that thinking, understanding and problem-solving all take place in one location in the human
brain, now known as working memory. The capacity of working memory is easy to measure,
with several standardized test now available. The capacity has been shown to grow with age to
about age 16, with adults possessing capacities between 5 and 9, the average being 7. This
means that the average working memory can hold 7 items of information at the same time. It has
been shown repeatedly that the capacity of working memory controls understanding as well as
influencing assessment success. While the capacity possessed by an individual cannot be
increased, its efficiency can develop with experience.
Highly conceptual subjects like mathematics are often found to be difficult for learners in
that many ideas have to be held at the same time to gain understanding. Thus, the capacity of
working memory has a direct effect on performance in mathematics. Thus, it is important that
teachers minimize working memory overload wherever possible and several strategies have now
been demonstrated to be effective. It is also important that learners do not have the functioning
of their working memories limited in any way during the learning processes. It is known that
extremes of temperature and oxygen deprivation affect cognitive functioning. This study aims to
explore the relationships between ambient temperatures and hypoxemic conditions (due to
altitude) and working memory capacity as well as mathematics performance.
Standard tests (Digit Span Backward Test and Block Recall Subset) will be employed
under various climactic conditions, while mathematics performance will be measured using a test
designed to fit the mathematics curriculum for elementary students. Gender will be considered as
well. Possible relationships and differences will be considered using Pearson correlation and t-
tests. Any insights will help in enabling allowances to be for students working under more
difficult conditions so that they are treated fairly.
KEYWORDS:
Working memory capacity, cognitive load, heat stress, cold stress, hypoxic stress.
Introduction:
If learners are to gain the most from their education journey, it is important that every
aspect of the educational provision works in harmony. This includes teacher education, teaching
materials, assessment of learning, as well consistent support from the school, education officials
and wider society. No matter how learning is organized or presented, the key to understanding
rests in being able to interact mentally with the material being taught. In his famous paper of
1956, Miller was the first to develop ways to measure what we now know as working memory.
His paper is one the most cited papers in all research literature and led to thousands of studies.
He was able to show that the capacity of working memory could be measured quickly and
reliably and that the working memory for an individual grew to about age 16 after which it was
fixed for life. He also showed that people varied in the capacity of their working memory.
This interaction is now known to take place in the working memory (Cowan, 2014). The
working memory has a limited capacity. While the capacity grow with age to about age 16, it is
then fixed and cannot be expanded. In essence, its capacity is controlled genetically. Research
has shown that its size is unrelated to ability or intelligence. It seems that it is the efficacy and
effectiveness by which we each use our working memory that is more closely related to ability.
There are may summaries in the literature, the reviews by Reid (2009a,b) being particular
relevant in the maths/science field.
A very large of studies has shown that the capacity of working memory controls success
in assessments (Johnstone and El-Banna, 1986, 1989; Johnstone, 1991, 1997). This applies to all
subject areas but is more acute in the maths-science areas, simply because of the abstract nature
of many concepts there. Studies have shown that this reflects the way assessments are usually
designed (Reid, 2002). The extent of control can be very large. In their collation of some
studies, the capacity of the working memory can control up to about 50% of the marks gained
but typical assessments depend on working memory to about 20% of the marks gained (Reid,
2009b).
While working memory is genetically determined, it has been found that there are no
differences related to either gender or culture. However, studies do indicate that there are
climatic factors that may be important. Cognitive performance can be associated with climate factors like heat, cold and hypoxia and sensory annoyance (Gaoua, 2010; Starcke and Brand,
2012). This study aims to explore the relationship between these climate factors and measured
working memory. If there is a relationship, then it follows that student performance will be
affected by climate factors.
Need of Study:
The aim of this study is to identify any association between climate factors (heat, cold,
hypoxia) and the working memory capacity of learners, specifically related to mathematics at an
elementary level. If, as previous research suggest, the functioning of working memory is affected
by any of these factors, then understanding will be affected. In turn, this can undermine
confidence and attitudes related to study will deteriorate (Reid, 2008; Jung and Reid, 2009; Maio
& Haddock, 2009). Mathematics is chosen as a specific subject because learning involves
thinking and logical reasoning while engaging in its activities, working memory having a critical
role here. Baddeley (1997, 1999) has explored the functions of working memory in considerable
detail over many decades. he has shown that there are visual spatial functions as well symbolic
functions (language and number). Mathematics involves all of them.
Literature Review:
Climate has been shown to have an impact on student learning capability and
performance. Evidence shows that there is a strong correlation between performance and
temperature. Students performance from 3
rd to 8
th grade in mathematics was low in hotter region
(Fahle et al., 2017).
High temperature and disease can have major impacts on human life and, perhaps
indirectly, on learning capabilities of learners as well as the workplace and economic activities
(Schwartz et al., 2004; Bleakley, 2010; Deschenes and Greenstone, 2011). Heat has a direct
impact on cognition and the physiology of humans. It may impair the ability of decision making
and generate significant discomfort. Teaching-learning processes are likely to be negatively
affected in hot classrooms (Seppanen et al., 2006; Schlenker et al., 2006; Albouy et al., 2016;
Garg et al., 2017; Shah and Steinberg, 2017). Cognitive functioning is likely to be affected and
this includes task which may be simple or complex (Lezak, 2004). Complex tasks required
multipart coordination and simple tasks required simple motor skills (Bradley and Higenbottam,2003). Evidence shows that heat has significantly effect on working memory functioning (digit
span test , pattern recognition), but little or no effect on simple tasks (Gaoua et al., 2011).
On the basis of evidence, it is also demonstrated that a cold climate also has negative
impact on cognition as individuals focus on feeling cold rather than to complete the assigned
tasks, so there is a significant association between in thermal comfort and assigned cognitive
tasks in cold (Muller et al., 2012; Watkins et al., 2014). Reduction in partial pressure of oxygen
availability is considered as hypoxia. When the partial pressure of oxygen is reduced at high
altitude (~1500–7500 m), there is negative association between altitude and cognition (Pickard,
2002; Rainford and Gradwell, 2006)
Mental capability to remember items, understand ideas, solve problems, and conduct
logical arguments is all dependent on the working memory capacity of individuals. The working
memory model consist on phonological loop, visual-spatial sketchpad and episodic buffer
(Alloway, Gathercole, Willis & Adams, 2004). Usually learners that are not capable to
remember the provided instructions, develop the connection between stored and processed
demands and lose their tracks, with low capacity of working memory face difficulties in
learning process (Gathercole et al., 2006).
The average capacity is found to be 7 for adults (over 16). This means that the working
memory can handle 7 items of information at any one time. He described a unit of information
as what was seen as a unit of information by each person and gave the name ‘chunk’, meaning a ‘unit of information’. Those who were more expert in any subject area were able to group ideas
together and the working memory now could see them as only one ‘chunk’, and this is an aspect
of the efficiency in the way we employ our working memories. However, the average working
memory capacity for a 12 year old is nearer 5 and it is less for those during primary education.
Most adults have capacities of 6,7 or 8, with some having 5 or 9. Capacities outside the range 5
to 9 are not common but are found from time to time.
What studies show consistently is that when working memory overloads, it simply cannot
function. Understand becomes impossible and the completion of any task faces almost
insuperable hurdles. This was the surprising finding from the findings of Johnstone and El-Banna
(1986, 1989). If an assessment task needed one more space than what was available, the average performance in assessment tasks would suddenly drop from over 75% to less than 25%. Many
studies have confirmed this (Reid, 2009b). Several studies have explored ways to help learners.
Some have shown how to redesign teaching materials at secondary stages to minimize working
memory overload (Danili and Reid, Hussein and Reid, Chu and Reid) while others have given
primary teachers some simple techniques to help learners work within their working memory
capacities and thus gain success (Gathercole & Alloway, 2009).
Objectives:
The research will be conducted to:
Measure the working memory capacity of groups of learners under various climatic
conditions including comfortable conditions at 18˚C
Explore the effects on measured working memory capacity of heat stress and cold stress
Explore the effects on measured working memory capacity of hypoxemia at higher
altitudes.
Correlate the measured working memory capacity with performance in mathematics at
elementary level.
To formulate any recommendations that can be deduced from the findings.
Look at any differences related to the impact of climate change on working memory
capacity on a gender basis.
Hypothesis:
HO: There is no significant effect of climate on working memory capacity of students in
learning mathematics at elementary level.
H1: There is a significant effect of climate on working memory capacity of students in
learning mathematics at elementary level.
Research Questions:
1. What is the extent of relationship between measured working memory capacity and
climate variations (involving temperature and altitude) with elementary learners? 2. What is the extent of relationship between measured working memory capacity and
performance in mathematics with elementary learners?
Methodology:
The sample will be drawn from elementary students from regions with different climates
variation like cold exposure (−20 to 10°C), heat exposure ( 35 to 50°C), hypoxemia exposure at
high altitudes (~1500–7500 m) and also comfortable warmth is 18 °C (64 °F) for normal. There
are several standard tests of working memory capacity and the project will employ the ‘Digit
Span Backwards Test’ (DSBT) to measure working memory capacity. The involves reading out
sets of numbers at a standard speed and the candidates her to write them down in reverse order.
The test is widely used and works well.
The Block Recall Subset (BRS) was designed by Corsi (1972) to measure the visuo-spatial
working memory. There will be set of blocks with 0 to 9 digits print on its one side of each block
and a sequence of the blocks defined by researcher. Respondent will be require to repeat the
same sequence that they have been seen. Time will be allocated to repeat the same sequence 2
second per block. Examiner may require to arrange in reverse order.
Pilot testing will be carried out to ensure researcher familiarity with tests and to resolve any
issues. Mathematics performance data will be gathered and standardized as necessary. The
mathematics test will be designed to match the 8
th grade curriculum. Correlation will be
employed to create performance to measure working memory capacity. Independent sample test-
test will be employed t comer measured working memory capacities from the various groups and
took at gender issues.
Limitation of Study:
To measure the correlation between working memory capacity climate change and
content achievement in mathematics 4 assessment tools will be used.
There will be diversity between respondents
There may be little variation in content achievement assessment tool according to
curricula.
Researcher have to travel in different regions to assess the climate change.
References:
Albouy, David, Walter Graf, Ryan Kellogg, and Hendrik Wolff (2016), “Climate amenities, climate
change, and american quality of life.” Journal of the Association of Environmental and Resource
Economists, 3, 205– 246.
Alloway, T.P., Gathercole, S.E., Willis, C. & Adams, A. (2004). A structural analysis of working memory
and related cognitive skills in young children. Experimental Child Psychology, 87,85-106.
Baddeley, A.D. (1997). Human memory: Theory and practice. Hove: Psychology Press Ltd.
Baddeley, A.D. (1999). Human Memory. Boston: Allyn and Bacon.
Bleakley, Hoyt (2010), “Malaria eradication in the americas: A retrospective analysis of childhood
exposure.” American Economic Journal: Applied Economics, 2, 1–45.
Bradley K., Higenbottam C. (2003). Cognitive performance: effect of drug-induced dehydration, in RTO-
MP-HFM-086 – Maintaining Hydration: Issues, Guidelines, and Delivery.
Chu, Y-C. & Reid, N. (2012). Genetics at school level: addressing the difficulties. Research in Science
and Technological Education, 31(1), 1-25.
Cowan, N. (2014) Working Memory Underpins Cognitive Development, Learning, and Education,
Educational Psychology Review, 26(2), 197–223.
Danili, E. & Reid, N. (2004). Some Strategies to improve performance in school chemistry, based on two
cognitive factors. Research in Science and Technological Education, 22(2), 203-226.
Deschenes, Olivier and Michael Greenstone (2011), “Climate change, mortality, and adaptation: Evidence
ˆ from annual fluctuations in weather in the us.” American Economic Journal: Applied Economics, 3,
152–185.
Fahle, Erin M, Benjamin R Shear, Demetra Kalogrides, Sean F Reardon, Richard DiSalvo, and Andrew D
Ho (2017), “Stanford education data archive.”
Gaoua N. (2010). Cognitive function in hot environments: a question of methodology. Scand. J. Med. Sci.
Sports 20, 60–70.
Garg, Teevrat, Maulik Jagnani, and Vis Taraz (2017), “Human capital costs of climate change: Evidence
from test scores in india.
Gathercole, S.E. & Alloway, T.P. (2009). Working mel110lY and learning: a practical guide for teachers.
Los Angeles: Sage
Gathercole, S.E., Lamont, E. & Alloway, T.P. (2006). Working memory in the classroom. In 45
Pickering, SJ. (Ed.), Working memory and education (pp. 219-240).
Hussein, F. & Reid, N. (2009). Working Memory and Difficulties in School Chemistry. Research in
Science and Technological Education, 27(2), 161-186.
Johnstone, A.H. (1991). Why is Science Difficult to Learn? Things are Seldom What They Seem. Journal
of Computer Assisted Learning, 7, 75-83.
Johnstone, A.H. (1997). Chemistry Teaching, Science or Alchemy? Journal of Chemical Education, 74(3) 262-268.
Johnstone, A.H. & El-Banna, H. (1986). Capacities, Demands and Processes: a Predictive Model for
Science Education. Education in Chemistry, 23(3), 80-84.
Johnstone, A.H. & El-Banna, H. (1989). Understanding Learning Difficulties - A Predictive Research
Model. Studies in Higher Education, 14(2), 159-168.
Jung, E-S. & Reid, N. (2009), Working Memory and Attitudes. Research in Science and Technological
Education, 27(2), 205-224.
Lezak M. D. (2004). Neuropsychological Assessment. New York, NY: Oxford University Press.
Maio, G., & Haddock, G. G. (2009). Psychology of attitudes and attitude change. London, England: Sage.
Miller, G.A. (1956). The magical number seven, plus or minus two: some limits on our capacity for
processing information. Psychological Review, 63, 81-87.
Muller M. D., Gunstad J., Alosco M. L., Miller L. A., Updegraff J., Spitznagel M. B., et al. .
(2012). Acute cold exposure and cognitive function: evidence for sustained impairment. Ergonomics 55,
792–798.
Pickard J. (2002). The atmosphere and respiration. Fund Aerospace Med. 3, 19–38.
Rainford D. J., Gradwell D. P. (2006). Ernsting's Aviation Medicine: Hypoxia and Hyperventilation.
Boca Raton, FL: Taylor and Francis.
Reid, N. (2006) Thoughts on Attitude Measurement, Research in Science and Technological Education,
24(1) 3-27.
Reid, N. 2003. Getting started in pedagogical research in the physical sciences. LTSN Physical Sciences
Practice Guide. Hull: LTSN.
Reid, N. 2008. A scientific approach to the teaching of chemistry, The Royal Society of Chemistry
Nyholm Lecture, 2006–2007. Chemistry Education Research and Practice 9, no. 1: 51–19.
Reid, N. (2009a). The Concept of Working Memory. Research in Science and Technological Education,
27(2), 131-138.
Reid, N. (2009b). Working Memory and Science Education. Research in Science and Technological
Education, 27(2), 245-250.
Reid, P.A. (2002). Problem solving by primary school children with particular reference to dyslexics. MSc Thesis, Glasgow: University of Glasgow.
Schlenker, Wolfram, W Michael Hanemann, and Anthony C Fisher (2006), “The impact of global
warming on us agriculture: an econometric analysis of optimal growing conditions.” The Review of
Economics and Statistics, 88, 113–125.
Schwartz, Joel, Jonathan M Samet, and Jonathan A Patz (2004), “Hospital admissions for heart disease:
the effects of temperature and humidity.” Epidemiology, 15, 755–761.
Seppanen, Olli, William J Fisk, and QH Lei (2006), “Effect of temperature on task performance in office
environment.” Lawrence Berkeley National Laboratory.
Shah, Manisha and Bryce Millett Steinberg (2017), “Drought of opportunities: Contemporaneous and
longterm impacts of rainfall shocks on human capital.” Journal of Political Economy, 125, 527–561.
Starcke K., Brand M. (2012). Decision making under stress: a selective review. Neurosci. Biobehav. Rev. 36, 1228–1248.
Watkins S. L., Castle P., Mauger A. R., Sculthorpe N., Fitch N., Aldous J., et al. . (2014). The effect of
different environmental conditions on the decision-making performance of soccer goal line officials. Res.
Sports Med. 22, 425–437.
Start Date
01 Oct 2020
End Date
30 Dec 2023