Essay: Cognitive load theory – implications for teaching

Cognitive load theory, first coined by Sweller (1988), is built upon two commonly accepted ideas (Centre for Education Statistics and Evaluation, 2017). Firstly, there is a limit to how much information the human brain can process at once, and secondly, there are no limits to how much information can be stored at once. Therefore, when learning, there is a limit to how much new information students can process at one time, but once it is learnt, the amount of information they can store is infinite (Peterson & Peterson, 1959). This has implications for teachers, as research suggests that this limit on the amount of information that can be processed at once varies in individuals, meaning that some individuals can process more new information during a lesson than others (Centre for Education Statistics and Evaluation, 2017). The aim of research on cognitive load is consequently to develop instructional techniques to allow teachers to maximise the amount of information individual students can learn at any one time.
The history of cognitive load theory can be traced back to the 1950s and the work of Miller (1956), who was the first to suggest that our memory for processing new information has a capacity. Miller cited this capacity as seven units of information plus or minus two, meaning that the average person can recall five to nine pieces of new information without rehearsal. Furthermore, cognitive load theory is based on the notion of working memory, the memory system where small amounts of information are stored for a short period of time (Peterson & Peterson, 1959). More recent research suggests that individuals can only hold up to four pieces of new information in their working memory at any one time (Cowan, 2001), although there is evidence that indicates differences in working memory capacity between individuals (Barrett, Tugade & Engel, 2004).
There are arguments that low ability pupils, and those with cognitive difficulties, have smaller cognitive loads than their peers (Paas, Tuovinen, Tabbers & Van Gerven, 2003), perhaps explaining their shortcomings in educational assessments that require individuals to memorise and recall large sets of information. If working memory is overloaded, there is a greater risk that the content being taught will not be understood by the learner, will be misinterpreted or confused, will not be effectively encoded in long-term memory, and that learning will be slowed down (Martin, 2016). This has multiple implications for teaching and learning environments, with British educationalist Wiliam (2017) describing cognitive load theory as “the single most important thing for teachers to know”. Several governments and educational institutions have produced guidance for teachers about strategies in which they can use to cater for individual’s cognitive load within their classrooms (Centre for Education Statistics and Evaluation, 2018).
Furthermore, having a small cognitive load has been found to correlate with having a low intelligence (Haapalainen, Kim, Forlizzi & Dey, 2010). There has been an abundance of research on children’s intelligence and its links to their performance at school. Perhaps the most well-known standard measure of intelligence, the IQ test, has been found to predict children’s attainment at school and success in future careers (Hogan et al., 2010; McDermott, 1984; Scarborough, 1998). Other measures of intelligence have also been shown to positively correlate with school success. For example, Laidra, Pullmann and Allik (2006) measured intelligence and academic attainment in 3618 primary school pupils. They found that intelligence, as measured by Raven’s Standard Progressive Matrices, was the best predictor of student’s attainment, as measured by grade point average, in all year groups. Additionally, the predictor variables used only accounted for a small percentage in variance of grade point average, suggesting that academic attainment relies on the same mechanism of intelligence throughout the school years.
The research on cognitive load, intelligence and academic ability implies that academic attainment is the basis of being a successful learner. Innumerable studies define success in life as achieving high grades at school and having a well-paid career as a result of this. For example, Laidra et al. (2006) defined having a high-grade point average as being successful at school, which they explained plays an important role in student’s future lives, making some opportunities in life more likely and eliminating others. Furthermore, York, Gibson and Rankin (2015) completed an analytic literature review to examine the use of ‘success’ in multiple fields, finding that grades such as grade point averages were the most commonly used measure of academic success.
However, success at school cannot be pinned entirely to academic success. For example, Oxford English Dictionary (2019) defines success as “the accomplishment of an aim or purpose”, which can apply to more than just academic grades. With this definition, any goal or milestone that is achieved can be defined as success. Success by nature is inherently perspective driven – it depends on the individual and what goal they are working towards. Say a pupil is working towards spelling words listed on the year 4 National Curriculum (Department of Education, 2014) while the rest of their class is learning words on the year 6 curriculum, if they achieve this goal of getting the words right, they have still been successful, even if not attaining the same level as their peers.
This definition of success relates to the difference between achievement and attainment, with achievement being the size of the progress made towards a goal, and attainment being the end goal (Callender, 2008). For example, if two pupils are taking the same standard assessment tasks (SATs) at the end of year 6, but one comes from a wealthy family with well-educated parents who invest time and money into making sure their child does well, and the other comes from a low income family with little support, but they both get age expected results, the pupil with less support will have ‘achieved’ more as they had a lower starting point. This means achievement is similar to success, in that an achievement is an amount of progress, and success can be any form of progress made on a goal. In my opinion, rather than attaining a specific goal or grade, success is about overcoming barriers, progressing, and achieving goals specific to the individual.
There was a particular child in my recent placement class whom the theories of cognitive load and subjective success apply to. This child, an 8-year-old boy, whom for the purpose of this essay shall be named ‘Student A’, is a low ability pupil within his year 3 class. During my 7-week placement within the class, I noted he struggled within all core subjects, and had challenging behaviour, not helped by other challenging pupils within the class. He was very easily distractible, but also had a tendency to rush through his work to get it finished, not taking caution and thus making careless mistakes. This was especially problematic in mathematics, as Student A did not have a secure foundation of mathematical concepts, therefore struggled with processing new methods. Within English, he would not take the time to read back his work to check it made sense, so I would have to get him to read a lot of his work back to me and he would not be able to decipher it himself either. Research suggests that pupils with low cognitive load exhibit behaviours such as being easily be distracted by novel or irrelevant information, tend to be imprecise, impulsive and non-systematic in collecting information and producing work, and tend to have a small knowledge base (Ferrari, 2001). I felt that these behaviours applied to Student A, and there was a clear opinion from his usual class teacher that most of the Year 3 mathematics and English curriculum was not accessible to him.
These observations interested me, as Student A was noticeably struggling with the curriculum that the rest of the class were learning, but not to the extent of a specific learning difficulty. In assessments, Student A was not achieving age-related expectations, but rather the age-related expectations for the year below. If he continued to progress at the same rate, this would impact him in the future for his year 6 SATs and secondary school GCSEs. Therefore, his cognitive load would affect his success in these assessments, so to some would not be seen as a ‘successful learner’.
As mentioned previously, success within school is subjective, and for Student A, I believe being a successful learner would be to mitigate the effects of his low cognitive load so that he can get the most out of his education. For other learners within the class, being a successful learner would take a different definition, such as moving up a reading level or ‘working at greater depth’. Not all pupils within the class face the same challenges to their learning, and for Student A his cognitive load and the adjacent behaviours are the main hurdle he faces; therefore, I believe Student A would be a more successful learner if he could utilise strategies to overcome his cognitive load.
This essay will critically analyse cognitive load theory in greater depth, and examine techniques and strategies used within practice to overcome low cognitive load, and how they may impact success as a learner. I will use both theories and empirical evidence to support my argument that cognitive strategies may help Student A to become more successful, however success as a learner should not be based solely on cognitive load and results in school assessments, but rather a more encompassing definition taking into account personal successes.
Discussion
Cognitive load theory
There is a large body of evidence to support cognitive load theory. The majority of this evidence comes for randomised controlled trials (RCTs), which are perhaps the most well respected from of scientific enquiry within educational research (Rothwell, 2005). This body of evidence suggests that instructions within teaching environments tend to be more beneficial when tailored according to how the human brain processes and stores information (Centre for Education Statistics and Evaluation, 2017).
In order to empirically research cognitive load theory, the majority of studies do not directly attempt to measure cognitive load itself, but rather the effectiveness of instructional techniques designed to overcome the limitations of cognitive load. However, some studies have attempted to measure cognitive load directly, for example, Paas and Van Merriënboer (1993) developed a construct, known as relative condition efficacy, which helps researchers to measure perceived mental effort, which is an index of cognitive load. It is based on the idea that individuals with a high cognitive load will use less mental effort to process information than individuals with a low cognitive load. Using this construct, Paas and Van Merriënboer found that learners who use worked examples were the most efficient in learning information.
This is supported by multiple other studies featuring worked examples, forming the ‘worked example effect’ (Sweller & Cooper, 1985), an instructional approach recommended by cognitive load research and supported by RCTs (Centre for Education Statistics and Evaluation, 2017). In an early study, Cooper and Sweller (1987) conducted a series of experiments whereby secondary school students were required to learn how to solve an assortment of simple algebra problems. They found that the students who were taught using worked examples learnt far more quickly than students who were given scaffolded opportunities to work the problems out for themselves. Furthermore, Cooper and Sweller found that students taught using worked examples were not only able to solve similar problems in subsequent tests to a higher level but were also able to solve problems in different contexts whereby the same algebraic rules were applied. This effect has since been replicated in a large number of RCTs and has been a basis for many instructional techniques for teachers (Centre for Education Statistics and Evaluation, 2017).
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