This is the sixth in a series of blogposts on Peter C. Brown, Henry L. Roediger III & Mark A. McDaniel’s seminal book on the science of learning, Make It Stick: The Science of Successful Learning (Harvard University Press, 2014). This week focuses on the sixth chapter, ‘Get Beyond Learning Styles’. For posts on previous chapters, see our blog.
In November, we hosted a webinar with the book’s authors, which is available on our podcast.
Jonathan Beale, Researcher-in-Residence, CIRL
In this chapter, the authors argue that while we all have learning preferences, there is insufficient evidence to show that we learn better when teaching is made to fit those preferences (pp. 132 & 145). They argue that there are nonetheless some learning differences that matter, which they outline. The term ‘learning styles’ is used to denote the view ‘that we learn better when the mode of presentation matches the particular style in which an individual is best able to learn’. They argue against this view (p. 145).
The chapter outlines several theories of intelligence and notions associated with current research on intelligence. One set of notions is the distinction between ‘fluid intelligence’ and ‘crystallized intelligence’, which is widely accepted by psychologists (p. 146). Fluid intelligence is our ability to reason, see relationships, think abstractly and hold information in our minds while we solve problems. Crystallized intelligence is the knowledge and mental models we have accumulated over our lives. A ‘mental model’ is ‘a mental representation of some external reality’ (p. 6) (on mental models, see this earlier blogpost, claims 11 & 12). The combination of fluid and crystallised intelligence ‘enable us to learn, reason, and solve problems’ (pp. 146-7).
The authors outline several theories of intelligence. The first is the standard theory, used as the method of assessment in Intelligence Quotient (IQ) tests. These assess a person’s logical and verbal potential. It was long held that IQ is fixed from birth, but the authors outline theories that challenge this view (p. 147).
Two challenging views have been put forward by the psychologists Howard Gardner and Robert J. Sternberg respectively. Gardner argues that human beings possess eight types of intelligence:logical-mathematical; spatial; linguistic; kinaesthetic; musical; interpersonal; intrapersonal; and naturalistic. While this theory accounts for the wide variety of abilities and differences in skills across people, it currently lacks sufficient empirical support (pp. 147-8).
Sternberg distinguishes three types of intelligence: analytical, creative, and practical. This theory has more empirical support in its favour (pp. 148 & 150). One of the main points the authors take from Sternberg is his argument that intelligence tests typically focus on static ability – our level of skill and what we know at a particular time – but do not tell us enough about our potential (pp. 149 & 151). The authors argue that a reliable theory of intelligence ought to be able to tell us more about our potential than IQ tests show.
The authors also argue that ‘traditional measures of intelligence fail to account for environmental differences’, such as complex cognitive skills and knowledge possessed by people who have received little education or do not score highly on IQ tests. As examples, the authors outline case studies showing the high levels of indigenous informal knowledge of herbal medicine possessed by children in rural areas of Kenya; the high-stakes knowledge and skills learned by some orphaned children in Brazil who quickly learn how to start and run successful street businesses in order to survive; and the complex mathematical and economic models developed by ‘expert handicappers’ at horse races (pp. 148-150).
In the final of those studies, the expert handicappers had devised highly complex mental models yet achieved average scores on standard IQ tests. The study found that ‘IQ is unrelated to handicapping ability’ and IQ tests do not assess the ability ‘to engage in cognitively complex forms of multivariate reasoning’ (p. 150).
The authors support Sternberg’s tripartite model of types of intelligence, which can accommodate skills and areas of knowledge such as those mentioned above. For example, the skills and knowledge possessed by the orphaned children in Brazil involves a high level of ‘practical intelligence’ – ‘our ability to adapt to everyday life – to understand what needs to be done in a specific setting and then do it’. The authors argue that a major problem with IQ tests is that cultures and learning environments draw upon analytical, creative and practical forms of intelligence in different ways, and ‘much of what’s required to succeed in a particular situation is not measured by standard IQ or aptitude tests, which can miss critical competencies’ (p. 150).
One of the arguments of this chapter is that standardised tests, such as IQ tests, focus too much on our ‘static’ abilities. As a method of testing that pays more attention to a person’s potential, the authors propose ‘dynamic testing’: a method of using testing to assess ability in a dynamic manner. Dynamic testing focuses on providing feedback on where a learner needs to improve to succeed, by helping to identify weaknesses and correct them (p. 151). This is based on the principle that ‘most of us can learn to perform nearer to our full potential in most areas by discovering our weaknesses and working to bring them up’ (p. 152).
Dynamic testing involves three stages:
1. a formative test of some kind is taken, the aim of which is to show the learner where they ‘come up short in knowledge or a skill’;
2. the student ‘dedicates’ themselves to becoming more competent at the areas in which they need to improve;
3. the student takes a formative test again, with the focus on paying attention to where more work is still needed to develop areas in need of improvement (p. 152).
The authors argue that dynamic testing has several learning benefits, some of which are argued to be advantages over standardised testing. Three key advantages are put forward. First, it provides a more accurate assessment of ability and potential. Second, it ‘focuses the learner and teacher on areas that need to be brought up rather than on areas of accomplishment’. Third, it is able ‘to measure a learner’s progress from one test to the next’, which ‘provides a truer gauge of his or her learning potential’ (p. 151).
The authors argue that there are cognitive differences in how people learn, but not the ones typically falling under the view they call ‘learning styles’. ‘Structure building’ is one such difference. This is the ‘act, as we encounter new material, of extracting the salient ideas and constructing a coherent mental framework [i.e., a mental model] out of them’ (p. 153). Constructing a narrative is an example of structure building (p. 154).
The authors distinguish between ‘low structure-builders’ and ‘high structure-builders’, the latter being those who possess higher levels of skills in structure building. Low structure-builders ‘struggle in figuring out and sticking with an overarching structure and knowing what information needs to fit into it and what ought to be discarded’ (p. 154). By contrast, high structure-builders,
- ‘learn new material better than low-structure builders’ (p. 153);
- ‘develop the skill to identify foundational concepts and their key building blocks’;
- ‘develop the skill to … sort new information based on whether it adds to the larger structure and one’s knowledge or is extraneous and can be put aside’ (p. 154).
As a strategy to develop the learning performance of low structure-builders, the authors recommend embedding questions in texts to help focus on the main ideas:
‘[When] questions are embedded in texts to help focus readers on the main ideas, the learning performance of low structure-builders improves to a level commensurate with high structure-builders’.
This is because the ‘embedded questions promote a more coherent representation of the text than low-structure readers can build on their own’ (p. 155).
Rule learning vs. example learning
Another distinction the authors draw is between ‘rule learning and ‘example learning’. Rule learners ‘tend to abstract underlying principles or “rules” that differentiate the examples being studied’. Example learners ‘tend to memorize the examples rather than the underlying principles’. Rule learners apply the rules they have learned to new situations whereas example learners generalise on the basis of examples from memory (p. 156).
Rule learning has learning advantages over example learning. Rule learners tend to ‘single out salient concepts from the less important information they encounter in new material’ and ‘link these key ideas into a mental structure’, both of which are habits of ‘more successful learners’. More successful learners also tend to ‘extract underlying principles or rules from new experiences’ rather than taking experiences at face value, the former of which is a tendency of rule learners and the latter is a tendency of example learners. Those who do not tend to extract underlying principles or rules often do not ‘infer lessons that can be applied later in similar situations’. Example learners might also generalise to new situations on the basis of examples that are not especially relevant to the new cases they encounter (p. 133).
Learning and applying rules is also required for learning to become ability knowledge (know-how):
‘Knowledge is not know-how until you understand the underlying principles at work and can fit them together into a structure larger than the sum of its parts’ (p. 158).
Some people are more naturally rule learners than example learners. But example learners can learn to develop their rule learning skills. The authors outline several strategies for this (pp. 155-7).
The authors recommend the following practical strategies:
- Construct mental models of what you have learned, relating new learning to existing knowledge and contextualising it within existing frameworks, applying it to relevant areas (p. 133).
- Reflect on your experiences and construct a narrative to function as a mental model: ‘cultivating the habit of reflecting on one’s experiences, of making them into a story, strengthens learning’. Structure building is proposed as theory of why this is so: ‘reflecting on what went right, what went wrong, and how I might do it differently next time helps me isolate key ideas, organize them into mental models, and apply them again in the future with an eye to improving and building on what I’ve learned’ (p. 155).
- Take responsibility over your learning: developing mastery requires long-term effort and motivation from the learner; regular revision; regular reflection on what went well and what needs improvement; adaptation of learning strategies to optimise improvement and focus on what most needs to be improved. All of these involve the learner taking responsibility over their learning (pp. 133 & 159).
- Take a wide view of intelligence: identify the knowledge or skills you want to possess and consider what is required to develop these; then, ‘list the competencies required [and] what you need to learn’. In other words, ‘take command of your resources and tap all of your “intelligences” to master the knowledge or skill you want to possess’.
- Practice dynamic testing: use this as a ‘learning strategy to discover your weaknesses, and focus on improving yourself in those areas’. This can help to develop your metacognition, so your awareness of your knowledge and skills is more accurate.
- Develop workarounds: where you lack a required skill in an area,try to‘develop workarounds or compensating skills for impediments or holes in your aptitudes’ (p. 159).
- Employ rule learning by distilling underlying principles: ‘abstracting the underlying rules and piecing them into a structure’ enables students to develop ability knowledge (know-how) in addition to propositional knowledge (knowledge-that), and this kind of mastery is highly advantageous for learning in general (p. 161). While this comes more naturally to rule learners, strategies are given for how example learners can improve their rule learning skills (p. 160).
- Follow the learning strategies best supported by current evidence, such as retrieval practice, spacing and interleaving. The ‘evidence currently available does not justify the huge investment of time and money that would be needed to assess students and restructure instruction around learning styles. Until such evidence is produced, it makes more sense to emphasize the instructional techniques … that have been validated by research as benefitting learners regardless of their style preferences’ (p. 146).
- Develop self-efficacy:‘self-efficacy’ is the belief we have in our own abilities, specifically our ability to meet the challenges we face and to successfully complete the tasks we need to. The authors write that a learning ‘difference that appears to matter a lot is how you see yourself and your abilities’ – i.e., your self-efficacy (p. 139).
Here are five questions we might consider.
1. When is constructing a narrative useful and when is it a ‘knowledge illusion’?
The authors argue that constructing a narrative that promotes belief in one’s abilities is important for success. One example is given of Richard Branson and his ‘personal narrative of exceptionalism’ (p. 140). However, in the previous chapter, our inescapable ‘hunger for narrative’ was warned as a danger which can lead to knowledge illusions (p. 109).
Sometimes, it seems that our narratives will need to be maintained against claims purportedly supported by evidence in order for us to reach our full potential. For example, we would need to protect our narratives from claims purportedly supported by evidence where they risk limiting our potential, such as a student being told they do not have potential in a certain area because of their performance in a standardised test. But maintaining narratives against purportedly evidential claims is also surely where narratives risk becoming knowledge illusions.
Given these points, we might ask, where do we draw the line between strategies that help us develop narratives that reflect reality and those that risk generating knowledge illusions?
2. Which instructional styles are best for subjects?
Evidence suggests that it is ‘important that the mode of instruction match the nature of the subject being taught’: ‘When instructional style matches the nature of the content, all learners learn better, regardless of their differing preferences for how the material is taught’. The authors give the examples of visual instruction for geometry and geography, and verbal instruction for poetry (pp. 145-6).
Which instructional styles would be best for subjects where it is not clear which style matches the nature of the content? In such cases, is a blend of styles the best approach?
For example, some areas of history might be best taught visually, to illustrate chronology; but history involves far more than understanding events in the historical order of their occurrence.
3. Dynamic testing and student motivation
The three stages involved in dynamic testing are highly learner-driven. The authors outline the stages as follows:
1. ‘a test of some kind … shows me where I come up short in knowledge or a skill’;
2. ‘I dedicate myself to becoming more competent…’
3. ‘I test myself again, paying attention to … where I still need more work’ (p. 152, emphasis added).
Must this method be learner-driven, or can it be teacher-driven? How successful is it likely to be, if the former?
4. Focusing on weaknesses rather than strengths
The authors recommend using dynamic testing to focus on improving weaknesses rather than strengths: ‘practice dynamic testing as a learning strategy to discover your weaknesses, and focus on improving yourself in those areas. It’s smart to build on your strengths, but you will become ever more competent and versatile if you also use testing … to continue to improve in those areas where your knowledge or performance are not pulling their weight’ (p. 159; emphasis added).
Should we not focus on developing strengths at least as much as improving weaknesses? Can students reach high levels of mastery over areas of knowledge or skills without focusing on developing strengths?
5. Dynamic testing is a whole other approach rather than a straightforward alternative to standardised testing
By the way dynamic testing is described in this chapter, it seems to be a whole other approach towards assessment of a person’s abilities rather than a straightforward alternative to standardised testing. Dynamic testing involved repeated formative testing over time; it is therefore a learning process, rather than a standalone means of assessing a person’s ability and potential. To use a distinction commonly employed when talking about assessment, dynamic testing is a form of formative assessment rather than summative assessment.
Since dynamic and standardised testing take different forms, it seems that we could not simply compare the results between a student taking a standardised test and also taking a dynamic test. Rather, we would need to compare a student’s progress over a course of dynamic testing with their performance in a standardised test, the results from which could then be compared with the student’s overall achievement across various areas of knowledge and skills.
Given these differences, we might ask, could dynamic testing not be used in conjunction with standardised testing, as a hybrid means of assessment?