There are many ways to teach and to learn. Some ways are not particularly effective or efficient (e.g., repetitive re-reading of the same material) and some ways are highly effective and efficient (e.g., correctly using flashcards). One would think that all teachers and students would abandon the ineffective ways and adhere to a diet of healthy study choices. Unfortunately, many who could benefit from an awareness of what would help them have not been presented with the information that they need to make the change. And, on other occasions, the affective qualities of some effective choices lead those who have been informed to still choose the poorer option. Whatever the reason, too many students are choosing ineffective study strategies. According to one study (Dunlosky et al., 2013), 65% of students report rereading as a regularly used study method, but this is one of the least effective ways. In this piece, I will look at six effective study / teaching strategies, discuss what makes them powerful tools for learning, and look at some ways that they can be used by both teachers and students.
Peeking in Other Silos
The area of Mind, Brain, and Education is an interdisciplinary field that offers the potential for the cross-pollination of ideas from one domain of study to another. That is, ideas that may be well established and understood in one academic field, may be completely novel and highly valued when they become known in another. This is the case with the effective learning strategies that I will discuss today.
These strategies, from cognitive psychology, are:
Spaced repetition (distributed practice): the studying of the same information distributed over time in multiple study sessions. This form of study is contrasted with massed practice, which is the covering of an amount of material in a single study.
Interleaving: the “inter-spacing” of learning episodes such that some new knowledge Thing A is alternated with related but different knowledge Thing B or Thing C.
Retrieval practice (the testing effect): the purposeful recalling of previously learned information to consolidate learning. Although it may take the form of an assessment, the goal is learning.
Elaboration: the conscious connection of new information to previously encoded information to better notice similarities and differences. This ultimately leads to improved transfer of knowledge from one domain to another.
Concrete examples: a form of elaboration that connects previous sensory and/or physical experiences to new knowledge.
Dual coding: the combination of words with visuals to increase the input that one can receive. Having two pathways of input also increases the number of elaborative connections to encoded knowledge that can be made.
These learning strategies have been known and studied for over forty years (Metcalfe & Kornell, 2007), but have not been presented to the education community until fairly recently. The United States Department of Education published a white paper to remedy this situation in 2007. Not surprisingly, not many teachers noticed. Teachers are busy managing the strain of day-to-day classes and do not have a lot of extra time to explore academic papers released by government agencies. When do they study how to be a maximally effective teacher? When they are enrolled in teacher training programs. Pomerance et al. (2016) examined the most commonly used teacher training textbooks and found that 59% made no mention of the effective strategies and only 15% devoted at least a page to explaining the strategies. Clearly, this is not enough to convey the what, why, and how of effective learning to teachers in training.
In 2018, I undertook a similar study. I read eleven popular books used in ESL/EFL training courses (a whopping 4353 pages worth!) and found that there was almost no coverage of the effective learning strategies that have been identified by cognitive psychology. I should be clear, there was mention of learner strategy use, but these were strategies such as those presented by Rebecca Oxford (2017) that highlight approaches that successful language learners employ, such as such as seeking out language partners or encouraging oneself. I was looking for evidence of strategies that have made it from the silo of cognitive psychology and into the realm of ESL/EFL.
According to Paul Nation (2013), the second most important thing that teachers should be doing is training learners in how to study effectively, but how can teachers convey what they have not been taught themselves? According to students, most teachers’ advice on how to study does not go much beyond telling them to “study hard.”
Students deserve better. Understanding these effective and efficient strategies from the research conducted within the cognitive psychology of learning can help you to be more effective in supporting your students’ learning.
The six strategies listed above can be loosely separated into two groups. The first three (spaced practice, interleaving, and retrieval practice) are hypothesized to derive much of their effective power from study that can been described as “desirably difficult.” That is an important concept, and I will explain it further in the next section. The other three effective learning/teaching strategies (elaboration, concrete examples, and dual coding) improve understanding by intentionally connecting to-be-remembered information with other information that is already encoded in memory. Because the underlying goals are different, I will address each separately.
Many teachers and students would love nothing more than to speed up their learning. On the surface, that sounds great, but if a sense of accelerated learning comes at the cost of the real retention of new information and the ability to retrieve that information when needed, then all that is being created is an inaccurate Judgement of Learning (JOL). Challenging tasks, such as spaced repetition, interleaving, and retrieval practice, that force one to work to recall previously studied material, are much more effective at promoting real learning. These types of tasks do not provide learners with a warm fuzzy sense of knowing: They are difficult, and students often do not like to do them. However, they are effective. It is this very difficulty that students do not like that is thought to result in the learning. Because they result in a positive outcomes, these challenges are known as desirable difficulties (Bjork, 2012; Bjork & Bjork, 1992). Desirable difficulties are part of Bjork’s broader New Theory of Disuse. The theory seeks to explain why long-term storage of information can be vast, yet the ability to retrieve that information can be quite limited and prone to lapses. Within the theory, each memory has two strengths: a retrieval strength (a measure of the accessibility of the memory) and a storage strength (the degree that a memory trace is consolidated in long-term memory.
There is a relationship between the two. Items that have a high retrieval strength (e.g., what you just did a moment ago) may have a weak storage strength. Conversely, some things that were once known very well (e.g., your best friend’s telephone number in high school) may be completely inaccessible now. With the appropriate cues, it may be possible to recall the memory, as it still exists; it is just very difficult to gain access. When you are studying or practicing, there is an increase in the storage strength and the retrieval strength of a trace; however, the greater the level of retrieval strength during that study, the less the storage strength will gain. Robert Bjork summed up the dynamic by saying: “When something is very, very accessible right now, virtually no learning can happen” (2012).
The relationship between retrieval and storage can be seen if you consider what is going on “under the hood” during massed versus spaced practice (Figure 8). During massed practice (a single extended period of study on one subject), the familiarity and the retrieval strength of the study subject increases with time spent on task. Therefore, the rate of increase in storage strength decreases with time spent on task.
In spaced practice, the start of each study session will have a decreased level of retrieval strength. Therefore, for a given period of study (e.g., one hour), having the time divided into separate twenty-minute study sessions would result in a greater increase in storage strength (learning) than a single massed session would.
The onset of a second or third or tenth spaced repetition study session inherently includes some retrieval practice if the lag time between study sessions is great enough to allow the retrieval strength to fade.
Pulling Things Out
Retrieval practice, also known as the testing effect, strongly reinforces the consolidation of memory for studied information. It also has many other indirect benefits that go beyond increasing what one can recall. Roediger et al. (2011) have identified ten benefits (direct and indirect) of using retrieval practice as a study/teaching strategy (see Figure 10). The indirect benefits of retrieval practice (as with the other desirably difficult approaches of spaced practice and interleaving) include learners and their teachers getting feedback on the current level of learning. Using this, learners can improve their Judgements of Learning (JOLs) and see where they need to focus their future study. Teachers can also use this information to help with lesson planning. A third benefit is that retrieval potentiates future learning (Pyc & Rawson, 2010) and improves the connection from a cue to a target memory. A fourth indirect benefit of using retrieval practice is that it improves the organization of knowledge, which, in turn, also supports the transfer of knowledge from one context to another. Weinstein et al. (2014) found that due to the anticipation of being tested, students “rose to the challenge” and adjusted their study habits. This increased their level of encoding for new information. Most people don’t like to be tested but, with frequent exposure to testing and retrieval activities, students’ anxiety levels around testing do diminish.
There are many ways to include retrieval practice (both as a stand-alone activity and when part of a schedule of spaced or interleaved study) into your teaching practice. Your students can do it during their personal study time: Show them how. Andre Hedlund and Hall Houston in this issue provide an excellent primer on how this can be done and I encourage you to integrate their ideas into your own teaching.
Putting Things Together
As was mentioned earlier, the final three effective learning/teaching strategies (elaboration, concrete examples, and dual coding) improve understanding by intentionally connecting to-be-remembered information with other information that is already encoded in memory. If you break the previous sentence down, you can see that the key components that explain how elaboration can be used as a teaching and study strategy are:
- That elaboration is conscious and intentional;
- That elaboration is about making associations;
- That the associations are with things currently in memory.
Some approaches to learning and teaching, such as interleaving, can happen without the student overtly knowing how this is helping them to improve their knowledge. This is not the case with elaboration. Elaboration is overtly making connections between one thing and another and then exploring what makes them the same and what makes them different. Through doing this, new knowledge is integrated into pre-existing knowledge structures (Weinstein et al., 2018). One way to guide students to recognize the associations is through a technique called elaborative questioning. This involves posing a series of open-ended, wh-questions that help the student see how something that they know relates to something else. For example, a teacher may want to point out to their class that many English phonemes contain meaning and that students can, sometimes, make predictions about the meaning of an unknown word through some key sound. The teacher could present the word “gleam” and ask the students, “What words do you know that start with gl-?” The students may then produce a list of word like “glow,” “glitter,” and “glass.” Then the teacher could ask, “What qualities do each of these words share?” To this question, the students may offer ideas like “shiny,” “bright,” “clear,” etc. The teacher could then point out that “gleam” does include the meaning of shiny and bright. They could also point out the not all gl-words do. For example, the word “glove” despite starting with gl- has no relation. Despite its cognitive benefits, one challenge of using elaborate questioning in the classroom is that it can be difficult to come up with the questions and can be quite time consuming.
Another form of elaboration is using concrete examples. As the “concrete” in the name implies, the connections in this form of elaboration are naturally associated with perceptual and/or motor experiences. When using concrete examples, the example should be broadened, through a process called “concreteness fading” (also known as “progressive idealization”), beyond the confines of the specific concrete form. In this process, the details of a concrete example are progressively lost and elements common to a variety of examples are identified. If concrete examples are not broadened to become more abstract, then the new information will not be well integrated with pre-existing knowledge and risks being forgotten. Goldstone and Son (2005) studied how effective this progression is for learning science concepts and found that subjects learned best when examples progressed from concrete to abstract. The reverse, abstract to concrete, did not generate the same level of learning.
Another specific form of elaboration is dual coding. The two codes referred to in this label are the visual input stream of information and the verbal stream. The theory of dual coding argues that human information processing handles visual/pictorial input and auditory/verbal input separately. One could certainly question this distinction and argue that written words are visual cues. This is not wrong, but it does highlight another benefit of dual coding. Verbal information is constructed of words joined by syntax and is sequentially processed. This allows any constraints in some units of input to slow processing. However, visual information, which is presented as a complete synchronous image at once, can be handled with parallel processing, which permits greater processing capacity—up to some maximum of informational load (Caviglioli, 2019). This assumption is in line with Baddeley et al.’s (2014) theory of working memory. (See Gillis-Furutaka in our Think Tank on Reading) The capacity of working memory is very limited. However, the combination of the two modes of input is additive, which results in an increase in overall processing capacity. This increase in neural network signal strength has the power to improve retention. When learners make connections between dual modes of information, they are also establishing connections between multiple brain regions.
Not all informational input is the same. Although input that is germane to the goal of the lesson will assist with comprehension and learning, extraneous sounds (e.g., music) and complex pictures (e.g., cute dancing dogs) can reduce overall comprehension as they compete for cognitive resources and move attention away from the relevant material (Mayer, 2002).
Now Take This and Run With It
At the beginning of this piece, I highlighted how the valuable information about effective and efficient learning strategies has not been readily adopted by the teacher training textbooks. The research on how to maximize learning has allowed researchers and those few teachers who have embraced the findings to demonstrate that the methods work. Now, I challenge you to give it a try. The only thing that is stopping you from being a great teacher, is being too comfortable being a good teacher. Go for it!
Baddeley, A., Eysenck, M. W., & Anderson, M. C. (2014). Memory. Milton Park, UK: Taylor and Francis.
Bjork, R. (2012, July 12). The theory of disuse and the role of forgetting in human memory [Video]. YouTube. https://www.youtube.com/watch?v=Hv6Vye1JCjo
Bjork, R. A., & Bjork, E. L. (1992). A new theory of disuse and an old theory of stimulus fluctuation. In A. F. Healy, S. M. Kosslyn, & R. M. Schiffrin, Learning processes to cognitive processes: Essays in honor of William K. Estes (pp. 35–67). London, UK: Psychology Press.
Caviglioli, O. (2019). Dual coding with teachers. Woodridge, UK: John Catt Educational.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266
Goldstone, R., & Son, J. (2005). The Transfer of Scientific Principles Using Concrete and Idealized Simulations. The Journal of the Learning Sciences, 14(1), 69-110. http://www.jstor.org/stable/1466883
Mayer, E. M. (2002). Multimedia learning. The Annual Report of Educational Psychology in Japan, 41, 27– 29.
Metcalfe, J., & Kornell, N. (2007). Principles of cognitive science in education: The effects of generation, errors, and feedback. Psychonomic Bulletin and Review, 14(2), 225–229.
Nation, P. (2013). What should every EFL teacher know? Seoul, South Korea: Compass Publishing.
Oxford, R. L. (2017). Teaching and researching language learning strategies: Self-regulation in context. New York, NY: Routledge.
Pomerance, L., Greenberg, J., & Walsh, K. (2016). Learning about learning: What every teacher needs to know. https://www.nctq.org/publications/Learning-About-Learning:-What-Every-New-Teacher-Needs-to-Know
Pyc, M. A., & Rawson, K. A. (2010). Why testing improves memory: Mediator effectiveness hypothesis. Science, 330(6002), 335– 335.
Roediger, H. L., Putnam, A. L., & Smith, M. A. (2011). Ten benefits of testing and their applications to educational practice. In J. Mestre & B. Ross (Eds.), Psychology of learning and motivation: Cognition in education (pp. 1-36). Oxford, UK: Elsevier.
Weinstein, Y., Madan, C. R., & Sumeraki, M. A. (2018). Teaching the science of learning” Cognitive Research: Principles and Implications 3(2). https://doi.org/10.1186/s41235-017-0087-y
Weinstein, Y., Szpunar, A. W., & McDermott, K. B. (2014). The role of test expectancy in the build-up of proactive interference in long-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(4), 1039-1048.
Jason Lowes is an associate professor at Fukuyama University. His current interests are in effective learning/teaching strategies, and predictive processing. He wishes that there were more hours in the day and fewer calories in chocolate (and beer).