Meaning First

Making meaning is the core principle in learning, on which all other principles build. Consequently, a lot has been said in education about how meaning making looks like in the classroom, what is meaningful learning, deep processing, transferable knowledge and how to achieve them. And yet it is also an elusive concept: the operational definition of ‘meaning’ is nontrivial and occasionally the importance of meaning is shadowed by other (also important) concepts in learning.

I wish to share here my operational conceptualization of ‘meaning making’, then highlight two instances where meaning is sometimes shadowed: when discussing Cognitive Load and Retrieval Practice, and altogether make the case for why we should consider meaning first.

What is ‘Meaning Making’?

Processing information meaningfully is known as the key factor to remembering learned information for the long-term, as was formulated by Craik and Lockhart (1972) in the ‘Level of Processing’ model of memory1 that distinguished shallow from deep processing.

Cognitive Psychologist Daniel Willingham further highlighted the idea that we remember only what we consciously think about2 and whether the knowledge is shallow or deep depends on the aspects that learners attend to, or are guided to think about. He coined a now familiar phrase “memory is the residue of thought3.

I wish to highlight here additional important aspects of 'making meaning'. I like to define 'making meaning' as describing a new concept in terms of other concepts that we already understand, in a way that allows to use it. In other words, we integrate the new concept into an existing network in a manner that enables us to close a loop between perception and action. For example, we ‘understand’ a new word once we can use it successfully to communicate.

Q: Is that all there is in understanding?

Yes, this simple principle is valid across levels of learning, and whether it is shallow or deep is determined by how useful it is in a given situation.

Q: What do you mean by ‘useful’?

'Useful' means that we can use the new concept to interact with the environment or to simulate such an interaction4. In lower, more concrete levels ‘use’ translates to an actual action or response: a child can respond to a question “do you want some water?” once she understands what ‘water’ is associated with. In higher levels, the useful response or action is simulated mentally, leading to an “a-ha moment" (for example when one learns that water is made of molecules, and how this idea helps in describing phenomena like melting). In every level, a new concept, that is learned as an arbitrary piece of information at first, becomes meaningful once the learner manages to integrate it into an existing system of concepts in a functional way.

Q: So ‘functional’ is context dependent?

Entirely, it is dependent on the context in which the concept was learned, context being the environment and the learner’s prior knowledge. If a child learns that ‘melting’ means ‘ice transforms into water’, the word is useful for her, and hence meaningful in many everyday scenarios. In order to take the same concept to a higher level it needs to gain features that would prove useful in additional contexts. For example, in science class she learns that other materials also melt, and that melting is the consequence of heating, thus “Melting is a change of a solid into a liquid when heat is applied” (source: Britanica). We could still argue that this is not ‘meaningful’ enough, as experts define this concept in terms of particles, forces and energy. However, meaning is context dependent, not a categorical distinction, and the most important question is whether the concept is useful enough - a useful concept is used, reinforced and remembered, otherwise it quickly decays.

To summarize, the idea is that ‘making meaning’ is the same process of utilizing a new concept into an existing functioning system, at every level, as illustrated here using a pyramid model .

I've written about it with additional illustrations here.


[Left] New concept (orange) is learned based on existing functional concepts to build a new functional unit (the pyramid). [Right] this unit,in turn, is the basis of the next level of learning, in which similar process takes place.

Practical takeaways

1. There is no point in comparing ‘Knowledge’ vs. ‘Understanding’. They are two parts of the same thing: ‘Knowledge’ is the structure and ‘Understanding’ is the function. In a sense, any knowledge has some level of meaning. We can only hold arbitrary information in our mind (e.g. a new word) for a very limited duration. When it is not useful it quickly fades, when it is useful it sticks.

2. When we teach, it is crucial to define the level of meaning that we strive for, and explicitly address:

· The prior knowledge, or the existing ‘loop’ between perception and reaction.

· How to demonstrate the function of the new concept.

· How to further demonstrate and practice the function in several different scenarios that allow to convey the desired level of meaning.

These points translate into teaching practices like activating prior knowledge, explicit explanations, using concrete examples, modelling applications, and designing effective practice to master application.

Meaning and additional important considerations

I doubt it if anyone disagrees that meaning is crucial. Fortunately, there are many examples by expert teachers of how to practically and explicitly approach this in the classroom (see below). And yet, meaning is obvious but at the same time elusive: clearly important, but unclear what is it exactly. This leads to a tendency to overlook meaning when other, also important but easier to grasp, concepts are discussed. I wish to explore here how the conversation around Cognitive Load and Retrieval Practice is sometimes prone to disregard meaning, and this is true in both practical and theoretical realms. While both Cognitive Load and Retrieval Practice are highly valuable, their value, so I intend to argue, is dependent on meaning, and hence meaning should be considered first.

Cognitive Load

Acquisition of semantic knowledge is dependent on making explicit connections, which requires attention and Working Memory (WM) resources. Fortunately, Working Memory has become a well known cognitive function in education. Daniel Willingham emphasizes its interaction with Long-Term Memory5 and I also wrote more about it here. John Sweller and his colleagues over the years emphasize the limitations of WM and the important role they play in effective instruction in what is widely known as the Cognitive Load Theory (CLT)6.

While the emphasis on WM limitations is essential, I argue that overemphasis on Cognitive Load specifically, shadows the role of meaning making. I read analyses, based on CLT, distinguishing the types of load, categorizing them according to their source and concluding how to optimize load to ensure effective learning. However, we should remember that the experience of load is the consequence of the effort invested in making meaning, rather than its cause. Therefore, our focus should be on the input, which we can design and control: what students should think about, rather than reverse engineer the effects of unmeasurable load.

Moreover, while it is important to manage students’ cognitive load as CLT emphasizes, we should also consider our own limited cognitive resources. Cognitive Load is an accessible and appealing term, since we all experience it personally. However, the theoretical framework around the different types of load takes away the appeal by overloading the mind with terms and complex interactions.

When we teach a new concept, we should consider two main factors: how to support students thinking on the concept’s main features and how to reduce distraction by irrelevant information. By including both under one umbrella (Cognitive Load) we risk overshadowing the first task by the second: figuring out how to eliminate distractions is easier and more concrete than thinking about how to support students in making meaning. We should support ourselves by prioritizing our planning tasks: reducing distractions is important but making meaning should come first.

Retrieval Practice

Making meaning in explicit ways is crucial yet insufficient. Effective practice is essential to make the concept accessible and useful. Thanks to cognitive research we know a lot about designing effective practice. When it comes to semantic knowledge, effortful and deliberate retrieval practice that is adequately repeated, varied and distributed is highly beneficial for long-term learning7. And yet, an important question would be: WHY are these factors important and what we should focus on when designing practice activities? Here too, the benefits come from focusing on meaning.

Retrieval Practice is not merely a “better kind of repetition”, its value stems from building new pathways of associations, closing gaps in knowledge and adjusting existing knowledge to current context. If we think of a concept as embedded in a network, then embedding the same concepts in additional networks, or perception-reaction loops, makes the concept more accessible and more useful in various situations. According to the definition above, the more useful the concept the more meaningful it becomes.

In every retrieval attempt we test the existing loops in different contexts and identify points where the associations are still dependent on the original learning context. For example, when asked to name the process where a metal piece turns into liquid, one can remember the association of the concept “melting”with ice turning into water and realize the meaning describes the process rather than the specific matter. In other cases, the context of initial learning may become utterly irrelevant and no longer helpful in recalling the information (e.g. who did the demonstration). In such cases, distributed retrieval attempts can help identify such gaps and replace them with more relevant information that will become more useful, and hence meaningful.

This explanation is in-line with the two leading cognitive models, suggested as a mechanism for Retrieval Practice, as described in a review on the topic by Jeffrey Karpicke (2017)8:

1. The elaborative retrieval account states that the semantic elaboration that occurs during Retrieval Practice enhances subsequent recall.

2. The episodic context account states that the original learning context is retrieved upon a retrieval attempt and then updated with the current context, and this updated context aids subsequent recall.

Karpicke presents them as two possible and even competing theories yet notes that they are not mutually exclusive. I would argue that the theories are not only “not mutually exclusive” but rather should be integrated into a more coherent explanation (and especially when more ecologically valid material is considered). According to this view, any attempt to recall a concept and to adjust previous to current context is done by elaboration. During any retrieval attempt, we assess similarities and differences between the learning context and the current, to identify the more relevant aspects of the concept (e.g. ‘melting’ describes the transition process rather than the matter involved). The concept becomes more general, and hence more useful or meaningful.

It follows that the key aspect of retrieval is ‘accommodation by elaboration’, and these are these small bits of meaning making that we are after. It is noteworthy that Retrieval Practice has proved more effective than elaborative techniques in some laboratory experiments, which suggests that there is more to retrieval than elaboration (Karpicke 2017, p.494). The benefit of retrieval may be in aligning the elaborative process with the existing gaps. Learners get to elaborate on the their “personalized missing links”.

Retrieval Practice is therefore about making meaning, and for this very reason it is difficult and requires effort. Robert and Elizabeth Bjork highlighted this aspect of effective practice approaches by coining the term “Desirable Difficulties”, according to which, practice should be difficult (to a limit) in order to be effective9. Here again, it is worth highlighting that the difficulty is the result of the effort invested in making meaning: difficulty is the effect rather than the cause. Hence, in line with the conclusion above: when planning we should consider meaning first, the effort follows. It is worth noting that in his review Karpicke (2017) describes two additional cognitive theories of retrieval practice:

3. Transfer appropriate processing- retrieval is effective because it mimics the final goal which is recall.

4. Strength and Retrieval Effort, or the Desirable Difficulties model by the Bjorks that argues that greater effort leads to greater learning.

These theories, argues Karpicke and I agree, do not provide mechanisms but are rather heuristics or descriptions of the process. It is meaning and context that we should focus on when designing retrieval practice activities.

Meaning first in every stage of learning

Often, I feel that the importance of meaning making is not considered deeply and explicitly either because it is obvious or because it is intangible. Here, I tried to take one step further to clarify the meaning of ‘meaning’, and argue that it should come first in every stage of learning:

In the initial stage we should plan how to embed a new concept into an existing network so it can be functional. In the later practice stages, we need to emphasize the utility of the concept in various relevant contexts to both extend and embed the desired level of meaning.

Other research-informed concepts like Cognitive Load and Retrieval Practice have crucial practical implications, but it is important to remember that they too make sense only in the context of the key principle of knowledge organization, meaning.

Directions in Practice and in Theory

The value in bridging the learning sciences and education is that it allows teachers to think critically about teaching strategies considering accumulating research findings. Exploring the WHYs, not only the HOWs, leads to more flexible yet critical approach to teaching. Luckily, we see more and more examples of evidence-informed teaching approaches. Below are a several great examples from books that were published in recent years, in each of them I’ve indicated the section that is most relevant to ‘making meaning’. They are written by expert teachers for teachers, and what I appreciate is the explicit approach to the learning process, leaving little space for chance: these experts make all the possible meaning of both teaching and learning – and I like to see this approach spreads, without losing its depth.

The other direction on the science-education bridge, is to inspire cognitive and educational research that focus on questions that stem from the field. I’d like to see greater consideration of meaning as the key principle in learning, when cognitive scientists suggest theories and design experiments to test the efficacy of ideas like Retrieval Practice and Cognitive Load. For example, I’d love to see some of the ideas presented in this post inspiring research and eventually evidence.

Recommended Book Chapters and Blogs

With clear explanations, techniques and examples, each book outlines an entire teaching framework, noted here are the chapters directly related to making meaning, but really, they are part of the whole:

  • The Learning Rainforest by Tom Sherrington - Chapter 8, and also Chapter 7 (C13-20)

Also see this blog by Tom Sherrington 'The #1 problem/weakness in teaching and how to address it'

Also see this blog by Harry Fletcher-Wood 'Deep learning? Preparing for knowledge transfer'

Also see this blog by Andy Tharby 'Connecting and organising knowledge in English literature'

More Blogs

There is plenty of valuable information, ideas, strategies and complete examples that put emphasis on making meaning in the classroom, here is a non-exhaustive (and always growing) collection:

Do they understand this well enough to move on? Introducing hinge questions by Harry Fletcher-Wood

Comprehension Strategies in the Classroom and Clear Teacher Explanations I: examples & non-examples by Pritesh raichura

Great Lessons 6: Explaining by Tom Sherrington

References:

1. McLeod, S. A. (2007). Levels of processing

2. Willingham, D. T. (2003). Students remember what they think about. American Educator, 27(2), 37-41.

3. Willingham, D. T. (2009). Why don't students like school?: A cognitive scientist answers questions about how the mind works and what it means for the classroom. John Wiley & Sons. Chapter 3.

4. Lakoff, G. (2009). The neural theory of metaphor. Available at SSRN 1437794 [PDF] also in this lecture: https://youtu.be/JJP-rkilz40

5. Willingham, D. T. (2009). Why don't students like school?: A cognitive scientist answers questions about how the mind works and what it means for the classroom. John Wiley & Sons. Chapter 1.

6. Cognitive load theory: Research that teachers really need to understand (2017) Centre for Education Statistics and Evaluation, NSW Department of Education, Sydney, Australia

7. Brown, P. C., Roediger III, H. L., & McDaniel, M. A. (2014). Make it stick. Harvard University Press.

8. Karpicke, J. D. (2017). Retrieval-based learning: A decade of progress. In J. T. Wixted (Ed.), Cognitive psychology of memory, Vol. 2 of Learning and memory: A comprehensive reference (J. H. Byrne, Series Ed.) (p. 487-514). Oxford: Academic Press.

9. Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. Psychology and the real world: Essays illustrating fundamental contributions to society, 2(59-68).


Published: October 2019, updated: December 2019