Metacognition: What’s the Fuss About?

“We don’t learn from experience, but from reflecting on experience.” (John Dewey, although disputed)

I know the buzzwords in education have invaded us in the past few years and, hopefully, we can navigate media better so as to not get trapped in them. As fancy as they may sound, they are not far from what we have already been doing as teachers and I am not certain an inflation of edu-words helps us in our practice, on the contrary. However, since these words are being circulated often, I decided to discuss one, “metacognition”. My source is Handbook of Research on Learning and Instruction, more specifically chapter 10 (pages 197-219).

Historical roots

In the educational literature two terms are being used interchangeably despite having different conceptual roots and theoretical perspectives.

Metacognition theory: it originated from developmental psychology with Piaget (Inhelder & Piaget, 1958) and Flavell (1970) as progenitors. It focused on “reflective abstraction of new or existent cognitive structures”; simply put, a person’s thinking about own thinking/cognition.

Self-regulated learning (SRL): emerged from metacognition theory above and Bandura’s self-regulation theory. The emphasis is on the regulation of learning processes and outcomes (Dinsmore et al, 2008; Zimmerman, 1995). In other words, knowing what, how and when to apply a specific strategy to monitor, carry, and self-evaluate learning.

The line between the two, however, became fuzzy over time and researchers themselves plead for more clarity in conceptual and operational definitions.…………………………………………………………………………………………………………………

Why does this matter?

Wang, Haertel, and Walberg (1990) concluded from their literature review that metacognition is the most important predictor of learning performance.

Veenman , in an overview of studies with learners of different ages, performing different tasks in various domains (2008), estimated that metacognitive skillfulness accounted for 40% of variance in learning outcomes.

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Transfer of Learning: Is There a Solution? (2)

In part 1 of this blog post I provided some reasons as to why transfer is difficult, particularly in mathematics. The use of concrete materials as well as of highly contextualized tasks may increase the probability of short-term comprehension but hinder learning in the future.

REASONS

• “Any extraneous detail in the presentation of information tends to distract learners from the relevant content, leading to poorer recall for that material. “ *see the “seductive details effect” (Garner et al., 1989; Harp and Mayer, 1998; De Loache 1991, 1995; DeLoache  and Burns, 1994; Son and Goldstone, 2009)

That can happen if the way you introduce a concept or practice a skill is overly “engaging” (I can think of many examples, starting with “food math” – pizza fractions, gummy bears counting etc., and ending with “pretty” worksheets).

• “More insidiously, even those concrete details that are integral and relevant to the examples may harm learning by impairing transfer to new situations.” (Clement et al., 1994; Goldstone and Sakamoto, 2003; Kaminski et al., 2008).

This means that the features that may have enabled students to perform an initial task made it difficult for them to transfer the learning to an analogous situation in which the surface details were changed, and to perceive the connection between the two contexts.

This leaves us with an apparent paradox: the very qualities that enable knowledge acquisition (concreteness, familiarity, personal relevance) are detrimental to knowledge transfer and generalization.

Transfer of Learning: Is There a Solution? (1)

Following some conversations with George Haines on Twitter, I attempted to embark on a very complicated topic: transfer of learning. The literature is full of unanswered questions and the research is equally equivocal or sparse.

What does “transfer of learning” mean?

The definitions seem to branch out with every paper that I read but, despite this variety, the basic meaning can be resumed to the ability to extend what is learned in one situation to new contexts. The major classification is between:

• near transfer – when knowledge is applied in a similar situation (e.g. adding in a class math –calculating change in a store)
• far transfer – application of knowledge or general principles to a more complex or novel situation (e.g. learning about the scientific method –applying its principles in designing and conducting an experiment, testing hypotheses, critiquing other experiments etc.)

Transfer is implied, to some extent, in any new learning otherwise we wouldn’t be able to learn anything new (you can’t really learn, say, how to conjugate verbs unless you have some previous knowledge about verbs).  Yet the ability to transfer information or ideas is not a given. Quite often, information learned in a specific way, or in a particular context, does not transfer to another. For instance, students may very well ace your vocabulary quiz yet fail to use the very same words in their writing. Or they may have very well learned a mathematical fact but do not know how to apply it in a new problem.

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Learning Stages

*I found a very interesting classification of learning stages by Hubert Dreyfus through this article  that sets his classification against online learning.  Although learning is not linear , I think it is worth considering at least for the sake of confronting personal experience in teaching with others’ perspectives. 

Stage 1: Novice
The novice typically gets instruction in the basics of a subject. In the Humanities lectures are still the most popular means to deliver this instruction. Students at this stage are principally consumers being taught simple rules and being provided with some factual content.

Stage 2: Advanced Beginner
Students need to understand relevant contexts for the rules and facts they learn. At this stage students learn to make some sense of what was mere information. The instructor typically becomes a kind of coach, ‘who helps the student pick out and recognise the relevant aspects that organize and make sense of the material’ (p.35). Ideally the instructor explains and picks out important features of cases as they come up in the course of learning.

21st Century Learning – An Ideology

It has been long I have seen a discourse about the “21st Century Learning” as sophisticated and deep as this one by Tobey Steves. I do encourage you to read it in full here  because we often tend to give in to urgency instead of importance. .

NOTE: Text features (bold, underline) are mine.  

 

“To illustrate the character of this (re/de)-valuation of teachers’ work, it is helpful to highlight the ideological prioritization of skills over content. Pring (2004) understands this economic skills agenda as relying on “the bewitchment of the intelligence by a misuse of language.” Pring critiques the vocationalistic skills strategy by suggesting that a skilled philosopher is not necessarily a good philosopher. A skilled philosopher, for instance, may be quite adept at the mechanics of philosophical argumentation without actually having “anything philosophically interesting to say.” This critique holds for lawyers, authors, musicians, and other professions. Therefore, Pring suggests that “to focus on skills traps us into a limited language which transforms and impoverishes the educational enterprise.” In other words, there may be noble hopes animating the push for skills and embedded in policies, but they may actually “impoverish the educational enterprise.”

In the same way, in my thesis I focus a great deal on learnification—the translation of all there is to know and say about teachers’ work into discourses of learning and learners.” 

Tobey S. ‘s thesis (which you can download here) is a complex one, debating the role of teachers in implementing “21st c Learning (“21st CL is more a policy for and on teachers not WITH them”) and dissecting the educational vs business needs. 

 

Difficulty vs. Complexity

As I am rereading one of the best books on differentiation – Differentiation in Action by Judith Dodge – I thought I would share a snippet from this excellent research.

     ” We should be aware of the few problems that new research has brought to our attention regarding taxonomies. Bloom and his colleagues attempted to use degree of difficulty as the basis for difference between the levels.  But in fact, the higher levels are not always more difficult (Costa, 2011). You have probably seen evidence of this yourself when you have asked a kid to judge the value of something or to rate the best or worst in a category (evaluation – the highest level). Most likely, s/he was able to give an opinion and support it with reasons. On the other hand, the student may have had more difficulty reading a passage and using it to analyze a character’s attributes (analysis – lower level on the taxonomy).

       Sousa refers to this problem as well when he suggest that we need to pay attention to the difference between DIFFICULTY and COMPLEXITY when designing learning tasks. Unintentionally, many of us assign more arduous tasks – tasks requiring more effort or time – when what we mean to do is challenge students with more rigorous tasks – tasks requiring more complex thought.  

Example: 

– increasing difficulty task: write two paragraphs about an author’s style

– increasing complexity task: write one paragraph comparing the author’s style in one story to the style of a different author from a previously read story.

Also we should keep in mind that: 

– the levels are not necessarily sequential. In actual practice, constructing knowledge does not happen in a lockstep manner. 

– the objectives at each level may overlap. It is not critical that students perform tasks at every single level of taxonomy. 

(…) Do not assume that students are not capable of thinking critically because they take more time to master basic concepts. Slower learners can reach higher levels of thinking if we help them focus on essential ideas and eliminate less important ones. ‘