The reactions people have to cognitive overload are varied. Some get angry, some withdrawn, some somewhere in the middle. What is common to all who experience it though is feeling overwhelmed, feeling uncomfortable, feeling frustrated and sometimes feeling worthless. Imposter syndrome can be common.
Understandably, it’s an experience we want to avoid. It can be exhausting.
How students handle it is largely determined by their temperament, which is affected by a multitude of factors. The more obvious reactions are the extremes: poor behaviour, lashing out, belligerence and compete withdrawal. Of course, poor behaviour and withdrawal is more complex than just cognitive overload, but at times it is certainly a factor, and I find it strange that little to no conversation ever discusses improving behaviour in the same way that we endorse the elimination of academic cognitive overload – through incrementally improving cognitive skills. Inculcating new behaviours surely needs the same level of design and commitment? Perhaps less obvious is cognitive overload in students who externally give few clues that they are experiencing it; perhaps they are not reacting because of compliance to the school’s rules or respect for authority, or perhaps because they don’t want to be seen as not understanding what is being taught; peer pressure is huge in all education sectors. Perhaps they are having a difficult time outside of the classroom, most certainly a factor affecting higher education students who may have lost their employment during COVID. Needless to say, cognitive overload reduces learning.
A practical and relatively simple solution to mitigate against too much cognitive load is the design of learning sequences that focus on the building of schema that include lots of formative assessment to check learning. Good communication with students also allows you to gauge how students are feeling in their learning, and this can be an extremely useful form of formative assessment too.
It’s not just students who feel it
It’s certainly not just students who experience it . Any time you are under pressure in a new situation you are likely to experience it to some degree as your mind grapples with the new content and searches relevant schema to connect it to: the more the pressure and the fewer the connections, the greater the load. You are likely to experience it when you attend a conference where presentations don’t adhere to multi-media principles, you are likely to experience it in a meeting when you don’t have the relevant background knowledge on a topic being discussed, and you are likely to experience it when you yourself are presenting/teaching and you don’t fully understand or believe in what you are discussing. Of course, the most obvious analogy is when your practice is being observed. All of these examples are the times when you are effectively a student, a novice learner. As an educator, it is important that you reflect on the feeling of cognitive overload and how easily it can occur, and use that knowledge to consider how you design and shape the learning experiences of your students so they experience it less, and learn more.
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger
Breaking a lecture up into distinct chunks or sections is a bit of a no-brainer. It is all to do with understanding the implications of cognitive load theory, specifically that the brain can only process a small amount of new information at once. Presenting more information than the brain’s architecture can handle leads to overloading the working memory, and usually a significant decrease in learning.
Breaking your lecture into chunks provides students a chance to process each chunk before new material is presented. Designing opportunities for students to be active (black boxes) in the processing of the content also assists in facilitating the content’s understanding, and eventual transfer into long term memory.
So, here’s a possible live -streamed lecture design that considers cognitive load implications, the need for the student to be active in their learning, and is very manageable for the lecturer. The model can be applied to both live and recorded lectures, but the recorded lecture will need some more specific context discussed, which I will do in another post.
I’ve talked before about the possible mixed-mode future of live lecturing, with it being able to facilitate a breakout room. The below model considers this as a possibility.
lesson segment
rationale
tech to assist
intro
The lesson begins with a retrieval quiz. The benefit of retrieval is enormous. It strengthens the memory of key ideas and content. The purpose of this is so the knowledge can be automatically brought to cognition when new learning is presented, without taxing the working memory. The more knowledge the student can draw from, the greater the opportunity to delve into more higher order independent learning, so building students’ schema through retrieval is is a bit of a no-brainer. The lecturer will place answers on the screen, and spend 2-3 minutes explaining answers if common errors were made.
Polls Echo 360 Mentimeter Quizziz Canvas quiz
teaching
Delivering content. 10-12 min. Incremental building to application is is a bit of a no-brainer. The lecturer is conscious of the need to present content clearly and simply, very much aware of multimedia principles that promote the efficient encoding of new information. They are also aware of the importance of modelling problem solving and incorporate worked examples into the presentation. Where appropriate, the lecturer connects the new learning to real world applications, not just to make the content relevant, but more so to build the mental patterns and analogies in the students’ schemata. The lecturer also frequently mentions the reasons why decisions in the teaching are being made so as to strengthen the students’ metacognition.
PPT slides. Document camera. Students can take notes in Echo, can raise confusion flag, and ask a question at precise point in either the live stream.
student activity
Strengthening understanding This provides students a chance to take in what has just been presented, and think about the concepts before tehy are presented with more content. Essentially the student is trying to convert the abstract to the concrete. Providing students with the opportunity to complete worked examples, practise solving similarly structured problems, or discussing with a peer possible analogies to the content is valuable at this point in the lecture, and is a bit of a no-brainer.
Breakout rooms. Mentimeter open question. Echo discussions. Canvas discussions. GoFormative.
teaching
Discussion of last task if necessary – may not be if practising or completing examples. Delivering content. 10-12 min. Incremental building to application is a bit of a no-brainer. The lecturer is conscious of the need to present content clearly and simply, very much aware of multimedia principles that promote the efficient encoding of new information. They are also aware of the importance of modelling problem solving and incorporate worked examples into the presentation. Where appropriate, the lecturer connects the new learning to real world applications, not just to make the content relevant, but more so to build the mental patterns and analogies in the students’ schemata. The lecturer also frequently mentions the reasons why decisions in the teaching are being made so as to strengthen the students’ metacognition.
PPT slides. Document camera. Students can take notes in Echo, can raise confusion flag, and ask a question at precise point in either the live stream.
Formative assessment
Checking for learning A quiz of short answer opportunity to see if what you have presented so far has been understood is is a bit of a no-brainer. The questions also provide another opportunity for a student to process the content and develop a better understanding.
Questions up on screen. Zoom polling. Using Canvas discussions as student answer repository. Mentimeter. Quizziz.
teaching
Check answers – you may need to pivot the lecture if misconceptions are still prevalent. Delivering content. 10-12 min. Incremental building to application is a bit of a no-brainer. The lecturer is conscious of the need to present content clearly and simply, very much aware of multimedia principles that promote the efficient encoding of new information. They are also aware of the importance of modelling problem solving and incorporate worked examples into the presentation. Where appropriate, the lecturer connects the new learning to real world applications, not just to make the content relevant, but more so to build the mental patterns and analogies in the students’ schemata. The lecturer also frequently mentions the reasons why decisions in the teaching are being made so as to strengthen the students’ metacognition.
PPT slides. Document camera. Students can take notes in Echo, can raise confusion flag, and ask a question at precise point in either the live stream.
student activity
Strengthening understanding This provides students a chance to take in what has just been presented, and think about the concepts. Essentially the student is trying to convert the abstract to the concrete. Providing students with the opportunity to complete worked examples, practise solving similarly structured problems, or discussing with a peer possible analogies to the content is valuable at this point in the lecture.
Breakout rooms. Mentimeter open question. Echo discussions. Canvas discussions. GoFormative.
summary
Recapping key ideas. Tying the lecture all together: linking it to previous learning and real word contexts. Discussion and questions asking students to link their learning is a great way to draw attention to the key concepts again, and is a bit of a no-brainer.
Mentimeter open ended question.
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger
THE 3 A’S OF KNOWLEDGE TRANSFER: ACQUIRE, ANALOGISE, APPLY PT 2
This is the third instalment of a 4 part series aimed at assisting an educator in designing a sequence of learning that drives towards the ultimate goal of knowledge transfer. The intro post is here and the post on the first stage of developing a context of transfer, acquiring knowledge, is here
PT 2:Analogous examples
The importance of presenting analogous examples to students to facilitate transfer is made apparent by Gentner and Ratterman (1991), who purport that people learning from a single example of content tend to encode it in a context-specific manner, with the result that later remindings are often based on more obvious surface aspects. Applying knowledge to a new context from only seeing one example of the learning is significantly harder compared to providing an analogy for students, but as Reeves and Weisberg suggest, it is not enough to simply provide a single analogous examples to your students.
The beginnings of analogical reasoning
Karl Duncker was a quite remarkable German psychologist.
He dedicated considerable energy into exploring how learners approach problem solving, and his thesis on how it happens is here. His candle problem highlighted the notion of Functional Fixedness, which highlighted that once a learner has established a schema for a certain function, it is difficult for the ‘function’ to be applied in another context. Before his tragic death, suicide at 37 years of age, Duncker created the ‘radiation problem’, where he established that learners asked to solve the problem could only do so 10% of the time. It is this very low number that paved the way for considerable research into how to assist the problem solving dilemma of functional fixedness.
Gick and Holyoak are perhaps the most notable of researchers to shed light on how to mitigate functional fixedness. They found that when students were given an analogy to the Duncker tumour problem that had the same underlying structural properties, the number of students who were able to solve the problem rose from 10% to 30%. Incredibly, when they experimented by providing a 2nd analogy prior to exposing students to the radiation problem they found that 80% of students could then solve the problem. When they provided a underlying principle to students, when only a single analogy was used it did not assist students, but when 2 analogies were given, the underlying principle increased student success to 82% with a verbal principle, and 92% with a diagrammatical principle.
Of particular note however, was Gick and Holyoak’s attention to the quality of a student’s schema when applying the analogies to the problem. What they found, consistently, was that when a student presented a good quality schema, found by having students articulate the similarities between analogies, 100% of students were able to solve the problem. This has enormous implications for the need to ensure that a well-developed schema is present when asking students to apply or transfer knowledge into a new context.
It highlights the fact that it is the bank of mental models and patterns that a student has that allows them to search and seek connections from the schema to the new learning context. If they have a good understanding of the general principles of a problem, characterised by identifying the deeper relational structure of a problem, then it is more likely they will be able to see the same structure in a new problem. As Duncker states, ‘one can transpose a solution only when one has grasped its functional value, its general principle, i.e., the invariants from which, by introduction of changed conditions, the corresponding variations of the solution follow each time.’
Gick and Holyoak’s work has been validated by other researchers. Alfieri, Nokes-Malach and Schunn conducted a meta-analytic review of research using comparison examples to assist problem solving, and found conclusive evidence that analogical reasoning using several comparisons benefits the transfer of knowledge. Jacobson et al report that providing students’ opportunity to search for the similarities of analogy structures improves problem solving and transfer capability, and adding to the weight of evidence, Markman and Gentner suggest that directing students to the structural similarities is what eventually builds the schema that the student will use to connect to new learning contexts.
So, analogy greatly assists students in being able to transfer knowledge into a new context. Providing at least 2 analogies and explicitly pointing students to the similarities appears to be the optimal context for developing the relevant schema for transferring and applying knowledge.
How to exploit this ability to transfer and apply knowledge will be the basis of the next post.
References
Language and the career of similarity. Gentner, D., & Rattermann, M. J. (1991). In S. A. Gelman & J. P. B yrnes (Eds.), Perspective on thought and language: Interrelations in development (pp. 225-277).New York: Cambridge University Press.
THE 3 A’S OF KNOWLEDGE TRANSFER: ACQUIRE, ANALOGISE, APPLY PT 1
Pt 1: ACQUIRING KNOWLEDGE EFFICIENTLY
This is the second instalment of a 4 part series aimed at assisting an educator in designing a sequence of learning that drives towards the ultimate goal of knowledge transfer. The intro post is here.
Concrete to abstract to concrete
Delivering new learning in concrete terms using concrete examples makes it easier for a student to encode the content. We assimilate or accommodate new information in the context of the patterns, mental models, examples, analogies and experiences that either already were or have been converted to the concrete in our brain, representations that have formed the basis of our schemata. Kolodner describes this reliance on prior learning as case-based scenarios1. The use of models help students to capture the mental patterns required for the knowledge to become understood. It could be suggested that this predisposition to preferring the concrete could lead one to theorise that the concrete represents understanding. However, a necessary process in the incremental development of a schema is the gradual introduction of abstractions, as they instigate the machinations of knowledge transfer.
A lot of what we teach in higher education is abstract in nature, and necessarily so: ‘The goal of learning an abstract concept is not simply knowledge of one instantiation; it is the ability to transfer, or apply conceptual knowledge to a novel isomorphic situation.’2 However, this creates two issues for learners: abstract content is inherently harder to learn than concrete content, and the links between abstract content and real word applications can often seem distant at best.
Why is it harder to learn through abstraction?
It seems our brains are wired to think in concrete terms3. So, if it is harder to learn through abstraction, why not simply avoid it altogether and convert material to the concrete for our students? Well, it all has to do with the facilitation of knowledge transfer. Resnick and Omanson4 assert that ‘learning with concrete objects supports initial understanding of the instructed concept but does not support the transfer of that knowledge to novel but relevant contexts.’ Pashler et al5 concur and extend this: ‘Many experimental laboratory studies and a growing number of classroom based quasi-experiments have found that teaching students about key principles or concepts using only abstract or only concrete representations of those concepts leads to less flexible knowledge acquisition and use than teaching students to recognize and use those key principles across a range of different situations’.
So how do we get the balance right? It appears that once we have set a foundation using concrete examples, turning to metaphor is the next step. Reece (2003)6 suggests that human analogical reasoning engaged through metaphor-based environments helps learners to incorporate new concepts into their existing mental schema. She advocates the use of metaphors believing them to be ‘especially appropriate when learners are introduced to new, abstract concepts’, and she cites Jonassen, (1981)7 who asserts that metaphor acts as a scaffolding ; that is, ‘a learner structures the to be-learned domain, the target, according to the relational structure of the concrete and more familiar domain, the source domain (see structure mapping theory in Gentner, 1983, 1989; Gentner & Markman, 1997).’
In designing effective metaphors, Carroll & Mack8 instruct that ‘Within pedagogical applications, an instructional metaphor source domain can be carefully structured so that it replicates the relational structure of the target domain’. It seems that this deliberate scaffolding of metaphor is consistent with the need to attend to the incremental building of a schema; the metaphor is designed to incrementally build abstraction by initially making connections to relational structures quite close, and then gradually making them more abstract. The student’s schema thereby develops by adding to the bank of patterns contained within.
But a well-developed schema does not necessarily mean that transfer of learning to new contexts is automatic. Gentner, Rattermann & Forbus (1993)9 report that ‘people often fail to access prior cases that would be useful, even when they can be shown to have retained the material in memory’. Transfer then, requires a lot more attention to design than just acquiring knowledge. One of the ways to achieve it is through analogous examples.
2. Do Children Need Concrete Instantiations to Learn an Abstract Concept? Jennifer A. Kaminski (kaminski.16@osu.edu), Vladimir M. Sloutsky (sloutsky.1@osu.edu), Andrew F. Heckler (heckler.6@osu.edu) – http://csjarchive.cogsci.rpi.edu/proceedings/2006/docs/p411.pdf
3. Lawson, A. E., Alkhoury, S., Benford, R., Clark, B. R., & Falconer, K. A. (2000). What kinds of scientific concepts exist? Concept construction and intellectual development in college biology. Journal of Research in Science Teaching, 37(9), 996-1018.
4. Resnick, L.B., and Omanson, S.F. (1987). Learning to understand arithmetic. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 3, pp. 41-95). Hillsdale, NJ: Erlbaum.
5. Pashler, H., Bain, P., Bottge, B., Graesser, A., Koedinger, K., McDaniel, M., and Metcalfe, J. (2007) Organizing Instruction and Study to Improve Stu. dent Learning (NCER 2007-2004). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ncer.ed.gov
6. Reece, D. 2003. Metaphor and content: An embodied paradigm for learning. Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Curriculum and Instruction (Instructional Technology).
7. Jonassen, D. H. (1981, April 7). Content treatment interactions: a better design model. Paper presented at the Association for Educational Communication and Technology, Philadelphia, PA.
8. Carroll, J. M., & Mack, R. L. (1999). Metaphor, computing systems, and active learning. International Journal of Human-Computer Studies, 51, 385-403.
9. Gentner, D., Rattermann, M. J., & Forbus, K. D. (1993). The roles of similarity in transfer: Separating retrievability and inferential soundness. Cognitive Psychology, 25, 524-575.
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger
THIS 4 PART BLOG SERIES is created to assist an educator design a sequence of learning that drives towards the ultimate goal of knowledge transfer. Consensus around the notion of transfer in learning is loose to say the least: some deny it’s existence, some accept it but differentiate the types of transfer possible, including near, far, (etc), and others dedicate entire epistemologies to its achievement. But whatever your position, few could deny that a major goal of education is to be able to apply what has been taught in the classroom to a broader context, whether that be work and/or the advancement of community, and so these posts attempt to position you better in being able to design a sequence of learning that strives as much as it is possible to transfer student learning to new contexts.
INTRO: Towards independent learning and transfer
One approach to facilitating transfer has been to teach students how to learn. The rationale seems sound: if a student understands and practises how to go about learning, then they should be able to do it in a new context, independently. Making a student aware of their metacognition is really important, but unfortunately the resulting pedagogy that most often subsumes this direction is a) inspired by the belief that strength and resilience in learning how to learn comes from the student constructing their own knowledge of the process and b) characterised by immersing students in the context of having to think and find knowledge independently. The ostensible bonus is the potential replacement of an anachronistic teaching practice with a modern 21st century student centered pedagogy.
But, the focus on a modern pedagogy liberating students from the shackles of the sage on the stage and all the imbalance of power that is associated with it misreads the argument of a large body of work* dedicated to providing caution to the increasing popularity of such a discovery/inquiry pedagogy. The sagacity that you need knowledge in a domain to become proficient in that domain, and more pertinently, that achieving the knowledge is more efficient if an expert scaffolds that journey for the novice, as opposed to the novice trying to find the knowledge themselves, is not driven by an impulsion to maintain a neoconservative agenda, or a thwart on choice or constructivist prerogative**, but ultimately driven by a goal to arrive at independent learning faster.
But student learning will be stronger if they have found the knowledge themselves, won’t it?
Interestingly, for such a widely held notion, I can’t find any evidence to support the idea that learning things on your own creates a stronger understanding than learning it from a teacher/peer, except if you are already quite proficient in a given topic/area of learning (reversal effect). Determining then how we teach the novice learner, who I contend, makes up quite a large percentage of the modern undergraduate cohort, to independence, needs a pedagogy that is less emotive and more scientific in its design, and one that is conscious of the reality of a curriculum that is starved of time.
Standing on the shoulders of giants
Initiating a context where novice students are expected to find knowledge on their own concomitantly initiates a context where novice students may not make the necessary connections between key ideas for a host of reasons: they may invest too much time in researching irrelevant knowledge, they may not ‘see’ the connections between ideas, they may, as John Sweller states, ‘use general problem-solving strategies such as means-ends analysis when faced with a problem’ and exhaust working memory, or worse, they simply may not engage with the autonomy of the context and do no work. Because much of what we teach is sequential, the consequence of students not arriving where we want them to be in the curriculum is that learning gaps will emerge, and these will have to be addressed in the limited time available. Understandably this ‘extra’ teaching is foregone by most, and this invariably leads to equity issues, with often only the highly motivated, intelligent or culturally literate students able to cope, as they are able to draw from schemata developed from these cultural and mental literacies. But I contend that even those students could be afforded a better more efficient pedagogy, one that scaffolds the acquisition of schema so that more meaningfully higher order thinking can be conducted sooner, and one that facilitates the creative extension of knowledge generated by ‘giant’ scholars.
The imperative of schema
The reason why it’s inefficient to not scaffold the development of a novice’s knowledge base is highlighted by schema theory. When presented with unfamiliar content, we attempt to either assimilate or accommodate it into our schema, but if the gap between what we have and what is new can’t be connected, the working memory essentially exhausts itself, cognitive dissonance ensues and little learning, if any, happens at that point. In light of encouraging efficient transfer of knowledge, Dunbar’s finding that novices struggle significantly to encode the deeper structures of problems is pertinent: without sufficient analogies in a schema, making a new context consonant with learnt contexts in troublesome.
The first step in building an appropriate schema is to teach in concrete terms with concrete examples. That is the base of the next post.
*here are some examples:
Assessment training effects on student assessment skills and task performance in a technology-facilitated peer assessment. Xiongyi Liua and Lan Lib. 2013
Why Education Experts Resist Effective Practices (And What It Would Take to Make Education More Like Medicine). Douglas Carnine
Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching . Kirschner, Sweller, Clarke. 2006
**well it may be for some, but for me it’s about efficiency in learning
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger
The mind is set up to process content in a myriad of ways, but 3 processes seem particularly relevant to education: the recognition of content, when what is presented requires little processing as it matches what is already understood by the student, the assimilation of content when there is sufficient difference between what is presented and already known and the brain ‘adds’ it on to the pile, and the accommodation of content when the brain has to change and adapt what it thought was sufficient understanding, thereby producing a new way of thinking.
Carroll and Mack’s paper on ‘Metaphor, computing systems, and active learning’ presents a really interesting view on the role of metaphor in education, and in arriving at their thesis they quite elegantly explicate the 3 processes outlined above:
Carroll & Thomas (1982), for example, suggested an account that appealed to consolidation and integration of new information. On their account material to be learned is apprehended and, by hypothesis, entered into working memory. Next, and as an automatic consequence, a framework of related general knowledge (Minsky, 1973) is retrieved from long-term memory and also entered into the working memory. Finally, with the apprehension of further new material, there is a need to consolidate and compress the contents of working memory into a more integrated format. One way that this can happen is for the new material to be assimilated to the retrieved frameworks.
The appropriateness of the retrieved knowledge framework for the new material being assimilated is crucial to this account. The retrieved framework cannot be completely appropriate, for, if it were, the “new” material would be recognized not assimilated. Hence, the framework must be partially appropriate and partially inappropriate. When it is not, additional mechanisms of inference come into play to modify the old structure to accommodate novel features of the new object of knowledge (see Bott, 1978, for further discussion of such mechanisms).
IRESEARCHNET also have a really nice definition of the terms.
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger
Life can be hard. Really hard. There are practically unlimited ways that one can become stressed, and I think I must have absorbed most of them over the last 25 years.
To cut a long story short, over these years I have incorrectly dealt with these stresses, and absorbed them into my shoulders and neck. Gradually, both became like rocks, stiff, tight and inflexible. But instead of doing anything about it, I let it build and build and build to the point of being in constant physical pain, often resulting in headaches, poor sleep and sadness. I know everything is relative, and that others are experiencing real pain and loss, but it was chronically affecting my entire being.
People: friends and family, always kept telling me that I needed to stretch to relieve the muscle pain and that I should go to a physio or massage therapist. I did that a few times, but it only touched the surface, so I would go onto YouTube and search for some free yoga as they suggested. Now I’ve done yoga before, but every time I did, I would always injure myself after a couple of sessions. The reason for that is because I would, unknowingly, choose a session that wasn’t suited to where my body was at. Or, if i got passed a few sessions unscathed, I would still be in pain and always think that it wasn’t working, and so stop.
Now, despite these bad experiences, I knew I couldn’t just leave it. I also knew that it made sense that the tension would ironically have to be reduced by exercising the muscles. Yoga must be the answer.
So I got to thinking, and then finally it occurred to me 3 months ago that doing yoga should be no different from doing anything as a novice, and that I absolutely needed modelled and scaffolded support to be able to get anything out of this ancient philosophy. I’m not sure why it took me so long to register this, especially since I have written about the imperative of modelling numerous times: here, here, here, and the importance of incremental knowledge building here and here, but anyways, eventually it clicked.
So what did I do?
1. The first thing was for me to accept a different motivation: learning, and not performance. I realised that I had to accept that the years of wrenching myself would not be unwound in a couple of yoga sessions. This meant that I would have to tell myself that the process was an extended one, and that if I was to measure any improvement, I would realistically need to give myself a month of continuous practice to evaluate any effectiveness, but even then, that it would be only small if at all.
2. The second thing was to realise that I needed to be taught by someone who knew what they were doing, who knew that building strength takes time, and who offered a continuum of learning where I could start at the very beginning. I found this in the reasonably priced platform www.glo.com.
3. The third thing was to realise that I needed to choose only two 20 minute classes that offered a chance to work on specific sections of the body (upper and lower), and that I would have to repeat those two classes, and alternate between them each day.
4. The fourth thing was to accept that even though some of the beginner class stretches were ostensibly too easy, that I was in fact strengthening muscles that I would need to support more difficult and inflexible muscles in other poses. This reminded me of Engelmenn, here and here.
5. The fifth thing was to discipline myself to doing the practice each day before work. And there were a lot of times where it would have been so easy not to do it – but I knew it was only 20 minutes, and I knew that I would be glad I had done it – I was able to self-regulate.
So after 90 sessions, where am I?
I don’t wake up anymore in more pain than when I went to bed.
I still experience pain, but nowhere near as much
I am still prone to headaches, but the frequency of them is significantly reduced.
I am a lot more flexible and have noticed that I feel better in my hips as well as my lower back.
I feel stronger.
I have begun to do some running as a result of this increased liberation.
Without any shadow of doubt, I am feeling better. My mental health has improved a lot. I am not constantly plagued by a physical pain that shouldn’t even be there. I know that I still have a way to go, but the cool thing is that because things have improved quite a lot, I’m obviously on the right path – and that gives me even more motivation. I know that I can also start to increase the level of difficulty now too to strengthen even more…and that’s exciting.
Develop a plan – like a sequence of learning
For me, the greatest realisation was that to strengthen my mental health, I needed to have a plan that allowed me to gradually develop and build my knowledge of a domain that could address it. For me, it was yoga. For you, it might be swimming, running, or cooking. Patience and discipline reign supreme, but planning an incremental curriculum that will help you achieve tangible benefits is more important.
I might check back in with you in another 90 days.
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow this blog for more stuff about education and follow me on Twitter @edmerger
This is part 2 of an essay based on self-regulated learning, and whether it needs to be taught for students to become skilled in it. Part 1 is here.
In part 1 I discussed how explicitly teaching and modelling to students how to think with knowledge potentially facilitates students being able to self-regulate such thinking. The proposition has implications for the explicit modelling of thinking critically and creatively. In this post I will expound on Zimmerman and Moylan’s 2009 paper that theorises that motivation is inextricably linked to these metacognitive processes, and just like everything else connected to learning, needs to be explicitly taught to students in equal measure for them to eventually be able to use the knowledge independently.
Zimmerman and Moylan suggest that there are 3 differentiated stages in achieving self-regulation. These can be equated with the EEFs appropriated terms: planning, monitoring and evaluation. The diagram below represents the cyclical processes of self-regulation.
FORETHOUGHT = PLANNING
IT’S A CASE OF WHICH COMES FIRST, the chicken or the egg, but in order for a student to get their learning off the ground, they need to be motivated to do so. Oftentimes in the school sector, this may not be an intrinsic motivation, with extrinsic rewards and punishments tending to dominate the setting. Upon presentation of a new learning activity, a student will process a range of thoughts evaluating whether they should in fact participate in the endeavour. Students immediately process the expectations against any prior experiences or knowledge, drawing on their schemata to ascertain the extent of having to set new goals and strategies to achieve the new learning, whilst probably concomitantly deciding if they have any intrinsic interest in the task. If they arrive at the conclusion that they don’t possess either of these motivators, your work is immediately cut out for you.
Compounding this will be the fact that students also naturally draw from that schemata the affective responses they had or indeed have built over time in dealing with similar types of activities or learning experiences. If this audit brings up negative memories, perhaps emanating from a lack of success, or serious disinterest, then this will heavily impact on their motivation to continue. It certainly won’t be the case that ‘If you build it they will come’. A student’s self-efficacy or belief that they will be able to positively engage in the task will most certainly affect their planning, strategy and goal setting capacity. So, besides forcing students to participate, what can be done to break this thought pattern?
METACOGNITION – Make explicit the possible reactions students may have to a new task: ‘You may have had a negative experience with this type of problem before, but this time is different because…’, ‘You may immediately think there’s no relevance to this task, but…’, ‘You may have not achieved the grade you wanted in the last task, but this time we are going to plan the response better…’. By making such reactions explicit, explaining how demotivating factors can arise, and providing explicit strategies that ‘show’ how a different outcome may eventuate, the teacher is training the student to think about the new context in a new way, and mitigating against poor self-efficacy inhibiting impetus.
Also crucial to setting up learning is making explicit the goal orientation of the task. Plenty of research suggests that ‘performance’ orientated goal setting, where students’ motivations to learn are primarily centered on comparison and competing against others, is tellingly inferior to having a ‘learning’ goal orientation: here. The positioning of a task’s import as being an opportunity to strengthen personal understanding against personal standards has been shown to facilitate a deepening of learning: ‘In this activity, let’s think about how we can incrementally improve our knowledge of the topic…’, ‘I want you to think about what your level of knowledge is on the topic and set yourself a goal of looking to strengthen it by the time we have finished….’, ‘In this task, we are going to concentrate on mastery…’ However, such ambition is made infinitely more difficult in a system predicated on accountability. Nonetheless, a good teacher will explicitly and inexorably focus their students’ attention on setting goals for self-improvement, and that learning is indeed a continuum that takes time and practice to master. When such purpose is part of the learning culture, once the task is successfully completed the student’s evaluation process then positively feeds into and strengthens the self-efficacy required to engage in a new learning context, regardless of how they fared compared to others in the cohort.
This personal growth rather than competitive epistemology is particularly relevant if you are trying to encourage students who are working hard but not quite succeeding – and observing others around them achieving – in the beginning of a course. These students not only need the explicit discussion of what success means (improvement against your last effort), but precise feedback that articulates what the gaps in knowledge are, and crucially, scaffolded activities that facilitate the opportunity for observed improvement against the last effort. Mastery pathways not only provide opportunity for incremental success, but also the chance to eventually catch up to the expected standard. Because success is the greatest motivator of all, when those achievements are explicitly labelled to the student, s/he will accommodate their self-efficacy to become more positive.
PERFORMANCE = MONITORING
During the task, drawing students’ attention to how they are solving problems and the progress they are making and the motivation required to do so will facilitate the eventual automaticity of such thinking. Modelling self-questioning and verbalisation of thinking processes whilst scaffolding learning through worked and completion examples builds the schema of such processes in students’ minds, and teaching students how to manage time and set up an appropriate learning space should never be assumed to be assumed knowledge. Providing as many opportunities as necessary to facilitate a culture where the student can control these learning strategies and can readily select the most appropriate tools to negotiate the context they find themselves in should be an engrained aspect of a teacher’s curriculum design. When students feel such control over the strategies they employ to negotiate the present task, their motivation and self-efficacy will be strong.
The explicit drawing of attention to higher order thinking processes during the task goes towards developing the schema for doing so in future, independent contexts. As argued in part 1, assuming students will engage in higher order thinking once knowledge is sufficiently acquired is not a good idea, as students may not do this unless they are highly motivated in the discipline or topic in question. Prompting with questions like ‘So if we know this about …., what would happen if …..?’, ‘What is the connection of this idea to the topic we looked at last week?’, ‘What would happen if we combined these 2 ideas?, ‘So imagine this scenario…., how would you solve the problem at hand?‘ If you model this thinking, students will use the model as a strategy when asked to think about knowledge in new contexts, and being able to do so will boost their confidence in engaging with knowledge in interesting ways. This confidence develops self-efficacy, and thus motivation.
SELF-REFLECTION = EVALUATING
From my experience, one of the most difficult things to do is to get students to reflect on their performance and planning after the event. This is especially difficult if the student entered the transaction with a performance goal orientation and wasn’t overly successful. The immediate deflation is palpable. Explicitly discussing this with the students is important at this very moment. But perhaps most importantly, understanding the causal attributions some students may have applied to their success or failure is necessary to ensure that they are able to benefit from the evaluation.
Many students attribute their experience to fixed ability, which is particularly detrimental if they engaged in the activity with a performance goal and didn’t succeed. The comparison against others that essentially results in a defeat if unsuccessful solidifies a negative self-efficacy, which in turn has a negative influence on the planning stage of the next learning moment. If however, the student can be persuaded by the learning continuum theory and that their ability in the task is not fixed and can in fact be improved by application of effort, practice and good revision and study techniques, then the probability of their motivation being secure for the next task is high.
Unfortunately, over time and repeated negative experiences in learning environments, some students develop entrenched negative evaluations that seriously inhibit motivation to continue or engage in future learning contexts. Procrastination may be a milder symptom of such a state, but more serious and damaging is learned helplessness, a notable defence mechanism employed that prevents a student from trying because they believe that there’s nothing that they can do to change an inevitable failure. Often, such a state becomes an unconscious default, and can only be changed by carefully designed scaffolded learning opportunities that promote success, as well as making the psychological context explicit. Of course it is time consuming, but a well-constructed audit of a student’s performance, including how they approached and revised etc for the task, will likely find a host of issues that could be rectified. A checklist may work in helping students evaluate their performance in a task, and the explicit discussion about how neglect in each element on the list is quite impactful could act as a motivator for a student to alter their preconceived beliefs that they aren’t in control of changing their learning potential.
TAKE AWAY
Teaching students about motivation and how past experiences affect the present, and helping students identify patterns of behaviour, their ‘real’ causes and how they can be adjusted is as imperative as teaching them content. Making thinking explicit can go a long way to positively affect how a student perceives a task and their ability to process, engage with, and succeed in it. The result is that students will willingly drink from the water you have led them to.
The next post will discuss how beneficial it can be for students to understand how learning actually happens.
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger
Achieving independence and self-regulation in learning is the holy grail of education, but how to go about it is as equally mystical. Essential to the quest is developing a rich schema through the building and interaction of knowledge, and whilst belief in the explicit teaching of students in how to think about their thinking processes (metacognition) and how to evaluate them as being an integral part of self-regulation is gaining momentum (EEF), this 2 part post will seek to extend the current understanding by discussing whether it is necessary to promote critical and creative thinking inside subject domains. The essay also expounds on Zimmerman and Moylan’s 2009 paper that theorises that motivation is inextricably linked to both of these metacognitive processes, can’t be omitted from the discussion, and in fact needs to be explicitly taught to students in equal measure. As Kuhn exhorts, ’People must see the point of thinking if they are to engage in it.’
WE ALL WANT 21ST CENTURY SKILLS
Whilst many argue that labelling skills such as critical thinking and creativity as ‘21st century’ does an injustice to those who for thousands of years exhibited such proficiency in them, few could argue that there is a growing demand for graduates to be strong in these areas in the age of increasingly automated and mechanised jobs. How to equip students with such skills then has become the mission of educators, but many well-intentioned educators have erroneously conflated the desired outcome with a direct pedagogy, succinctly stated by Kirschner: the epistemology of a discipline should not be confused with a pedagogy for teaching or learning it. The practice of a profession is not the same as learning to practise the profession. There are plenty of excellent voices who assent to this notion, none better then Daisy Christodoulou, specifically pointing to the fact that thinking critically or creatively relies entirely on a strong bedrock of knowledge and can’t be taught in the abstract. If we think about this it seems rather logical – you can’t think about things you have no knowledge of, and most creativity is the accommodation of knowledge already in existence. Such constraints make the application of such skills heavily context and domain dependent. But what tends to be lacking from such unequivocal pedagogy is the answer to this question: once the foundations of knowledge are secure, do students need explicit modelling of how to think critically and creatively with that knowledge? I contend that the answer is yes.
If we consider how learning is characterised by the acquisition of schema, and how crucial modelling is in that continuum, I would argue that modelling how to play with knowledge is no less important than modelling the knowledge itself. However, it is something that is often overlooked in modern curricula for three reasons:
Because we sometimes assume that students will naturally think in these ways
Because of the need to fit in so much content in so little time
Because it is hard to assess, relying on subjective and therefore unstable evaluation
The first relies on Geary’s theory of primary vs secondary knowledge. The exposition of the theory is that once sufficient knowledge is obtained, the mixing/matching and challenging/critiquing of what is understood should become axiomatic. From my experience though, without the continuous prompting by the teacher to engage with the knowledge in this way, such an outcome tends to rely heavily on a student being highly motivated in a specific domain of knowledge, with the less interested, but equally as capable student, content with achieving in assessment but not necessarily interested in exploring the content further. But what is notable however about the self-motivated student, is that they still will undertake a process of learning in how to mix and match and challenge what they know, albeit, independently: it is through the experimentation of their thinking and its evaluation that they may eventually arrive at something unique and interesting, but this ostensibly natural skill is actually being practised and refined to be maximised – and quite possibly, inefficiently, compared to what some guidance in the process could afford. When motivation to pursue a discipline is not as high, students need to be prompted to engage in ‘higher order’ thinking. Interestingly, sometimes it is only after these higher order prompts that real interest and motivation is sparked, and so the explicit provocation of them in a learning environment is important.
Sweller’s addition to Geary’s thesis, that : ‘Organizing general skills to assist in the acquisition of subject matter knowledge may be more productive than attempting to teach skills that we have evolved to acquire automatically…’ supports the earlier statement that teaching critical and creative thinking in the abstract is pointless, but it is the focus on the word ‘organising’ that is crucial here: the conclusion then is that it’s not enough to assume students will naturally engage with this type of thinking – it is only through the explicit organisation and modelling of it that will facilitate students being able to self-regulate this thinking.
Practising the application of critical and creative thinking needs time and space for it to be strengthened, and this is why the existence of the 2nd obstacle in educational contexts is so concerning. The impetus of non-invigilated exams has certainly made apparent the need for assessment to involve the application of knowledge. But to do so requires a carefully designed curriculum that facilitates such opportunity in the sequence of learning. I tend to promote a sequence patterned by the rhythm: learn, practise, apply. New knowledge is introduced by the expert, students interact with and practise using the knowledge to confirm understanding, students then apply their knowledge to do something with it. The application doesn’t have to be a large project type task. It may simply be the asking of higher order questions that include hypothesising, creating analogies, exploring various points of view, wondering if the content can be applied in other contexts, what the connections are to other aspects of the course, or brainstorming with a view to generate new ideas for a real-world context. The latter is especially relevant for the later stages of higher education.
It is such a pattern of learning that models for students how to interact with the understood knowledge they now have in their possession, a modelling process that observes what Volet (1991) imports as the necessity of identifying and making explicit how an expert thinks. This is relevant to not just when the expert is presented with new problems, but also how they think with the knowledge they already have. Palincsar &Brown (1989) concur, ‘By demonstrating the different activities by which subject matter may be processed, problems solved, and learning processes regulated, the teacher makes knowledge construction and utilization activities overt and explicit that usually stay covert and implicit.’ Like all learning, the goal is to take the metacognition to automaticity so the propensity for self-regulation in the next sequence of learning isn’t compromised by cognitive overload.
WHAT ABOUT TRANSFER?
Whether or not this explicit process of thinking within specific domains can be transferred to new contexts remains to be seen, but Simon, Anderson, & Reder (1999) arouse our curiosity when they suggest that transfer happens far more frequently than we might think. They cite reading as a prime example, but more specifically challenge a famous study by Gick and Holyoak who demonstrated that students were unable to see the abstract similarities between two problems even when they were presented side by side:
‘One of the striking characteristics of such failures of transfer is how relatively transient they are. Gick and Holyoak were able to increase transfer greatly just by suggesting to subjects that they try to use the problem about the ‘general’. Exposing subjects to two such analogues also greatly increased transfer. The amount of transfer appeared to depend in large part on where the attention of subjects was directed during the experiment, which suggests that instruction and training on the cues that signal the relevance of an available skill might well deserve more emphasis than they now typically receive–a promising topic for cognitive research with very important educational implications.’
They then continue to suggest that: ‘Representation and degree of practice are critical for determining the transfer from one task to another, and transfer varies from one domain to another as a function of the number of symbolic components that are shared.’ It follows then that for Dignath and Buttner’s claim to be valid, in their meta-analysis on Components of Fostering Self-regulated Learning, that ‘Providing students with opportunities to practice strategy use will foster the transfer of metastrategic knowledge to real learning contexts’, relies on students being able to recognise patterns or connections between contexts where they can apply their metacognition.
As stated earlier, you can’t think critically and creatively without a strong foundation of knowledge, and further, some of that thinking will be only relevant in specific domains. But it does seem likely that some of the higher order strategies stated above (hypothesising etc) would be able to be applied in a range of disciplines, and that a student observing the modelled thinking processes of a teacher in a second context will recognise some (if not many) elements learnt from their first. Once reinforced through this observation, students will begin the regular learning continuum of taking the skills to automaticity through practice. Once achieved, being able to apply the thinking in new contexts is made more possible – it will be up to further research to ascertain whether, having met these conditions, such transfer is actually possible.
WHAT DO WE WANT FROM EDUCATION?
Another consideration when teaching critical thinking draws from Kuhn, who exhorts that the development of epistemological understanding may be the most fundamental underpinning of critical thinking. In no uncertain terms, she beseeches that teachers provide the opportunity for students to reach an evaluative level of epistemological understanding, realising that simply possessing an absolute epistemology constrains and in fact eliminates a need for critical thinking, as does a ‘multiplist’ stance, allowing students a degree of apathy characterised by statements such as “I feel it’s not worth it to argue because everyone has their opinion.” The explicit modelling of an evaluative epistemology, where students are encouraged to the fact that people have a right to their views with the understanding that some views can nonetheless be more right than others, sets up a learning culture where students see the ‘weighing of alternative claims in a process of reasoned debate as the path to informed opinion, and they understand that arguments can be evaluated and compared based on their merit (Kuhn, 1991).’ Such a pedagogy may satiate an interesting question posed by Martin Robinson: ‘Should the result of a good education include all students thinking the same or thinking differently?’
The 3rd obstacle also looms large. Assessing creativity especially is a difficult thing due to its subjectivity. Rubrics are notoriously imprecise as a reliable reference in determining success or failure of creativity: what I may think satisfies one element of a rubric may be argued against by a colleague; maintaining consistency even with myself in marking is difficult. And if we don’t assess, will students not particularly interested in the topic lose motivation, and make the process a challenging one to manage? I think the answer lies within the answer to Martin Robinson’s question: surely we don’t want everyone robotically programmed. We want students to engage critically and creatively with concepts, and participate in the building of a dynamic and interesting world, so we have to have faith that the knowledge taught to our students, when learnt well, will provide avenues for curiosity that will engage them to participate. Such an epistemology then satisfies stakeholder desires to employ graduates who can think critically and creatively in a modern workplace.
So how is motivation linked to it all?
In the next post, I will extrapolate on Zimmerman’s imperative that metacognition is inextricably linked to motivation, and how educators can ensure they incorporate both in learning design.
References
Anderson, J. R., Reder, L.M., & Simon, H.A. (2000, Summer).Applications and Misapplications of Cognitive Psychology to Mathematics Education.Texas Educational Review.
Dignath, C., Buttner, G. (2008). Components of fostering self-regulated learning among students. A metaanalysis on intervention studies at primary and secondary school level. Article in Metacognition and Learning · December 2008 retrieved from here
Geary, D. (2001). Principles of evolutionary educational psychology. Department of Psychological Sciences, University of Missouri at Columbia, 210 McAlester Hall, Columbia, MO 65211-2500, USA here
Palincsar, A. S., & Brown, A. L. (1989). Classroom dialogues to promote self-regulated comprehension. In J. Brophy (Ed.), Advances in research on teaching, Vol. 1 (pp. 35–67). Greenwich, CO: JAI Press.
Sweller, J. (2008) Instructional Implications of David C. Geary’s Evolutionary Educational Psychology, Educational Psychologist, 43:4, 214-216, DOI: 10.1080/00461520802392208
Volet, S. E. (1991). Modelling and coaching of relevant metacognitive strategies for enhancing university students’ learning. Learning and Instruction, 1, 319–336.
Zimmerman, B., Moylan, A. R. (2009). Self-Regulation from: Handbook of Metacognition in Education. Routledge.
I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger
When trying to offer advice on how to modify exams for the coming semester exams, some subjects have presented with unique issues. Mathematics, for example, has the unenviable dilemma of not being able to set calculation type questions as students can simply type them into an online calculator and be presented not just with the solution, but the workings out too.
The remedy presented to other subjects that require numerical calculations, such as statistics and accounting, of randomising questions, both through the formula question type in Canvas as well as question banks, is not appropriate for mathematics.
The only hope of confidently reducing the amount of ‘Googling’ during the exam is to create more complex questions, questions that require deeper understanding or the application of knowledge, which also requires deeper understanding. Whilst this is of course the ultimate goal of any subject, if such application demands haven’t been taught, then the likelihood of students producing quality answers in exams is limited. If the amount of content that has been introduced determines that only superficial understanding is possible, a breadth rather than depth approach, then question types in the exam can’t change because it’s now open book – students simply wouldn’t have been prepared sufficiently, and thus the exam will not produce valid inferences.
In defense of mathematics, many of the calculation questions that an ordinary invigilated exam would test are designed as such to strengthen fundamental processes and skills that are required for further study in the discipline. The building of the schema is essential to be able to apply understanding in further contexts. But open book exams now pose a large threat to such a design of curriculum. It may be in the future that a depth rather than breadth approach is the only feasible option, so that the depth of understanding in less of the content can open opportunity to assess the application of the knowledge, and thus mitigate against cheating.
Baby with the bathwater?
However, there is something that mathematics’ exam designers should also be conscious of before eliminating all questions that a student could simply look up. The beginning of an exam should really be designed to ease students into the process, to provide a quick boost as they solve a question they find relatively easy. The anxiety, practically 100% concomitant with sitting university examination, is immediately partially assuaged, and thus reduces cognitive overload and allows a student to think more clearly. Exams that begin with very difficult problems can throw off students’ confidence significantly, even those who know enough to pass. It may be that you still set those initial questions as fundamental skill questions that could be looked up but knowing that for the majority, who won’t need to look them up, they will benefit from gaining some confidence in the initial stages of the exam that will facilitate better attempts at the more difficult questions later on.
In the end, it’s not about those who will cheat, it’s about those who won’t.
I’m Paul Moss. I’m a Learning Designer at the University of Adelaide. Follow me on Twitter @edmerger
