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  • Christian Moore Anderson

A framework for giving feedback: a shared understanding of learning

Updated: Apr 7

They say that feedback is not about improving the work, but about improving the learner. But what aspect of the learner? Their knowledge and understanding of the subject domain, or their understanding of their own learning? I’d like to explore an idea for how we can do both. But before I do, let me take you back to 2012, the year I joined Twitter.


Back then I was introduced to an idea called SOLO taxonomy and was instantly impressed. It was an abstract framework that appeared useful for both assessment and for students to analyse their own work. It looked like this:

The steps were incremental, basically beginning with no knowledge, some unconnected knowledge, connected knowledge, and finally connected knowledge that allowed you to be creative.


However, the problem was that it didn’t discriminate on the grounds of the quality of knowledge. Additionally, it wasn’t so clear how the final two steps differed, nor the two unconnected-knowledge steps.


It can be simplified to a taxonomy of unconnected to connected knowledge, which in itself could be useful, as some teachers discovered.


Nevertheless, as a pragmatic tool for teachers and students, it lacked two key aspects: differentiation between the quality of the information, and a defined and observable difference between unconnected and connected knowledge.


In Easter of 2021 my paper was published entitled: Designing a curriculum for the networked knowledge facet of systems thinking in secondary biology courses: a pragmatic framework.


It contains a useful framework, specific to biology education, but possibly more widely. For this blog, I’ve adapted the image, and wording, to make it more accessible but the theory is the same.


The idealised framework (Figure 1) distinguishes both: Quality, and connectedness, of knowledge. It’s simple enough to be pragmatic for both teachers and students, and complex enough to be useful.





Figure 1. Adapted framework from Moore-Anderson, C. 2021. “Designing a Curriculum for the Networked Knowledge Facet of Systems Thinking in Secondary Biology Courses: A Pragmatic Framework.” Journal of Biological Education. doi:10.1080/ 00219266.2021.1909641


The model explained


A major distinction in biology is between knowing about things; what they’re called, where they’re found, what they do; and knowing about how things happen. It’s a distinction between function and mechanism.


In this model, function is considered of lower quality knowledge as it only allows knowledge of inputs and outputs. Mechanistic knowledge is taken to be of higher quality as it allows knowing how inputs lead to outputs, considering the interactions that take place in between.


When we add the additional dimension of the connectedness of knowledge, things get more interesting, and explanatory.


When knowledge is unconnected, it lacks meaning. This is represented by rote learning. Pure memorisation without connections to other knowledge the person holds.


Novice knowledge


In the upper left quadrant we can find: rote novice knowledge. This is exemplified by just random bits and pieces a student can remember. Here’s a quote from my paper:


‘This could be a student who knows the functions of some organs, and that of some cells (such as neurons) and biomolecules (such as DNA). While knowledge of functions of entities that are spatially integrated in a system may enhance understanding of the system, here knowledge of how the functions are connected is not present.’


Rote knowledge


Here students have diligently learnt certain mechanisms, but they are not connected to other knowledge and so the knowledge is isolated and lacks meaning. They can answer ‘how’ questions, which require the learner to know how a process happens, but cannot explain the possible effect of changes to the process.


Here’s a quote from my paper to give an example:


‘Students may be able to explain exactly how mitosis occurs, but without much notion of how this process relates to the living organism. Furthermore, it is possible to observe temporal isolation when biological mechanisms that are highly connected to others are taught in different topics without explicitly revisiting prior knowledge. For example, the processes of the digestive system may be taught during one year of secondary school, and the circulatory system another year. If students cannot remember the details of the digestive system well enough, the knowledge may then remain isolated from the circulatory system.’


The quadrants in the framework of connected knowledge represent understanding, let's look at those now.


Inflexible understanding


Understanding arises when we have connected, meaningful knowledge. Just as Bruner stated, it is “Grasping the structure of a subject [and] understanding it in a way that permits many other things to be related to it meaningfully. To learn structure, in short, is to learn how things are related” (Bruner, 1960, p. 7).


When students have knowledge of the functions of entities, and how they are connected, then they can understand many phenomena. They can answer ‘what for’ questions, which require the learner to know what entities do and can link them to observed phenomena. However they cannot answer how the phenomena happen, which constraints the flexibility of their understanding. Here’s a quote from my paper:


‘Students may learn the abstract details of the members of food webs, such as the producers, consumers, and decomposers, plus the additional details of trophic levels. A student that has an inflexible understanding could name all of these, and their functions in the community, such as producers provide organic molecules, and decomposers recycle minerals/nutrients. The knowledge structure is meaningful as the structures are connected and causal reasoning of inputs and outputs is possible by considering function alone. However, the knowledge is not [flexible] because the student cannot reason about how disruptions to the system may alter its functioning.’


Flexible understanding


This is our goal for the core systems we have in our curricula. While knowledge of function is useful, at the core of the systems we need to know both how the processes occur and how they are connected. Only then can we have truly flexible understanding.


Students here can answer ‘what if’ questions, which require the learner to explain how changes in a mechanism would affect the usual outputs. Here’s a quote from my paper:


‘For instance, a question may ask a student why patients suffering from pancreatic

cancer lose weight. Here students must visualise the connection between knowledge structures pertaining to the behaviour of malignant tumours, and the mechanisms of the digestive system. Both of these are typically found in distinct curricular topics. Another related example is asking a student to suggest the effects of a gastric bypass on the digestive system’s functioning, and possible outcomes for that human.’


Furthermore:


‘It is to zoom in to visualise the interactions of components and zoom out for a view of the effects on higher levels. It is to see the whole and the parts depending on the will of the student’s gaze, to ponder the systems’ ability to respond and buffer changes dynamically.’


A feedback tool for improving the student’s knowledge, and their self awareness


So how can we use this model for giving students feedback?


Not all feedback is equal; the beneficial effects are associated with sharing the ideal answer rather than merely informing whether a response is correct or incorrect (Pashler et al., 2005). A further factor is that for feedback to be effective it must be attended to actively (Metcalfe, 2017).


We need both, an ideal answer and action, to improve learning. Let’s quickly consider some feedback formats before getting back to the framework and how to use it.


A major distinction in feedback format is between correct answers feedback (CAF), and elaborative feedback (EF) that provides the correct answer plus additional information. This could include an additional example, an explanation, or presentation of the originally studied materials. A reason for why EF could be beneficial is that the additional information allows the learner to have a better understanding of their error (Kulhavy & Stock, 1985).


The real application of feedback for learning is ultimately the classroom, and it is therefore imperative that it be considered in this environment rather than in a controlled laboratory setting. In a classroom of twenty-five students pragmatic reasoning becomes more pertinent.


As feedback must be actively engaged with, the ability to focus all the students’ attention on feedback could provide a larger overall benefit for the class than any feedback technique that is not interacted with fully. Therefore, CAF can be beneficial if all the students are required to read and correct their own answers as all students are actively engaged in feedback.


Conversely, EF in a classroom requires giving students time to think and discuss, as it is not a one-on-one setting; other students must wait as discussion takes place and the ultimate correct answers are delayed. During this time, some students may not be actively participating, verbally or mentally. If CAF obtains more active participation than EF in a classroom, it may outweigh the benefits of elaborating on feedback when thinking collectively.


How can we get both, maximum participation and elaborative feedback?


Siegler (1995; 2002) discusses some of their his studies on EF with primary school children, which highlighted the importance of guiding students to focus on the underlying rationale of feedback, rather than merely checking their own performance. In one study, the participants were allocated to three different treatments: 1. Control: Feedback only, 2. Participants explain their own reasoning, and 3. Participants explain the reasoning of the experimenter, with the latter being the most effective.


Siegler suggested that ‘Having the children explain another person’s correct reasoning has the advantage of both discovery and didactic approaches to instruction. It is like discovery-oriented approaches in that it requires the child to generate a relatively deep analysis of a phenomenon without being told how to do so. It is like didactic approaches in that it focuses the child’s attention on the correct reasoning.’ (Siegler, 2002 p.40).


Importantly, he concluded (among others things) that:

  • Explaining why an answer is correct is more useful for learning than explaining one’s own answer.

  • But it is even more effective to juxtapose correct and incorrect answers and have students explain why they are correct and incorrect.


Using the framework with students


So let’s now return to the framework and fit the pieces together. How can we use it to get maximum participation, elaborative feedback, and also improve the learners awareness of what good learning is?


By sharing the framework it provides students a scaffold for elaborative feedback, to give them confidence in knowing why one answer is better than another, and what they should strive to learn themselves. With a shared understanding, whole class feedback, using student examples (shared with students), should become much more streamlined, participative and effective.


In my experience, students are not at all aware of the distinction between function and mechanism, nor are they aware of how much more useful it is to hold a knowledge of mechanisms. When I have shared this with them, they have told me that it has changed their view of learning.


Finally, a concrete suggestion for its use


When students are familiar with the framework, ask them to write an extended response on a well designed question. The question must give scope for all quadrants. So asking ‘tell me about the digestive system?’ is unlikely to supply an answer that fits the flexible understanding quadrant.


To allow a wider range of answers and participation, these type of question may work better:

  • ‘Tell me about the human intestine, and what may happen to a human who has half his intestine removed due to a disease.’

  • 'Tell me about the cardiac cycle, and what may happen if there is a hole in the septum.'

  • 'Tell me about population dynamics in this marine food chain, and what may happen if fishing of X was banned.'


Answers can be collected and a sample could be read, selecting good and bad answers. Preferably, just two examples that can be juxtaposed and projected to the class, would be ultimately selected.


During the lesson, ask students to think in silence to which quadrant they would assign the answers, and to self explain why an answer is good, and why the other is bad. Students could write it, discuss it, or just think it, before a quick class discussion.


In the process we are not just providing students with ideal answers, we are developing, metacognition, or self-awareness of how to improve their own thinking and learning in the future via the framework.


If you’d like to read my full paper about the framework, send me a message.


Christian Moore Anderson

@CMooreAnderson (follow me on twitter)


Follow this link to my other blog posts


References


Bruner, J., 1960. The process of education. Cambridge, MA: Harvard University Press.


Kulhavy, R., Stock, W., 1989. Feedback in written instruction: The place of response certitude. Educational Psychology Review, 1, pp.279-308.


Metcalfe, J., 2017. Learning from Errors. Annual Review of Psychology, 68(1), pp.465–489.


Moore-Anderson, C. 2021. “Designing a Curriculum for the Networked Knowledge Facet of Systems Thinking in Secondary Biology Courses: A Pragmatic Framework.” Journal of Biological Education. doi:10.1080/ 00219266.2021.1909641


Pashler, H., Cepeda, N., Wixted, J., Rohrer, D., Nelson, T., 2005. When Does Feedback Facilitate Learning of Words? Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(1), pp.3–8.


Shute, Valerie J., 2008. Focus on Formative Feedback. Review of educational research, 78(1), p.153.


Siegler, R., 1995. How Does Change Occur: A Microgenetic Study of Number Conservation. Cognitive Psychology, 28(3), pp.225–273.


Siegler, R. 2002. Microgenetic studies of self-explanation. In Microdevelopment: A Process-Oriented Perspective for Studying Development and Learning, ed. Garnott, N., Parziale, J., pp. 31–58. Cambridge, UK: Cambridge University Press


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