Responsibility, the pedagogical act, and the freedom to learn

In an article that I wrote with a few colleagues of mine, we made the point that the notion of responsibility needs disambiguation, as it may refer to very different pedagogical “scenarios”. Let’s see why.

Lucas in his book on responsibility argues that responsibility establishes a triadic relationship: I am responsible for something to somebody. In our article we called this “answerability” to stress that this conception of responsibility always involves in one way or the other to respond to somebody (for something). There are a few things that I may draw from that, which has important consequences for education – I believe.

The first is that what we are held responsibility for should be known in advance. This implies that this something I am responsible for should described or made fully explicit. This is typical of contractual relations. In education, these are the learning objectives. What is it that the student will eventually acquire? Skills, competences – all these should be fully described in a specific way.

The second thing is that, if I am held responsible for something, then this something should be within my reach. Say, that I am appointed to make coffee for breakfast. So, I am responsible for preparing it. However, if the next morning the coffee machine breaks down, then, I cannot be held responsible. As far as education is concerned, this is a very interesting point. Learning is interpreted by some as acquiring certain skills, knowledge or competences defined beforehand. That is what a student is responsible for. But here comes the third actor in the triad. If I am held responsible for making coffee, as I said, that should be within my reach. Indeed, the machine may get broken. Yet there is another condition under which I may not be able to fulfill my “duty”. And that is when I don’t know how to make coffee. When, in other words, I have to learn. Being in such a state is a quite interesting one. And the reason is this: I may not be able to make coffee now, but I can learn and be able to make afterwards.

If that is the case, then there must be somebody who should teach me or, at least, assist me in the process. In schooling this role is fulfilled by the teacher. Interestingly, the teacher is in the pedagogical process that one the student responds to. But because of the pedagogical relationship the teacher is also the one becoming responsible for the students’ learning process. The teacher is, in other words, responsible for bringing the student into the state of knowing. The teacher is the one who posses pedagogical knowledge. That is, the kind of knowledge that would in theory allow the student to get to know what he/she has to know. At the same time, the teacher is also the subject supposed to know. That is, the state the student should achieve. So, the student responds to her.

So, to go back to my example. I am supposed to make coffee. But I lack the skill to do so. Therefore, a teacher is appointed to teach me how to make coffee. The teacher has pedagogical knowledge. That is, she tells me what to do in order to learn the skill. My responsibility now is not to make coffee, but to follow her instructions, which would allow me to acquire the skill. So, I am now responsible to her, while at the same time she has become (temporarily) responsible for me making coffee. This is what Lucas calls the “upward spiral of responsibility”, which seems to be a fundamental element of schooling.

Now, it goes without saying that the upward spiral establishes the conditions for hierarchy to emerge, because different people become engaged with one another in a sort of co-dependent relationship of duties – I respond to you, you respond to her, etc..  Some would call this “chain of fools”.

We can use responsibility as answerability to describe the state of affairs in education. But is there another way to interpret responsibility?

As I said, answerability establishes a triadic relationship: I am responsible for doing something to a third party. If I am not capable yet to fulfill my duty, then the third party becomes responsible for my learning process, whereas I become responsible for following what the third party – my teacher – tells me.

We can, though, deconstruct this. And that can be done, if we do not interpret responsibility as something related to an outcome to achieve or secure (e.g. making coffee). But as a process. Or, more specifically, as a type of engagement while doing something, which, though, may not be entirely specified. If we go back to the example of coffee – a trivial one, indeed – we may say that, if I am responsible for breaksfast, then what that means is that I am the one who will be taking care of preparing breakfast. I may make coffee, bake a cake, make toasts, etc..What counts is not so much what I am going to prepare as my own engagement, which eventually will lead to preparing something.

This is quite a radical departure from the triadic relationship. We may say that we are still somehow responsible to somebody for doing something. Yet such connections are somehow relaxed or loosened up. What counts is more something coming out of oneself rather than mere compliance. This has indeed very important repercussions on education and the way in which we may conceive it. For example, the learner is not necessarily to be seen as an executor of the teacher’s plan (for his own good, indeed). Conversely,  the student becomes a subject of a process that is essentially open to his “will to learn”. In other words, to educational trajectories that very much depend on his own ideas and plans, not teachers’, school’s or society’s. The teacher then is not taking responsibility for the learner’s learning process. Conversely, she becomes an ally of the student in the process.


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Types of predictions

The term prediction is fundamental ambiguous and therefore more prone than other terms to all sorts of intellectual stupidities.


Predictions can be generated inductively. Today it is sunny. Yesterday it was sunny. The day before yesterday was sunny. And it’s been sunny from the last 10 days. What can I infer, if I just look at? I can predict that tomorrow it will be sunny.  Predictions are generated by projecting past occurrences onto the future. Indeed, there might be way more sophisticated predictions than this example. However, no matter how complicated they can be, there is one crucial point that is shared: I don’t need to know or understand why the weather will be sunny – to stick to the example. I just need to see patterns in the past and project them onto the future.


A second type of prediction is generated deductively. So, imagine that I am waiting for a colleague of mine. Since I have seen him coming to work, I say to myself: if he has come, then, I will see his laptop on his desk in his office. This is a different type of prediction. My prediction is generated by deriving the logical consequences that follows from stating a hypothesis. This is an important piece of equipment for scientists, because such type of prediction allows us to put our hypothesis to the test. Interestingly, it does not “predict” a future event. It just informs us about the epistemic value of a hypothesis. If we are right, we will see this happening. If we are wrong, we won’t. In this sense such type of prediction informs us about the truthfulness of our ideas, not about the future. It has, in other words, an epistemic function.


There is a third type of prediction, which is different from the previous two. This is a type of prediction that like the first type tries to say something about the future. But unlike the first one it is not generated by projecting past occurrences onto the future. For example, if I see that a student is not particularly engaged, often forgets to attend classes, shows very little interest in what he is studying, what I may predict is that this person is going to drop out. And I do that abductively. Indeed, my prediction is still generated by looking at what happened in the past. But unlike the case of inductive predictions, the past provides me with clues, which hint at a possible explanation, in the light of which a certain “future” might appear more plausible than others. Plausibility, not probability, is the discriminating factor here. What is crucial here is that this type of predictions requires understanding. In other words, we need to understand how things “work”. In the example, we need to have some sort of understanding as to why students drop out.



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Functions, purposeful use and tinkering

Last week I visited a school here in Tartu with the students of our master’s programme in educational technology. While we were leaving, one of the students had an interesting remark. He said that an educational technologist is not a technician.  One day later during the course “use of technology in education” a colleague of mine engaged the students in a very interesting task. Students were divided in three groups. Each of them got a little robot to play with. One of the group was asked to think what an Art teacher could do with it. Another one was asked to think what a Music teacher could do with it. And the last group got the Math teacher.

Do the two episodes have anything in common?

One thing that I observed during the task was that students spent a fair amount of time getting familiar with how to program the behavior of the robot, which meant how to use the application in the tablet that came along with the robot itself. In common parlance, we say that they were learning “how to use” the robot. After they got a bit more familiar with how to use the robot, the students, assorted in the different groups, started thinking about how the music teacher, the art teacher and the math teacher, respectively, can use it.

In both cases we use the expression “how to use it”. Yet that bears a fundamental ambiguity, which is in my opinion at the core of philosophy of educational technology or of any attempt to understand a bit more what kind of role technology could have in education (and perhaps in society too).

If we wanted to use a better terminology, we could say that, when the students were getting familiar with the application that controlled the robot, they were trying to understand how to communicate with it to do something. In other words, they were trying to find a “common language”. What happens if I push this? What happens if I push that? – that sort of things. In other words, what they were tying to understand were the “functions” of the robot.

All this that I briefly described is not what my colleague had in mind when he asked the students to think of how, for example, the music teacher could use the little toy in his class. One way to disambiguate the term “use” is to add the adjective “purposeful”.  Purposeful use does not refer to the fact that we are able to interact with a robot using its functions. But that we are able to connect the use of a robot to some educational purposes, that is, to show something that is good to know about music, different types of rhythms, for example.

I believe that this is a distinction that most of people would not have any problem to understand. And it is captured by several different other concepts. For example, Heidegger’s distinction between ready-to-hand and present-at-hand. That is, a more practical/pre-reflective approach to things and a more theoretical/reflective one.

But let’s try to make a step forward. When we interact with a piece of technology – a robot, for example, we are bound to the type of “language” that the piece of technology “understands”. And that kind of language gives us access to its “functions”. So, for example, I am now writing in a form and there are buttons that allow me to mark some part of the text in bold. I can do that by going with the mouse over one of those buttons – the one marked with the letter “B” – and click on it.

Now, the question is: can we deploy the same kind of mindset for purposeful use? Can we talk about functions on a different level – the level of purposes? The answer is yes. Yet…

Interestingly, when my colleague asked the students to think of how a music/art/math teacher could use the robot in her/his class, which was “purposeful use” to stick to the terminology introduced above, the students – by their own admission – started tinkering. What does that mean? If we stick to the sort of definition that tinkering has, it means that they are sort of messing around or playing McGyver. At first approximation that’s what students started to do. But this is not the whole story.

Tinkering has several characteristics. One of those is its open-endedness. When we say that we start tinkering, this means that we try to figure out how we can actually use something without having a specific idea in mind: we start playing and build on top of that. It is a meaning-making process through doing . Interestingly enough, this happens at both levels described above. The level of using the functions of a piece of technology and the level of using a piece of technology purposefully. The difference, though, is that in the latter case the degree of freedom is way more vast. As I mentioned above, a device comes with certain functions (e.g., marking this word bold). We can explore the functions via tinkering, yet the degree of freedom for playing would be very small if non-existent. In this case, tinkering would characterize the process, not the result.

Things are different with the second level. The reason is that what we are engaged with is no longer the exploration of the possible functions of an object. But the meaning/purpose that the object can have in a specific cultural practice. In this case, the kind of use we talk about is ontologically different. It’s no longer about the piece of technology, but the kind of things we can often creatively do as members of a practice. In other words, the device ceases to be a “technical” object, as it becomes “cultural” – for lack of a better term, that is, fully embedded in the particular cultural practice.

This that I am trying to elaborate is somehow connected to the comment that the student made during the school visit. An educational technologist must indeed be familiar with how to use a piece of technology. To suggest a music teacher how to use a robot during one of her/his class, one must know how to make the robot move. Yet the technical aspect is not the constitutive of being educational. And this is the biggest challenge that this particular professional figure is facing at the moment: how to tinker and tinker well.

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Predictive knowledge vs predicting the future

I am reading von Mises’ Human action. A Treatise on Economics and in Chapter VI – dedicated to Uncertainty – there is almost an aphoristic passage that says:

If man knew the future, he would not have to choose and would not act. He would be like an automaton, reacting to stimuli without an will on his own.

He then makes another bold statement about the kind predictability that is assumed in the so-called natural sciences:

Natural science does not render the future predictable. It make it possible to foretell the results to be obtained by definite actions.

A few pages earlier he had made another interesting remark:

The temporal order in which knowledge is acquired must not be confused with the logical simultaneity of all parts of this aprioristic deductive system. […] Anteriority and consequence are essential concepts of praxeological reasoning. So is the irreversibility of events.

What von Mises is very clear about is that, whenever we approach human action to study it (hence the term praxeology – the science of action), we tend to generate, say, “systems of thought” – such as those belonging to logic and mathematics – that are simply out of time. This means, more specifically, that all events and occurrences – including our own actions, either as consequences or motives  – are seen synchronously as part of a broader picture that is cast upon reality. There are no lapses of time. Nor are there past, present and future.

The suppressing of time is  a typical characteristic of an objective approach to the world and human affairs. Notions such as after and before, antecedent and consequent, etc. make sense because they are part of a sequence of events that we can arrange before our eyes – this leads to this that leads to this, etc..

As von Mises brilliantly notices, indeed we can come to predict the consequence of an action. For example, we put coffee on the stove and  we can quite accurately predict that the water will start boiling and coffee will be ready. However – and this is a central point – the accuracy of a prediction (in other words, whether we get it right or not) is always relative to the conditions under which we make our prediction. Let’s go back to the example of making coffee.

Indeed, the water on the stove will come to boil. But, if we use an electric stove and after a few minutes there is a blackout, it simply won’t. Is it an exception? As paradoxical as it may sound, It is not an exception to the rule. The conditions under which the rule can be applied is the exception. The simple fact that we can wake up and make coffee implies a concomitant occurrence of a number of events that would make us tremble in disbelief.

The more general lesson that we derive from von Mises’ treatment is that we can predict the consequences of certain specific actions. But that does not mean that what we are doing is predicting the future. And the reason is that predicting the future would imply predicting something regardless of  the actual conditions.


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Simulations, duplicates and the fallacy of misplaced concreteness

Searle has a very interesting argument – among many others – against the strong AI program (and all the rest of it). He says that simulation is not duplicate (he seems to love punchlines, by the way). This means that we can simulate, say, cognition in a computer, but that does not mean that we have then a duplicate of cognition, that is, something that IS cognition in the computer. Ontologically speaking, what a simulation creates (or embodies) is nothing but a model, a representation. What Searle adds is that it lacks causal power. This is obvious, if we think of a flight simulator. It can be as accurate as possible, as close to reality as possible. Yet, guess what? Nobody would ever think that with that machine we could actually fly. Indeed! As Searle says, it lacks causal power, that is, the power to do exactly the same thing as a real aircraft.

Now, why is it that we think that with cognition (or intelligence or reasoning) things are different? A full answer to this is worth an entire philosophy course of AI and computation. But one short way to answer to the question is that we are committing a fallacy, the so-called “fallacy of misplaced concreteness”, which was introduced by British mathematician and philosopher Whitehead. The fallacy states that when we come up with a theory or a model of how something works, for example, our cognition, we tend to assume that that theory or model is characterized ontologically by the same very way as that which it models or theorizes about. So, a theory of cognition is (erroneously taken for) cognition. Or a theory of the brain is the brain. As we know, theories of the brain have changed over the past centuries, while the brain has interestingly remained the same over the same period of time.

Indeed, there is one thing that changes. A new theory of the brain or cognition may change the way we use the brain or our cognition! And this is worth a few more words. Suppose that I say I have 100 Euros in my pocket when in fact I have nothing. Would saying that  have that money magically make 100 Euros appear in my pocket? Indeed, nobody would say Yes. The difference is eminently ontological. Same thing as above.

However, in a way, it is true that those 100 euros exist. But they exist as a mere verbal representation – something that I say. Indeed, 100 euros would not exist as, say, a banknote to exchange at the supermarket. Now, this representation is not without meaning. It is a representation after all.  So, if I say to you that I have 100 euros, and you think that I am a man of my word (pun intended), then you would start acting as if I actually have that money in my pocket. So, you may give me, say, your watch, because, well, I have the money!

Now, coming back to cognition and computers, this simple example shows one important thing. That the issue concerning the attribution of some sort of “cognition-like” power to computer becomes a matter of practical concern. That is, it is more about what we then do than what it actually is.

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Interaction, incorporation of new skills and abilities, and the meaning of educational technology

In preparation of one of my courses for the fall – the one dedicated to creative re-use and tinkering – I decided to read again Chapter 1 of Where the action is by Paul Dourish. The book was published in 2001, but I believe it’s still a very good reading even in 2017.

In this first Chapter Dourish merges our ideas of what interaction is with the history of computation. Dourish walks us through some 60 years of history during which computation has increasingly colonized our life. The main argument put forward by Dourish is that over the last decades we have seen a widening of the range of skills and abilities being incorporated in the interaction with computers. There was a time when only experts could interact with a computer, which, by the way, would occupy a space as large as a medium-seized gym. Now it’s completely different.

We have here a curious phenomenon. On the one hand, the actual interaction with computers is dramatically enriched. For example, since the introduction of the so-called graphical user interface (GUI), I can incorporate in the interaction with a computer – and thus make use of – my spatial reasoning skills, which radically improves the usability of a so-called computer. On the other, the incorporation of an increasingly wider range of skills and abilities made the so-called “technical knowledge” not so fundamental as it was before to operate a computing machine. It is not that all of a sudden we no longer need engineers and programmers. We still need them to build a computer, but when it comes to  interaction, and consequently the kind of experience we may or can have, things have changed. Dramatically.

To put it another way: the interaction with a computing machine is less and less tied to the kind of knowledge we need to build it. It follows that what we can do with computers is less affected by our “technical” skills, while it is increasingly affected by other skills and abilities we have, which, in turn, are less and less related to building the thing.

Now, one thing that we may add to Dourish’s argument is that in adding layers to the interaction with computing machines, we lose control, as complexity kicks in. Computers were used at the beginning for specific tasks and they needed an entire task force of engineers to operate. Not quite the same as today. But the price we pay is, as I said, losing control. So, once the interaction of a computer incorporates social skills, this also means that the computer gets embedded in a much more complex “game” – the social one. The result is that we can no longer assume the same kind of linear connection between a machine and its function.

This is to me quite a big issue when it comes to educational technology. It is clear to me that educational technology is not something technical. It is not about becoming engineers or getting the right sets of skills. The computer is already interacting and thus incorporating skills and abilities pertaining with teaching and learning. But here comes the big question: have these skills and abilities changed? Or are we still dealing after all with teaching and learning?


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Practical deductions vs practical abductions (or algorithmic thinking vs heuristic thinking)

Deduction has the function of a decision. Deduction can be defined as the act of subsuming a case under a general rule. This means that the resulting action (what in a syllogism would be the conclusion) is identified by the application of a general rule (if x, then y) to a particular case b.

If x then y.

b is x.

Then, y

Such a framework for decision is also called “purposive-rational action” (see my previous post). This model or frame of decision-making is in essence the practical side of algorithmic thinking. Algorithmic thinking proceeds this way.  It is the generation of a chain of practical deductions. Since it’s logical, it can easily be embedded in a computer and in this way it makes decisions without any further human intervention (provided that a viable system of input and output is in place).

This approach based on practical deductions “works” – so to say, if we can connect the particular case at hand back to a known category –  a sort of specimen. This is what the act of subsuming a case under a general rule actually means. So, a case is never taken for what it is, but as a member of a broader category. This makes very hard for a practical deductive system to handle any case that is not immediately connected back to a known category.

Interestingly, a practical deductive system may actually force a particular case into a known category. Practical deductive systems are in fact procrustean beds (I will come back to this point in another post). There are indeed false positive and false negative to deal with.

Practical deductions never quite deal with individual cases. A case is always a token of a type – to use a more technical jargon. In other words, it needs to engage with abstraction. Which means in this case to reduce or attenuate the number of levels of (possible) descriptions (see Mark Johnson’s post on this). Assigning a case to a broader category means precisely reducing the variety of ways in which we can describe something to a single one (or a few). This is the problem when we simply classify things.

Leaving procrustean beds aside for the time being, practical deductions are not particularly good when it comes to actual decision-making. Indeed, they look good. Very good. That’s because they rely on purposeful deliberation: we know what we are going to do and why. However, they are not able to adapt. They need explicit knowledge. They do not tolerate uncertainty and ignorance. While practical deductions are safe and provide a good deal of certainty and control, they inevitably deal with fixed abstract entities and therefore do not adapt to circumstances. (Seemingly, practical deductivism maps pretty much on what the great Alexander Luria said about classical science, which he viewed as “the reduction of living reality with all its richness of detail to abstract schemas”.)

Let me now turn to practical abductions, which I started dealing with in a previous post.

Essentially, in practical abductions we are not able to recognize the case at hand. And that impairs the possibility to apply a practical deductivist approach. An interesting example is unlocking the door. If everything works, what we have to do is to put the key in the lock and turn the key left (or counterclockwise). The thing is, if something goes wrong (because of a faulty lock, for example),  we are plunged into a state of ignorance: what should we do? Practical deduction is a powerless option precisely because it requires that the case is known. The case at hand cannot be subsumed under the general rule, simply because we don’t know what the case is.  We need therefore a creative type of practical inference. Here is where practical abduction comes into play.

In the syllogistic framework, an abduction is composed of a major premise, which is a piece of knowledge, a habit, a rule, or, more in general, something that we assume to be true; a minor premise, which is something like a cue, a trace left that we detect in the here and now. The conclusion of the abduction establishes a state of things – a case. So, I can make the following example:

Paul comes to office with his laptop (Major premise – habit).

I see Paul’s laptop on his desk (Minor premise – cue)

Paul has come to the office (Conclusion – case)

What makes abduction particularly interesting is that the generation of a conclusion depends on the selection of a major premise, which happens in connection with the cue, the trace left. In a non-practical abduction the conclusion we reach is an explanation.

In a practical abduction we have something similar. First of all, we have something like a cue, a trace left. In our example it is that we can’t turn the key right. The major premise is not a piece of knowledge, but an action, a doing. It can be, for example, pulling the key out of the lock by a few millimeters. This action is somehow cued by the situation. We simply can’t turn the key right. Now, this action – this doing – has then an effect on the world. So, the “conclusion” is not an explanation like in the case of a non-practical abduction. It is some sort of change that we see happening in the world. If we prefer, it is a manipulation of the world brought about by an action (pulling out the key) cued by something in the environment (we can’t turn the key right) that we took as relevant.

What is interesting is that the conclusion in the practical abduction is known only after we have actually acted. This is what gives the sense of tentativeness to practical abduction. Indeed, the resulting change can meet our expectations or not. Or it may create something that eventually meet our expectations, although it was not intended to be so. This is the case of serendipity. Now, what is interesting to stress is that this way of proceeding is not algorithmic, that is, governed by rules that we apply to known cases. The selection of the major premise – the action – has a pure heuristic function. We do something in the expectation that the change produced will somehow be helpful for us to understand a bit better the world around us. But there is no certainty.




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