There's an interesting reading group in Cambridge on the theory of educational technology at the moment. Naturally enough, the discussion focuses on the technology, and then it focuses on the agency of those operating the technology. Since the ontology of technology and the ontology of agency are mired in metaphysics, I'm not confident that the effort is going to go anywhere practical - although it is good to see focus on Simondon, and the particularly brilliant Yuk Hui.
But that raises the question: What is the thing to focus on if we want to get practical (i.e. make education better!)? I don't think it's technology or agency. I think it's institutions - we never really talk about institutions! And yet all our talk is framed by institutions, institutions pay us (most of us), and institutions determine that it is (notionally) part of our job to think about a theory of educational technology. But what's an institution? And what has technology done to them?
It is at this point that my theoretical focus shifts from the likes of Simondon, Heidegger, and co (great though I think this work is), to Luhmann, Stafford Beer, Leydesdorff, von Foerster, Ashby and Pask.
Luhmann is a good place to start. What's an institution? It is a autopoietic system which maintains codes of communication. "Autopoietic" in this sense means that codes of communication are reproduced by people ("psychic systems"), but that the "agency" of people in communicating is driven by the autopoietic mechanism (in Luhmann's jargon, it is "structurally coupled"). "Agency" is the story we tell ourselves about this, but it is really an illusion (as Erich Hörl has powerfully discussed in his recent "The archaic illusion of communication")
By this mechanism, institutions conserve meaning. I wonder if they also conserve information, and Leydesdorff has done some very important work in applying Shannon's information theory to the academic discourse.
Ashby's insight into information systems becomes important: "Any system that categorises effectively throws-away information" he wrote in his diary. That seems perverse, because it means that our so-called information systems actually discard information! But they do.
For Luhmann, discarding information means that the probability that communications will be successful (i.e. serve the mechanism of autopoiesis in the institution) will be reduced. As he pithily put it in his (best) book "Love as Passion": "All marriages are made in heaven, and fall apart in the motorcar". What he means is that when one person in a couple is driving, their lifeworld is completely different to their partner's. The context for meaningful communication is impaired by the mismatch in communicative activity which each is engaged in.
In our social media dominated world, where alternative lifeworlds metastasise at an alarming rate, the effect of technology in damaging the context for the effective conservation of meaning is quite obvious.
In the technocractic world of the modern university, where computer systems categorise students with so-called learning analytics, it is important to remember Ashby: with each categorisation, information is thrown away. With each categorisation, the probability that communications will be successful is diminished as the sphere of permissible speech acts becomes narrower. Instead of talking about the important things that matter most deeply, conversations become strategic, seeking to push the right buttons which are reinforced by the institutional systems: not only the bureaucratic systems of the university, but the discourse system of the publishers, and the self-promotion system of social media. This is the real problem with data.
The problem seems quite clear: Our institutions are haemorrhaging information. It is as if the introduction of information systems was like putting a hole in the hull of the institutional "ship".
Stafford Beer knew this problem. It is basically what happens when the coordination and control part of his "viable system model" (what he called "System 3") takes over, at the expense of the more reflective and exploratory curious function that probes the environment and examines potential threats and opportunities (what he called "System 4"). In companies, this is the R&D department. It is notable that universities don't have R&D departments! Increasingly, R&D is replaced by "analytics" - the system 4 function is absorbed into system 3 - where it doesn't belong.
But let's think more about the technology. System 3 tools categorise stuff - they have to - it's part of what system 3 has to do. This involves selecting the "right" information and discarding the rest. It is an information-oriented activity. However, the opposite of information is "redundancy" - pattern, repetition, saying the same thing in many ways... in education, this is teaching!
Machine learning is also predominantly a redundancy-based operation. Machine learning depends on multiple descriptions of the same thing from which it learns to predict data that it hasn't seen before. I'm asking myself whether this redundancy-oriented operation is actually a technological corrective. After all, one of the things that the curious and exploratory function of system 4 has to do is to explore patterns in the environment, and invent new interventions based on what it "knows". Machine learning can help with this, I think.
But only "help". Higher level coordination functions such as system 4 require human intelligence. But human intelligence needs support in being stimulated to have new kinds of conversations within increasingly complex environments. Machine learning can be incredibly and surprisingly creative and stimulating. It can create new contexts for conversations between human beings, and find new ways of coordinating activities which our bureaucratic systems cannot.
My hunch is that the artists need to get on to this. The new institutional system 4, enhanced by machine learning, is the artist's workshop, engaging managers and workers of an organisation into ongoing creative conversation about what matters. When I think about this more deeply, I find that the future is not at all as bleak as some make out.
But that raises the question: What is the thing to focus on if we want to get practical (i.e. make education better!)? I don't think it's technology or agency. I think it's institutions - we never really talk about institutions! And yet all our talk is framed by institutions, institutions pay us (most of us), and institutions determine that it is (notionally) part of our job to think about a theory of educational technology. But what's an institution? And what has technology done to them?
It is at this point that my theoretical focus shifts from the likes of Simondon, Heidegger, and co (great though I think this work is), to Luhmann, Stafford Beer, Leydesdorff, von Foerster, Ashby and Pask.
Luhmann is a good place to start. What's an institution? It is a autopoietic system which maintains codes of communication. "Autopoietic" in this sense means that codes of communication are reproduced by people ("psychic systems"), but that the "agency" of people in communicating is driven by the autopoietic mechanism (in Luhmann's jargon, it is "structurally coupled"). "Agency" is the story we tell ourselves about this, but it is really an illusion (as Erich Hörl has powerfully discussed in his recent "The archaic illusion of communication")
By this mechanism, institutions conserve meaning. I wonder if they also conserve information, and Leydesdorff has done some very important work in applying Shannon's information theory to the academic discourse.
Ashby's insight into information systems becomes important: "Any system that categorises effectively throws-away information" he wrote in his diary. That seems perverse, because it means that our so-called information systems actually discard information! But they do.
For Luhmann, discarding information means that the probability that communications will be successful (i.e. serve the mechanism of autopoiesis in the institution) will be reduced. As he pithily put it in his (best) book "Love as Passion": "All marriages are made in heaven, and fall apart in the motorcar". What he means is that when one person in a couple is driving, their lifeworld is completely different to their partner's. The context for meaningful communication is impaired by the mismatch in communicative activity which each is engaged in.
In our social media dominated world, where alternative lifeworlds metastasise at an alarming rate, the effect of technology in damaging the context for the effective conservation of meaning is quite obvious.
In the technocractic world of the modern university, where computer systems categorise students with so-called learning analytics, it is important to remember Ashby: with each categorisation, information is thrown away. With each categorisation, the probability that communications will be successful is diminished as the sphere of permissible speech acts becomes narrower. Instead of talking about the important things that matter most deeply, conversations become strategic, seeking to push the right buttons which are reinforced by the institutional systems: not only the bureaucratic systems of the university, but the discourse system of the publishers, and the self-promotion system of social media. This is the real problem with data.
The problem seems quite clear: Our institutions are haemorrhaging information. It is as if the introduction of information systems was like putting a hole in the hull of the institutional "ship".
Stafford Beer knew this problem. It is basically what happens when the coordination and control part of his "viable system model" (what he called "System 3") takes over, at the expense of the more reflective and exploratory curious function that probes the environment and examines potential threats and opportunities (what he called "System 4"). In companies, this is the R&D department. It is notable that universities don't have R&D departments! Increasingly, R&D is replaced by "analytics" - the system 4 function is absorbed into system 3 - where it doesn't belong.
But let's think more about the technology. System 3 tools categorise stuff - they have to - it's part of what system 3 has to do. This involves selecting the "right" information and discarding the rest. It is an information-oriented activity. However, the opposite of information is "redundancy" - pattern, repetition, saying the same thing in many ways... in education, this is teaching!
Machine learning is also predominantly a redundancy-based operation. Machine learning depends on multiple descriptions of the same thing from which it learns to predict data that it hasn't seen before. I'm asking myself whether this redundancy-oriented operation is actually a technological corrective. After all, one of the things that the curious and exploratory function of system 4 has to do is to explore patterns in the environment, and invent new interventions based on what it "knows". Machine learning can help with this, I think.
But only "help". Higher level coordination functions such as system 4 require human intelligence. But human intelligence needs support in being stimulated to have new kinds of conversations within increasingly complex environments. Machine learning can be incredibly and surprisingly creative and stimulating. It can create new contexts for conversations between human beings, and find new ways of coordinating activities which our bureaucratic systems cannot.
My hunch is that the artists need to get on to this. The new institutional system 4, enhanced by machine learning, is the artist's workshop, engaging managers and workers of an organisation into ongoing creative conversation about what matters. When I think about this more deeply, I find that the future is not at all as bleak as some make out.
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