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Category Archives: Problem Based Learning

Does The Great Onlining Offer Opportunities to move from Teaching Content to Teaching Thinking?

One often hears the view expressed that one of the benefits of the enforced move to teaching online is that it will entail a move away from teaching content, and open up opportunities for a new vision of teaching that foregrounds students’ problem-solving skills. One of the many educational trends that have been rained down on teachers like the ten plaques of Egypt, is the idea that content is outdated, and that what counts in the Twenty First Century is Problem-Solving or Thinking Skills. It is an idea that has become all pervasive. At every Educational Technology Conference I’ve ever attended, at some stage a keynote speaker will express this point of view. Especially if they come from industry. “What we need is not people with paper qualifications,” they say, “it is people who can think and problem-solve!”

But can thinking be distilled from all context and taught as something discrete? Knowledge is changing so fast, the argument goes, that it will become outdated as soon as you teach it, and therefore what we need to be doing is teaching students to think, rather than teaching them content. This idea is seductive because of course it appeals to a kernel of truth. Knowledge is changing really fast. What I learned about the structure of the atom in high school is certainly not what is taught today! And yet the notion that somehow education’s core business has suddenly changed is somewhat ludicrous. Did teachers not teach students how to think pre-millenium? What does thinking that is separated from content look like, anyway?

My own career as a teacher has been affected by this movement towards explicit teaching of thinking. I teach a class called Thinking Skills. In this class we use problem-based approaches together with introducing the Harvard Visible Thinking Routines and cognitive tools such as the De Bono Thinking Hats, David Hyerle’s Thinking Maps and Costa and Kallick’s Habits of Mind. These thinking tools and strategies are embedded in every school subject, but the purpose of the Thinking Skills class we do in our grades 8-10 is to give importance to thinking itself, and to provide a platform for explicit teaching of the range of cognitive tools we use across the school. I am in two minds about how effective this is as an approach. Thinking, after all, is always about something. Thinking divorced of content makes no sense, and thinking always has a context. How you think as an historian, a musician or as a scientist is different. Learning to think in one context surely confers benefit, and surely fuels habits and dispositions which are transferable to other contexts. But how this happens is not easy to pin down, or easy to demonstrate. Nor is it automatic. We assume that it happens, but we cannot definitively demonstrate that it does. We hope that an awareness of different cognitive tools, and familiarity with using different thinking strategies will improve our students’ thinking skills. We try to teach them to notice when they need to reach into their cognitive tool kits, and develop their capacity to reflect on their own thinking, and to become better at choosing appropriate cognitive strategies. But all the documentation in the world does not add up to proof that this is effective. And as much as I think the Thinking class I teach is useful, I do not believe it supplants Maths or English classes in any way. Students still need to learn to think like a mathematician, or think like an artist!

There is some anecdotal evidence of course, that our approach to cognitive education does work. Visitors to the school express amazement at how well our students engage with problem-solving tasks. As encouraging as this feedback is, it does not amount to proof. The benefit of an explicit Thinking course is not really about improving performance in other subjects, the aim is to improve the ability to think in any context. I think what students enjoy about it is that they get to think about real-world problems without the pressure of assessment or swotting. I think it is also important in that it signals that what the school values is thinking, and the development of thinking dispositions. I believe that this approach has benefits because solving problems helps improve the ability to solve problems. Not least it builds confidence in the ability to solve problems. As anyone who has ever tackled problems like crossword puzzles, for example, will know, once you start to understand how the puzzles are set, and develop strategies for solving the clues, the easier it becomes to work through the clues. And even a difficult seven across will be tackled with a level of confidence that it can be solved given enough time. The ability to solve crosswords does not necessarily make one a better problem-solver in another context, such as Chess problems. One can be quite good at solving one type of problem, but quite bad at another. In our class we try to tackle different types of problems and help students develop strategies and tools for approaching problems. The hope is that each student will develop a sizable toolkit of cognitive tools, and an awareness of which tools are good in different situations.

So, whilst I believe that teaching Thinking has value, I do not believe it can be done divorced from the curriculum. At my school the explicit teaching of thinking is limited, we wish it to be embedded in our curriculum, rather than becoming the focus of the curriculum. It would be lovely to believe that the move online would allow teachers to throw off the yoke of curricula and standardized testing and teach students to think, to problem-solve. Sadly I do not think that it does. It is rather naive, to believe that students, simply by doing an online project rather than more formal classes, will develop thinking skills miraculously. Thinking skills need to be carefully scaffolded and nurtured. Even in a Thinking class tasks are contextualised and we seek to draw students’ attention to opportunities for transferring their skills across the curriculum. As any teacher who has ever set an open-ended project will know, the success of the project depends on how carefully it was scaffolded and supported. Remote learning will not suddenly unlock hidden abilities in our students. If we want those abilities to emerge we need to put in the pedagogical work to develop them. And remote teaching is hard, it is hard enough teaching the regular curriculum.

Doing the kind of work needed to foster advanced thinking skills over Zoom?

I don’t think so.

 

The Importance of Teaching Media Creation Skills

There is an abiding myth that kids today are born digital natives. Anyone who has ever taught ICTs in any form will know that this is simply not the case. Digital skills very much have to be taught! Kay and Goldberg have described computers as a metamedium, a medium, in other words used in the creation of other media. As such it would seem axiomatic that computing should be taught to everyone. And yet this is far from the case. All over the world computing has to fight for a space in the curriculum. No doubt much of this contention stems from the expense of acquiring computing resources, and from securing adequately trained teachers. The great onlining of education has shown us the importance of computers as a medium of communication, but as a medium of creativity it can scarcely be less important. I have taught PhotoShop, Flash and Dreamweaver for many years, often in the context of web design, or game creation. I find that it is an excellent way to segue into coding for middle school students. Computers can be used to create all manner of digital content, but games are particularly alluring for students.

In this blog post I would like to walk through my thoughts about how the nature of remote teaching will have to change my curriculum and instructional design. I would like to cover the same basic concepts: namely photo-editing and game design introducing elementary programming procedures.

Starting with image manipulation in PhotoShop one can teach not only photo-editing skills, but also copyright issues. I usually teach students to use the Creative Commons Search Engine to find suitable images to use that are copyright free. There are many plarforms available for games creation. Up until last year I used Flash, despite the increasing difficulties as the platform becomes less and less supported. I have been considering using Scratch instead, but the seamless integration inside websites and the ability to run in a browser still made Flash a viable choice. My school had an Adobe licence, so justifying that expense was also a concern. I usually teach students how to create buttons in flash and use interactive behaviours. This requires starting to use ActionScript. We use existing scripts and learn how to tweak them. After a few tutorials I get the students to design their own games and then help them get it to work. The graphic shows one of the games created by students which depended upon drag and drop behaviours to work.

So, here’s my problem. I am due to start teaching this unit in May with my grade 8 class, and yet we are likely to be on lockdown, and I am wondering if it is a unit of work I can teach remotely. Certainly not with PhotoShop and Flash, as students are unlikely to have the Adobe Suite. But apart from the problem around access to the software and the necessary data or devices – most of my students use iPads if they do not have a laptop. This presents a number of problems. Firstly, I will be really sad not to have the linkage between image editing and games creation. Realizing that everything about remote teaching and learning takes longer, I will have to concentrate on the game design alone. For remote teaching an online Photo editor such as Photopea appears to work well. The crucial skill is removing a background and saving as a gif with transparency. I am not sure that I will be able to adequately support students through photo-editing online, and the games design, however. So I will have to play this aspect by ear.

In my experience getting students to the point where they can design their own games requires a good few basic tutorials teaching base skills, and then a great deal of scaffolding the process of discovery, especially where it requires coding beyond my own capacity! Tackling this online presents problems. It is difficult to help students debug their code when you can’t see their screen, or where you have to reconstruct it to test it on your own screen! It also needs to be something that can be done on an iPad if a student does not have access to a laptop or pc. It should also not involve any downloading of software or purchase of an app.

So I have decided to use Scratch on the MIT platform which works inside a browser, and apparently works fairly well on an iPad and allows students to use a free account. Students can also share their projects with others. This is crucial because I would like students to work in small groups. I usually get students to do a few tutorials online and then set the project as a group project. Working with groups might prove tricky during remote teaching and learning, but might also help overcome some of the isolation of working from home.

To test the versatility of the platform I created a quick pong game and a tamigotchi game, and it seems to me that Scratch works very well at enabling game creation. The platform also has tutorials which allow for students to work on their own, and develop capacity beyond any tutorials and tasks I create for the class. It also has an extension for the BBC micro:bit controller, which I use for robotics. I have not been able to explore this, but it seems to me that it creates some potential tie-ins, which is important. I also use the MIT platform for mobile app design with my grade 9s, so using Scratch on the MIT platform to introduce coding seems a good fit all round.

To my mind the key to instructional design in a case like this is to have a programme in mind which can be cut short, or can be extended, depending upon the time available and the capacity of the students. In this case the vagaries of remote teaching becomes a particular concern. I will write a follow up post after completing the unit.

Bibliography

A. Kay and A. Goldberg, “Personal dynamic media,” Computer, 1977, pp. 31-41.

 

Towards a Taxonomy of Educational Games using Bernstein as a Guide

Games and gaming have increasingly become a part of the educational landscape, both in analog and digital formats. Teachers are keen to find out if they can use games in their classrooms to improve student learning and performance. It is often easy to demonstrate an uptake in engagement, but less easy to justify the time spent on a game, if educational benefit cannot be quantified. Taxonomies of games are largely based on their genre or features, the degree to which chance is present, or the complexity of the rules. This is great if you are trying to classify games, but not very helpful if your interest lies in its pedagogical value. One approach has been to try to map the affordances of game genres to educational concepts derived from Bloom’s taxonomy of educational objectives, Gagne’s five categories of learning outcomes, and Jonassen’s typology of problem solving (O’Brien, et al, 2010). This approach is promising, but suffers, I think from a surfeit of base concepts. By trying to account for too much, we end up with the kind of diagram beloved of these post-post times, so complex that it differs little from anecdote, and illuminates nothing.

I would like to suggest instead that a fruitful avenue might start with the work of Basil Bernstein (2004). Bernstein’s sociology of education has offered many researchers insight into the problems they were researching and a shared language which can illuminate different concerns, at different scales from the macro socio-political level to the individual lesson. By bringing this language to an analysis of types of games in education it seems to me we might be able to leverage a common language to understand better what it is in a game that might bring use value to the educational setting. I am not going to go into a lengthy summary of Bernstein’s work, which is often dense and difficult to navigate. Bernstein was basically interested in the ways in which education reproduced inequality in society, the rules and processes by which middle class students are advantaged, and working class students disadvantaged. A key tool of analysis for Bernstein was to see pedagogic practice in terms of two concepts: classification and framing.

Classification refers to the content of pedagogic discourse, the boundaries and degree of insulation between discourses. This answers the question of what knowledge is considered valid and legitimate. For example, in a Science class there is a strong sense of a body of knowledge that constitutes Science and doing Science. Even within different Science classes, some teachers may organize around tightly drawn boundaries of what constitutes doing Science, but others may operate around learning Science through problem-based approaches. A Social Studies class may have less of a sense of what might constitute legitimate knowledge in the field. In Social Studies there is more cross-disciplinary work being done, and the boundaries of the field are less tightly drawn. A class might quite legitimately be engaged in gender studies or in studying ancient history. Classification, in other words can be strong or relatively weak. Some schools organize work around themes rather than distinct subject areas. Problem-based learning probably represents the weakest classification of all.

Framing refers to the “how” of pedagogical practice, and sets out how control operates within a classroom, the ways in which the curriculum is sequenced, paced and evaluated. Strong framing reflects very much a teacher-centred approach, while weak framing is where students have greater control over what and how they are learning.

Both classification and framing are described as strong (+) or weak (-) and allowed Bernstein to identify two codes – collection codes which result in the acquistion of specialised knowledge and integrated codes in which the boundaries between subjects are weaker as are the boundaries between everyday knowledge and school knowledge. By visualizing these continua of weak to strong as a Cartesian plane – as below – we can start to identify recognizable pedagogical modes and ways of describing shifts in pedagogical practice over time. While teachers tend to favour one style or another, effective teaching relies upon the ability to shift between pedagogical modes according to the needs of the moment.

Figure 1: Pedagogies analysed with classification and framing (adapted from Jónsdóttir & Macdonald, 2013 in March et al (2017)

As Maton and Howard (2018) have shown, integrative knowledge building is dependent on movement between fields of knowledge – what they term Autonomy Tours. I have summarised what is meant by autonomy tours in a previous blog, but what research indicates is that successful lessons involve more than just sticking to the subject or topic being studied. Effective teaching involves turning everyday knowledge, knowledge from other bits of the curriculum to the purpose at hand. A Science teacher will often need to use Maths knowledge in her lesson. A History teacher might use Geography, and all teachers tend to use knowledge from students’ everyday experience to unpack and understand the concepts being built upon in their discipline. To teach effectively teachers need to take tours through content that is relevant to their field and knowledge outside their field and turn it to the purpose of teaching the topic at hand. In this way knowledge across the curriculum becomes more integrated.

It seems to me that in a similar way, effective teaching depends upon Pedagogical Tours, movements between pedagogical modes. There are times when it is appropriate for students to explore a topic on their own or with minimal guidance, but it is also appropriate for much more teacher-directed activities at other times. Movements between student-centred and teacher-centred pedagogies are necessary for learning to take place. It might well be that teachers are more comfortable in one or other pedagogical mode, but it is hard to see how effective learning can take place without movements between modes.

How are we to understand the role played by educational games?

I would argue that educational games can similarly be described through the lens of classification and framing.

Classification here would refer to the relative insulation of the game content. Some games have highly specialised content, while others have more integrated or open content. A game of Maths Blaster, for example, is clearly focused on mathematical concepts and skills, despite a space age theme. The content of the game displays strong classification (C+). On the other hand a game of I Spy with my little Eye incorporates content from everyday life around the players, and has very weak classification (C-). All games have relatively stronger or weaker classification along a continuum. Chess, for example, although it has warlike pieces and is nominally a game of conflict, is clearly more integrated in terms of general cognitive skills than a tactical wargame, which has more specialized military content.

Framing here would refer to the locus of control. Some games are tightly controlled through the operation of the rules, or software. Progress and sequencing is determined by the rules of the game and players have little opportunity to choose their own path. For example, in a game of tic-tac-toe, possible moves are heavily circumscribed. Players can only ever place a nought or a cross, and there are only nine possible starting positions. The Framing here is strong (F+). On the other hand, in a role play game, although the Games Master may have circumscribed the action by setting out a particular setting or scenario, players are generally free to try anything within their imaginations. The Framing here is much weaker (F-). In between of course might lie a continuum of games with relatively stronger or weaker framing. Chess, for example has more pieces and more possible moves than tic-tac-toe, although the framing is still strong because players cannot deviate from a set of possible board positions or legal moves. A tactical wargame might have weaker Framing because there are more pieces, more freedom to move in any direction and fewer restrictions on what a player may choose to do.

If we put the two together on a Cartesian plane, we can start to plot different games as follows:

 

Clearly we might differ in where we position any particular game on this matrix, and these are just a few examples of both analog and digital games. By using classification and framing, it seems to me that we can easily see the affordances of games for educational purposes, without being clouded by its features, genre and so on. By superimposing the two diagrams we might begin to identify possible code matches and code clashes between educational games chosen for use in a classroom and pedagogical styles. A code match is where the classification and framing of both pedagogical style and game match each other, and a code clash where this match is absent.

 

 


What exactly does this tell us though beyond a common sense understanding that teachers that value a great deal of control over the pacing and sequencing of their teaching are unlikely to choose to use a role play game in their classroom because it surrenders a great deal of control over to their students? Or that a teacher who values insulated academic boundaries is more likely to explore History through a game like The Oregon Trail than through creating an alternate world in Minecraft because there is simply more historical content in the former and learning is more tangential in the latter. This may seem obvious, but many teachers are genuinely confused by the range of material available to them, are easily swayed by sales reps, and misunderstand the affordances of the games they select for use.

What this taxonomy does offer, I believe, is a clear way into looking at those very affordances to be able to understand better the choices that teachers make. I think it also represents a useful research tool for looking at games in education generally and being able to relate it to pedagogical choices.

 

Bibliography

Bernstein, Basil. 2004. The Structuring of Pedagogic Discourse. Vol. 23. Routledge.

March, Jackie & Kumpulainen, K. & Nisha, Bobby & Velicu, Anca & Blum-Ross, Alicia & Hyatt, David & Jónsdóttir, Svanborg & Levy, Rachael & Little, Sabine & Marusteru, George & Ólafsdóttir, Margrét & Sandvik, Kjetil & Thestrup, Klaus & Arnseth, Hans & Dyrfjord, Kristín & Jornet, Alfredo & Kjartansdottir, Skulina & Pahl, Kate & Pétursdóttir, Svava & Thorsteinsson, Gisli. (2017). Marsh, J., Kumpulainen, K., Nisha, B., Velicu, A., Blum-Ross, A., Hyatt, D., Jónsdóttir, S.R., Levy, R., Little, S., Marusteru, G., Ólafsdóttir, M.E., Sandvik, K., Scott, F., Thestrup, K., Arnseth, H.C., Dýrfjörð, K., Jornet, A., Kjartansdóttir, S.H., Pahl, K., Pétursdóttir, S. and Thorsteinsson, G. (2017) Makerspaces in the Early Years: A Literature Review. University of Sheffield: MakEY Project.

Maton, K. and Howard, S. K. (2018) Taking autonomy tours: A key to integrative knowledge-building, LCT Centre Occasional Paper 1 (June): 1–35.

O’Brien, D., Lawless, K. A., & Schrader, P. G. (2010). A Taxonomy of Educational Games. In Baek, Y. (Ed.), Gaming for Classroom-Based Learning: Digital Role Playing as a Motivator of Study. (pp. 1-23).

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Computational Thinking – a new modality of thought or just what coders do?

I want to pose a question for consideration. There is a great deal of debate and disagreement over what Computational Thinking means. For some it describes how computer scientists go about what they do, akin perhaps to the scientific method for scientists (Wolfram, 2002), and is applicable only to computer scientists. For others it is a skill set that has implications beyond the field of computer science, a set of generalizable skills of benefit to all (Wing, 2006). A third view is that it represents something of a new mode of thought capable of unique explanations (Papert, 1980) and knowledge building. In this sense it goes beyond a set of procedures, like the scientific method, and might represent a mode of thought distinct from the paradigmatic (argumentative) and narrative modes of thought proposed by Bruner (1986).

The paradigmatic mode represents knowledge founded on abstract understanding or conceptions of the world,. For example, I could explain why an apple fell to the ground by referencing the theory of gravity. This is largely the language and understanding of Science. The narrative mode of thought represents an understanding of the world founded in human interactions. I might explain why an apple fell by referencing a sequence of events in which my elbow knocked it off the table and I was not deft enough to catch it. Of course there is a continuum along which both modalities of thought intersect and interweave. So, my question is whether computational thinking represents a separate mode of thought in its own right, or simply new combinations of paradigmatic and narrative modes. If I were to model a world of apples, elbows and tables, my understanding of why apples fall might be based on a more complete understanding of how apples behave under different circumstances. The use of computational models allows for new ways of understanding the world, new ways of gaining understanding and knowledge. Chaos Theory, for example, emerged out of computational model building. Paradigmatic formulations of the world followed from computational modelling, rather than the other way round.

When we create a computational model of a weather system and run our algorithms through computers with slightly different inputs to make a hurricane path forecast, for example, or use machine learning algorithms to predict heart disease more accurately, are we deploying a new kind of thought which is somewhat different from both paradigmatic and narrative modes?

The need to ask this question rests, perhaps, on the rapid development of Machine Learning and how it threatens to disrupt our world. Machine Learning has brought us to a point where we might soon be farming most of our thinking to intelligent machines. And while probabilistic approaches to artificial intelligence allow human beings to trace back what the machine has done with our algorithms: neural networks, with their black box approaches represent thinking that is to a large extent opaque to us. It seems entirely possible then, that in the not too distant future machines will be delivering to us knowledge of the world, and we will not be able to explain the thinking behind it.

The idea of Computational Thinking (CT) has a history, and it is interesting to unpack some of it. The term was coined by Seymour Papert (1980) and popularised by Jeanette Wing (2006) and there is general consensus that it refers to the thinking skills employed by computer scientists when they are doing computer programming, derived from the cognitive processes involved when you are designing an algorithm for getting “an information-processing agent” (Cuny, et al, 2010) to find a solution to a problem. For some, information-processing agents should refer only to machines, but for others it could include human beings when they are performing computational tasks. Differences in how applicable CT is beyond computer science hinges on these nuances of understanding. I have often heard it said that getting students to design an algorithm for making a cup of tea represents CT and if students were to study designing algorithms through leaning to code they would therefore be improving their general problem solving skills.These claims are difficult to assess, but they are important because if CT applies only to the context of computer science, then its place in the curriculum occupies something of a niche, important though it might be. If, however, as claimed, it leads to benefits in general problem solving skills there is a solid case to be made for getting all students to learn programming. Equally, the case for exposing all students to some coding might rest on other claims unrelated to the transfer of CT to other domains.

Let’s start by looking at the claims made by the Coding for all lobby. Wing (2206) argued that CT skills have transferable benefits outside of computer science itself because they entail five cognitive processes, namely:

  1. Problem reformulation – reframing a problem so that it becomes solvable and familiar.
  2. Recursion – constructing a system incrementally on preceding information
  3. Decomposition – breaking the problem down into manageable bites.
  4. Abstraction – modelling the salient features of a complex system
  5. Systemic testing – taking purposeful actions to derive solutions  (Shute, et al, 2017)

Wing’s claim has received a great deal of attention and has become the bedrock for the Computer Science for All movement, the idea that all children should be exposed to CT, by teaching them to code, both because such skills will become increasingly important in an increasingly digital world, but also because they equip students for the kinds of problem solving that is increasingly important. It is debatable, though, as to whether these cognitive processes are unique to computational thought. Abstraction and decomposition, in particular, might seem to be thinking processes shared by any number of activities. Wing’s thesis that computational thinking is generalizable to all other fields could perhaps be stated in the reverse direction. Perhaps general cognitive processes are generalizable to computation? This point is not trivial, but still might not threaten the thesis that learning to code or create algorithms is excellent for developing good problem solving skills applicable to other fields.

The question of the transfer of skills gained in one context to another is, however, fraught with difficulty. Generally speaking it seems to me that knowledge and skills are gained within the framework of a particular discipline, and that the application of knowledge and skills in other contexts is always problematic to some extent. There is a close relationship between knowledge itself and what we call thinking skills. It is hard to imagine, for example, anyone possessing dispositions and thinking skills in History or Mathematics without possessing knowledge in those disciplines. As Karl Maton (2014) has pointed out, all knowledge has both knowledge and knowing structures. There is the stuff that is known and the gaze of the knower. In different fields, knowledge structures or knower structures may have greater or lesser relative importance, but one cannot distill out something which is pure knowledge, or pure knowing. Therefore the question of the transfer of skills from one context to another, from one field to another, is not a simple one. Of course we do achieve this feat. At some point in my life I learned basic numeracy skills, within the context of elementary arithmetic classes presumably, and I have been able to apply this basic knowledge and skill set to other contexts, for example computer programming. But I am not so sure that the thinking dispositions I gained while studying History at University, and my appreciation for the narrative mode of explanation are altogether much use when thinking about Computational Thinking and what I ought to be doing as a teacher of ICT skills. I am painfully aware that there are limits to the general applicability of the enquiry and data analysis skills that I learned when training to become an historian. I did not train to become a computer scientist, and therefore I am very wary of commenting on how transferable skills in computational thinking might be to contexts outside the field. But I do believe we should be wary of claims of this sort. Peter Denning (2017) has argued that the idea that all people can benefit from CT, from thinking like computer scientists, is a vague and unsubstantiated claim. For Denning, the design of algorithms (algorithmic thinking) rests not on merely setting out any series of steps, but speaks specifically to the design of steps controlling a computational model. It is context bound.

My understanding from this is that the case for teaching everyone to code cannot rest solely on an argument that CT transfers benefits. This case has yet to be proven. It does not mean that teaching coding to all is not a good thing. I believe that learning to code represents a rigorous discipline which is good for the mind, has benefits because we are living in a world where computer programs are increasingly important, and because coding involves problem solving and this too benefits the mind. All in all I think the case for teaching coding to all is extremely cogent.

I also have this sneaking suspicion that the question I posed in my opening remarks is going to be raised more and more frequently as artificial intelligence gets applied, and if so, having a population trained in some level of competence with computational thinking is probably a really good idea.

Bibliography

Bruner, J. (1986). Actual Minds, Possible Worlds. Cambridge, Mass: Harvard University Press.

Cuny, Jan,  Snyder, Larry, and Wing, Jeanette. 2010. “Demystifying Computational Thinking for Non-Computer Scientists,” work in progress.

Curzon, Paul, Tim Bell, Jane Waite, and Mark Dorling. 2019. “Computational Thinking.” In The Cambridge Handbook of Computing Education Research, edited by S.A. Fincher and A.V. Robins, 513–46. Cambridge. https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/57010/Curzon Computational thinking 2019 Accepted.pdf?sequence=2&isAllowed=y.

Denning, Peter J. 2017. “Remaining Trouble Spots with Computational Thinking.” Communications of the ACM 60 (6): 33–39. https://doi.org/10.1145/2998438.

Guzdial, M. 2011. “A Definition of Computational Thinking from Jeannette Wing.” Computing Education Research Blog. 2011. https://computinged.wordpress.com/2011/03/22/a-definition-of-computational-thinking-from-jeanette-wing/.

Maton, K. (2014). Knowledge and Knowers: Towards a realist sociology of education. London, UK: Routledge/Taylor & Francis Group.

Papert, Seymour. 1980. Mindstorms: Children, Computers, and Powerful Ideas. The British Journal of Psychiatry. New York: Basic Books. https://doi.org/10.1192/bjp.112.483.211-a.

Shute, Valerie J., Chen Sun, and Jodi Asbell-Clarke. 2017. “Demystifying Computational Thinking.” Educational Research Review 22 (September): 142–58. https://doi.org/10.1016/j.edurev.2017.09.003.

Wing, Jeannette. 2006. “Computational Thinking.” Communications of the ACM 49 (3): 33–35. https://doi.org/10.1145/1118178.1118215.

Wolfram, Stephen. 2002. A New Kind of Science, Wolfram Media, Inc. https://www.wolframscience.com/nks/

 

EduTech Africa 2018 – Moving Beyond the Technology to Make a Difference t

Over the last decade or so the focus of the ed tech conferences I have attended has shifted increasingly away from the technology itself towards what we can do to transform education. In the early years it was as if ed tech enthusiasts were like magpies, dazzled by every shiny new tool. Some of that sense of wonder still exists, of course, and is healthy. We need to be alive to new possibilities as technology evolves. But over the years we have learned to become more discriminating as we found what tools actually worked in our classrooms, and learned not to try to do too much at one time. The focus started shifting towards pedagogy, towards how to use the tools effectively. Behind this was always some thought as to the significance of the impact of technology on education. Common refrains have been the development of 21st Century Skills, personalised learning, a movement away from teacher-centred to student-centred approaches, problem-based learning, what technologies will disrupt education and learning based on the burgeoning field of neuroscience. The overall sense has been one of promise, that technology has the potential to make teaching and learning more effective, and that education will become transformative in liberating humanity from a model  grounded in the factory system and a mechanised reproduction of knowledge and skills.

 

This year’s conference was no different in content although the technologies have changed somewhat. The focus has shifted towards Artificial Intelligence, robotics and coding, especially how to involve women in STEM and how to infuse computational thinking across the curriculum. However, this is the first time the sense I have is not one of advocacy, but of militancy. Speakers from the world of work were united and adamant in a condemnation of schooling itself. A clear preference for extra-curricular learning and the futility of academic qualifications was presented stridently. Employers, we were told, prefer people able to solve problems. If any learning is required it can be delivered, just-in-time at the point of need, online via MOOCs. Tertiary qualifications should be modular and stackable, acquired over time when required to solve real world problems. Educators endorsed this stance stressing personalised learning and the use of Artificial Intelligence and even real-time feedback from brain activity. The sense was one of an urgent need for a curriculum based on problem solving rather than subject disciplines. If you need some Maths to solve a problem you can get it online. You don’t need to study Maths divorced from real world imperatives.

 

The very idea of tertiary institutions is clearly under massive assault, and it cannot be long before they come for secondary schools as well. What scares me about this is not that I don’t agree that learning should be problem-based at some level, or that degree programmes should not be using MOOCs and blended models to achieve greater modularity and be more student-driven. What scares me is what we lose by doing that. My fears are based on two premises.

 

Firstly, I believe that knowledge should be pursued for knowledge sake rather than for the needs of the world of work alone. Of course our education should prepare us for employment or entrepreneurship. To argue that it shouldn’t is folly. But knowledge has its own trajectory and logic. Mathematical knowledge, for example, represents a body of knowledge bounded by rules and procedures. It forms a coherent system which cannot be broken up into bite-sized chunks. Can one quickly study calculus without studying basic algebra just because you need calculus to solve a problem? Historical knowledge is not just about reading up on Ancient Sumeria on Wikipedia quickly. Historical knowledge is founded on a system of evidentiary inquiry within a narrative mode of explanation. I worry that just-in-time knowledge will lack a solid enough base. If we erode the autonomy of the universities and do away with academic research, what happens to knowledge? It will become shallow and facile.

 

Secondly, I believe that the discovery model of learning is deeply flawed. Of course, if left to our own devices, following our curiosity, we can discover much. It is a fundamental learning principle. But it is not very efficient. There is no earthly reason why teaching should be ditched. Being told something by someone else is as fundamental a learning principle as learning something for yourself. It is an effect of socialised learning. We learn from each other. Teaching is an ancient and noble profession, and there seems no reason to ditch it now. The scholar’s dilemma is that it is unusual to discover anything unless you know it is there, and this requires guides and mentors. The world we live in is complex and vast and we need a working knowledge of a great deal. Without extensive teaching, it is difficult to see how we could acquire the knowledge we need.

 

I would argue that we need a broad-based liberal education, focusing on critical thinking and problem solving, which gives us a grounding in Mathematics, the Sciences, the Arts and Humanities. At this stage, after a first degree, say, the best approach could well be just-in-time content delivery delivered online.

 

Just because technology can disrupt education doesn’t mean it should. Teachers have been very conservative in their adoption of new technologies, and I think this is a good thing. Education and knowledge are just too important to change willy nilly. We need to be certain that we are not destroying our evolutionary advantage, our ability to think, simply because we can.

 

EduTech Africa 2018 – Day 2 of Just-in-time Learning

 

Dr Neelam Parmar

On the second day of the Conference the focus seemed to shift from what schools should be doing, to the nature of learning itself. Dr Maria Calderon took us on a whistlestop tour of what neuroscience has to tell us about learning. Key to understanding this is the surprising role played by emotion in mediating learning experiences. If the amygdala is too excited learning is blocked. Ian Russell then stressed the importance of changing the way learning happens in schools so that it reflects how the world now works and students are better prepared for the world of work. Learning needs to be flexible and delivered just in time. Employers are interested in your skills not your qualifications. The days of students earning a degree and then entering the world of work are gone. Mark Lester amplified this idea by stressing how tertiary learning is increasingly blended and modular. Life-long learning is the new norm.

Dr Neelam Parmar presented us with a model for weaving together technology and pedagogy. Choices around technology and pedagogy are driven by decisions around curriculum and finding a match between schools and the world of work. She left us with an image of the accelerated use of AI in schools: robots in China that monitor student attention and nudge them awake when they fall asleep.

It is in many ways an image which encapsulates the future and its possibilities. Technology can deliver a more personalised, seamless tracking of educational achievement, much of it delivered online. Students of all ages can learn what they need to learn just in time, building their own curriculum. The curriculum can be based on the task, the challenge at hand. And yet there is a danger, a danger that we will lose the ability to discriminate out what it is that is important to learn. The dilemma of self directed study is that you can’t know what you need to learn until you have learned it.

There is a strong movement away from traditional school disciplines, towards problem based learning, and I believe this is a mistake. Knowledge is coherent because it is bounded by a field. If it becomes nothing more than fodder for solving problems we lose something very valuable and that is the pursuit of knowledge for knowledge sake. Something happens when you do history for its own sake, not just to prepare for a career in politics, for example. Or if you do maths just for engineering. You lose a certain perspective, you lose knowledge itself. Knowledge is not just something you gain to live, it is something, almost tangible that enriches our lives because it throws up surprising perspectives and unleashes powerful forces of change.

The conference this year had a strong sense that the teacher is increasingly irrelevant, and I’m not that convinced that wide awake robots are the best solution. I think the teacher will be with us for quite a while yet!

 

 

EduTech Africa 2018 – Day 1 Just-in-time Teaching


The first day of the Conference started with an impassioned plea from Sameer Rawjee to make schools places where possible futures could be prototyped rather than relying on the reproduction of the present. He envisioned a future world of technology where the role of technology was to make our lives easier and liberate humanity. Schools should be places where this vision of a future where humanity has a place and can thrive is fostered and explored. This set the tone for a conference where coding, robotics and Artificial Intelligence was foregrounded, and where the role of technology was to transform pedagogical practice, empowering flexible, life long learning focusing on the development of skills, attitudes and dispositions in tune with a changing world.

Chris Rodgers spoke next on robotics and the importance of makerspaces in fostering learning and problem solving as a basis for integrating and reorganizing the curriculum. When solving problems, students arrive at a diversity of solutions, and draw on what they need to know, when they need to know it. Teaching becomes just-in time interventions, reflecting the way the world works.

In the break away sessions this theme was amplified. The role of the teacher has to change. Learning needs to become more flexible, and with this change comes the need for relevant knowledge on demand. A move from a push to a pull model, if you like. The classroom of 2030 will have to reflect this out we will have failed or students.

 
 
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