Assembling the heterogeneous elements for (digital) learning

Category: digitalIgnorance

Exploring Dron’s definition of educational technology

Pre-COVID the role of technology in learning and teaching in higher education was important. However, in 2020 it became core as part of the COVID response. Given the circumstances it is no surprise that chunks of that response were not that great. There was some good work. There was a lot of a “good enough for the situation” work. There was quite a bit that really sucked. For example,

Drake Hotline Bling Meme

Arugably, I’m not sure there’s much difference from pre-COVID practice. Yes, COVID meant that the importance and spread of digital technology use was much, much higher. But, rapid adoption whilst responding to a pandemic was unlikely to be better (or as good?) qualitatively than previous practice. There just wasn’t time for many to engage in the work required to question prior assumptions and redesign prior practices to suit the very different context and needs. Let alone harness technology transformatively.

It is even less likely if – as I believe – most pre-COVID individual and organisational assumptions and practices around learning, teaching and technology were built on fairly limited conceptual foundations. Building a COVID response on that sandy foundation was never going to end well. As individuals, institutions, and vendors (thanks Microsoft?) begin to (re-)imagine what’s next for learning and teaching in higher education, it is probably a good time to improve those limited conceptual foundations.

That’s where this post comes in. It is an attempt to explore in more detail Dron’s (2021) definition of educational technology and how it works. There are other conceptual/theoretical framings that could be used. For example, postdigital (Fawns, 2019). That’s for other posts. The intent here it to consider Dron’s definition of educational technology and if/how it might help improve the conceptual foundations of institutional practices with educational technology.

After writing this post, I’m seeing some interesting possible implications. For example:

  • Another argument for limitations in the “pedagogy before technology” argument (pedagogy is technology, so this is an unhelpful tautology).
  • A possible explanation for why most L&T professional development is attended by the “usual suspects” (it’s about purpose).
  • Thoughts on the problems created by the separation of pedagogy and technology into two organisational universities (quality of learning experience is due to the combination of these two, separate organisational units, separate purposes, focused on their specific phenomena).
  • One explanation why the “blank canvas” (soft) nature of the LMS (& why the NGDLE only makes this worse) is a big challenge for quality learning and teaching (soft is hard).
  • Why improving digital fluency or the teaching qualifications of teaching staff are unlikely to address this challenge (soft is hard and solutions focused on individuals don’t adress the limitations in the web of institutional technologies – in the broadest Dron sense).

Analysing a tutorial room

Imagine you’re responsible for running a tutorial at some educational institution. You’ve rocked up to the tutorial room for the first time and you’re looking at one of the following room layouts: computer lab, or classroom. How does Dron’s definition of educational technology help understand the learning and teaching activity and experience you and your students are about to embark upon? How might it help students, teachers, and the people from facilities management and your institution’s learning and teaching centre?

Computer lab Classroom
Czeva , CC BY-SA 4.0 via Wikimedia Commons Thedofc, Public domain, via Wikimedia Commons

Ask yourself these questions

  1. What technology do you see in the rooms above (imagine you can see a tutorial being run in both)?
  2. What is the nature of the work you and your students do during the tutorial?
  3. Which of the rooms above would be “best” for your tutorial? Why?
  4. How could the rooms above be modified to be better for tutorials? Why?

What is the (educational) technology in the room?

Assuming we’re looking at a tutorial being carried out in both images. What would be on your list of technology being used?

A typical list might include chairs, tables, computers, whiteboards (interactive/smart and static), clock, notice boards, doors, windows, walls, floors, cupboards, water bottles, phones, books, notepads etc.

You might add more of the technologies that you and your students brought with you. Laptops, phones, backpacks etc. What else?

How do you delineate between what is and isn’t technology? How would you define technology?

Defining technology

Dron (2021) starts by acknowledging that this is difficult. That most definitions of technology are vague, incomplete, and often contradictory. He goes into some detail why. Dron’s definition draws on Arthur’s (2009) definition of technlogy as (emphasis added)

the orchestration of phenomena for some purpose (Dron, 2021, p. 1)

Phenomena includes stuff that is “real or imagined, mental or physical, designed or existing in the natural world” (Dron, 2021, p. 2). Phenomena can be seen as belonging to physics (materials science for table tops), biology (human body climate requirements), chemistry etc. Phenomena can be: something you touch (the book you hold); another technology (the book you hold); a cognitive practice (reading); and, partially or entirely human enacted (think/pair/share, organisational processes etc).

For Arthur, technological evolution comes from combining technologies. The phenomena being orchestrated in a technology can be another technology. Writing (technology) orchestrates language (technology) for another purpose. A purpose Socrates didn’t much care for. Different combinations (assemblies) of technologies can be used for different purposes. New technologies are built using assemblies of existing technologies. There are inter-connected webs of technologies orchestrated by different people for different purposes.

For example, in the classrooms above manufacturers of furniture orchestrated various physical and material phenomena to produce the chairs, desks and other furniture. Some other people – probably from institutional facilities management – orchestrated different combinations of furniture for the purpose of designing cost efficient and useful tutorial rooms. The folk designing the computer lab had a different purpose (provide computer lab with desktop computers) than the folk designing the classroom (provide a room that can be flexibly re-arranged). Those different purposes led to decisions about different approaches to orchestration of both similar and different phenomena.

When the tutorial participants enter the room they start the next stage of orchestration for different, more learning and teaching specific purposes. Both students and teachers will have their own individual purposes in mind. Purposes that may change in respone to what happens in the tutorial. Those diverse purposes will drive them to orchestrate different phenomena in different ways. To achieve a particular learning outcome, a teacher will orchestrate different phenomena and technology. They will combine the technologies in the room with certain pedagogies (other technologies) to create specific learning tasks. The students then orchestrate how the learning tasks – purposeful orchestrations of phenomena – are adapted to serve their individual purposes.

Some assemblies of technologies are easier to orchestrate than others (e.g. the computers in a computer lab can be used to play computer games, rather than “learning”). Collaborative small group pedagogies would probably be easier in the classroom, than the computer lab. The design of the furniture technology in the classroom has been orchestrated with the purpose of enabling this type of flexibility. Not so the computer lab.

For Dron, pedagogies are a technology and education is a technology. For some,

Them's fighting words

What is educational technology?

Dron (2021) answers

educational technology, or learning technology, may tentatively be defined as one that, deliberately or not, includes pedagogies among the technologies it orchestrates.

Consequently, both the images above are examples of educational technologies. The inclusion of pedagogies in the empty classroom is more implicit than in the computer lab which shows people apparently engaged in a learning activity. The empty classroom implicitly illustrates some teacher-driven pedagogical assumptions in terms of how it is laid out. With the chairs and desks essentially in rows facing front.

The teacher-driven pedagogical assumptions in the computer lab are more explicit and fixed. Not only because you can see the teacher up the front and the students apparently following along. But also because the teacher-driven pedagogical assumptions are enshrined in the computer lab. The rows in the computer lab are not designed to be moved (probably because of the phenomena associated with desktop computers, not the most moveable technologies). The seating positions for students are almost always going to be facing toward the teacher at the front of the room. There are even partitions between each student making collaboration and sharing more difficult.

The classroom, however, is more flexible. It implicitly enables a number of different pedagogical assumptions. A number of different orchetrations of different phenomena. The chairs and tables can be moved. They could be pushed to sides of the room to open up a space for all sorts of large group and collaborative pedagogies. The shapes of the desks suggest that it would be possible to push four of them together to support small group pedagogies. Pedagogies that seek to assemble or orchestrate a very different set of mental and learning phenomena. The classroom is designed to be assembled in different ways.

But beyond that both rooms appear embedded in the broader assembly of technology of formal education. They appear to be classrooms within the buildings of an educational institution. Use of these classrooms are likely scheduled according to a time-table. Scheduled classes are likely led by people employed according to specific position titles and role descriptions. Most of which are likely to make some mention of pedagogies (e.g. lecturer, tutor, teacher).

Technologies mediate all formal education and intentional learning

Dron’s (2021) position is that

All teachers use technologies, and technologies mediate all formal education (p. 2)

Everyone involved in education has to be involved in the orchestration of new assemblies of technology. e.g. as you enter one of the rooms above as the teacher, you will orchestrate the available technologies including your choice of explicit/implicit pedagogical approaches into a learning experience. If you enter one of the rooms as the learner, you will orchestrate the assembly presented to you by the teacher and institution with your technologies, for your purpose.

Dron does distinguish between learning and intentional learning. Learning is natural. It occurs without explicit orchestration of phenomena for a purpose. He suggests that babies and non-human entities engage in this type of learning. But when we start engaging in intentional learning we start orchestrating assemblies of phenomena/technologies for learning. Technologies such as language, writing, concepts, models, theories, and beyond.

Use and particpation: hard and soft

For Dron (2021) students and teachers are “not just users but participants in the orchestration of technologies” (p. 3).

The technology that is the tutorial you are running, requires participation from both you and the students. For example, to help organise the room for particular activities, use the whiteboard/projector to show relevant task information, use language to share a particular message, and use digital or physical notebooks etc. Individuals perform these tasks in different ways, with lesser or greater success, with different definitions of what is required, and with different preferences. They don’t just use the technology, the participate in the orchestration.

Some technologies heavily pre-deterimine and restrict what form that participation takes. For example, the rigidity of the seating arrangements in the computer lab image above. There is very limited capacity to creatively orchestrate the seating arrangement in the computer lab. The students participation is largely (but not entirely) limited to sitting in rows. The constraints this type of technology places on our behaviour leads Dron to label them as hard technologies. But even hard technologies can orchestrated in different ways by coparticipants. Which in turn lead to different orchestrations.

Other technologies allow and may require more active and creative orchestration. As mentioned above, the classroom image includes seating that can be creatively arranged in different ways. It is a soft technology. The additional orchestration that soft technologies require, requires from us additional knowledge, skills, and activities (i.e additional technology) to be useful. Dron (2021) identifies “teaching methods, musical instruments and computers” as further examples of soft technologies. Technologies that require more from us in terms of orchestration. Soft technologies are harder to use.

Hard is easy, soft is hard

Hard technologies typically don’t require additional knowledge, processes and techniques to achieve their intended purpose. What participation hard technologies require is constrained and (hopefully) fairly obvious. Hard technologies are typically easy to use (but perhaps not a great fit). However, the intended purpose baked into the hard technology may not align with your purpose.

Soft technologies require additional knowledge and skills to be useful. The more you know the more creatively you can orchestrate them. Soft technologies are hard to use because they require more of you. However, the upside is that there is often more flexibility in the purpose you can achieve with soft technologies.

For example, let’s assume you want to paint a picture. The following images show two technologies that could help you achieve that purpose. One is hard and one is soft.

Hard is easy Soft is hard
Aleksander Fedyanin CC0, via Wikimedia Commons Small easel with a blank canvas CC0

Softness is not universally available. It can only be used if you have the awareness, permission, knowledge, and self-efficacy necessary to make use of it. Since I “know” I “can’t paint”, I’d almost certainly never even think of using of a blank canvas. But then if I’m painting by numbers, then I’m stuck with producing whatever painting has been embedded in this hard technology. At least as long as I expect the hardness. Nor is hard versus soft a categorisation, it’s a spectrum.

As a brand new tutor entering the classroom shown above, you may not feel confident enough to re-arrange the chairs. You may also not be aware of certain beneficial learning activites that require moving the chairs. If you’ve never taught a particular tutorial or topic with a particular collection of students, you may not be aware that different orchestrations of technologies may work better.

Hard technologies are first and structural

Harder technologies are structural. They funnel practice in certain ways. Softer technologies tend to adapt to those funnels, some won’t be able to adapt. The structure baked into the hard technology of the computer lab above makes it difficult to effectively use a circle of voices activity. The structure created by hard technologies may mean you have to consider a different soft technology.

This can be difficult because hard technologies become part of the furniture. They become implicit, invisible and even apparently natural parts of education. The hardness of the computer lab above is quite obvious, especially the first time you enter the room for a tutorial. But what about the other invisible hard technologies embedded into the web technologies that is formal education.

You assemble the tutorial within a web of other technologies. As the number of hard technologies and interconnections between hard technologies increases, the web in which you’re working becomes harder to change. Various policies, requirements and decisions are made before you start assembling the tutorial. You might be a casual paid for 1 hour to take a tutorial in the computer lab shown above on Friday at 5pm. You might be required to use a common, pre-determined set of topics/questions. To ensure a common learning experience for students across all tutorials you might be required to use a specific pedagogical approach.

While perhaps not as physically hard as the furniture in the computer lab, these technologies tend to funnel practice toward certain forms.

Education is a coparticipative technological process

For Dron (2021) education is a coparticipative technological process. Education – as a technology – is a complex orchestration of different nested phenomena for diverse purposes.

How it is orchestrated and for what purposes are inherently situated, socially constructed, and ungeneralizable. While the most obvious coparticipants in education are students and teachers there are many others. Dron (2021) provides a sample, including “timetablers, writers, editors, illustrators of textbooks, creators of regulations, designers of classrooms, whiteboard manufacturers, developers and managers of LMSs, lab technicians”. Some of a never ending list of roles that orchestrate some of the phenomena that make up the technologies that teachers and students then orchestrate to achieve their diverse purposes.

Dron (2021) argues that how the coparticipants orchestrate the technologies is what is important. That the technologies of education – pedagogies, digital technologies, rooms, policies, etc. – “have no value at all without how we creatively and responsively orchestrate them, fuelled by passion for the subject and process, and compassion for our coparticipants” (p. 10). Our coparticipative orchestration is the source of the human, socially constructed, complex and unique processes and outcomes of learning. More than this Dron (2021) argues that the purpose of education is to both develop our knowledge and skills and to encourge the never-ending development of our ability to assemble our knowledge and skills “to contribute more and gain more from our communities and environments” (p. 10)

Though, as a coparticipant in this technological process, I assume I could orchestrate that particular technology with other phenemona to achieve a different purpose. e.g. if I were a particular type of ed-tech bro, then profit might be my purpose of choice.

Possible questions, applications, and implications

Dron (2021) applies his definition of educational technology to some of the big educational research questions including: the no significant different phenomena; learning styles; and the impossibility of replication studies for educational interventions. This produces some interesting insights. My question is whether or not Dron’s definition can be usefully applied to my practitioner experience with educational technology within Australian Higher Education. This is a start.

At this stage, I’m drawn to how Dron’s definition breaks down the unhelpful duality between technology and pedagogy. Instead, it positions pedagogy and technology as “just” phenomena that the coparticipants in education will orchestrate for their purposes. Echoing the sociomaterial and postdigital turns. The notions of hard and soft technologies and what they mean for orchestration also seem to offer an interesting lens to understand and guide institutional attempts to improve learning and teaching.

Pulling apart Dron’s (2021) definition

the orchestration of phenomena for some purpose (Arthur, 2009, p. 51)
seems to suggest the following questions about L&T as being important
1. Purpose: whose purpose and what is the purpose?
2. Orchestration: how can orchestration happen and who is able orchestrate?
3. Phenomena: what phenomena/assemblies are being orchestrated?

Questions that echo Fawn’s (2020) argument using a postdigital perspective to argue against the pedagogy before technology purpose and landing on the following

(context + purpose) drives (pedagogy [ which includes actual uses of technology])

Withi this in mind, designing a tutorial in one of the rooms would start with the content and purpose. In this case the context is the web of existing technologies that have led you and your students being in the room ready for a tutorial. The purpose includes the espoused learning goals of the tutorial, but also the goals of all the other participants, including those that emerge during the orchestration of the tutorial. This context and purpose is then what ought to drive the orchestration of various phenomena (which Fawn labels “pedagogy”) for that diverse and emergent collection of purposes.

Suggesting that it might be useful if the focus for institutional attempts to improve learning and teaching aimed to improve the quality of that orchestration. The challenge is that the quality of that orchestration should be driven by context and purpose, which are inherently diverse and situated. A challenge which I don’t think existing institutional practices are able to effectively deal with. Which is perhaps why discussions of quality learning and teaching in higher education “privileges outcome measures at the expense of understanding the processes that generate those outcomes” (Ellis and Goodyear, 2019, p. 2).

It’s easier to deal with abstract outcomes (very soft, non-specific technologies) than with the situated and contexual diversity of specifics and how to help with the orchestration of how to achieve those outcomes. In part, because many of the technologies that contribute to institutional L&T are so hard to reassemble. Hence it’s easier to put the blame on teaching staff (e.g. lack of teaching qualifications or digital fluency), than think about how the assembly of technologies that make up an institution should be rethought (e.g. this thread).

More to come.


Arthur, W. B. (2009). The Nature of Technology: What it is and how it evolves. Free Press.

Dron, J. (2021). Educational technology: What it is and how it works. AI & SOCIETY.

Fawns, T. (2019). Postdigital Education in Design and Practice. Postdigital Science and Education, 1(1), 132–145.

Random meandering notes on “digital” and the fourth industrial revolution

In the absence of an established workflow for curating thoughts and resources I am using this blog post to save links to some resources. It’s also being used as an initial attempt to write down some thoughts on these resources and beyond. All very rough.

Fourth industrial revolution

This from the world economic forum (authored by Klaus Schwab, ahh, who is author of two books on shaping the fourth industrial revolution) aims to explain “The Fourth Industrial Revolution: what it means, how to respond”. If offers the following description of the “generations” of revolution

The First Industrial Revolution used water and steam power to mechanize production. The Second used electric power to create mass production. The Third used electronics and information technology to automate production. Now a Fourth Industrial Revolution is building on the Third, the digital revolution that has been occurring since the middle of the last century. It is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres.

Immediate reaction to that is that the 3rd revolution – with its focus on electronics and information technology – missed a trick with digital technology. It didn’t understand and leverage the nature of digital technologies sufficiently. In part, this was due to the limited nature of the available digital technology, but also perhaps due to the failure of a connection between the folk who really knew this and the folk trying to do stuff with digital technology.

The WEF post argues that “velocity, scope and systems impact” are why this fourth generation is distinct from the third. They could be right, but again I wonder if ignorance of the nature of digital technology might be a factor?

The WEF argues about the rapid pace of change and how everything is being disrupted. Which brings to mind arguments from Audrey Watters (and I assume others) about how, actually, it’s not all that rapid.

It identifies the possibility/likelihood of inequality. Proposes that the largest benefits of this new revolution (as with others?) accrues to the “providers of intellectual and physical capital – the innovators, shareholders and investors”.

Points to disquiet caused by social media and says more than 30% of the population accesses social media. However, the current social media is largely flawed and ill-designed, it can be done better.

Question: does an understanding of the nature of digital technology help (or is it even required) for that notion of “better”? Can’t be an explanation for all of it, but some? Perhaps the idea is not that you need only to truly know the nature of digital technology, or know only the details of the better learning, business, etc you want to create. You need to know both (with a healthy critical perspective) and be able to fruitfully combine.

Overall, much of this appears to be standard Harvard MBA/Business school like.

The platform economy – technology-enabled platforms – get a mention which also gets a mention in the nascent nature of digital technology stuff I worked on a couple of years ago. Platforms are something the critical perspective has examined, so I wonder if this belongs in the NoDT stuff?

Links to learning etc.

I came to this idea from this post from a principal come consultant/researcher around leading in schools. It’s a post that references this on building the perfect 21st Century worker as apparently captured in the following infographic.

Which includes the obligatory Digital skills which are listed in article as (emphasis added)

  • Basic digital literacy – ability to use computers and Internet for common tasks, like emailing
  • Workplace technology – using technologies required by the job
  • Digital learning – using software or online tools to learn new skills or information
  • Confidence and facility learning and using new technologies
  • Determining trustworthiness of online information

Talk about setting the bar low and providing a horrendous example of digital literacy, but then it does tend to capture the standard nature of most attempts at digital literacy I’ve seen, including:

  • A focus on using technology as is, rather than being able to renovate and manipulate it.
  • Revealing a ignorance of basic understanding. e.g. “software or online tools”, aren’t the “online tools” also software?
  • Continuing the medium divide, i.e. online information or online tools are somehow different from all the other information and tools I use?

(Not to mention that the article uses an image to display the bulleted list above, not text)

What if our digital technologies were protean?

On Friday the 30th September 2016 I will present the paper – What if our digital technologies were protean? Implications for computational thinking, learning, and teaching – co-written by Elke Schneider and I at the ACCE’2016 conference.

Other resources include:

  • A 1 question poll; and
    An attempt to explore whether people experience their organisational information systems as protean or not.If you haven’t already, do please take the time to complete the poll.
  • Stories of digital modification.
    A copy of the Google doc we originally used to gather the data for the paper. This data was then analysed for themes.


Not for the first time, the transformation of global society through digital technologies is driving an increased interest in the use of such technologies in both curriculum and pedagogy. Historically, the translation of such interest into widespread and effective change in learning experiences has been less than successful. This paper explores what might happen to the translation of this interest if the digital technologies within our educational institutions were protean. What if the digital technologies in schools were flexible and adaptable by and to specific learners, teachers, and learning experiences? To provide initial, possible answers to this question, the stories of digital technology modification by a teacher educator and a novice high school teacher are analysed. Analysis reveals that the modification of digital technologies in two very different contexts was driven by the desire to improve learning and/or teaching by: filling holes with the provided digital technologies; modelling to students effective practice with digital technologies; and, to better mirror real world digital technologies. A range of initial implications and questions for practitioners, policy makers, and researchers are drawn from these experiences. It is suggested that recognising and responding to the inherently protean nature of digital technologies may be a key enabler of attempts to harness and integrate digital technologies into both curriculum and pedagogy.

Any pointers to an old, ancient game?

Way back in 1986 I started studying undergraduate computer science at the University of Queensland. One of our first year programming assignments was to use the fancy, new Macintosh computers to add some code to a game.  I’m looking for pointers to the name of the game and any online resources about it. A working version on some contemporary platform would be great.

Any help more than welcome.

The game

The game was played with a grid. Typically 4 by 4 grid that looked something like this.

Grid 001

The idea is that there were random mirrors hidden throughout the grid. The aim of the game was to figure out what type of mirrors were located where within the grid. To do this you had a flashlight that you could shine through one of the holes on the outside. The light would exit the grid at another location, depending on the location and type of mirrors it would encounter. A bit like this
grid 002

There were three types of mirrors. Two diagonal mirrors / and ; and, a X mirror.  The diagonal mirrors would change the direction of the light depending on how the light struck the mirror. The X mirror would direct the light back the way it came.

The following image shows one potential layout of mirrors to explain how the light behaved in the above image.

Grid 003

The light travels straight ahead until it hits the first diagonal mirror. This mirror causes the to change direction directly up. Where it immediately hits another diagonal mirror which send the light traveling right again until it exits the grid.

Nature of digital technology? Part 2 – expansion

@damoclarky has commented on yesterday’s Part 2 post. A comment that’s sparked a bit of thinking. I’ve moved my length response into this post, rather than as a reply to the comment.

What is it? Stable or unstable?

@damoclarky writes

There also appears (at least to me) to be an irony in your blog post. On the one hand, we have technology as unstable, with constant change occurring such as Apple iOS/Phone updates, or 6monthly Moodle releases. Then on the other, we have:

“… commonplace notions of digital technologies that underpin both everyday life and research have a tendency to see them “as relatively stable, discrete, independent, and fixed” (Orlikowski & Iacono, 2001, p. 121).”

Part of the argument I’m working toward is that how people/organisations conceptualise and then act with digital technology doesn’t align or leverage the nature of digital technology. This lack of alignment causes problems and/or lost opportunities.  This is related to the argument that Orlikowski & Iacono make as they identify 5 different views of technology, illustrate the differences and argue for the importance of theorising the “IT artifact”.

The “relatively stable, discrete, independent, and fixed” view of technology is one of the views Orlikowsi & Iacono describe – the tool view. There are other views and what I’m working on here is a somewhat different representation.  I’m actually arguing against that tool view.  The discrepancy between the “relatively stable, discrete, independent, and fixed” view of digital technology and the unstable and protean nature of digital technology is evidence (for me) of the problem I’m trying to identify.

Actually, as I’m writing this and re-reading Orlikowski and Iacono it appears likely that the other nature of digital technology described in the part 2 post – opaque – contributes to the tool view. Orlikowski and Iacono draw on Latour to describe the tool view as seeing technologies as “black boxes”. Which aligns with the idea of digital technologies as being increasingly opaque.

Stable but unstable

For most people the tools they use are black boxes.  They can’t change them. They have to live with what those tools can or can’t do. But at the same time they face the problem of those tools changing (upgrades of Moodle, Microsoft Office etc), of the tools being unstable. But even though the tools change, the tools still remain opaque to them, they still remain as black boxes.  Black boxes that the person has to make do with, they can’t change it, they just have to figure out how to get on.

Perceptions of protean

Is it just perception that technology is not protean? There is a power differential at play. Who owns technology? Do you really “own” your iPhone? What about the software on your iPhone? What controls or restriction exist when you purchase something? What about your organisation’s OSS LMS software? It is very opaque, but who has permissions to change it?

Later in the series the idea of affordances will enter the picture. This will talk a bit more about how the perception of a digital technology being protean (or anything else) or not does indeed depend on the actor and the environment, not just the nature of the digital technology.

But there’s also the question of whether or not the tool itself is protean. Apple is a good example. Turkle actually talks about the rise of the GUI and the Job’s belief at Apple of controlling the entire experience as major factors in the increasing opacity of digital technology. While reprogrammability is a fundamental property of digital technology the developers of digital technology can decide to limit who can leverage that property. The developers of digital technology can limit the protean nature of digital technology.

In turn the organisational gate keepers of digital technology can further limit the protean nature of digital technology. For example, the trend toward standard course sites within  University run LMS as talked about by Mark Smithers.

But as you and I know, no matter how hard they try they can’t remove it entirely. The long history of shadow systems, workarounds, make-work and kludges (Koopman & Hoffman, 2003) spread through the use of digital technologies (and probably beyond). For example, my work at doing something with the concrete lounges in my institution’s LMS. But at this stage we’re starting to enter the area of affordances etc.

The point I’m trying to make is that digital technologies can be protean. At the moment, most of the digital technologies within formal education are not. This is contributing to formal education’s inability to effectively leverage digital technology.

Blackboxes, complexity and abstraction

Part of the black box approach to technology is to deal with complexity. Not in terms of complexity theory, but in terms of breaking big things into smaller things, thus making them easier to understand. This is a typical human approach to problem solving. If we were to alter the opacity of technological black boxes, how much complexity can we expect educators to cope with in then being able to leverage their own changes?

When I read Turkle in more detail for the first time yesterday, this was one of the questions that sprung to mind. Suchman is talking about being able to perceive the bare technology as being transparent, but even as she does this she mentions

When people say that they used to be able to “see” what was “inside” their first personal computers, it is important to keep in mind that for most of them there still remained many intermediate levels of software between them and the bare machine. But their computer system encouraged them to represent their understanding of the technology as knowledge of what lay beneath the screen surface. They were encouraged to think of understanding as looking beyond the magic of the mechanism (p. 23).

She then goes onto argue how the rise of the GUI – especially in the Macintosh – encourage people to stay on the surface. To see the menus, windows and icons and interact with those.  To understand that clicking this icon, that menu, and selecting this option led to this outcome without understanding how this actually worked.

The problem I’m suggesting here isn’t that people should know the details of the hardware, or the code that implements their digital technology. But that they should go beyond the interface to understand the model used by the digital technology.

The example I’ll use in the talk (I think) will be the Moodle assignment activity. I have a feeling (which could be explored with research) that most teachers (and perhaps learners) are stuck at the interface. They have eventually learned which buttons to push to achieve their task. But they have no idea of the model used by the Moodle assignment activity because the training they receive and the opaque nature of the interface to the Moodle assignment activity doesn’t help them understand the model.

How many teaching staff using the Moodle assignment activity could define and explain the connections between availability, submission types, feedback types, submission settings, notifications, and grade? How many could develop an appropriate mental model of how it works?  How many can then successfully translate what they would like to do into how the Moodle assignment activity should be configured to help them achieve those goals?

What about the home page for a Moodle course site? How much of the really poorly designed Moodle course home pages is due to the fact that the teachers have been unable to develop an effective mental model of how Moodle works because of the opaque nature of the technology?

How many interactive white boards are sitting unused in school classrooms because the teacher doesn’t have a mental model of how it works and thus can’t identify the simple fix required to get it working again?

I imagine that the more computational thinking a teacher/learner is capable of, the more likely it is that they have actively tried to construct the model behind that tool, and subsequently the more able they are to leverage the Moodle assignment activity to fit their needs.  The more someone sees a digital technology as not opaque and as protean, the more likely I think that they will actively try to grok the model underpinning the digital technology.

This isn’t about delving down in the depths of the abstraction layer. It’s just trying to see beyond the opaque interface.

Another interesting research project might be to explore if modifying the interface of a digital technology to make it less opaque – to make the model underpinning the digital technology clearer to the user – would make it easier to use and eventually improve the quality of the task they wish to complete?  e.g. would it improve the quality of learning and teaching with digital technology?

Can you do anything? How?

Without sounding too dramatic (or cynical), without industry-wide changes to how digital technology is viewed, are attempts to address the issues outlined in your blog post futile?

How do you bring about industry-wide change in attitude and thinking?

The funny thing is that significant parts of the digital technology industry is already moving toward ideas related to this.Increasingly what software developers – especially within organisations – are doing is informed by the nature of digital technologies outlined here. But that hasn’t quite translated into formal education insitutions. It is also unclear just how much of this thinking on the part of software developers has informed how they think about what the users of their products can do. But in some cases, the changes they are making to help them leverage the nature of digital technologies are making it more difficult, if not impossible, to prevent their users from making use of it.

For example, both you and I know that the improvements in HTML have made it much easier to engage in screen scraping. The rise of jQuery has also made it much easier to make changes to web pages in tools like Moodle. But at the same time you get moves to limit this (e.g. the TinyMCE editor on Moodle actively looking to hobble javascript).

This is something that will get picked up more in later posts in this series.

So it’s going to happen, it’s going to be easy, but I do think it’s going to get easier.


Koopman, P., & Hoffman, R. (2003). Work-arounds, make-work and kludges. Intelligent Systems, IEEE, 18(6), 70-75.

The nature of digital technology? Part 2

This is a followup to yesterday’s Part 1 post and a continuation of an attempt to describe the nature of digital technology and to think about what this might reveal about how and what is being done by formal education has it attempts to use digital technology for learning and teaching. This post moves from the fundamental properties of digital technologies (yesterday’s focus) to what some suggest is that nature of digital technologies.

Note: this is not the end of this series. There’s a fair bit more to go (e.g. this is all still focused on a single black box/digital technology, it hasn’t touched on what happens when digital technology becomes pervasive). I’m not entirely comfortable with the use of “nature” at this level, but the authors I’m drawing on use that phrase.

Recap and revision

Yesterday’s post aimed to open up the black box of digital technology a touch by explaining the two fundamental properties (data homogenization and reprorammability) of digital technology proposed by Yoo, Boland, Lyytinen, and Majchrzak (2012).  This was original represented using this image.

Fundamental Properties

I don’t think the image makes the point that these are fundamental properties of the black box, the digital technology. Hence, the following revised image. The idea being is that data homogenization and reprogrammability are properties that are “baked into” digital technology.  Identifying these properties has opened up the black box a little. This is going to be useful as I attempt to develop the model of digital technology further.
Fundamental Properties embedded

Nature of digital technologies

The aim here is to move up a bit from the fundamental properties to look at the “nature” of digital technologies.  As mentioned above, I’m not entirely happy with the use of the phrase “nature” at this level, but I don’t have a better term at the moment, and I’m drawing on Koehler and Mishra (2009) here who argued (emphasis added)

By their very nature, newer digital technologies, which are protean, unstable, and opaque, present new challenges to teachers who are struggling to use more technology in their teaching. (p. 61)

As they argue the combination of protean, unstable, and opaque makes the use of digital technology by teachers (and others) difficult. The following seeks to expand and explore that a bit more.

The following representation (I’m not a designer by any stretch of the imagination) is attempting to illustrate that this “nature” of digital technology sits above (or perhaps build upon or become possible due to) the fundamental properties introduced in the last post.

Nature of Digital Technology


In this context, Koehler and Mishra (2009) define unstable and “rapidly changing” (p. 61). Which version of the iPhone (insert your preference) do you have? The combination of data homogenization and reprogrammability mean that digital technologies can be changed, and other external factors tend to make sure that they do. Commercial pressures mean that consumer digital technologies keep changing. Other digital technologies change to improve their functionality.

But beyond that is the argument that digital technology shows exponential growth. Bigum (2012) writes

To most, the notion of an exponential is something that belongs in a mathematic’s classroom or perhaps may somehow be related to home loan repayments. Exponential change is not something with which we have had to become familiar, despite the fact of Moore’s Law and other Laws that map the growth of various digi- tal technologies and which tell us that the price of various digital technologies is halving roughly every 18 months to 2 years and that their performance is doubling on about the same time scale….The fact is that the various digital technologies that end up in laptop computers, mobile phones, and an increasing number of things that we tend not to associate with computers, are still doubling their performance and halving their cost in fixed time periods, i.e. we are seeing exponential growth. (p. 32-33)


Koehler and Mishra (2009) draw on Turkle (1995) to define opaque as “the inner workings are hidden from users”. Turkle (1995) talks about people having “become accustomed to opaque technology”. Meaning that as the power of digital technologies have increased we no longer see the inner workings of the technology. She suggests that computers of the 1970s “presented themselves as open, ‘transparent’, potentially reducible to the underlying mechanisms”. Perhaps more importantly she argues that

their computer systems encouraged them to represent their understanding of the technology as knowledge of what lay beneath the screen surface. They were encouraged to think of understanding as looking beyond the magic to the mechanism. (p. 23)

Earlier this year, as part of an introductory activity, I asked students to find and share an image (or other form of multimedia) that captured how they felt about digital technologies. The following captures just some of the images shared, and also captures a fairly widespread consensus of how these pre-service educators felt about digital technology. I’m guessing that it resonates with quite a few people.
Perceptions of computers
The increasingly opaque nature of digital technology combined with our increasing reliance on digital technologies in most parts of our everyday life would seem to have something to do this sense of frustration. Ben-Ari and Yeshno (2006) found that people with appropriate conceptual models of digital technologies were better able to analyse and solve problems. While learners without appropriate conceptual models were limited to aimless trial and error. I suggest that it is the aimless trial and error, due to a inappropriate conceptual model of how a digital technology works, is what creates the feelings of frustration illustrated by the above image.


This is the characteristic that I’ve written the most about.  The following two paragraphs are from the first version of Jones and Schneider (2016).

The commonplace notions of digital technologies that underpin both everyday life and research have a tendency to see them “as relatively stable, discrete, independent, and fixed” (Orlikowski & Iacono, 2001, p. 121). Digital technologies are seen as hard technologies, technologies where what can be done is fixed in advance either by embedding it in the technology or “in inflexible human processes, rules and procedures needed for the technology’s operation” (Dron, 2013, p. 35). As noted by Selwyn and Bulfin (2015) “Schools are highly regulated sites of digital technology use” (p. 1) where digital technologies are often seen as a tool that is: used when and where permitted; standardised and preconfigured; conforms to institutional rather than individual needs; and, a directed activity. Rushkoff (2010) argues that one of the problems with this established view of digital technologies is that “instead of optimizing our machines for humanity – or even the benefit of some particular group – we are optimizing humans for machinery” (p. 15). This hard view of digital technologies perhaps also contributes to the problem identified by Selwyn (2016) where in spite of the efficiency and flexibility rhetorics surrounding digital technologies, “few of these technologies practices serve to advantage the people who are actually doing the work” (p. 5). Digital technologies have not always been perceived as hard technologies.

Seymour Papert in his book Mindstorms (Papert, 1993) describes the computer as “the Proteus of machines” (p. xxi) since the essence of a computer is its “universality, its power to simulate. Because it can take on a thousand forms and can serve a thousand functions, it can appeal to a thousand tastes” (p. xxi). This is a view echoed by Alan Kay (1984) and his discussion of the “protean nature of the computer” (p. 59) as “the first metamedium, and as such has degrees of freedom and expression never before encountered” (p. 59). In describing the design of the first personal computer, Kay and Goldberg (1977) address the challenge of producing a computer that is useful for everyone. Given the huge diversity of potential users they conclude “any attempt to specifically anticipate their needs in the design of the Dynabook would end in a disastrous feature-laden hodgepodge which would not be really suitable for anyone” (Kay & Goldberg, 1977, p. 40). To address this problem they aimed to provide a foundation technology and sufficient general tools to allow “ordinary users to casually and easily describe their desires for a specific tool” (Kay & Goldberg, 1977, p. 41). They aim to create a digital environment that opens up the ability to create computational tools to every user, including children. For Kay (1984) it is a must that people using digital technologies should be able to tailor those technologies to suit their wants, since “Anything less would be as absurd as requiring essays to be formed out of paragraphs that have already been written” (p. 57). For Richard Stallman (2014) the question is more fundamental, “To make computing democratic, the users must control the software that does their computing!” (n.p.).

Implications for formal education

The above – at least for me – opens up a range of questions about how formal education uses digital technology for learning and teaching. A small and rough list follows.

Unstable changes everything

If digital technologies are fundamentally different and if they are unstable (rapidly – even exponentially – changing) then everything will change.  Bigum (2012)

Taken together and without attempting to anticipate how any of these technologies will play out, it is nevertheless patently clear that doing school the way school has always been done or tweaking it around the edges will not prepare young people who will grow up in this world (p. 34)

Bigum (2012) then draws on this from Lincoln

The dogmas of the quiet past, are inadequate to the stormy present. The occasion is piled high with difficulty, and we must rise – with the occasion. As our case is new, so we must think anew, and act anew. We must disenthrall ourselves, and then we shall save our country

The increasing neo-liberal/corporatisation fetish within formal education on efficiency etc. appears to be placing an emphasis on refining what we already do. Dropping the dogmas of the quiet past would mean admitting that people had it wrong….etc.  It’s difficult to see how such change will happen.

Moving beyond recipe followers?

Since digital technology is increasingly opaque, it is increasingly difficult for people to develop conceptual models of how digital technology works. As a result, many people have developed recipes that they follow when using digital technology. i.e. they know that if they press this button, select that menu, and check that box this will happen. They don’t know why, they just know the recipe.

Increasingly, a lot of the training and documentation provided to help users use digital technologies are recipes. They are step-by-step examples (with added screen shots ) of the recipe to follow to achieve this specific goal. If they don’t have the recipe, or the recipe doesn’t work then they are stuck. They don’t have the conceptual models necessary to analyse and solve problems.

What can be done to digital technologies and the methods used to support them to help people develop better conceptual models? If you do that, does that improve the quality of learning and teaching with digital technology?

If your documentation and training is a collection of recipes, why aren’t you automating those recipes and building them into the technology? i.e. making use of the protean nature of digital technology?

What or whom drives the change? What is the impact?

My institution has adopted Moodle, an open source LMS. One of the benefits of open source is that it is meant to be more protean. It can change. The Moodle release calendar shows the aim of releasing a major upgrade of Moodle every six months. It appears that my institution aims to keep reasonably up to date with that cycle. This means that every 6 months a change process kicks in to make staff and students aware that a change is coming. It means that every 6 months or so it is possible that staff and students will find changes in how the system works. Changes they didn’t see the need for.

To make matters worse, since most people are recipe followers, even the most minor of changes cause confusion and frustration. Emotions that make people question why this change has been inflicted upon them. An outcome not likely to enhance acceptance and equanimity.

Perhaps if more of the changes being made responded to the experiences and needs of those involved, change might be more widely accepted. The problem is that because most institutional digital technologies aren’t that protean, changes can only be made by a small number of specific people who are in turn constrained by a hierarchical governance process. A situation that might lead a problem of starvation where the priority is given to  large-scale, institutional level changes, rather than changes beneficial to small numbers of specific situations.

Would mapping who and why changes are being made to the digital technologies reveal this starvation? How can institutional digital technologies be made more protean and more able to respond to the needs of individuals? What impact would that have on learning and teaching? Is this sort of change necessary to respond to exponential growth?

Opaque technology creates consumers, not producers

Kafai et al (2014) talk about the trend within schools of transforming “computer class” into the study of how to use applications such as  word processors and spreadsheets. Approaches which they argue

These technology classes promote an understanding of computers and software as black boxes where the inner workings are hidden to users. (p 536)

In contrast they argue that

working with e-textiles gives students the opportunity to grap- ple with the messiness of technology; taking things apart, putting them back together, and experimenting with the purposes and functions of technology make computers accessible to students

Which importantly has the effect of

by engaging learners in designing e-textiles, educators can encourage student agency in problem solving and designing with technologies. This work can disrupt the trend that puts students on the sidelines as consumers rather than producers of technology

Currently, most digital learning environment within formal education tend to lean towards being opaque and not protean. Does this contribute toward a cadre of learners and teachers that see themselves as consumers (victims?) of digital technologies for learning and teaching? Would the provision of a digital learning environment that is transparent and protean help encourage learner and teacher agency? Would this transform their role from consumer to producer? Would this improve the use of digital technology for learning and teaching within formal education?


Ben-Ari, M., & Yeshno, T. (2006). Conceptual Models of Software Artifacts. Interacting with Computers, 18(6), 1336–1350. doi:10.1016/j.intcom.2006.03.005

Kafai, Y. B., Fields, D. A., & Searle, K. A. (2014). Electronic Textiles as Disruptive Designs: Supporting and Challenging Maker Activities in Schools. Harvard Educational Review, 84(4), 532–556,563–565. doi:10.17763/haer.84.4.46m7372370214783

Koehler, M., & Mishra, P. (2009). What is Technological Pedagogical Content Knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70. Retrieved from

Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for Innovation in the Digitized World. Organization Science, 23(5), 1398–1408.

What is the nature of digital technology? Part 1

Formal education in most of its forms is still struggling to effectively harness digital technology to enhance and transform learning and teaching. Even with a history for 40+ years of various attempts. The reasons for this are numerous and diverse. The following is an attempt to look at one of the reasons. A reason, at least to me, which seems to have be somewhat ignored.

The technology. Does digital technology have a unique nature/set of capabilities/affordances that sets it apart from other types of technology? If so, what is it? What might understanding the nature of digital technology have to say about how formal education is attempting to use it to transform learning and teaching?

The following is a first attempt to frame some thinking that is moving towards a presentation I’ll be giving in a couple of weeks.  This is only the first step, there’ll be follow up posts over the coming week or two. These posts will aim to develop my own understanding of a model that aims to capture the nature of pervasive digital technology. It’s a model that will draw largely on the work of Yoo, Boland, Lyytinen, and Majchrzak (2012) combined with a few others (e.g. Papert, 1980; Kay, 1984; Mishra & Koehler, 2006). That model will then be used to look at current attempts within formal education to use digital technology for learning and teaching.

Views of Digital Technology

For most people digital technology is a black box. Regardless of what type of digital technology, it’s a black box.

DT black box
Orlikowski and Iacono (2001) label this the tool view of technology which

represents the common, received wisdom about what technology is and means. Technology, from this view is the engineered artifact, expected to do what its designers intend it to do. (p. 123)

They go onto cite work by Kling and Latour to describe this view and its’ limitations before going on to examine 4 other views of the IT artifact. The motivation for their work is that “The IT artifact itself tends to disappear from view, be taken for granted, or is presumed to be unproblematic once it is build and installed” (Orlikowski & Iacono, 2001 p. 121). They go proceed to describe 4 additional “broad metacategories” of the IT artifact “each representing a common set of assumptions about and treatments of information technology in IS research” (Orlikowski & Iacono, 2001 p. 123). Metacategories or views of technology that draw on a range of perspectives outside of their discipline such as Actor-Network Theory etc.

My attempt here at opening up the black box of digital technology perhaps best fits with Orlikowski & Iacono’s (2001) fourth view of technology – the computational view – where the interest is “primarily in the capabilities of the technology to represent, manipulate, store, retrieve, and transmit information, thereby supporting, processing, modeling, or simulating aspects of the world” (Orlikowski & Iacono, 2001 p. 127). My focus here is on trying to explore what is the unique nature of digital technology. Not as an end in itself, but as a starting point that will draw on (at least) the other four views of technology suggested by Orlikowski & Iacono (2011) in attempting to understand and improve the use of digital technology within formal education.

Fundamental properties of digital technology

Yoo, Boland, Lyytinen, and Majchrzak (2012) argue that the “fundamental properties of digital technology are reprogrammability and data homogenization” (p. 1398)
Fundamental Properties

Data homogenization

Whether a digital technology is allowing you to talk to friends via Skype (or smartphone or…); capture images of snow monkeys; listen to Charlie Parker; measure the temperature; analyse the the social interactions in a discussion forum; or, put your students to sleep as you read from your powerpoint slides (which they’re viewing via some lecture capture system) all of the data is represented as a combination of 0s and 1s. All the data is digital. Since all digital technologies deal with 0s and 1s, in theory at least, all digital technologies can handle all data.The content has been separated from the medium (Yoo, Henfridsson & Lyytinen, 2010).

Analog technologies, on the other hand, have a tight coupling between content and medium. If you had bought “Born in the USA” on a record, to play it on your Walkman you had to record it onto a cassette tape. Adding it as background to that video you recorded with your video camera involves another translation of the content from one medium to another.

Data homogenization is the primary reason why you – as per the standard meme – can now carry all of the following in your pocket.




It’s not just the content that is represented digitally with digital technology. Digital technology also stores digitally the instructions that tell it how and what to do. Digital technologies have a processing unit that will decode these digital technologies and perform the task they specify. More importantly those instructions can – in the right situations – be changed. A digital technology is reprogrammable. What a digital technology offers to the user does not need to be limited by its current function.

Questions  for formal education?

The above is but the first step in building a layered model for the nature of digital technology. The intent is that each layer should include a couple of questions related to how formal education is using digital technology. The following are a rough and fairly weak initial set. Really just thinking out loud.

Where is the convergence?

If data homogenisation is a fundamental property of digital technology, then why isn’t there more convergence within formal education’s digital technologies? Why is the information necessary for learning and teaching kept siloed in different systems?

When I’m answering a student question in the LMS, why do I need to spend 20 minutes heading out into the horrendous Peoplesoft web-interface to find out in which state of Australia the student is based?

Should we buy? Should we build?

I wonder if there is a large educational institution anywhere in the world that hasn’t at some stage, somewhat within the organisation had the discussion about whether they should buy OR build their digital technology? I wonder if there’s a large educational institution anywhere in the world that hasn’t felt it appropriate to lean heavily toward the buy (and NOT build) solution?

What is gained and/or lost by ignoring a fundamental property of digital technology?


Orlikowski, W., & Iacono, C. S. (2001). Research commentary: desperately seeking the IT in IT research a call to theorizing the IT artifact. Information Systems Research, 12(2), 121–134.

Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21(4), 724–735. doi:10.1287/isre.1100.0322

Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for Innovation in the Digitized World. Organization Science, 23(5), 1398–1408.

Digital technology ignorance and its implications for learning and teaching

Slides and abstract for the presentation can be found below.

A video recording of the presentation is also available.



Digital technology is increasingly a pervasive presence in contemporary society. The knowledge and skills required to utilise digital technologies are increasingly seen as necessary for both individuals and organisations, if they wish to become successful participants in and contributors to society. For the individuals and organisations involved in education there is a growing expectation that they are not only required to help learners develop the necessary digital technology knowledge and skills, but that they have the knowledge and skills to effectively use digital technology to fulfill that requirement. Recent history suggests that many individuals and institutions involved in education are struggling to fulfill this expectation (Bigum, 2012; Johnson, Adams Becker, Estrada, & Freeman, 2015; Masters, 2016; Mcleod & Carabott, 2016; OECD, 2015; Willingham, 2016).

There are numerous factors that contribute toward these on-going struggles. However, this talk will propose that ignorance of and the subsequent failure to harness the true nature of digital technology is a significant, under-examined, and in some cases deemed an unimportant factor (Kirschner, 2015). Drawing on a range of literature (Kay, 1984; Mishra & Koehler, 2006; Papert, 1993; Yoo, Boland, Lyytinen, & Majchrzak, 2012; Yoo, Henfridsson, & Lyytinen, 2010) this talk will develop a model for understanding the fundamental properties and unique affordances of digital technology. The talk will illustrate how this model can be used to identify and understand significant shortcomings with existing practice and research at all levels of education. Lastly, the talk will use the model to map out potentially, fruitful areas of future research around questions such as:

  • Why will growing up using digital technology everyday never be sufficient to make you a digital native?
  • Why might 88.5% of teachers and 74% of students in Auburn, Maine prefer laptops over iPads, and what might that say about the value of tablets as computing devices?
  • Why is the Moodle assignment activity so hard to use in my course and why does the provided documentation not help?
  • What’s next after the Learning Management System?
  • Why is the current push to embed the teaching of coding in primary schools likely to fail and what might be done about it?
  • How might an educational institution leverage the fundamental properties and unique affordances of digital technology to be “a leader in physical and digital higher education learning experiences geared to a diverse student constituency“?


Bigum, C. (2012). Schools and computers: Tales of a digital romance. In L. Rowan & C. Bigum (Eds.), Transformative Approaches to New Technologies and student diversity in futures oriented classrooms: Future Proofing Education (pp. 15–28). London: Springer.

Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2015). NMC Horizon Report: 2015 K-12 Edition. Austin, Texas.

Kay, A. (1984). Computer Software. Scientific American, 251(3), 53–59.

Kirschner, P. a. (2015). Do we need teachers as designers of technology enhanced learning? Instructional Science, 43(2), 309–322.

Masters, G. (2016). Five challenges in Australian School Education.

Mcleod, A., & Carabott, K. (2016). Students struggle with digital skills because their teachers lack confidence. The Conversation. Retrieved May 30, 2016, from

Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

OECD. (2015). Students, Computers and Learning: Making the Connection. Paris.

Papert, S. (1993). Mindstorms: Children, Computers and Powerful ideas (2nd ed.). New York, New York: Basic Books.

Willingham, D. (2016, May 15). The false promise of tech in schools: Let’s make chagrined admission 2.0. New York Daily News. Retrieved from

Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for Innovation in the Digitized World. Organization Science, 23(5), 1398–1408.

Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21(4), 724–735.

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