Maintaining your OLI - the problem

Earlier, I argued that online learner identities (OLIs) are pivotal in a learning ecology that is personal in that it takes the learner as its starting point and is social in that it puts this starting point at the centre of an online network of people with kindred interests (see more on this Berlanga and Sloep, 2011, Towards a Digital Learner Identity). Such an ecology thrives on the services with which it is populated. Such services come in different kinds, but these are the main categories:

• social services - they are services that intelligently match a learner with others in his or her social network; other learners come in a variety of roles, such as fellow learner, team buddy, coach, mentor, tutor, supporter, supervisor, assessor, etc., basically all the different roles teachers in ordinary formal education adopt, and a few more

• content services - they are services that match a learner's learning objectives or needs with content that could help fulfill those needs; such content will often be in the form of (preferably openly accessible) documents (explicit knowledge), but could also be in the form of implicit knowledge, only accessible by approaching the people who bear this knowledge.

The people in your social network are good candidates to fulfill the various roles in your learning ecology. And your search behaviour speaks to the things you want to learn as do, say, your blogs and wikipedia entries; they also reveal your level of expertise. Presumably, the more detailed the data about your network, about your search behaviour, your posts, tweets, etc., that is, the richer the description of your OLI, the better the social and learning services would be able to facilitate your learning. So, learning benefits from a rich OLI description.

Providing such a rich description, however, poses a privacy risk. The risk may be as grave as to result in identity theft, that is,  in somebody intentionally posing as some other person whose personal data have been stolen with the intention to harm that individual; or the risk may be moderate as when two similar but different individuals accidentally, without harmful intentions become mixed up. So the individual learner is faced with a dilemma. She should reveal everything about herself as this improves the learning experience, but she should reveal nothing at all to lower the risks involved with privacy loss. How can this dilemma be tackled? The answer is that a learner should be able to provide differential access rights to her OLI data: different groups of people get different rights. Thus, people whom one has grown to trust are provided with more rights that complete strangers. Also perhaps, people affiliated with a well-known educational institution are endowed with more rights. Etc. In this conception, controlling one's privacy is equivalent to controlling the access rights to one's data. In a next installment I will explain a schema for how this could in principle be achieved technically. However, and this is the topic of the present post, implementing any such solution which puts a user in control of her OLI data, is hard if not impossible to achieve in the current social web.

First, in the current social web data are provided freely. Web users provide them in exchange for the services that social web sites provide. So Google allows people to carry out searches, in return for the searcher's consent to Google to collect and compile a user profile, which furthers their commercial interests. And something similar goes for Facebook, Twitter, etc.  Although in principle you may decide not to agree with such schemes, in practice this is no more an option than disconnecting yourself from the electricity grid. If you want to search, you use Google, if you want to make online friends, you use Facebook, if you want to microblog, you use Twitter; etc. Second, the data are fragmented as they are scattered over various sites. This nature makes controlling them harder as you need to visit multiple sites. Moreover, sites such as Google, Facebook, etc. are walled gardens, they do not let your data escape, again because those data are the very foundation upon which their business rests. So, they are not just fragmented but your data are also deliberately kept out of your control. Clearly, in the face of this, no individual person really stands much of a chance to control his or her personal data, that is, ultimately also his or her privacy (see my earlier post on this issue).

Interestingly, even scarily if you think about it, the issue of privacy does not seem to bother the majority of the Internet users. The discussion on privacy occasionally flares up, for instance when privacy settings turn out to reveal more than previously as a consequence of a license update (Facebook) or when location data on private wifi networks turn out to have been collected and stored (Google). But the big picture of the massive amounts of data that already have been collected and stored, are used on a regular basis, fails to upset people. Wrongly so, as I have argued.

[adapted and updated December 29, 2011

Why we need the Internet to stay a Commons

Thanks to Sir Tim Berners-Lee's vision we have the hyperlinked online network that we call the Web, in which we share information and ever more engage socially with each other. Thanks to the American Department of Defence the Web runs on a distributed logical infrastructure, which gives it the character of a commons: something we use and own collectively, without a central authority to govern or dictate what goes on. This character has been under threat from two sides. The Media Industries who see their business model of 'selling culture in containers' jeopardized. They focus their grievances on the peer-to-peer networks in which content is freely shared. However, independent artists of all kinds who sell their creative products directly to their fans and customers, will in the long run be even more of a threat as they cut out the middleman that the Media Industry is. This is the real game changing event as for the first time it is feasible to also cater for the long tale of people's interests. The second threat comes from governments, which are naturally inclined to fear loss of control and use national security arguments to clamp down on an Open Net. The recent measures that various governments took to make life hard for the WikiLeaks site bear witness to this.

For good measure, I should rapidly add that existing intellectual property rights need to be respected, also on the Internet; and attempts to overthrow governments, threaten its institutions or plan terrorist attacks should be nipped in the buds, anywhere, so also on the Internet. However, this is not an all-or-nothing argument. Rather, of each measure taken benefits and drawbacks should be weighted against each other. Thus, enforcing the old model of selling music, films, books in containers that one pays for or attempting to impart such a model on Internet transactions (Digital Rights Management!) stifles innovation. It keeps transaction costs high and it disallows artists and independent small producers and publishers their seat at the table. Also, imparting too much government control on the Internet brings in its wake the Kafkaesque dangers of intransparant data aggregation, data exclusion and data distortion I discussed in my previous post.

Arguments more detailed than the ones I have given can be found in a recent paper by Yochai Benkler entitled WikiLeaks and the protect-ip Act: A New Public-Private Threat to the Internet Commons.  He also reveals how governments and the Media Industry indeed have joined forces in their attack on the openness of the Internet. According to him, an Open Internet, one which embraces the idea of a commons is not faultless but it offers numerous and large benefits. One of those is its support for democracy and freedom: a democracy only thrives if the populace is well educated and divergent opinions are allowed to be aired; another benefit is its support for innovation and welfare: creativity thrives in heterogeneous environments, where many people gather freely and talk openly, with whom they like, when they like. In a recent 15 minutes interview Benkler reiterates this all very succinctly and eloquently.


Finally, Internet as a Commons is also crucial for the innovation of education. In a world that needs people to be better educated and needs more of them, it is imperative to experiment with different models of learning and teaching. Even though there will always be room for formal learning as in schools and universities, this cannot be the whole story (see Tony Bates' recent blog on this). Experiments with forms of informal learning or combinations of both formal and informal learning are badly needed (see also this recent report on the Future of Learning). To the extent that these are networked - and I have argued in many blogs and papers they should be - only the Internet as a Commons offers enough room for experimentation. Open Educational Resources are a key element but run of course counter to the interests of the Media Industries. Individuals as the sole owners of their profiling data are essential (see my previous post), but runs counter to the interest of governments (who want privileged access) and the Social Media Industry (who want ownership themselves or at least give people a hard time themselves to exert ownership). Long-tail education offers unprecedented opportunities for personalization and customization, but only thrives if providers of such educational opportunities have few hurdles to take, that is, on a web that is as little regulated as feasible. The easiest and ultimately most rewarding way to do this, I believe, is to defend the current character of the Internet as a Commons, surely against attacks such as described by Benkler in his paper and interview.

Privacy and your online learner identity

This post is prompted by an article I happened to read in the Chronicle of Higher Education of May 15th, 2011 entitled Why privacy matters even if you have nothing to hide, written by Daniel Solove, a professor of law at George Washington University. It is a prequel to a book called Nothing to Hide. My interest in it stems from an article Adriana Berlanga and I wrote about online learner identities. The question we address there is how best to balance the need to know as much as you can about a lifelong learner to be able to offer him or her the best possible learning arrangements (in an online learning environment) with the justified worry that yielding all those data may easily invade that person's privacy.

Fundamental to our argument is the observation, made by many, that the online realm or cyberspace becomes ever more a place where we lead our social lives, also our live as a (lifelong) learner and worker. Consequently, we need to build online identities, which we dubbed a online learner identity in so far as that identity should allow us to 'live' in networked environments geared for learning and professional development (Learning Networks, if you like). However, since these identities are fragmented across the various social networking sites out there (Facebook, Google, Ning, LinkedIn, ...) it is difficult for an individual user to build, let alone maintain, such an identity. One needs to repeatedly update various sites and, even harder, one needs to imagine what the big picture of oneself is that emerges this way. So technical solutions may be attempted that allow data to be automatically exchanged between those sites. Perhaps a kind of dashboard that aggregates data from various sources is a good idea. (This assumes the hosting parties would allow that, which does not go without saying as sharing with such a dashboard site lowers traffic and thus is not in their interest.) Also, a learning perspective is needed to dictate what data the dashboard should collect. Past education, for instance, seems more important than the kinds of movies one likes.

However, there is another issue that is inextricably linked to these technical and learning-theoretical issue. It is whether we as users of such a dashboard do indeed want to aggregate our existing fragmented identities. It does not go without saying that we do. Facebook, for instance, once was a fun site only but increasingly has earned itself a bad reputation for revealing ever more data about its users without asking them explicitly beforehand. And every service Google offers us for free betrays Google's hunger for our (profiling) data. This should not come as a surprise, of course. Somebody should foot the bill for the services provided to us. It turns out that we ourselves do so by giving up our data for free, allowing the Facebooks and Googles of this world to make money through targeted advertising and selling of profiling data to third parties. But we need at least ask the question if this is the way we want it, for Facebook and Google but also for dashboard-like services that ostensibly only have the best intentions. At face value, this question is about privacy issues. Solove's paper shines an illuminating light on helping us understand it that way.

His point of departure is the often voiced argument that if you have nothing to hide, it is ok for the government to know anything there is to know about you. The counterargument is that this constitutes an invasion of your privacy. Parenthetically, in the discussion that follows the article someone rightly points out that privacy is a Human Right (number 12) granted to you by birth and that invasions thereof are a privilege that needs to be granted through proper argument, even by governments. However, to make the counterargument stick we need to understand what privacy is. Solove attempts to delineate the notion by using two metaphors, a quite ingenious move in my view. Some aspects of privacy are addressed by George Orwell in his Nineteen Eighty-Four novel, by describing the omnipresent state which watches and stores in huge databases our every step. This is the surveillance aspect of privacy. The other metaphor is discussed by Franz Kafka in his Der Prozess (The Trial). This is about someone who has to stand trial but has no idea what he is accused of nor is he allowed to have access to the accusations and the reasoning behind it. This aspect of privacy Solove calls information processing, it addresses the government as a bureaucracy, which lacks transparency and refuses to be accountable for what it does with those data. He then argues: the problems [with privacy invasions] are not just Orwellian but Kafkaesque. Government information-gathering programs are problematic even if no information that people want to hide is uncovered. In The Trial, the problem is not inhibited behavior but rather a suffocating powerlessness and vulnerability created by the court system's use of personal data and its denial to the protagonist of any knowledge of or participation in the process. The harms are bureaucratic ones—indifference, error, abuse, frustration, and lack of transparency and accountability.

So, one should not so much worry about the mere storage of data, that which George Orwell denounced, but about the subsequent processing of them in opaque ways, that which worried Franz Kafka so much. To unpack the processing, data aggregation is one way of data processing, 'the fusion of small bits of seemingly innocuous data'. Aggregation may be objected to since the picture of someone that emerges after aggregation is not apparent in the constituting bits. The whole is more than the sum of its parts, sums this up nicely. Exclusion, preventing people 'from having knowledge about how information about them is being used' and barring them 'from accessing and correcting errors in that data', is another way. Exclusion goes to the heart of the Kafka objection. Job applicants whose application was turned down because they were unable to remove online pictures taken of them taken in a moment of weakness understand the harm exclusion can do full well. This problem is exacerbated when secondary use of those data is made, as the route from misuse to the data source is now even harder to trace. Distortion is a third kind of data processing, meaning that, necessarily, stored data only show part of a personality, which may lead to a distorted picture of that person. When first impressions matter, as in job interviews, distortion can do much harm.

In the case of a learner's online identity, Adriana and I argued against the fragmentation of someone's identity across the various social media sites in existence. This is a variation of the distortion argument. Even if we admit that people may have good reasons to maintain several, separate online identities (one for work, one or more for your leisure activities), what such an identity should look like should be under the identified person's control and only his or her control. After all, only that person can oversee the degree and kind of allowable distortion. Thus, the practical argument we leveled against fragmentation proves to have a privacy aspect as well. This brings us to the exclusion argument. People need to have access to the data stored about them to correct those data, extend them, prune them, etc. In our paper, we offered a practical argument for this, arguing that people should be able to build an online identity qua learner that suits their learning and professional development best. This argument too turns out to have a privacy twist to it, being that control over one's data is a matter of principle (privacy) and not only convenience. And finally, the defragmentation that we argued for of course is a form of aggregation. However interesting the technical challenges may be to overcome defragmentation and however useful it may be from a learning perspective, doing so inevitably also impacts our privacy. That is the key value of Solove's argument.

Solove thus exposes the nothing-to-hide argument as too simplistic. Privacy is multifaceted, nothing to hide only addresses the data surveillance aspect of it, not the data processing aspect. Data processing itself is complex, encompassing such things as aggregation, exclusion and distortion. Any one of these impinges on efforts to arrive at the consolidated online learner identity we argued for in our paper. Solove, in focusing on debunking the nothing-to-hide argument, does not offer any solutions on how someone's privacy may be safeguarded against the aggregation, exclusion and distortion of their data. But perhaps this cannot be discussed in general terms, perhaps it can only be understood in the concrete case of, for instance, building a consolidated digital identity for learners. If so, his refined understanding of what privacy is about should help us do so. It should help us to reap the benefits of online learning while giving due attention to the privacy challenges that come in its wake.

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October 22, 2012. Note added after publication: It has come to my attention that there is an EU funded, 7th framework project that goes by the name of Trusted architecture for securely shared services (TAS3). I quote from their summary: TAS3 will develop and implement an architecture with trusted services to manage and process distributed personal information. [...] TAS3 will focus an instantiation of this architecture in the employability and e-health sector allowing users and service providers in these two sectors to manage the lifelong generated personal employability and e-health information of the individuals involved. This sounds like an architecture that should also work for online learner identities, even though TAS3 will focus on data in offline databases and we are more interested in online databases (behind social media interfaces). Second, the EIfEL team has published a blog post with the intriguing title: To create a trustworthy Internet respectful of our privacy, shouldn't we simply make our personal data public? Without going into detail, their solution is to spread your personal data over various sites, but anonymously. You as the owner keep a bundle of private keys through which you can grant access to those data in a piecemeal fashion. This way, you can allow whoever you want to access and disallow everybody else access. Quite ingenious, although I am not sure Facebook and Google would like the idea of only having uninformative bits and pieces of your personal profile data hidden behind an alias. Even so, Google just said are considering allowing aliases on their Google+ service.

Educational Data Mining Conference 2011, Eindhoven

Since is was practically around the corner and I'd been wanting to acquaint myself with the latest news on educational data mining for some time already, I decided to spend three days at the Educational Data Mining conference, which was held in Eindhoven, July 6 through 8, 2011. Having sat it all out, I have to say that my feelings are mixed, saw very good stuff and some work that makes you wonder. A couple of general observations first, then some details on a few papers and posters. A confession out the outset: I am interested in informal (non-formal) kinds of learning, so I was specifically on the lookout for uses of data mining that would foster this kind of learning.

First, the EDM community is heavily dominated by people of US extraction. That inevitably brings a bias in that 'educational' is surreptitiously being defined as 'in accordance with the US educational system'. This is not necessarily bad, but it is something to keep in mind. Second, and perhaps as a consequence of this, data mining seems almost congruent with intelligent tutoring systems. Even though the title of a paper may suggest something different, ITSs are never far away. Third, and most importantly in my view, the conference's take on what data there are to mine is a very narrow one. This is connected to their narrow view of what constitutes education: school-based, teacher-led formal learning, with no concept of other forms of learning. This may simply be a choice, which is already narrows down the field. However, it gets worse as within the confines of formal learning, their sole educational model is that of the teacher as the sage on the stage, who may be assisted by ITSs to relieve them from some of the drudgery of repeatedly having to answer the same questions. I am exaggerating, true, but not all that much. My main problem with this is that to the extent that EDM is successful, it acts as a conserving force, reinforcing received testing methods and having little eye for educational innovation. From which indeed follows that I do not see EDM as espoused in the conference as innovation of education, at best as innovative methods to support traditional forms of learning.

That being out of the way, there were several papers and posters of interest to be seen and heard at the conference. A few observations on just three of them. First, Kelly Wauters et al. from K.U. Leuven discussed a novel means of rating proficiency in their Monitoring Learners' Proficiency: Weight Adaptation in the ELO Rating System. They used a modified version of the ELO rating system that chess players use for this and apply it to rate proficiency on learning items. If you want to sequence learning items adaptively, you not only need to know how 'difficult' the items are, but also how good someone is at particular ones. That way, you can provide learners with items that in terms of their difficulty match their proficiency. Second, and breaking away from tradition, Worsley and Blikstein from Stanford also worry about proficiency or expertise. They wonder What is an Expert? and seek an answer in the use of Learning Analytics to identify emergent markers of expertise through automated speech, sentiment and sketch analysis. Thus they look at say speech utterances and sketches to acquire an impression of someone's expertise at a particular subject. Interestingly, both novices and experts reveal little lack of confidence, the former since they are sure not to know, the latter since they are sure they do know. Both (short) papers are fun for their innovativeness, they are also useful in the context of informal learning (in, say, Learning Networks) as they provide means to characterize learners' expertise and thus means better to help them.

Third and finally, there was a nice poster by Anna Lea Dyckhoff from the computer supported learning group, informatics at RWTH Aachen, practically our neighbours at OUNL. Although still in its infancy, she is developing a learning analytics toolkit (eLAT) that allows teachers to gauge their students interaction with the content in Personal Learning Environments. I am not sure whether the use of the term teacher in connection with a PLE is entirely fortunate - after all, if PLEs are really personal they must by definition also refer to informal learning situations in which the role of teachers is not self-evident. However, such toolkits are very valuable as they provide a means to help personal learners that self-guide their learning, or so I would hope. In this same vein, R. Pedraza-Perez et al. from Cordoba, Spain offer a Java desktop tool to mine Moodle log files, and García-Saiz et al. from Cantabria have built an E-Learning Webminer (EIWM) that, by discovering student's profiles, is intended to help them navigate and work in distance taught courses.

On professional development in Learning Networks

In the present day and age professionals cannot afford to stop learning after their graduation, they should continue to learn incessantly throughout their professional lives. They need to do so in order to secure their own continued employment, but also to ensure the viability of our modern knowledge society. The latter is of course a societal responsibility rather than a personal one. Where knowledge becomes obsolete at an increasing pace because of technological and societal innovation, knowledge workers need to yield an ample supply of new, relevant knowledge and share it widely with each other lest the innovation grinds to a halt. And innovation is needed for the continued economic health of modern, Western society.

These observation are not new, they have been made by several people in all walks of society, from academics to politicians, from educationalists to labour union representatives. However, it is not easy to unpack all that it implies. In particular, it is hard to ensure that ways are found to yield new and more knowledge. At first sight, it seems plausible to rely on the educational establishment for this - schools, colleges and universities. However, a moment’s reflection reveals that one cannot just the rigid structures that they represent to exercise sufficient flexibility to live up to these expectations.

Necessary Conditions
Because of the challenges our current society sets professionals, they can only be expected to develop themselves professionally if three conditions have been fulfilled. First, they need logistic flexibility, that allows them to learn wherever and whenever they want as well as to take charge of their own learning. Second, they not so much need set degree programmes, but rather agile learning opportunities. These should address their specific problem in exactly the right depth (level complexity), have exactly the right extent (size), and be offered in ways that are commensurate with their preferred learning modes. This one may call content flexibility. Third, the metaphor of knowledge transfer between someone who is in the know (a teacher) and others who are clean slates (the students) is inapt. Professionals are all experts in some way, be it all on slightly different topics and in differing degrees. So they alternate between the role of teacher and student, depending on what the topic is and who asks. This one may call didactic flexibility, the ability to see learning as a social process of knowledge creation and exchange.

This list of demands shows why traditional forms of learning with ‘sages on the stage’ who lecture in halls of brick-and-mortar building for one hour at weekly intervals do not work. There’s limited logistic flexibility as the institutional calendar dictates the students calendar, rather that the other way around. There’s no content flexibility as learning opportunities are packed in lectures, courses and curricula. And finally, there’s no didactic flexibility because teacher and learner are not roles but occupations. The rapid switching of roles that knowledge sharing and creation demand is significantly hindered this way. I do not claim that schools, colleges and universities are in principle unable to change and offer these needed flexibilities. But I do argue that it is notoriously hard for any institution significantly to alter its organisational structure and products, particularly if it comes to the kind of paradigmatic shift I believe is needed. It is my conviction that we need to approach things from the other end. We should not start with educational institutions as we know them and wonder how we can make them fit the demands of modern-day professionals. Rather, we should develop - conceptually first, practically later - a novel learning environment that does suit professional development. The question whether and, if so, how extant educational institutions could adopt this, is secondary. The learning environment sought, I call a Learning Network.

The nature of a Learning Network
Learning Networks may be defined as online social networks that have been designed specifically to facilitate professional development. They may be likened to the familiar online networks we all know, networks which form around such generic social networking applications as Hyves, Facebook, LinkedIn, but also around more specialist ones such as Delicious, Slideshare, Flickr, Academia or Yammer, to name a few. A Learning Network is a social network like these but different in that it is centred around a particular topic that lies at the heart of the interests of its users, i.e. professionals. It is important also to note that a Learning Network is different than a community of practice. Communities are relatively small (tenths of users), networks are larger, (hundreds of people). Community users share a common goal, network users share a common interest and typically have a diversity of different goals. Community members are strongly tied to each other, fellow network members maintain both strong and weak ties. Importantly, the weak ties allow them to connect with new people, thus accessing new knowledge. Communities of practice (and learning) may be part of a Learning Network. They arise if a group of users has a shared need (through their common goals) and vanish if that need disappears; they may overlap since users typically have a variety of goals. Thus a Learning Network user typically is a member of zero to several communities, the number changing as function of opportunity, need and demand. This way, a Learning Network becomes a dynamic collection of communities, that wax and wane, come to overlap and drift apart in response to the participants needs and wants. In such a community professional development (learning) and being professionally active can become two sides of the same coin.

Networked learning, what does it look like?
In a Learning Network, learning (professional development) takes place by accessing relevant resources. These are are in the first instance the Learning Network’s participants themselves. They are the primary sources of expertise. They then adopt a teaching role and direct fellow participants to (online) artefacts - such as texts, presentations, videos, blogs, Twitter feeds, shared bookmarks - relevant communities they participate in, or other experts they know. However, they will also act as providers of all kinds of support - as learning coaches, mentors, critical friends, business contacts. All of this is quite labour intensive, however. As explained, the potential of Learning Network lies in exploring the weak links between its participants. They are the as yet unknown sources of new knowledge and support. Being only weakly linked to them, participants do not know whom to contact for what. Broadcasting request for help to the entire Network of course would rapidly overload its participants, significantly decreasing their willingness to contribute. Participants therefore need to receive requests for expertise and support that fit their profile, and recommendations that fit their requests. This is achieved by equipping the Learning Network with a variety of request-and-recommend tools that support its participants in every needed way. To the extent that these tools function adequately the Networks continued viability is guaranteed. Many of these tools are similar to what existing social network sites offer. However, such tools typically leave something to be desired when it comes to their supporting typical learning (knowledge sharing and creation) functions. These tools are unique to Learning Networks and need to be developed specifically. So a tool is needed that helps a participant find fellow participants in the Network who can honour requests for expertise or support; a tool is needed to help participants find fellow participants who would be suitable to jointly form a topical community; a tool is needed to help participants find artefactual resources and perhaps concatenate them in sensible ways; etc. The design element in the definition of a Learning Network refers to the design of these tools, although the way they are orchestrated to work together and are operated on by the Network participants should not be left out.

The need for further investigation
A Learning Network thus designed is able to fulfil the demands for flexibility discussed. Being an online network, logistic flexibility is guaranteed almost by definition. Content flexibility depends on the Network’s composition, on the people and their expertise and experience. It is the participants joint expertise that will allow people to develop themselves further and to engage in activities that produce novel insights. It is the participants joint experience, professionally and with these form of knowledge exchange and building, that dictates how smoothly this all goes. Also, a degree of heterogeneity is required. Participants should be different enough to make for a rich user experience, but similar enough to ensure cohesion. Didactic flexibility, finally, is guaranteed by the overall design of the Learning Network, by using the power of online networks and of tools custom made for networked learning. The overall design ultimate determines the quality of the Learning Network as an environment for professional development. If the network design leaves to be desired, any potential for knowledge sharing and creation that is hidden in the participants will not come to fruition.

There is a host of questions which have not been addressed yet. Most of them are detailed ones, having to do with the social aspects of networked live and learning, and the technical aspects of creating adequate tools. Answering them is the subject of ongoing research. Some of the most pressing issues, however, relate to the question of how one actually employs such networks. Are they compatible with existing virtual learning environment (VLEs)? (I don’t think so.) Can they be built on top of existing social networking sites? (It depends, but it will be difficult to realise their full potential.) Can they be built on top of an integration of a variety of different social networking tools amplified with custom-made tools? (That’s an interesting challenge, they should.) Will existing educational institutions - schools, colleges, universities - be agile enough to adopt and embrace such a model? Time will tell ….

Learning in Learning Networks (2)

In a previous post I concluded that the non-formal learning that is a prime characteristic of Learning Networks may well be an internally inconsistent notion. Either non-formality points to the absence of the structuring that teachers provide and then the question arises of whether non-formal learning is learning at all; or teachers are allowed in to develop learning content and learning tasks, provide assistance, etc., but then non-formal learning starts to coincide with formal learning. The problem can be resolved by looking more carefully at the dichotomy of formal and non-formal. The key is to differentiate between learning as an activity and the contexts in which this takes place. To make sure this distinction is crystal clear, I conceive of learning as an activity people undertake to update or extend their range of beliefs and abilities. In order to learn, they need a context, an environment, that provides them with the right kind of stimuli to learn. The problem arises because we tend to think that teachers should be part of that environment as only they can ensure that learning is effective, efficient and satisfactory.

To make my point, I use of a distinction that Suzanne Verdonschot in her thesis called Learning to innovate uses; she synthesizes the ideas of a variety of other authors to arrive at this distinction. Suzanne distinguishes three kinds of learning:

1. learning that prepares one for the workplace (training or acquisition learning)
2. learning that helps one better to perform at the workplace (learning sensu stricto or participation learning
3. learning that is needed to resolve novel problems (productive or creative learning)

Her context is one of learning at the workplace, but that does not detract from a more general usefulness. Since Learning Networks target professional development, her distinction is certainly useful for the present discussion.

Training (acquisition learning) is associated with clear-cut tasks that need to be fulfilled and with carefully described competences that one needs to acquire to fulfill those tasks. Competence gaps that people have with respect to a particular job situation can therefore easily be identified, and instructional-design-type methods can be deployed to design learning content and tasks that help people to fill these gaps. Such environments are rather schoollike in that there is an important role for teachers who design and develop the learning environment. Such an approach, moreover, is possible, because of the relative immutability of the situation: the work tasks are known, the competences needed to carry them out also, so learning tasks and content may be developed in advance and will remain useful for some time to come. If anything, this kind of learning may be described as formal, even though it still differs from the archetypal school-based learning by children and adolescents. (Parenthetically, children and adolescents learn not merely to prepare for the workplace, enculturation and socialisation are important other functions.)

Learning in a narrow sense (participation learning) is what one does on the job. Although the tasks to be carried out do not change fundamentally, there's always room for improvement; procedures may be optimized, services may be fine-tuned, products may be enhanced. A social setting with colleagues is crucial for this kind of learning to occur. Communities of practice is a term that comes to mind immediately and the example of maintenance personnel that John Seely Brown and Paul Duguid give in their book The Social Life of Information (p. 99 et seq.) quite well illustrates this kind of learning. It cannot be planned ahead nor does it lend itself to competence-gap-type of analyses as it is not known beforehand what needs to be learned. Although it is still individuals who learn, it is the community which provides their learning environment. The structuring comes about through the way their work is organized, such as through the early-morning dispatch meetings with a cup of coffee that Brown and Duguid mention. This kind of learning takes place in non-formal context, as there are no curricula, no lecture time-tables, no teachers, only colleagues who may occasionally act as teachers.

The third kind of learning is yet different. It can best be described as 'working and thereby learning'. Were in the previous kinds at least the work tasks stable, we now even do away with that assumption. Indeed, the work tasks are the problem as they have to be replaced by other ones in order to create novel products, services or procedures. Clearly, the kind of learning that talks competences is no use here. To be sure, competences matter, but learning tasks to acquire those cannot be defined. One doesn't know the prospective work tasks, let alone the learning tasks (and associated competences). (The only exception may be meta-competences such as being able to solve problems, to collaborate and communicate, to guide one's own learning, etc.) This kind of learning is described as productive or creative learning as at its heart lies the need to produce or create new knowledge. Although it is social in nature, the notion of a community of practice does not apply. Such communities consist of relatively stable groups of people, where creative learning requires inputs from new people, even though one may not yet know from whom! Productive learning thus thrives on a networked approach, one in which a great many people with a great many different backgrounds are available for collaboration. The network forms a valuable source upon which one can draw for learning creatively and productively and thus be innovative. The idSpace project, discussed in a previous post, was about this kind of learning. Clearly, this is the kind of learning that George Siemens with his connectivist view targets: even though learning is not equivalent to making and breaking of connections, making and breaking them amounts to creating a learning environment for oneself. Clearly also, this kind of learning demands non-formal contexts.

Back to Learning Networks. As argued, they encompass quite naturally productive learning. In so far as a Learning Network consists of an ensemble of relatively stable, established communities of practice, it also supports participation learning. Either way, Learning Networks are non-formal learning environments. Now back to the original problem of how to design a Learning Network as an environment that still allows one to learn efficiently, effectively and satisfactorily. This problem still stands. Suzanne Verdonschot's thesis is one long attempt to develop design principles for productive learning environments. She lists 11 of them but concludes that they 'do not have a prescriptive function, [... but] present various perspectives that offer the designer starting points for the design of interventions.' (ibid. p. 240). This is much better than nothing, but can of course not be equated with the tried and tested principles of instructional design. So, the logical problem of being inconsistent when talking about non-formal learning in Learning Networks has been resolved, but a practical problem has come in its place, and not a simple one at that!

Limited data retention through selective data degradation

The social web can only thrive if its participants are willing to share personal data, data about themselves, with each other. So, you have an account with some social network (Twitter, del.ico.us, LinkedIn, etc) in order to allow others to read your Tweets, peruse your presentations or, quite generally, find out who you are and what you do. With the advent of the semantic web, of systems that can make inferences on the basis of the data that are fed to them, this is all the more true. Individual users profit from the services that the web offers to them, often for free; the service providers profit, mainly from the advertisements that accompany their services. Although there are other business models, this is the prevailing one, it seems.

So far so good then. But what if service providers sell the data they have acquired in the course of their business to other providers; or worse even, what if these data end up in the hand of others because of clumsiness (a stolen USB stick, a lost laptop) or criminal intent (hacking servers, bribing personel)? Admittedly, you may decide to shut down your Facebook account or give up Twittering, but this freedom of choice is absent for many services. What about your loyalty card with your favourite grocery store, which not only registers your purchasing behaviour but also gives you access to sizeable discounts; or your public transportation travel pass, a system recently introduced in The Netherlands, which allows you to travel throughout the country with one card but registers routes and start and end times in its database; or a road use system installed in your car which helps prevent traffic congestions but does so by logging your car's GPS track data in its central database? In each of these examples - and many more can easily be given - data about a person are logged into a database and it is not transparent to the data providing individual what the associated privacy risks are.

In 1981 the states that jointly form the European Council signed a ‘Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data’. Among other things, it stipulates that no more data may be stored than needed for a particular, identified purpose, and that those data may not be kept for longer than strictly needed. The lack of transparency compels the individual to simply trust the database manager to abide by these rules. Experience teaches us that often this trust is misguided, even if we ignore cases of intentional theft and accidental loss of data. The issue of whom to trust with what data is a complex one. It touches upon the closely related questions of what data to collect and whom to allow to access them. The other day, I read a PhD thesis that sheds an interesting light on the first one of these questions (Harold J.W. van Heerde (2010) Privacy-aware data management by means of data degradation; making private data less sensitive over time. Universiteit Twente).

Ignoring for now the possibility to grant differential access rights, someone can decide to make her particular personal data available or decide not to do so. A LinkedIn profile may contain a photo but need not. Something similar goes for the data that are collected through someone's public transport travel pass. If one uses the pass, route and time data will be collected and stored. Could a user still decide to remove or replace her photo, the storage of travel data is fully beyond her control. The point to note here is that the decisions are all-or-none decisions. Someone’s photo is there or it isn’t, travel data or logged or aren’t. There is no middle ground. Van Heerde shows that a sensible middle ground does exist. He introduces a limited retention principle, meaning that data degrade over time. So, the public transportation database may remain fully intact for a month to allow sending out bills. The data may subsequently be degraded to the level of the route and day of the week someone has travelled to allow sending out special offers. This level of detail is maintained, say, for a year. After one year only the cumulative frequency of use or routes per day of the week, decoupled from the individual, are still available. This still allows the statistical analysis of travel data, for planning purposes for instance. Data degradation allows for a more subtle marriage of the interests of the individual (new, better, cheaper services) with those of the service provider (a more efficient and effective business). Of course, there are all sorts of theoretical and practical problems to be dealt with. Van Heerde discusses many of them, he also suggests how to solve them. For me, the importance of his contribution is his description of how one may come one step closer to heeding the European Council's admonition only to store just enough data for just long enough. This is in the interest of both web service users (aren't we all) and web service providers.

Learning in Learning Networks

With a group of people at the Centre for Learning Sciences and Technologies at the Open Universiteit (Netherlands), I am doing research on so-called Learning Networks. Networked Learning or Learning (in) Networks is a popular catch phrase these days. As long ago as 1995 Linda Harasim and colleagues already dubbed their book Learning Networks: A field guide to teaching and learning online, and this May the fifth Networked Learning Conference was held in the city of Aalborg in Denmark. Inevitably the notion of a Learning Network harbours a great many different opinions on what it actually is. For Harasim cs, any form of learning and teaching for which networks (the Internet) were used, fitted the bill. The visitors of the Networked Learning Conference seemed very much interested in pedagogies for networked learning and in positioning networked learning properly with respect to such theories as Engeström's version of Activity Theory. And of course, ours is yet a different take. I don't necessarily see this is a problem: conceptual growth besides theory development is the hallmark of a growing and evolving scientific field.

For us, Learning Networks are online, social networks that have been designed to facilitate non-formal learning. Here, in line with what is customary, non-formal learning is like formal learning intentional (in contract with informal or accidental learning). However, it differs from formal learning in that it rigorously puts the demands of the learner centre stage. Therefore, it does not necessarily rely on such institutions as curricula, experts in the capacity of teachers, cohorts, schools as buildings or institutions, etc. So far, so good.

This definition of a Learning Network may seem rather weak. After all, it only specifies that the network should be designed in a particular way, not how that should be done. In terms of the how, it only determines that it should not contain such ingredients as cohorts as curricula. Yet, we feel this definition is an apt one as it serves as a workable starting point for empirical research into the how question. What network constellations work in the sense of allowing non-formal learners to learn, and what not?

There is a large variety research questions that we try to solve (see publications). A particularly important set relates to the role competences and competence taxonomies should play, for instance for inventorying someone's prior competences or for charting out someone's learning objectives. Since Learning Networks are online networks, another set of questions is related to appropriate software tools that should help the inhabitants of the Learning Network to learn collectively. The assumption is that being united in such an online network is an important asset, something that helps learning. However, a typical learner may only be acquainted with a few of his peers, if any. So what, in a social-network analytical terms, is a good mix of weakly and strongly linked people, and how can such a mix arise?

These questions are all about how to dress up, to tool the Learning Network. But there is one question that precedes all these: how do individual people learn in a Learning Network, how do they acquire the competences they need? In a recent paper on Connectivism in the Enterprise, George Siemens defines learning as 'the process of forming and pruning connections through social and technological networks'. Although I obviously have no quarrel with the network part, it cannot be that learning only is a matter of forming and pruning links. Ultimately, it is individuals who learn or do not learn. Networks may play a part in that, indeed I argue they should, but it is individuals who decide to make or break links and their decisions hang on what fosters their learning, which is different than being their learning. So, how can people be helped to learn in a Learning Network?

If anything, formal learning is teacher led. This means that teachers develop activities, in which their students engage with their help, through which the students learn; they organise the times and the order of engagements. A lot has been written about this by people such as Robert Gagné (nine events of instruction), Dave Merrill (first principles of instruction) and Jeroen van Merriënboer (4 components instructional design model), to mention a few. All have in common that they prescribe how what it is that needs to be learned should be 'packaged' so as to opimise learning effectiveness (what you learn), learning efficiency (at what costs you learn) and perhaps learner satisfaction.

Non-formal learning, on the other hand, is learner led. So students themselves should somehow organise their own learning. But how are we to understand that? As argued, making and breaking of links with fellow-learners may contribute to learning, it cannot be learning. Merely consuming content cannot be equated with learning either. After all, we do not lock up our students in a library for a few years, wish them all the best, and have them sit in on exam at the end. The various instructional principles and theories we have developed, including those just mentioned, indicate that there are more sensible ways to learn than being flooded with content. Put differently, if learners themselves develop learning activities, decide what learning activities to engage in, when and with whom to do so, how can they be sure they do so most effectively, efficiently and satisfactorily? This is the design question. Also, where do these learning activities come from in the first place? This is the library with books or an online network with content resources is not the same as a collection of learning activities. Perhaps teachers should develop learning activities according to sound instructional design principles and subsequently make them available to learners, for instance as open educational resources. This is the development question. But if teachers interfere in both the developing of learning activities and their structured provision, one could well argue that non-formal learning is teacher led after all. According to that argument, non-formal learning is an internally inconsistent notion: either it isn't learning or it is formal (teacher-led) learning in disguise!

I have no ready-made answer, I do have a couple of ideas. I'll discuss those in a next installment. Meanwhile, suggestions are welcome.

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Note added after publication: In a follow-up post, I further develop the argument, solving the apparent logical problem of internal inconsistency, but replacing it with a practical one.

idSpace project successfully completed

idSpace is an EU FP7 project that started April 1st, 2008, ran for two years and just got its (informal) final ok from the EU reviewers. This is both something cheerful and something sad. Cheerful because we got the official recognition for a job well done. Sad because there's no denying now anymore that the project really is over and that we're really out of funds to continue our R&D and software development work. What was the project about? To quote from its website, The ultimate goal of the idSpace project was to build ... the idSpace environment that should come to the aid of distributed teams of innovators who want to collaborate on product design, thereby making use of earlier results achieved by themselves or even others. So what it tries to do is not so much to make people more creative per se, but to provide them with a software platform in which they can achieve their creative potentials to the full. The environment does so by offering the innovators a choice of (as yet only a few) scenarios for sharing ideas and related knowledge, a module for entering ideas and connecting them in graphs, a variety of context-sensitive recommendations on for instance relevant new group members and helpful resources. Admittedly, this all sounds a bit complicated and you do indeed need a knowledgeable moderator to steer the whole collaborative innovation process, but we are convinced there's a lot of potential here. The platform really still is a prototype, so it needs improvement in many respects. But even in its present state it is quite useable. To help users and to satisfy the curiosity of prospective users, an extensive online user guide and a series of tutorials have been created. So far so good. But how can the potential we believe the platform has be unleashed? How can its further development be financed and how can further R&D work, that provides the input for future improvements, be guaranteed? So far we've been able to come up with a rather predictable answer: new project applications. However, even though this may in principle finance R&D work, new proposals require novel lines of enquiry, while we really only want to continue along the already familiar lines. Also, the platform itself needs to prove its usefulness in actual practice, for which its useability needs to be maximised first, something for which R&D proposals typically do not pay. We need to be more imaginative, more creative, if you like. How about turning this into an open source project so that others can contribute to the platform development too? How about tapping into regional innovation funds? How about even obtaining private funding? Or, to add a wild suggestion, how about asking for funds at such sites as Kickstarter (see the Diaspora project, an attempt to build an open Facebook version, for how easily funds can be raised if the cause is right). Many options, no firm answers, but certainly exciting opportunities. And of course, all suggestions are welcome!