19 November 2011

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

13 November 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.

21 July 2011

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.

the identity question, finger print with that text
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.

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.

10 July 2011

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 GarcĂ­a-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.

28 January 2011

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 ….