While searching for a movie using Google Search last night I got (for the first time that I can recall) the option to tag the result, as described in this article. I was pleased to discover that the tool they provide for this is virtually identical (albeit with a much slicker and more refined modern interface overhaul) to the CoFIND system that underpinned my PhD, that I built over 20 years ago now. You are presented with a list of tags, and can select one or more that describe the movie, and/or suggest your own, effectively creating a multi-dimensional rating system that other users can use to judge what the movie is like. When I rated the movie last night, for instance, popular tags presented to me included ‘terrible acting’, ‘bad writing’, ‘clichéed’, ‘boring’ and so on. Having seen the movie, I agree about the bad writing and clichés – it was at the terrible end of the scale – but actually think most of the acting was fairly good, and it was not very boring. What is interestingly different about this, compared with other tagging systems currently available, is that this kind of tag is fuzzy – it represents a value statement about the movie that exists on a continuum, not a simple categorization. The sorting algorithm for the list of tags presented to you appears (like my original CoFIND) to be based mainly on simple popularity though it is possible that (like CoFIND) it uses other metrics like tag age and perhaps even a user model as well. It’s vastly more useful and powerful than the typical thumbs-up/thumbs-down that Google normally provides. The feature has sadly not reappeared on subsequent movie searches, so I am guessing that Google is either still testing it or trying to build up a sufficient base of recommendations by occasionally showing it to people, before opening it up to everyone.

Just in case Google or anyone else has tried to patent this, and to assert my prior art, you can find a description and screenshots (p183 and p184) of my original CoFIND system in chapter 6 of my PhD thesis as well as in many papers before and since, not to mention in a fair few blog posts. It’s out there in the public domain for anyone to use. The interface of my system was, even by the standards of the day, pretty awful and not even a fraction as good as the one provided by Google, but those were different times: it did work in exactly the same way, though. As I developed it further, the interface actually became much worse. Over the course of a few years I experimented with quite a range of methods to get and display ratings/tags, including an ill-conceived Likert scale as well as a much more successful early use of tag clouds, all of which added complexity and reduced usability. Some of these later systems are described and discussed in my PhD too.  In its final, refactored, and heavily evolved form that postdates my PhD by several years, a version of Cofind (last modified 2007) is actually still available, that almost reverts to the the Google-style tag selection approach of the original, with the slight tweak that, in CoFIND, you can disagree about any particular tag use (for instance, if you don’t believe it to be inane then you can cast a vote against that tag).  The interface remains at least as awful as the original, though, and not a patch on Google’s. The main other differences, apart from interface variations, are that the nomenclature differs (I used ‘qualities’  rather than ‘tags), and that CoFIND could be used for anything with a URL, not just movies. If you’re interested, click on any resource link in the system and you’ll see my primitive, ugly, frame-based attempt to do very much the same as Google is doing for movies (nb. unless you are logged in you cannot add new qualities but, for authorized users, a field appears at the end that is just like Google’s). Though primarily intended to share and recommend educational resources, CoFIND was very flexible and was, over the years, used for a range of other purposes from comparing interface designs to discovering images and videos. It was always flaky, ugly, and unscalable, but it worked well enough for my research and teaching purposes, and (because it provides RSS feeds) it was my go-to tool for sharing interesting links right up until 2007, after which I reverted to more conventional but better-maintained tools like the Landing or WordPress. 

A little bit of CoFIND background

I’ve written a fair bit about CoFIND, formally and informally, but not for a few years now, so here’s a little background for anyone that might be interested, and to remind myself of a little of what I learned all those years ago in the light of what I know now.

An evolving, self-organizing, social bookmarking tool

I started my PhD research in 1997 with the observation that, even then, there was a vast amount of stuff to learn from that could be easily found on the Web, but that it was really difficult to find good stuff, let alone stuff that was actually useful to a particular learner at a particular stage in their development. Remember that this was before Google even started, so things were significantly worse then than they are now. Infoseek was as good as it got.

I had also observed that, in any group of learners, people would find different things and, between them, discover a much larger range of useful resources than any one learner (or teacher) could do alone, a fact that I use in my teaching to this day. These would likely (and, it turns out, in reality) be better than what a teacher could find alone because, though individual learners might be less able to distinguish low from high quality, they would know what worked for them and sufficient numbers of eyes would weed out the bad stuff as long as there was a mechanism for it. This was where I came in.

The only such mechanisms widely available at the time were simple rating systems. However, learners have very different learning needs, so I immediately realized that ‘thumbs-up’ or simple Likert scales would not work. This was not about finding the one ‘best’ solution for everyone, but was instead concerned with finding a range of alternatives to fill different ecological niches, and somehow discovering the most useful solution in that niche for a given learner at a given time.  My initial idea was to make use of a crowd, not an individual curator, and to employ a process closely akin to natural evolution to kill bad suggestions and promote good ones, in order to create an ecosystem of learning resources rather than a simple database. CoFIND was a series of software solutions that explored and extended this initial idea.

CoFIND was, on the face of it, what would eventually come to be called a social bookmarking system – a means for learners to find and to share Web resources (and, later, other things) with one another, along with a mechanism for other learners to recommend or critique them. It was by no means the first social bookmarking system, but it was certainly not a common genre at the time, and I don’t think such a dedicated system had ever been used in education before (for all such assertions, I stand to be corrected), though other means of sharing links, from simple web pages or wikis or discussion forums to purpose-built teacher-curated tools were not that uncommon. A lot of my early research involved learning about self-organization and complex systems, in particular focusing on evolution and stigmergy (self-organization through signs left in the environment). As well as the survival-of-the-fittest dynamic, evolution furnished me with many useful concepts that I made good use of, such as the importance of parcellation, the necessity of death, ways to avoid skyhooks, benefits of spandrels, ways to leverage chance (including extinction events), and various approaches to supporting speciation.  As a result of learning about stigmergy I independently developed what later came to be know as tag clouds. I don’t believe that mine were the first ever tag clouds – weighted lists of one sort or another had been around for a few years – but, though mine didn’t then use the name, they were likely the first uses of such things in educational software, and almost certainly the first with this particular theoretical model to support them (again, I am happy to be corrected).

A collaborative filter

The name CoFIND is an acronym for ‘collaborative filter in n-dimensions’. The n dimensions were substantiated through what we (my supervisors and I) called qualities. We went through a long list of possible names for these, and I was drawn for a while to calling them ‘values’, but (unfortunately) we never thought of ‘tags’ because the term was not in common use for this kind of purpose at the time. After a phase of calling them q-tags, I now call qualities by the much more accessible name of ‘fuzzy tags’. Fuzzy tags are not just binary classifications of a topic but tags that describe what we value, or don’t value, in a resource, and how much we value it. While people may sometimes disagree about binary classifications (conventional tags) it is always possible to have different opinions about the application of fuzzy tags: some may find something interesting, for instance, while others may not, and others may feel it to be quite interesting, or incredibly so. Fuzzy tags are to do with fuzzy sets, that have a continuum of grades of membership, which is where the name comes from. Different versions of CoFIND used different ways to establish the fuzziness of a tag – the Likert Scale used in a few mid-period versions was my failed attempt to make it explicit but this was a nightmare for people to actually use.  The first versions used the same kind of frequency-based weighting as Google’s movie tags, but that was a bit coarse – I was uncomfortable with the averaging effect and the unbridled Matthew Effect that threatened to keep early tags at the top of the list for all time, that I rather coarsely kept in check with a simple age-related weighting that was only boosted when they were used (the unfortunate side effect of which being that, if a system was not used for a few weeks, all the tags vanished in a huge extinction event, albeit that they could be revived if anyone ever used one of the dead ones again). The final version was a bit in-between, allowing an indefinitely large scale via simple up-down ratings, balanced with an algorithm that included a decaying but renewable novelty weighting that adjusted to the frequency of use of the system as a whole. This still had the peculiar effect of evening out/initializing all of the tags over time if no one used the system, but at least it caused fewer catastrophes.

‘Traditional’ collaborative filters simply discover whether things are likely to be more valued or less valued on a usually implicit single dimension (good-bad, liked-disliked, useful-useless, etc). CoFIND’s qualities/fuzzy tags allowed people to express in what ways they were better or worse – more interesting, less helpful, more complex, less funny, etc, just as Google’s movie tagging allows you to express what you like or dislike about a movie, not just whether you liked it or not. In many tag-based systems, people tend to use quite a few simple tags that are inherently fuzzy (e.g. Flickr photos tagged as ‘beautiful’) but they are seldom differentiated in the software from those that simply classify a resource as fitting a particular category, so they are rarely particularly helpful in finding stuff to help with, say, learning.

I was building CoFIND just as the field of collaborative filtering was coming out of its infancy, so the precise definition of the term had yet to be settled. At the time, a collaborative filter (then usually called an ‘automated collaborative filter’) was simply any system that used prior explicit and/or implicit preferences of a number of previous users (a usually anonymous crowd) to help make better recommendations and/or filter out weaker recommendations for the current users. The PageRank algorithm that still underpins Google Search would perhaps have then been described as a collaborative filter, as was one of its likely inspirations, PHOAKS (People Helping One Another Know Stuff), that mined Usenet newsgroups for links, taking them as an implicit recommendation within the newsgroup topic area. By this definition, CoFIND was in fact a semi-automated collaborative filter that combined explicit preferences with automated matching. Nowadays the term ‘collaborative filter’ tends to only apply to a specific subset of recommender systems that automatically predict future interests by matching individual patterns of behaviour with those of multiple others, whether by item (people who bought this also bought…) or user (people whose past or expressed preferences seem to be like yours also liked…). I think that, if I built CoFIND today, I would simply refer to it more generically as a recommender system, to avoid confusion.

Disembodied user models

Rather than a collaborative filter, back in the late 90s Peter Brusilovsky saw CoFIND as a new species of educational adaptive hypermedia, as it was perhaps the first (or at least one of the first) that worked on an open corpus rather than through a closed corpus of linked resources. However, he and I were both puzzled about where to find the user model, which was part of Peter’s definition of adaptive hypermedia. I didn’t feel that it needed one, because users chose the things that mattered to them at runtime. In retrospect, I think that the trick behind CoFIND, and what still distinguishes it from almost all other systems apart from this fairly new Google tool, is that it disembodied and exposed the user model. Qualities were, in essence, the things that would normally be invisibly stored in a user model, but I made them visible, in an extreme variant of what Judy Kay later described as scrutable adaptation.  In effect, a learner chose their own learner model at the time they needed it. The reasoning behind doing so was that, for learners, past behaviour is usually a poor predictor of future needs, mainly because 1) learning changes people (so past preferences may have little bearing on future preferences), and 2) learning is driven by a vast number of things other than taste or past actions: we often have a need for it thrust upon us by an extrinsic agency, like a teacher, or a legislative demand for a driving licence, for instance. Qualities (fuzzy tags) allow us to express the current value of something to us, in a form that we can leave behind without a lot of sticky residue, and that future users can use. In fact, later versions did tend to slightly emphasize similar things to those people had added, categorized, or rated (fuzzily tagged) earlier, but this was just a pragmatic attempt to make the system more valuable as a personal bookmark store, and therefore to encourage more use of it, rather than an attempt to build a full-blown collaborative filter in the modern sense of the word.

Moving on

I still believe that, in principle, this is an excellent approach and I have been a little disappointed that more people have not taken up the idea and improved on it. The big and, at the time, insurmountable obstacles that I hit were 1) that it demands a lot of its users to provide both tags and resources, with little obvious personal benefit, so it is unlikely to get a lot of use, 2) that the cold-start problem that affects most collaborative filters (it relies on many users to be useful but no one will use it until it is useful) is magnified exponentially by every one of those n dimensions so it really demands a big lot of users, and 3) that it is fiendishly hard to represent the complex ecological niches effectively in an interface, making the cognitive load unusably high. Google seems to have made good progress on the last point (an evolution enabled by improved web standards and browsers combined with a simplification of the process, which together are enough to reduce the cognitive load by a sizeable amount), and has plenty sufficient numbers of users to cope with the first and second points, at least with regard to movie recommendations. It remains challenging to see how this would work in an educational setting in anything less than the largest of MOOCs or the most passionately focused of user bases. However, I would love to see Google extend this mechanism to OERs, courses, and other educational resources, from Quora answers to Kahn Academy tutorials, because they do have the numbers, and it would work well. For the same reasons, it would also be great to see it applied to something like StackExchange or similar large-scale systems (Reddit perhaps) where people go to seek solutions to learning problems. I doubt that I will build a new version of CoFIND as such, but the ideas behind it should live on, I think, and it’s great to see them back on a system as big as Google Search, even if it is so far only experimental and, so far, just used to recommend movies.

I’ve reached the end of my first week of not Chairing the School of Computing & Information Systems here at Athabasca University, which is now in the capable hands of the very wonderful Ali Dewan.

Along with quite a few people that I know, I am amazed that I stuck it out for over 3 years. I was a most reluctant Chair in the first place, because I’d been in middle management roles before and knew much of what to expect. It’s really not my kind of thing at all. Ideologically and temperamentally I loathe hierarchies but I’d rather be at the top or at the bottom if I have to be in one at all. However, with the help of some cajoling, I eventually convinced myself that being a Chair is essentially much the same as being a teacher, which is an activity that I both enjoy and can mostly do reasonably well. Like a teacher (at least one that does the job well), the job of a Chair is to help nurture a learning community, and to make it possible for those in that community to achieve what they most want to achieve with as few obstacles as possible. Like teaching, it is not at all about telling, but about listening, supporting, and helping others to orchestrate the process for themselves, not so much about leadership as followership, about being a supportive friend. It’s a bit about nudging and inspiring, too, of sharing the excitement of discovery and growth with other people. It’s a bit about challenging people to be who they want to be, collectively and individually. It’s a bit about solving problems, a bit about being a shoulder to cry on, a bit about being a punchbag for those needing to let off steam, an arbiter in disputes. It could be fun. And I could always give it up after a few months if it didn’t work out. That was what I convinced myself.

On the bright side, I don’t think that I broke anything vital. I did help a couple of good things to happen, and I think that most of my staff were reasonably happy and empowered, a few of them more than before. One or two were probably less happy. But, in the grand scheme of it all, I left things much the same as or a little better than I found them, despite often strenuous efforts to bring about far more exciting changes. My tenure as Chair was, on the whole, not great, but not terrible. I have been wondering a bit about why that happened, and what I could or should have done differently, which is what the next part of this post is about.

Authority vs influence, responsibility vs power

One of my most notable discoveries (more accurately, rediscoveries) is that authority and responsibility barely, if at all, correlate with power and influence. In fact, for a middle management role like this, the precise inverse is true. One of the strange paradoxes of being in a position of more responsibility and authority has been that, in many ways, I feel that I’ve actually had considerably less capacity to bring about change, or to control my own life, than I had as a plain old professor.  It’s just possible that I may have overused the joke about a Chair being the one everyone gets to sit on, but it resonated with me. And this is not to contradict Uncle Ben’s sage advice to Spiderman – it may be true that with great power comes great responsibility, but that doesn’t mean that with great responsibility comes great power.

Partly the problem was just the myriad small but draining demands that had to be done throughout the course of a typical day (most of which were insufferably tedious and mostly mindless bureaucratic tasks that anyone else could do at least as well), as well as having to attend many more meetings, and to engage in a few much lengthier tasks like workload planning. It wore me down. I put a lot of things that were important to me, but that didn’t contribute to my role, to one side because there were too few chunks of uninterrupted time to do them. Blogging and sharing on social media, for instance.

Partly it was because I felt that my role was primarily to support those that reported to me – I had to do their bidding much more than they had to do mine. Instead of doing what I would intrinsically wish to do, much of the time I was trying to do what those that I supervised required of me. This was not just a result of my own views on leadership. I think a lot of it would have affected most people in the same position.

Partly it was because I often felt (with a little external reinforcement) that I must shut up and/or toe the line because I represented the School or the Dean or the University. Being the ‘face’ of the school meant that I often felt obliged to try to represent the opinions and demands of others, even when I disagreed with them. Often, I had to present a collective agenda, or that of an individual higher up the foodchain, rather than my own, whether or not I found it dull, mistaken, or pointless. Also, being a Chair puts you in some sensitive situations where a wrong step can easily lead to litigation, grievance proceedings, or (worse) very unhappy people. I’m not naturally tactful or taciturn, to say the least, so this was tricky at times. I sometimes stayed quiet when I might otherwise have spoken out.

The upshot of it is that, as a Chair, I was directly responsible both to my Dean and to the people I supervised (not to mention more or less directly to students, visitors, admins, tech staff, VPAs, etc, etc), and I consequently felt that I had very little control over my own life at all. Admittedly it was at least partly due to my very intentional approach to the role, but I think similar issues would emerge no matter what leadership style I had adopted. There’s a surprising amount of liberty in being at the bottom of a hierarchy, at least when (like all academics) you are expected – nay, actually required – to be creative, self-starting, and largely autonomous in your work. Academic freedom is a wonderful thing, and some of it is subdued when you move a little way up the scale.

Some compensations 

There have been plentiful compensations, of course. I wouldn’t have stayed this long if it had been uniformly awful. Being a Chair made some connections easier to make, within and beyond the university, and has helped me get to know my colleagues a lot better. And I have some great colleagues: it would have been much harder to manage had I not had such friendly, supportive, smart, creative, willing, and capable team to work with. I solved or at least made fair progress on a few problems, none huge but all annoying, and helped to lay the groundwork for some ongoing improvements. There were opportunities for creativity here and there. I will miss some of the ways I could help shape our values and systems simply thanks to being a Chair, rather than having to actually work at it. I’ll miss being the default person people came to with interesting ideas. I’ll miss the very small but not trivial stipend. I’ll miss being involved by default in most decisions that affect the school. I’ll miss the kudos. I’ll miss being a formal hub in a network, albeit a small one.

Not quite like teaching

In most ways I was right about the job being much like teaching. Most of the skills, techniques, goals, and patterns are very similar, but there’s one big difference that I had not thought enough about. On the whole, most actual teachers engage with learners over a fairly fixed period, or at least for a fixed project, and there is a clear beginning, middle, and end, with well defined rituals, rules, and processes to mark their passage. This is even true to an extent of more open forms of teaching like apprenticeship and mentorship. Although this in some ways relates to any kind of project, the fact that people, working together in a social group, are both the focus and the object of change, makes it fairly distinctive. I can’t think of many other human activities that are particularly similar to teaching in this regard, apart from perhaps some team sports or, especially, performing arts.

To be a teacher without a specific purpose in mind is a surprisingly different kind of activity, like producing an improvised play that has no script, no plot, no beginning, and no end. Although a teacher is responsible to their students, much as I was responsible to my staff, the responsibility is tightly delimited in time and in scope, so it remains quite manageable, for the most part. In retrospect, I think I should have planned it better. I probably should have set more distinct goals, milestones, tasks, sub-projects, etc. I should have planned for a very clear and intentional end, and set much firmer boundaries. It would not have been easy, though, as many goals emerged over the years, a lot changed when we got our new (and much upgraded) administration, and a lot depended on serendipity and opportunism. I had, at first, no idea how long I would stick with the role. Until quite some time into it, I had only a limited idea about what changes I might even be allowed to accomplish (not much, as it happens, with no budget, a freeze on course development, diminishing staff numbers, need to fit faculty plans, etc). It might have been difficult to plan too far ahead, though it would have been really useful to have had a map showing the directions we might have gone and the limits of the territory. I think there may be useful lessons to be learned from this about support for self-directed lifelong learning.

Lessons for learning and teaching

A curse of institutional learning can be the many scales of rigid structure it provides, that too often take agency away from learners and limit support for diversity. However, it also supports an individual learner’s agency to have a good map of the journey ahead, even if all that they are given is the equivalent of a bus route, showing only the fixed paths their learning will take. I have long grappled with the tensions and trade-offs between surfing the adjacent possible and following a planned learning path. I spent a lot of time in the late 1990s and early 2000s designing online systems that leveraged the crowd to allow learners to help one another to learn, but most of them only helped with finding what to do next, or to solve a current problem, not to chart a whole journey. Figuring out an effective way to plan ahead without sacrificing learner control was one of the big outstanding research problems left to be solved when I finished my PhD (in self-organized learning in networks) very many moons ago, and it still is. There are lots of ineffective ways that I and others have tried, of course. Obvious approaches like matching paths through collaborative filtering or similar techniques are a dead-end: there are way too many extraneous variables to confound it, way too much variation in start and end points to effectively cater for, even if you start with a huge dataset. This is not to mention the blind-leading-the-blind issues, the fact that learning changes people so past activity poorly predicts future behaviour, and the fact that there is often a narrative context that assumes specific prior activities have occurred and known future activities will follow. Using ontologies is even worse, because the knowledge map of a subject developed by subject experts is seldom if ever the best map for learning and may be among the worst. The most promising approaches I have seen, and that I had a doctoral student working on myself until he had to give up in the mid 2000s, mine the plans of many experts (e.g. by looking at syllabuses) to identify common paths and branches for a particular subject, combining them with whatever other information can be gleaned to come up with a good direction for a specific learner and learning need. However, there are plenty of issues with that, too, not least of which being the fact that institutional teaching assumes a very distinctive context, and suffers from a great many constraints (from having to be squashed into a standardized length to fitting preferred teaching patterns and schedules), that learners unhindered by such arbitrary concerns would neither want nor need. Many syllabuses are actually thoughtlessly copied from the same templates (e.g. from a professional association model syllabus), or textbooks, and may be awful in the same ways. And, again, narrative matters. If you took a chunk out of one of my courses and inserted it somewhere else it would often change its meaning and value utterly.

This is a problem I would dearly love to solve. Though I stand by my teaching approaches, one of the biggest perennial complaints about the tools and methods I tend to use is that it is easy to feel lost, especially if the helping hands of others are not around when needed. There are always at least a few students who would, as a matter of principle, rather be told what to do, how to do it, and where to go next. The majority would prefer to work in an environment that avoids the need for unnecessary decisions, such as where to upload a file, that have little to do with what they are trying to learn. My role (and that of my tutors, and the design of my courses) is to help them through all that, to relieve them of their dependency on being told what to do, and to help them at least understand why things are done the way they are done. However, that can result in quite inconsistent experiences if I or tutors let the ball slip for a moment. It can be hard for people who have been taught, often over decades, that teaching is telling, and that learning can reliably be accomplished by following a set of teacher-determined steps, to be set adrift to figure it out in their own ways.

It is made far worse by the looming threat of grades that, though eliminated in my teaching itself, still lie in wait at the end of the path as extrinsic targets. Students often find it hard to know in advance how they will meet the criteria, or even whether they have met them when they reach the end. I can and do tell them all of this, of course, usually repeatedly and in many ways and using many media, but the fact that at least some remain puzzled just proves the point: teaching is not telling. Again, a lot of manual social intervention is necessary. But that leads to the issue that following one of my courses demands a big leap of faith (mainly in me) that it will turn out OK in the end. It usually takes effort and time to build such trust, which is costly for all concerned, and is easily lost with a careless word or a missed message.  It would be really useful for my students to have a better map that allows them to plan detours and take more alternative transit options for themselves, especially with overlays to show recommended routes, warnings of steep hills and traffic, and real-time information about the whereabouts of people on their network and points of interest along the way. It would, of course, also be really handy to have a big ‘you are here’ label.  I would have really liked such a map when I started out as Chair.

Moving on

Leaving the Chair role behind still feels a little like stepping off a boat after a rough voyage, and either the land or my legs feel weird, I’m not sure which. As my balance returns, I am much looking forward to catching up with things I put to one side over the past 3 years. I’m happy to be getting back to doing more of what I do best, and I hope to be once more sharing more of my discoveries and cogitations in posts like this. It’s easier to move around with your feet on the ground than when you are sitting on a chair.