We all know that user experience designers come from a range of different backgrounds. But what do these experiences bring to UX? In our first of a series of articles, Boon Chew reflects on his start in computer science.

Human-Computer Interaction has strong roots in Computer Science, and user experience design is almost exclusively a technology-focused practice. How much does UX design share with its engineering-focused sibling? I’m going to share some thoughts about my experiences from making the transition from software engineering to UX, and how my past career has made an influence in my roles as a user experience designer today.

Computer Science to User Experience — a strong legacy

A number of notable UX experts have come from a strong CS background. Alan Cooper, founder of interaction design company Cooper and author of the seminal book The Inmates are Running the Asylum, is also widely known as the Father of Visual Basic. Jared Spool, who is one of the industry’s most prominent speaker and usability expert, also had his beginnings as a software developer and programmer. I also know many UX practitioners who were formerly web developers, most of them specializing in front-end programming. A handful work in hybrid roles, juggling UX and software development in their day-to-day work.

Needless to say, UX is a natural route for CS practitioners keen on improving the user experience of interactive systems and beyond.

Why?

I pursued Computer Science because I wanted to work with computers. I was also excited about the web and in particular web design. I never got into design very seriously, feeling that I needed to start from scratch to learn the fundamentals – and engineering school was demanding enough. Still, I grew to love software engineering and found developing for the web very interesting and fulfilling, so that became my focus of my career for several years.

What  I Did

In my eight year career as a software developer, I worked primarily on web applications. While my first job involved developing Java applets for a NASA research project as a Computer Engineering student, my first ‘real’ job got me building web applications for a telecoms solutions company. It was the early 2000s, and the big Internet boom meant almost every software developer in the world were involved in building software for the web.

I started off working on many projects where I was the sole web developer, and I grew acquainted with many web frameworks and web technologies, both front-end, back-end, as well as rich internet applications. In short, I was a generalist developer for the web, and for many years, I was responsible for translating requirements from project managers and sales executives into usable websites.

I increasingly worked with teams of other developers, but my teams were fairly small and grew no larger than 5 people. It was a very business-like environment and long hours in the office were very common. Despite the challenging nature of the job, I continually pursued a deeper interest in the craft of web development across several different industries – first in telecoms, then in a technology startup, then as a prototype engineer in a research division of a large Fortune 500 company.

Interestingly, it was business innovation and research that sparked my interest in user experience. I already had a strong love for web and graphic design, but I couldn’t have predicted being in the UX field – it was like a whole new world had opened before me.

How I moved into UX

In 2006, I was hired into a research team specialising in developing prototypes with the aim if generating innovative and creative ideas for a large Fortune 500 company. There was a big buzz around business innovation and design thinking, and I poured myself into as much literature as I could to get a handle on the creative process. But it wasn’t until two years later that I came across About Face 3 by Cooper et al, which completely changed my perception of running software projects. The book provided me a practical methodology to apply user-centred design into my work and it made so much sense, it was very hard to go back to the way things were.

Also, one of the main motivations pushing me towards UX was my desire to create better digital experiences. I constantly struggled to influence design decisions as a developer, partly because I was so far removed from any business or design context. I also lacked the necessary skills and design experience, which often left me frustrated thinking that my contributed a part to the overall experience influenced only part of the solution.

One of the main motivations pushing me towards UX was my desire to create better digital experiences. I constantly struggled to influence design decisions as a developer, partly because I was so far removed from any business or design context.

At the end of 2008, I enrolled in a Masters program in Human-Computer Interaction to kick-start my career in UX. It was a difficult transition, and after I graduated I still continued to work as a developer cum UX designer before making the leap into UX full-time — initially as a contractor, then finally as a full-time UX designer at SapientNitro.

What I Bring to UX From It

It’s hard to pin down specific things that bring to UX from CS. A lot of it I probably take for granted, such as understanding the intricacies of web technologies, complex software practices, and how design and technology can feasibly work together in one seamless whole. Often times, UX seems like an extension of my overarching goal as a technologist to deliver amazing experiences. I’m also not unaware of how poor implementation can happen when designers don’t understand how even the best ideas still need to translate well into engineering. These days, it takes a lot of different skills to deliver complex solutions way beyond the ability of individual engineers, designers or entrepreneurs.

Often times, UX seems like an extension of my overarching goal as a technologist to deliver amazing experiences.

I’ve also noticed that I’ve developed good practices as a developer that benefit my productivity, especially when it comes to using complex production tools, managing digital artifacts, multi-tasking, and prototyping. For example, I’m as comfortable using visual design tools such as Omnigraffle, Fireworks, InDesign and Illustrator as I am using the Eclipse IDE. I keep a healthy trail of artifacts using version control systems, dropbox, and other means. The tools seldom get in the way of my work, and I pick up new tool skills very quickly.

Finally, I think that all that software problem solving has come in handy. Some design problems are extremely complex and require some heavy analytical work. I seem to have developed enough patience to break down complex problems into manageable chunks, structure it in a more coherent solution, while putting my information architect hat on.

What I’ve Had to Work On

All the benefits I described have not come without a price. I obviously lack experience and exposure in many aspects of design, such as the design process, visual literacy, creative savvy, and working in creative teams. I’m learning a lot of this on the job now, but I can’t say it’s been easy – I sometimes struggle to comprehend my teammates who have worked in the field for much longer, as part of me silently begs for more answers on “how things work around here”.

I’ve had some experience facilitating teams in brainstorming sessions and running meetings, but I still feel I have a long way to go in running a full-blown workshop to generate compelling ideas and solutions. There’s a lot to learn here, too – some professionals dedicate their entire lives to this one craft.

I’m also fairly new to a role that involves more client-facing opportunities. This requires a lot of savvy, preparation, good people skills and good communication skills as well. I don’t think UX can exist in a bubble (unlike some software roles), and as a designer, I’m constantly required to take a stand and provide compelling reasons for my decisions, while considering the business and user goals. That’s one hard task!

UX can’t exist in a bubble (unlike some software roles), and as a designer, I’m constantly required to take a stand and provide compelling reasons for my decisions, while considering the business and user goals

In addition to this, I have to have a firm handle on what the overall experience should be. I’ve learnt from others and from my own experience that no single UX method rules them all. Things can get overly complicated and overwhelming very easily, and I’m still developing my own personal style and approach that works best for me.

Tips for Those Making the Move

UX is a very subjective, diverse, and inclusive design practice. It pays to be intellectually curious, to prefer problem solving through collaboration, and be comfortable tackling complex problems. As a UX designer, you will gradually have to dig deep through social, behavioural and technical layers to ensure your designs can hold up well in specific contexts. And since no single person can provide all answers, you’ll have to be resourceful and be thinking on your feet a lot.

I would also encourage stepping completely away from the computer for a while, at least until you’ve developed a personal approach to practicing UX. Start by putting some methods into practice and learning from that. Generate designs prolifically and with the assumption of throwing away much of what you’ve designed through rapid iteration. Don’t be afraid of starting from scratch with your designs repeatedly and testing them – it will build confidence in your ability to research and design.

Also, force yourself to design with others. This will help build a richer perspective of the world and how to design for it, and you’ll learn a lot about collaborative design in that process.

And while you’re at it, keep a journal of every lesson you’ve learnt. Not just written, but artifacts, concepts and sketches too. These will go handy in preparing a portfolio when you’re finally ready to hit prime time.

What I’ve Found About Moving Into UX

While the transition hasn’t been easy, I don’t regret the decision I made to switch. There is a lot of demand for UX designers of diverse backgrounds, and CS represents one of the primary foundations for UX as a domain. While it isn’t necessary to code while doing UX, a strong CS foundation provides a real world basis for UX problem solving. As technology gets even more complex, the role of a UX designer bridging the gap between technology and people is becoming increasingly important. I’m grateful for the opportunity I have now, and see no end to the possibilities ahead.

This is the first of a series of Johnny Holland articles on the different stories and skills people bring to UX. Have your own to add? Please contact us.

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Photo: NC-by-CC-2.0 by deltamike

Whether by design or by accident, every social tool is an instance of a generalizable social model. The model is defined by the types and modes of social interaction built into the system as enabling bias. As they are used over time by users, social tools become social systems — a necessary combination of both technical features and user practices. Again, over time, individual user practices communicate (to other users), and cause reinforcing feedback loops and iterative cycles within the system such that social practices emerge. These practices are stabilizing and binding, for they lay down tacit codes of conduct and behavior that facilitate the growing competencies of users.

As social tools develop over time, with their users, they become increasingly complex. Timed right, this complexity is embraced by users — it not only “enhances” user experiences, increases diversity and variety of features and functionalities, extends to other systems/tools, it differentiates the system. In systems theoretical terms, this internal differentiation is required if a system is to handle increased levels of activity and information. In social systems, this increased activity comprises of both mediated symbolic action and communication. Symbolic action takes the form of represented, normalized, and signifying activity: ratings, votes, likes, leaderboards, rankings, and many other non-individual, impersonal, generalizable forms of social differentiation and action. (A Like for one is a Like for all.) Communication is personal, individual, communicative and expressive, and is non-generalizable — thus unavailable to the system for generalized absorption. There is no “aggregation” or interpretation of user communication within social systems. At best, limited sentiment and semantic extraction (trending topics, number of communications (posts, tweets, @replies, comments), and user-supplied semantic declarations (#hashtags, tags, and supplemental sentiments such as ratings and votes).

Internal differentiation of a social system also results in greater social differentiation — a must for social tools. Users must be as able to distinguish themselves and others in mediating social systems just as they do in everyday life. Again, social tools avail themselves of the palette of symbolic media: ratings, votes, likes, +1, checkins, followers, and so on. For these can be normalized and thus easily aggregated for the purpose of system-produced social rankings, leaderboards, recommendations, and so on. (An impossibility in the old days of Myspace testimonials — hence no leaderboards for social rank.)

Social differentiation permits the system to increase its internal referentiality. That is, internal connectedness and the functionality of connectedness. Actions connect to reactions, confirmations, shares, permalinks, and so on — each nest of connections enabling greater access to system contents now and in the future. Emphasis by the system on real time connectedness speeds up system activity. Emphasis on horizontal connectedness increases the system’s navigability. Think twitter vs Wikipedia. The former is designed for realtime content consumption; the latter, for topical content relatedness.

A further and salient feature of social tools is their inclusion and exclusion of user actions and activity, communication included. The amplification, distortion, symbolic representation, and bracketing of social interaction is a universal feature of mediating social systems. Some acts and actions are amplified (distorted) by how they are made to appear online. Others are bracketed out. Users adapt to and become competent at how these mediation effects reflect on them, represent activities of others, and produce ongoing social interaction. Social systems become uniquely world-like, and each is as distinct a world as, say, genres of film distinguish themselves. What works in some social systems doesn’t work in others (we call this frames, or context).

It’s interesting, then, to compare social tools to tease out their interaction models. Let’s try a few, in brief.

Facebook is built on friend relationships, lacks social ranking systems, and has designed for a wide variety of interactions and communication. One might say that it has a tonal preference for mediated relationships — a design reflecting the psychology of its founder, perhaps, or simply a design born out of college-age socialities. Facebook friending is interpersonal (mutual friending), Likes have become a universal gesture of attentiveness, attentiveness is baked into algorithmic news item surfacing, and symbolic activity is related to people and to the personal. Facebook is about the “status of our friendships” — and offers a means to maintain friendship.

Twitter is for speed, and is on the development path to recuperate and preserve content (tweets) lest it implode under its own velocity. Speed and volume of activity in twitter results in a social system that creates invisibility problems — further complicated by the fact that the counter to invisibility is repetition and redundancy. “Am I being heard” might be the catchphrase for twitter, because the tool is designed to fashion the illusion of conversation with users followed, when in fact tweets are read by followers. The follower model, which is asymmetric and unilateral, offers a solution for some: reciprocity. But reciprocal following is no guarantee that followers pay attention: this is twitter’s fundamental social challenge, and the greatest threat to its longevity. Perhaps for this, its current drive to build features based less on tweeting and more on the user/brand, on its platform as plumbing, and on “tweet this” as a global action available from any web page.

Turntable.fm is interesting in that it is a synchronous experience. If twitter is “am I being heard,” turntable is “am I being listened to.” That one is being heard is a given (for the most part). The site seems interested in developing towards greater social differentiation of users: this by points and followers for djs, and navigation not by sound but by visual representation of social activity (named rooms, number of listeners in room, proportion of dj spots used, etc). Liking (or not) a song feeds into points for djs, allows a room to fall into synch (the visual of bobbing heads), and of course qualifies “am I being listened to” (do they like what I’m playing). The manner in which turntable.fm transforms and extends the user’s ego online is very compelling — music users like that others like, too, creates a strong social affirmation because it persists for a stretch of “shared” time.

Empire Avenue, one could say, is about the “sum of me.” The majority of user actions and thematic activities on Empire Avenue involve numbers and the peculiar world of magnitudes. I say this because it is not just numbers, as increments or proportions, or even as relative numbers, but also the culturally-informed numbering quality that is “magnitude.” (Magnitude is a numerical quality meaning “great,” “greater,” and “greatest” and is the property of a type of number that has all three of these in social form — it’s a socially meaningful, or socialized number.) Empire Avenue might appeal to those who relate to and enjoy counting and being counted, who get a reward from quantifying, and who grasp numbers as a substitute for Self worth and an attribute of Self worth. Of course, the game references the stock market, and so numbers are expressed as prices. The core social activity in Empire Avenue is not content creation, but the symbolic transaction of buying/selling other users. Not surprisingly then, reciprocity becomes a social tactic — effective but ultimately merely a means of iterating game play.

Google+ is brand new, and so it is early yet to say what it’s interaction model will become. But interestingly, Google+ Circles makes a metaphorical adaptation of a social concept — that people do not relate to each other socially across one graph equally. That people’s relationships are asymmetric, unequal, dynamic, and non-transferable is of course a social fact. Google+ has chosen to make this lumpiness explicit, allowing users to assign people to named circles. The catchphrase for Google+ might therefore be “in or out,” for this is the social dilemma friend circles poses. Circles are not transparent among users, so social solutions (other than reciprocation, which is based on trust and faith, for there is no social norm possible until there is transparency and a means to hold users accountable for reciprocation) are hard to come by. Furthermore, Google’s emphasis on personal utility over social system design is reinforced by the fact that Circles are for content/feed consumption more than for talk amongst Circles. They can as of yet not be shared, overlapped, or properly targeted and addressed. There’s on other peculiarity of circles:  they do not include the user. Why Google chose the circle, which is for “a group of others not including me,” rather than a hub, I’m not sure. For a hub is a more accurate social relationship model — and in its metaphor, is more inclusive and socially group-like than a circle (which is more a grouping than group-like).

Social interactions emerge within social systems in the shape of socio-technical competencies and practices. Each system is unique, technically and socially. We are still only learning how technical, feature, and system architectures result in varying social dynamics, interactions, practices, and outcomes. But it should seem clear from this comparison that differences between systems are indeed real and profound, each resulting in social dynamics that express the particular social milieu a system’s design supports. Dynamics that in turn shape not only a company’s future prospects, but in many ways those of the industry overall.

In a world of unlimited access to information and infinite choices, it can be hard to make decisions. We’ve created tools to help us sort the humongous mass of information we’ve created; tools to help us find what we’re looking for. We’ve grown so used to using these tools that we rarely notice their downsides and limitations. This article aims to highlight some problems related to findability and discoverability and encourage you to find alternative solutions to the existing paradigms.

What is serendipity and why is it important?

Serendipity denotes the property of making fortunate discoveries while looking for something unrelated, or the occurrence of such a discovery during such a search.

The experience of browsing items in a physical space or online catalogue can differ substantially. For example when you’re browsing records in a store you often come across items you weren’t actively looking for but which you instantly recognize as desirable. Online stores offer some mechanisms for discovery but they’re highly limited in scope when compared to physical environments. They may offer a much higher number of items on sale than a physical store, but because screen space is scarce catalogues have to resort to categorisation trees, so users are only exposed to a small subset of the full range of possibilities. Online, users have less peripheral vision and a limited awareness.

“First we shape our tools, thereafter they shape us” – Marshall McLuhan

Existing Paradigms

So far we’ve had three paradigms for enabling findability: search, categorisation and social recommendation.

Search – the mirror

Search is great when users have a precise idea of what they’re looking for, but lacks the ability to propose related content that could bring new insights into the subject the user is researching. It acts as a mirror, you only get what you put into it. The problem is compounded by predictive text, which actively influences a user’s decision process while she’s typing a search query. The problem is the algorithms behind the search and autocompletion mechanisms are based on statistical data culled from the collective behavior of all users. This leads to the progressive erosion of the less used words from the suggestions mechanism and can have a huge impact on a user’s perception of a given subject. This can have terrible implications, for example consider how someone’s perception of a political event can be shaped or influenced by the keywords being suggested to them while they’re conducting searches. Would you be happy to have your worldview influenced by Google’s auto-complete?

Add to the equation that Google is a private company whose profits depend on advertising and sponsored results, it is a no-brainer to imagine that predictive search will soon start featuring sponsored suggestions too.

What is popular becomes exponentially popular, and everything else gets buried with no chance of surfacing. The price we pay for Google’s efficiency is having our perception of the rich complexity and variety of the world filtered and reduced to its average sum.

Category drill-down – a box inside a box inside a box

As a user traverses a navigation tree, her choices exclude a high number of paths that could provide unexpected insights. Of course users can trawl up and down the tree, and cross-referencing mitigates this limitation, but this becomes unpractical for very large collections. Cross-referencing is often limited in scope, what usually happens is you have a group of objects sharing some common theme pointing to each other but this forms a closed loop, so if you follow the cross-reference links you end up going full circle and sooner or later you find yourself looking at the initial item again.

Social recommendation – The echo chamber

Recommendation engines are the prevalent method for adding serendipity to navigation systems, however they operate in a rather linear fashion: they analyze a user’s past behavior and that of his peers, and use that information to extrapolate recommendations. The intended purpose of recommendation engines is to suggest to users objects or information which might interest them. But because the recommendations are based on a user’s past behavior, it tends to only offer back to the user items within their existing range of interests. So it reinforces a user’s tendency towards a certain behavior and never triggers alternative responses. Instead of offering something truly new to a user, recommendation creates a self-referential loop where a user’s body of knowledge is limited by her existing frame of references.

The notion of “Echo chamber” describes this process: “ [...] participants may find their own opinions constantly echoed back to them, and in doing so reinforce a certain sense of truth that resonates with individual belief systems.  This can create some significant challenges to critical discourse within an online medium.

the echo chamber

The echo-chamber effect may also impact a lack of recognition to large demographic changes in language and culture on the Internet if individuals only create, experience and navigate those online spaces that reinforce their “preferred” world view.  Another emerging term used to describe this “echoing” and homogenizing effect on the Internet within social communities is “cultural tribalism” [...] “ – Wikipedia (On a related note but from a journalism point of view Eli Pariser talks about what he calls the “filter bubble”, worth checking out.)

Mob Rulz

Because people spend so much time on social network platforms, it is safe to assume this will have an impact on culture. It impacts how we relate to those who don’t share our worldview, creating invisible walls between people rather than connecting them. The reverse side of connecting with some people on social networks is we are simultaneously isolating ourselves from all the other people we are not connected with. Users are implicitly creating two groups, those who belong to the circle and those who don’t. We create safe bubbles for ourselves where only the echo of our own preferences can be heard, and opposing views never get a chance to be heard. It’s like a child who is given the option to only ever eat what they please, and exclude anything they don’t know or like, as on Willy Wonka and the Chocolate factory. Seducing if you’re 5 years old perhaps, but would you really enjoy only eating snacks and candy for the rest of your life?

Alternatives

So what can we do to increase the serendipity of the systems we design? Being aware of the issues is already a good first step, I suspect what happens is designers specify a recommendation system but never get down to thinking about the details of how the algorithm behind actually works. So it’s up to the developers to decide and this usually defaults to the “top averages” approach. In most cases no-one even notices these funneling effects taking place.

Search – revisited

In the case of search there is no easy answer, it’s supposed to be functional and work as a mirror. What can help in some contexts is to offer alternative search terms to be combined with the initial query, or showing loosely related results clearly identified as such. Google has changed their results page while this article was being written and now it sometimes displays a “Something different” category as part of the faceted navigation for filtering results. For example if you type “Johnny” it assumes you’re looking for Johnny Depp and under “Something different” it suggests “george clooney, orlando bloom, matt damon, leonardo dicaprio, brad pitt”

It’s going through the most popular results, doing a semantic analysis that identifies “Johnny Depp” as a popular film star and suggesting other popular film stars. This is the El Dorado of semantic search, and it is great to a certain extent, but is not without its problems, for example Johnny Holland or Johnny Hallyday won’t get a chance of being suggested because they don’t fit the semantic class of “hollywood film star”. Funneling towards the most popular expressions is still firmly in place.

Spezify for example does something interesting, it displays related search terms next to the initial search term, so when you click on them the corresponding search is executed. I must admit I am puzzled as to how they determine which terms to display, as sometimes the relation is really unclear. Regardless, the result is you can certainly enjoy a chase down the proverbial rabbit-hole by surfing the suggested search terms. A very successful serendipity tactic, albeit overly random at times.

Categorisation – revisited

A few years ago while working on the information architecture for a VOD catalogue of independent cinema I noticed their films didn’t fit the traditional genres (drama, action, comedy,…) so devised a different system based on assigning multiple tags to each film and proposing a navigation system that would let users switch from tag to film to tag. This enabled a high degree of serendipity without the unwanted randomness. This wasn’t implemented but it’s certainly a viable model.

I built a prototype to test the idea and after some experimentation came up with a second model similar to Spezify’s but with a distinct difference: As you search for a keyword, you receive a set of results, each one with its related set of tags. These tags become the suggested tags. Clicking on a suggested tag adds it to the search query, and filters the results. So it’s like progressive filtering, the suggested keywords get added to the mix as your search query grows in size. This model provides a high degree of serendipity without the unwanted side-effect of making suggestions appear random, there is a logical progression as the user creates a growing query. Here is a screenshot of the prototype:

Social recommendation – What else?

Other possible tactics are…

Following strangers

Instead of presenting recommendations based on a user’s peers, show instead recommendations based on the preferences of non-peers, and identify them as such.

Parsing multiple sources of information

During UX Lisbon 2011 Chris Fahey suggested having an app collecting a user’s information from multiple platforms (eg facebook+twitter+last.fm+amazon+netflix) and using the information from multiple sources to make more intelligent suggestions. I suspect Google is about to pull this sort of trick in the near future, if your Google account becomes your identifier across several products (eg. google+, picasa, mail,youtube, maps), it would be easy to combine it all and make more accurate predictions. The prospect will send a chill down the spine of privacy advocates…

Friend of a friend of a friend

Considering the 6 degrees of separation theory, which has some flaws but is nevertheless interesting for our purposes, a system could make recommendations based on indirect relations, for example, showing what films a friend of a friend likes to watch.

Seeing the world through someone else’s eyes

Twitter has recently introduced a nice little feature which lets you see Twitter the way someone else does, and jump between the people you follow. What if this was extended to the people you’ve never heard of? Google+ is doing that really well, you can merrily jump from profile to profile in a highly serendipitious way.

Polysemy

Because images can have multiple meanings and are more open to interpretation than textual information, they can be used as powerful suggestion mechanisms. If combined or sequenced in certain ways they encourage free association of ideas and discovery.

Get our more

It’s good to let go of digital environments and let real life surprise us. Encourage your users to go out more and place themselves in situations they’re not usually comfortable with, nudge them to venture beyond their comfort zones. Location-based mobile apps are the key here.

There’s a lot to be done in this area and I encourage you to take these issues in consideration when you’re next creating your design solutions, and remember how algorithms can limit user’s choices in a detrimental way.