How our addiction to meaning benefits tech comm

Join me for my presentation “Addicted to Meaning: How Good Technical Communication is Like Bad Magic Tricks” at tekom/tcworld in Wiesbaden on Tue 23 Oct at 1:45 pm.

In the session, I will explore how “meaning” works in technical communication, why it sometimes fails and how you can improve its chance for success. Being meaningful in your work is harder to measure than being correct, concise or consistent. However, it is just as essential: Understanding how and why communication is meaningful to your readers can help you to make your documentation more effective and to distinguish good from bad.

Using examples from topic-based authoring and minimalism, I will illustrate the underlying working of semiotics and mental models to show:

• Why minimalism works, but FAQ’s don’t
• Why asking a friend is effective, even when he doesn’t know the answer
• How readers create meaning from documentation
• … and how good documentation is like bad magic tricks

I will put our familiar tech comm tool box into a new context, so you can get a deeper understanding and a fresh perspective on tech comm and how it fits into the bigger picture of meaningful communication.

I’ve set up the topic in two earlier posts which give you an idea how I’ll tackle the issue:

• But do we create documentation with meaning?
• How meaning works in technical communication

Improve tech comm by knowing a foreign language

Knowing a second language can help your tech comm work in a couple of ways. The benefit is probably not great enough in itself to justify learning a language, but if you have or had other reasons, it’s worth to consider these side benefits.

Making decisions in a foreign language

I got to think about this when I read a story in Wired that “thinking in a second language reduced deep-seated, misleading biases”. Psychologists at the University of Chicago conducted a study (abstract, full text in PDF) that asked “Would you make the same decisions in a foreign language as you would in your native tongue?”

In a foreign language, we use the same experiences and processes to evaluate situations and estimate risks. However, “a foreign language is like a distancing mechanism. It’s almost like you’re a slightly different person,” says Boaz Keysar who led the study (Business Week). According to the study, thinking in a non-native language emphasizes the systematic, analytical reasoning process. Thinking in our native tongue, on the other hand, leaves more room for the complementary intuitive, emotional decision process: “The researchers believe a second language provides a useful cognitive distance from automatic processes, promoting analytical thought and reducing unthinking, emotional reaction” (Wired). (Whether an analytical process yields “better” decisions is an entirely different story…)

Making tech comm better with a foreign language

For the past 12 years that I’ve worked full-time as a tech writer, I’ve written almost exclusively in my second language English, though I did occasionally translate my English writing into my native German. The study’s conclusion that a second language provides a “useful cognitive distance … promoting analytical thought” explains what I’ve experienced in my work in either language, beyond the limits of actual study:

1. A second chance to learn how language works. Many writers I’ve talked to have a solid grasp of their native tongue, but cannot necessarily explain the rules why something is right or wrong. When you learn a second language consciously, you also learn about grammar (again), its powers and limitations. And you can understand how something what works in one language can be similar or even different in another. For me, writing in English certainly made me a more conscientious “grammarian” in either language.

2. Mirroring the “distance” of users. In my experience, the distance that a second language brings is basically pragmatic incompetence: In a foreign language, I’m not as fully aware of the social context, of how time, space and inferred intent contribute to any communication act. I may trip over an idiom I don’t understand, or I may fail to see the irony of a statement and take it at face value.

In tech comm, this linguistic challenge is actually a benefit, because many of my readers share in that distancing experience. My readers may read my documentation in their second language. Or they might use the product in a context and for a purpose that is more or less different than intended and documented. This is why localization is harder than just translation. Internationalization can even become an accessibility issue, when a product no longer works properly in a certain context. So facing similar pragmatic uncertainties makes me a better advocate of the users I write for.

Your turn

If you know a second language, do you find it helps your writing? Do you have other reasons or benefits beside the ones I listed?

A. Ames & A. Riley on info experience models at STC12

Andrea Ames and Alyson Riley, both from IBM, presented a dense whirlwind tour on “Modelling Information Experiences” that combine four related models into a heavy-duty, corporate information architecture (IA).

While the proceedings don’t include a paper on this session, Andrea posted the slides, and the presenters published a related article (login required) “Helping Us Think: The Role of Abstract, Conceptual Models in Strategic Information Architecture” in the January 2012 issue of the STC’s intercom journal.

The session proceeded in six parts. First, Alison explained IA models in general and how they work. Then Andrea described each of the four model types that make up an IA specifically.

IA models as science and art

Information architecture relates to science as its models draw on insights and theories of cognition. And its models relate to art as they aim to create a meaningful experience. Both aspects are important. Only if IA models manage to blend science and art can they touch the head and the heart.

The session focuses on IA models instead of theories (which are too vague and abstract) or implementations (which are too specific and limited). Thanks to the in-between position of IA models, we can use them to

• Ask the right questions to arrive at a suitable, feasible IA
• Tolerate the ambiguities of “real life”

Models present descriptive patterns, not prescriptive rules. They don’t say how stuff must be, but how it can be represented. They differ from real life, but real life is still recognizable in them.

That means you cannot simply implement a model on autopilot and get it right. Instead, you have to

• Think how to implement the model
• Vary the model for your users’ benefit
• Listen to the right knowledgeable people when implementing

Models help you think

To arrive at your concrete IA, you take the model’s abstract patterns and apply your project-specific details to them, the who, what, where and when.

This task is less mechanical and less copy-and-paste than it sounds. It’s not so much a question of following rules and recipes, but of making abstract patterns come to life in a coherent, flexible whole. (If you’ve ever tried to create meaningful concept or task topics by following an information model, you know it’s more than just filling in a DITA template. You need to use your own judgment about what goes where to achieve and maximize user benefit.)

Since there are four related models, you need to think carefully how each of these models should benefit your users. And the models help you think, they scale and adapt to:

• How your business and its information requirements develop over time, how they grow and expand in desired directions
• How your customers find, understand and apply the information they need

The four IA model types

The IA model that Andrea then explained consists of four related model types:

use model (content model + access model = information model)

Each of these model types needs to be developed and validated separately.

The use model defines how users interact with information. It describes standard scenarios for optimal user experience within the product or system context. It outlines what information users need and why and how they use it. It includes use scenarios for the entire product life cycle and user personas that outline different types of users. Fortunately for us technical communicators, Andrea pointed out, describing all this is part of our core skill set.

The content model defines how information is structured effectively. This could be DITA (in the case of the company I work for, this is what we call our DITA-derived “information model”). It includes the granular information units and their internal structure, for example, task topics and their standard sequence of contained information. It also describes how these granular units are combined into deliverables.

The access model defines how users access the information efficiently. It makes provided information useable, thanks to a navigation tree, a search function, a filtering function to increase the relevance of search results, an index, etc. Artefacts of this model type are wireframes, storyboards, a navigation tree and the like.

The information model defines how users get from one stage to the next. It uses the other three model types as input and fills in the gaps. It combines the content and access models which probably work fine during the installation and configuration processes, but may not yet carry a user persona from one stage to the next. As such, the information model is sort of the auxiliary topic that you sometimes need to insert between concept, task and reference topics to make a complete book out of them. At the same time, this model type is more detailed than the use model and closer to the other two types.

My takeaways

I was extremely grateful for this session and rank it among the top 3 I’ve seen at the summit – or any tech comm conference I’ve been to! Yes, it was fairly abstract and ran too long – my only complaint is that it left only 2 minutes for discussion at the end.

As abstract as much of the session was, it actually solved a couple of problems I couldn’t quite put into words. After designing and teaching our company’s DITA-derived information model (which is a “content model” by this session’s names), I thought our model was applicable and useful, but it had two problems:

• Our model lacked context. – Now I know that’s because we haven’t mapped out our use model in the same detail and failed to connect the two.
• Our model was baked into a template for less experienced writers to fill in – with varying results. – Now I know that’s because the models are not supposed to provide templates, but require writers to use their own judgment and keep in mind the big picture to deliver effective information.

Another lesson I learned is that many structured information paradigms seem to include a less rigid element that sweeps up much of the miscellaneous remnants. In DITA, there’s the general topic which is used for “under-defined” auxiliary topics to give structure, especially to print deliverables such as manuals. In this IA model, there’s the information model which fills the gaps and ensures a more seamless user experience than the other three models can ensure.

- For an alternative take, see Sarah Maddox’ post about this session.

What I learned from my pattern recognition talk at STC12

My session on pattern recognition for technical communicators was a very rewarding experience which taught me a lot. I thank Paul Mueller, Conference Manager, and Alyssa Fox, Program Committee Chair, for inviting me to speak, even though this was my first summit. Their friendly, indefatigable support set the tone for a high-energy, well-run conference.

If you want to revisit the session, here are the slides and the article from the proceedings. (If you haven’t attended the session, the article will probably be more helpful, for reasons explained below.)

Here’s a look behind the scenes of my talk and what I learned:

Two different roles

Attending a conference is not the same as speaking at one. Well, duh…

But what I mean is this: I took care to be an observant attendee before my own session, so I could gauge expectations and behaviors of attendees. Bluntly put: As attendee, I want my money’s worth. As speaker, I need to give my audience their money’s worth. Observing and knowing the first helped me achieve the second – or so I hope.

When I was still in academia, I was often put off by conference presenters whose ignorance of the audience’s interests and demands could be quite arrogant – and usually didn’t help the conference as a whole, either. So I wanted to avoid that.

Plus, one of the mantras of technical communicators is: “Know your audience!” How could I afford to ignore it at a tech comm conference?

“Unusual” works

I was unsure about my topic, because it was a bit unusual and off-the-wall: Tying the psychology of perception to technical communications – only to confirm what we do anyway, such as topic-based authoring and parallelism?

(Photo courtesy of Jamie Gillenwater)

On the other hand, I know from attending previous conferences, how much I enjoyed and benefitted from such sessions. A-ha moments are fun and enlightening, they work in TV science programs, so maybe they’ll work at a conference as well…

During the session, I was too busy to count heads, but I’m guessing I might have had an audience of 70 people maybe. There were other sessions to choose from. Or breakfast, since I was in the 8:30 slot. So I decided early on to get over my worries and trust in the general curiosity of tech writers.

Rehearsing pays

So practicing helps… Again: Well, duh…

Specifically, it allowed me to move beyond bullet points. I’ve seen many a session (less so at the summit) where presenters mainly read their slides. To me, those are usually not the best presentations. I don’t need great showmanship, but reading the slides seems as if the speaker serves the slides rather than vice versa.

I’ve tried to make at least the a-ha moments less reliant on words and bullet lists and more like an illustrated story. And I’ve found that decent images will remind me of the story just fine. (The last section about actually applying pattern recognition in tech comm has more bullet lists, so people could take notes.)

In addition, I found that rehearsing also helps me to “know time” (a pet obsession of mine; I even have a blog post about it). I’ve seen excellent presentations, but it peeves me a bit if they take up 58 of 60 minutes. I also learn by asking questions and by engaging with the speaker or others in the audience. And to me, it seems a bit careless to mar an excellent session by running overtime.

Budget your energy

I am really glad (and almost a little proud) that we’ve had such a lively, engaging discussion after my presentation. People suggested additional sources, propped up some of my arguments and ran with others, bringing up evolution, Edward Tufte’s information graphics and – privately afterwards – even Immanuel Kant!

My one regret is that by that time I was a bit exhausted and didn’t always do justice when repeating the question or comment for everybody in the room and for the recording. Not sure how I can achieve it, but I want to save not only time, but also energy to facilitate the discussion afterwards.

But all in all I think the session went well, I’ve really enjoyed the experience and am glad to contibute to our curious, friendly and supportive tech comm community!

Art vs. online: 2 dimensions of curating

Curating is a cool word, or trendy jargon, for what happens in web technologies and in art museums, but they are fundamentally different activities.

In this post, I want to add an alternative view to Rachel Potts who recently wrote about “When software UX met museum curation“. Where Rachel emphasises similarities, I’d like to focus on the differences, especially as they relate to art museums.

Your artefacts

One serious limitation and difference in curating at art museums, compared to anything in software and online, is that you need to care for original, unique works. If you mount a special exhibition, you need to procure them to begin with. And sometimes you cannot get them, no matter how much you want them in the show to present an artist or an era in history or to make your case.

• Some works don’t travel because they’re fragile or because the insurance is too costly or because they’re centerpieces in the collection that owns them.
• Some owners won’t lend works to you, because you cannot satisfy security requirements or because you’re too small a museum or because they don’t like your director.
• Some works are simply lost.

Of course, you can always do with fewer or lesser works or, in the case of historic artefacts, with copies, but that invariably hurts the critical response and your attendance.

"Stalking Christina" - other people regarding my favorite painting

Your objectives

Another difference is that for many art museums “enabling users to learn” is one objective among many. And several other objectives are, unfortunately, at odds with it:

• Some pieces are too sensitive to light or touch or movement to allow more than very few people to experience them.
• Some museums need to please or placate donors (who may influence what’s shown and what not) and trustees (who may influence what gets paid for and what not).
• Some museums don’t have the means: They lack the manpower to accommodate visitors more than a few hours per week. Or they don’t have the expertise to allow them to learn well.

Your audience

A third difference is that art museums who put on ambitious, critically well-regarded exhibitions find that attendance is surprisingly low. The reason is simple and disappointing: Many people don’t want to be enabled to learn in art museums. They don’t want to learn new things, much less have their beliefs challenged. Instead, many people visit an art museum, because of the way it makes them feel:

• Many go to be in the presence of beauty or to be awed. Hence the success of any show whose title mentions a best-selling artist or any of the words “Impressionist”, “Gold” or “Gods” – even if the title is far-fetched and the show mediocre at best. “Dinosaurs” gets kids, and anything that flies or shoots gets their dads.
• Some go to feel cool. Hence the success of after-work parties in modern art museums.

The words

Roger Hart once told me, it’s futile to try to stop linguistic change. And the web is a great change agent of language:

• How many kids today know that women warriors (or a river) gave their name to an online store?
• The German language has known about “email” for centuries (though we only spell it thus after a recent change in orthography); in English, it’s known as “enamel”.

But if language is to represent the real world, I advocate to respect the differences within one word, such as curating. Conflating two similar activities into the same word cheapens our experience of the stuff that surrounds us.

Proving the benefit of consistency in tech comm

To ensure efficiency and accessibility of technical communications, use consistent, common formatting, for example, for interface elements. What sounds obvious to many technical communicators is actually proven in academic studies. This post is for people looking for proof that consistency has a benefit in technical communications.

I’m taking my cue from a question that appeared in a LinkedIn group:

[I need to] find studies or tests that show that it is value-adding to have consistent formatting on e.g. User Interaction elements (such as buttons or handles) in your instructive documents. Can anyone share studies or tests in this area?

I can offer an answer on two levels:

• The general benefits in terms of human perception
• The particular benefits of consistent documentation

The neuroscience of consistency

Human perception favors consistency. The mind groups things easier, faster and with more confidence, if they’re consistent and have something in common.

Gestalt law of similarity: The mind groups similar elements into collective entities, from Wikipedia.

Psychological studies have shown two principles by which human perception groups things: Proximity and similarity. For a comparison of these two principles and further references, see Han, S., Song, Y., et al. (2001). Neural substrates for visual perceptual grouping in humans. Psychophysiology, 38, 926-935. Han and colleagues actually quote several studies that “proximity is a more salient cue than similarity.”

In technical comunications texts, we usually can’t practically lump all names of windows or fields across all topics together to show they’re related.

But we can resort to similarity to help readers understand that we mean a GUI element at each occurrence. If we always write their names in bold, for example, readers will recognize that similarity across topics and learn to scan for it (whether they’re aware of it or not). If we always mark up GUI elements somehow, sometimes in bold, sometimes in italics, readers are more likely to wonder if the different markup has a meaning – and they won’t be able to scan the text as quickly and reliably.

For more psychological research and how it applies to technical communications, refer to Chris Atherton‘s excellent and accessible article “What and where?” in ISTC’s Communicator quarterly, Spring 2011, pp. 28-29.

Studies of applied consistency

So much for the theory. But does consistency, for example in formatting GUI elements, have an actual benefit in documentation?

One very good resource to argue for such consistency is the Research-Based Web Design & Usability Guidelines. This 292-page tome sets out “to provide quantified, peer-reviewed Web site design guidelines”. It was published by the US Department of Health and Human Services in 2006 and is available for free download, as a book and as individual chapters.

Chapter 11 on “Text Appearance” has a couple of applicable guidelines:

#2 Format common items consistently

Common, recurring items, such as telephone numbers, time records, button and window names should be formatted consistently, according to expert recommendations in: Ahlstrom, V. & Longo, K. (2001). Human factors design guide update (Report number DOT/FAA/CT-96/01): A revision to chapter 8 – computer human interface guidelines.

#4 Ensure visual consistency

Visual consistency, including the appearance of characters in interfaces, reduces user errors. “Studies found that tasks performed on more consistent interfaces resulted in (1) a reduction in task completion times; (2) a reduction in errors; (3) an increase in user satisfaction; and (4) a reduction in learning time.” The quoted studies include:

• Adamson, P.J. & Wallace, F.L. (1997). A comparison between consistent and inconsistent graphical user interfaces. Jacksonville: University of Northern Florida, Department of Computer and Information Sciences.
• Eberts, R.E. (1997). Cognitive modeling. In: G. Salvendy (ed.), Handbook of Human Factors and Ergonomics, 2nd ed., New York: John Wiley & Sons.
• Ozok, A.A. & Salvendy, G. (2000). Measuring consistency of web page design and its effects on performance and satisfaction. Ergonomics, 43(4), 443-460.
• Schneider, W., Dumais, S.T. & Shiffrin, R.M. (1984). Automatic and control processing and attention. Varieties of Attention. New York: Academic Press, 1-27.
• Schneider, W. & Shiffrin, R.M. (1977). Controlled and automatic human information processing: detection, search and attention, Psychological Review, 84, 1-66.

Specifically, Ozok and Salvendy in 2000 confirmed the earlier studies that visually inconsistent interfaces lead users to poorer performance and more errors, see the summary in the Human Factors International newsletter.

- I hope this little field trip into academia can help you to prove that consistency not merely seems somehow desirable and logical, but has actually cognitive benefits proven in studies.

“Statistics without maths” workshop at #TCUK11

Technical Communication UK 2011 is off to good start with around 100 people attending six pre-conference half-day workshops on Tuesday. Even the night before saw about 20 attendees joining the organisers to help with last-minute setup chores, not to mention drinks and dinner.

On Tuesday afternoon, I attended the workshop “Statistics without maths: acquiring, visualising and interpreting your data” by Mike K. Smith, Chris Atherton and Karen Mardahl.

Mike K. Smith encourages us to insist on hard evidence

The workshop was virtually free of math in terms of formulas and calculations. Nonetheless, its introduction of concepts such as different average measurements mean vs. median vs. mode, or such as standard deviation vs. standard error challenged tech communicators. Personally, I’m more familiar with the finer points of language, not mathematical concepts, so it was a bit of a stretch for me.

The focus, however, was on general principles that give well-done statistics the power to infer a greater whole from representative data:

• Strength of evidence, meaning the amount of data is large enough
• Quality of data, meaning the data is good and useful to answer the question

A simple example illustrated these points:

1. Survey a group of people whether they like Revels, a British candy that comes with different fillings and hence different flavours, in general.

2. Hand out one Revel each to a smaller group of people and ask them how many liked the specific Revel they were given.

Frequently, the results of #2 are interpreted to mean #1. And that’s not even taking into consideration the alternative suggested by the workshop audience:

3. Watch a smaller group eat Revels (best without their knowing that they’re being watched) and draw your your conclusions how many really like Revels.

Another principle that was presented and discussed was that correlation measured by studies and statistics is not the same as causation: Two things that frequently or always occur together don’t mean that one causes the other. They could both be caused by a third overarching force. Or maybe there’s no causal relation between them at all…

The workshop about these concepts with dozens of examples also showed up a few cultural differences: Statisticians seem to strive for accuracy and precision to the point of not quite intelligible anymore, at least not outside their peer group.

I think some of the finer points about the definitions of averages and standard measurements (see above) were lost on some of us tech comm’ers. Still, the general message resonated with many: Statistics deserve close scrutiny, for the numbers they present, for the conditions in which they were measured and for the questions they seek to answer.

As Mike Smith put it towards the end:

What do we want?
Evidence-based change!
When do we want it?
After peer review!

Are serifs or non-serifs easier to read?

Are serif fonts easier to read than non-serif ones? No, is the conclusion in Alex Poole’s exhaustive post.

After reviewing more than 50 studies, Alex comes to the conclusion that “serifs or the lack of them have an effect on legibility, but it is very likely that they are so peripheral to the reading process that this effect is not even worth measuring”.

Serif vs. non-serif fonts as illustrated by Wikipedia

Thanks to Chris Atherton for pointing me to this post which perfectly complements one of my most popular posts: Ragged-right or justified alignment?

I must admit this came as a surprise to me, since I always liked the argument “Serifs are used to guide the horizontal ‘flow’ of the eyes”. But unfortunately, simply liking an argument doesn’t make it so. It might just be a case of confirmation bias, as in this case…

Typography primer

Today’s bonus link is to a cool, comprehensive article in Smashing Magazine about typography including justified text, hyphens, dashes, smart and dumb quotes: Mind Your En And Em Dashes – Typographic Etiquette.

Recommended read: Degree helps tech writers

Here’s an example of how graduates of a tech comm program put their skills to use in a high tech company.

Specifically, National Instruments at Austin contributes two guest posts to the Texas Tech STC Student Chapter blog. Yes, these are recruiting posts, but they do ring true. I think they reflect well how a company can take tech comm seriously and benefit from the formal training and academic skills of graduates. It may be an exception among the employers of tech writers, but it’s an encouraging example. So I recommend these two posts:

Tech Writing…It’s Not Just For Tina Anymore
… asks Texas Tech alumni who now work as tech writers and their managers how they can apply what they’ve learned and what advice they have to students and prospective employees:

• Rayo emphasize task analysis before tool skills – which I’m glad to hear.
• Robin gives SME interviews a reality check that strikes a balance between what’s desirable and what’s possible.
• Heather explains how she determines which documentation formats get produced.

Technical Writing Skills
… has Nathan Stokes list and explain 5 skills which every tech writer can benefit from – especially in this less than obvious combination:

• Editing, including yourself: If you can edit yourself, you’ll be a better writer.
• Productivity, i.e., project management skills to keep yourself organized.
• Enthusiasm
• Curiosity, which can drive you and help you to grow.
• Computer Knowledge

- Previously in these pages, I’ve covered a how a (non-tech comm) degree can help a tech writer.

Your turn

Are these skills useful in the tech writer workplace? Can you learn them – or at least expect to learn them – in a tech comm program? If you have any thoughts or experience, please leave a comment.

How a degree helps a technical writer

A college degree can help you in technical writing, though maybe not in the ways you expect.

How relevant is a college education for the field of technical communication? A couple of very good and influential tech writing blogs have recently discussed this issue:

• Why Tech Comm Is a Career Path of Last Resort for Students, by Tom Johnson, about an English department
• Rhetorical theory vs. tech comm reality, by Ryan Fulcher, Scriptorium, about a tech comm program

The question is both pertinent and impertinent, because Tom and Ryan frame it in a way that devalues college education [... at least in the specific program they are criticizing, see Ryan's comment below. 18 Nov 2010]. Tom says tech comm “should not be taught in the context of an English department, because [it] is not understood or encouraged in traditional English curricula.” Ryan says he’s gained more useful knowledge in 5 months in the job than in his entire time in the tech comm MS program. I cannot argue with their experiences, and I cannot hope to convince them otherwise.

To help anyone with similar questions who’s in college now or has recently graduated, I can offer my own alternative assessment: How a college degree helped me become a passionate and, I dare say, good technical writer.

What I sought

Computer Science and Business as a combined major was how I started college. I sought to learn how to build software and how to run a business. What I got was learning by rote, too much how it’s done and not enough why and how it could be done better. I dropped the major after a semester.

I had embarked on rational and reasonable education and found that my heart wasn’t in it. I just couldn’t see myself spending several years getting a degree as a means to an end. I expected college to teach me something that was interesting in its own right, not a promise that I could apply it in a future job, or maybe not.

American Studies is what I declared as my major after two weeks of soul searching. That’s where I found my academic home. The curriculum was heavy on literature, social history and culture. The emphasis was on understanding what holds the USA and its culture together, to come to terms with its cultural and artistic developments, and to use whatever academic theories could be made useful.

Over ten years ago, I’ve received my M.A. in American Studies. I’m a technical writer by choice and practice, with the heart and the outlook of an Americanist.

What I learned

In American Studies, I learned a lot of things. Almost all of them do not directly relate to technical writing. Here are some things that I’ve found useful and applicable as a technical writer:

• How to write long, coherently argued, understandable papers in correct English. It took me a long time to get it right. It took me longer yet to realize the importance of tailoring my message and language to my audience. And it took me even longer to realize that all this combined is a rare and marketable skill.
• How to explain something that defies explanation to people who think they already know how it works. After you’ve ever tried to explain America to Germans who have it all figured out from movies and news media, writing user manuals is actually pretty easy. Most products I’ve dealt with are less complex than a country of 310 million people, even if you only regard the most recent 400 years.
• How to cope with complexity. Literary studies can appear pretty neat, especially when you deal with only one author or one book. Studying a country and its culture is a more daunting task, not least because the people carry on so, with no regard for your studies. Trying to keep your insights reconciled with an ever-changing reality is a good preparation for your survival in large corporate environments and their organisational quirks.
• How to organize to finish. Formulating a thesis and then framing and arguing it was part of my later assignments. That was a good preparation for writing user manuals from scratch. The “thesis” in that case is the easy part. The customer wants to do stuff with the product and look cool while doing it. But the rest is again up to the writer: Framing the text in a context of use, consulting all available sources, explaining it in the most understandable and most efficient way.
• A turn of phrase occasionally: That a question can be both pertinent and impertinent (see above) is something that I learned from Thoreau’s Walden, the second paragraph of the “Economy” chapter to be precise.

What I know now

A college education can work very well for you, if you take it for what it is and don’t expect something that it isn’t. Stanley Fish makes a very astute argument for what a university can be and do:

When it comes to justifying the humanities, the wrong questions are what benefits do you provide for society (I’m not denying there are some) and are you cost-effective. The right question is how do … your program of research and teaching fit into what we are supposed to be doing as a university.

It’s important to realize that this kind of education comes with no guarantee: It guarantees you neither a job, nor happiness, nor that you’ll always be right or make the right decisions.

But it gives you the tools to gather information, take responsibility and make the decisions that affect your life. In short, such an education can give you hope. In the words of Václav Havel, writer, dramatist, and the first President of the Czech Republic:

Hope … is not the conviction that something will turn out well, but the certainty that something makes sense, regardless of how it turns out.

Your turn

What did you get out of a college education? Was it useful for immediately applicable skills? Was it instrumental to become who you are? Or was it a waste of time?