Applying SEO to the Course Catalogue

Just before Christmas I gave a talk at the department awayday that I’d intended to do in the style of a participatory lecture but, as is the way of these things, it turned into a total palaver and lost most of the lunch-addled audience within the first 20s;-)

Anyway, anyway, one of the parts of the talk was to get everyone to guess what course was being described based on a tag cloud analysis of the course description on the corresponding page of the course catalogue (got that?)

Here’s the relevant part of the presentation:

(The course codes were actually click-revealed during the presentation.)

Note that the last slide actually shows a tag cloud of the search terms that brought visitors into the OU website and delivered visitors to the specified course page, rather than a tag cloud of the actual course description.

See if you can spot which is which – remember, one of the following is generated from the actual course description, the other from incoming search terms to that page:

2009-01-12_2319

T209 description tag cloud

I’m not going to explore what any of this “means” in this post (my blogging time is being increasingly sidelined, unfortunately:-( suffice to say that whilst I was giving the original presentation I heard my self strongly arguing something along the lines of the following:

It’s pointless writing the course description on the course catalogue web pages using the terminology you want students to come out of the course with (that is, using the language you expect the course to teach them). What the course description has to do is attract people who want to learn those terms; so YOU have to use the words that they are likely be using on Google to find the course in the first place.

It strikes me that a similar sense of before/after language might also apply to the way we phrase learning objectives at the start of a learning activity in everyday, why we’re bothering learning thisat all, type language, and then clarify the learning outcomes in jargon heavy, terminology laden, worthy sounding terms at the end of the activity?;-)

See also: Measuring the Success of the Online Course Catalog, which looks at the design of a course catalogue from an SEO/actionable analytics point of view.

Author: Tony Hirst

I'm a Senior Lecturer at The Open University, with an interest in #opendata policy and practice, as well as general web tinkering...

7 thoughts on “Applying SEO to the Course Catalogue”

  1. Tony

    You make a very good point and SEO work looking at search terms can increase traffic to a site – we do it on OpenLearn to great effect. But there is the other side of whether the words on the site they come to matches their perceived needs and that often becomes more specific and the search terms possiby longer into search phrases. Otherwise they just bounce off elsewhere. Now course descriptions are of necessity short and do need to be written up in better ways for both online and print but a better match between search terms and need comes more from having some of the content freely available, as on OpenLearn, as the spidering of all the content as well as metadata increases the range of terms and can hit the long tail of increasingly speficic search needs. You can see the effect in searches for very specific terms like Hume or Eutrophication or IT and Computing where OpenLearn often comes out in the first 10 returns.

  2. interested to know what software you used to produce your tag clouds, they look a lot more interesting than the ones I’ve produced with tagcrowd.com

  3. Tony,

    This reply is a bit late, but thanks so much for the mention.

    I completely agree that it’s time we re-work our course catalog descriptions. The issue is two-fold, and I do acknowledge that these are generalizations. On most higher ed websites, course descriptions are still written by those who are writing for print, and not versed in SEO or writing for the web.

    Also, in traditional higher ed, it’s hard to convince schools to stop writing their website content in academic-speak, even when you show them data that clearly shows users are *not* searching for the terms contained in their descriptions.

    Shelby

  4. @shelby “Also, in traditional higher ed, it’s hard to convince schools to stop writing their website content in academic-speak, even when you show them data that clearly shows users are *not* searching for the terms contained in their descriptions.”

    So play a game… Here’s something I’ve thought about before ( http://ouseful.open.ac.uk/blogarchive/009726.html ) and will repost here:

    —————
    Take one prediction market framework (something like Betocracy for example), add possible successful course predictions in place of the bets and combine it with something like the search engine referrers stats from a web analytics package (like Google Analytics for example) to feed automated bets into the system…

    In an OU ‘what courses should we be providing?’ sense, imagine something like the following:

    Example 1:

    1. Descriptions of potential courses are posted as ‘stories’;
    2. students vote on the ones they like.

    Result: potentially popular courses float to the top.

    Example 2:

    1. Descriptions of potential courses are posted as ‘stories’;
    2. search terms used by students on the OU course catalogue are treated as votes that can be cast;
    3. a vote is given to a story/proposed course if a search term that would lead to the story/proposed course is used on the course catalogue.

    Result: predicted potentially popular courses float to the top.

    Example 3
    As Example 2, except that votes are only cast if the proposed course would be returned as one of the top three (?) ranking results if it were included in a live/real search.
    Result: a more robust version of 2, assuming people only click through the top few hits!

    Example 4
    As Example 2, except that votes are only counted if a student clicks through to request further materials (or even better, actually clicks through th register) on a real course after using search terms that would have turned up the proposed course (maybe as one of the top three (?) ranking results if you want to tighten things up further) if it were included in a live/real search.
    Result: predicted potentially popular courses that people might actually register on float to the top.

    What I’m leading towards here is the idea of ‘invisible vote casting’ (predictive marketing?!) used to help identify courses that might appeal to students on the basis of their course descriptions.

    I suspect the approach could also be used as a basis of forecasting the number of students who might register on a proposed course (search powered forecasting!)?
    ————-

    So to recap, the idea is:
    – to post SEOd course decriptions, and old course decriptions, as ‘ideas’ in a prediction market;
    – take a feed of real search traffic, both organic incoming and local search;
    – run the search traffic against the stories as search terms;
    – top hit gets a vote in the prediction market;
    – compare SEOd and current pages in the rankings…

    I’ve pitched this several times over last two years, but no-one gets what I say (maybe I keep explaining the idea badly)…

    …and it’s too big a job (at the mo) for me to think I can hack it out in half an hour…

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