Integrating Course Related Search and Bookmarking?

Not surprisingly, I’m way behind on the two eSTEeM projects I put proposals in for – my creative juices don’t seem to have been flowing in those areas for a bit:-( – but as a marking avoidance strategy I thought I’d jot down some thoughts that have been coming to mind about how the custom search project at least might develop (eSTEeM Project: Custom Course Search Engines).

The original idea was to provide a custom search engine that indexes pages and domains that are referenced within a course in order to provide a custom search engine for that course. The OU course T151 is structured as a series of topic explorations using the structure:

– topic overview
– framing questions
– suggested resources
– my reflections on the topic, guided by the questions, drawing on the suggested resources and a critique of them

One original idea for the course was that rather than give an explicit list of suggested resources, we provide a set of links pulled in live from a predefined search query. The list would look as if it was suggested by the course team but it would actually be created dynamically. As instructors, we wouldn’t be specifying particular readings, instead we would be trusting the search algorithm to return relevant resources. (You might argue this is a neglectful approach… a more realistic model might be to have specifically recommended items as well as a dynamically created list of “Possibly related resources”.)

At this point it’s maybe worth stepping back a moment to consider what goes into producing a set of search results. Essentially, there are three key elements:

– the index, the set of content that the search engine has “searched” and from which it can return a set of results;
– the search query; this is run against the index to identify a set of candidate search results;
– a presentation algorithm that determines how to order the search results as presented to the user.

If the search engine and the presentation algorithm are fixed, then for a given set of search terms, and a given index, we can specify a search term and get a known set of results back. So in this case, we could use a fixed custom search engine, with know search terms, and return a known list of suggested readings. The search engine would provide some sort of “ground truth” – same answer for the same query, always.

If we trust the sources and the presentation algorithm, and we trust that we have written an effective search query, then if the index is not fixed, or if a personalised ranking algorithm (that we trust) is used as part of the search engine, we would potentially be returning search results that the instructor has not seen before. For example, the resources may be more recent than the last time the instructor searched for resources to recommend, or they better fit the personalisation criteria for the user under the ranking algorithm used as part of the presentation algorithm.

In this case, the instructor is not saying: “I want you to read this particular resource”. They are saying something more along the lines of: “these are potentially the sorts of resource I might suggest you look at in order to study this topic”. (Lots of caveats in there… If you believe in content led instruction, with students referring to to specifically referenced resources, I imagine that you would totally rile against this approach!)

At times, we might want to explicitly recommend one or two particular resources, but also open up some other recommendations to “the algorithm”. It struck me that it might be possible to do this within the context of a Google Custom Search approach using “special results” (e.g. Google CSEs: Creating Special Results/Promotions).

For example, Google CSEs support:

– promotions: “A promotion is simply an association between a pre-defined set of query terms and a link to a webpage. When a user types a search that exactly matches one of your query terms, the promotion appears at the top of the page.” So by using a specific search term, we can force the return of a specific result as the top result. In the context of a topic exploration, we could thus prepopulate the search form of an embedded search engine with a known search phrase, and use a promotion to force a “recommend reading” link to the top of the results listing.

Promotion links are stored in a separate config file and have the form:

<Promotions>
  <Promotion id="1"
    queries="wanderer, the wanderer" 
    title="Groo the Wanderer" 
    url="http://www.groo.com/"
    description="Comedy. American series illustrated by Sergio Aragonés."
    image_url="http://www.newsfromme.com/images5/groo11.jpg" />
</Promotions>

– subscribed links: subscribed links allow you to return results in a specific format (such as text, or text and a link, or other structured results) based on a perfect match with a specific search term. In a sense, subscribed links represent a generalised version of promotions. Subscribed links are also available to users outside the context of a CSE. If a user subscribes to a particular subscribed link file, then if there is an exact match against of one the search phrases in the subscribed link file and a search phrase used by a subscribing user on Google web search (i.e. on google.com or google.co.uk), the subscribed link will be returned in the results listing.

In the simplest case, subscribed links can be defined at the individual link level:

If your search term is an exact match for the term in the subscribed link definition, it will appear in the main search results page:

It’s also possible to define subscribed link definition files, either as simple tab separated docs or RSS/Atom feeds, or using a more formal XML document structure. One advantage of creating subscribed links files for use within in custom search engine is that users (i.e. students) can subscribe to them as a way of augmenting or enhancing their own Google search results. This has the joint effect of increasing the surface area of the course, so that course related recommendations can be pushed to the student for relevant queries made through the Google search engine, as well as providing a legacy offering: students can potentially take away a subscription when then finish the course to continue to receive “academically credible” results on relevant search topics. (By issuing subscription links on a per course presentation basis (or even on a personalised, unique feed per student basis), feeds to course alumni might be customised, or example by removing links to subscription content (or suggesting how such content might be obtained through a subscription to the university library), or occasionally adding in advertising related links (so if a student searches using a “course” keyword, make recommendations around that via a subscribed links feed; in the limit, this could even take on the form of a personalised, subscription based advertising channel).

Another way in which “recommended” links can be boosted in a custom search result listing is through boosting search results via their ranking factors (Changing the Ranking of Your Search Results).

In the case of both subscribed links and boosted search results, it’s possible to create a configuration file dynamically. Where students are bookmarking search results relating to a course, it would therefore be possible to feed these into a course related custom search engine definition file, or a subscribed link file. If subscribed link files are maintained at a personal level, it would also be possible to integrate a student’s bookmarked links in to their subscribed links feed, at least for use on Google websearch (probably not in the custom search engine context?). This would support rediscovery of content bookmarked by the student through subscribed link recommendations.

Just by the by, a PR mailing in my inbox today threw up another example of how search and bookmarking might be brought more closely together: SearchTeam (screenshots [pdf]).

The model here is based around defining search contexts that one or more users can contribute to, and then saving out results from a search into a topic based bookmark area. The video suggests that particular results can also be blocked (and maybe boosted? The greyed plus on the left hand side?) – presumably this is a persistent feature, so if you, or another member of your “search team” runs the search, the blocked result doesn’t appear? (Is a list of blocked results and their corresponding search terms available anywhere I wonder?) In common with the clipping blog model used by sites such as posterous, it’s possible to post links and short blog posts into a topic area. Commenting is also supported.

To say that search was Google’s initial big idea, it’s surprising that it seems to play no significant role in Google’s offerings for education through Google Apps. Thinking back, search related topics were what got me into blogging and quick hacks; maybe it’s time to return to that area…