Years ago, I used to spend quite a bit of time playing with Google Custom Search Engines, which allow you to run searches over a specified list of sites, trying to encourage librarians and educators to think about ways in which we might make use of them. I was reminded of this technology yesterday at the a Community Journalism conference, so thought it might be worth posting a quick how to about how to set up a CSE, in particular one that searches over the websites of hyperlocals listed on LocalWebList.net. (If you don’t want to see how it’s done, but do want to try it out, here’s my half-hour hack LocalWebList UK hyperlocal CSE.)
One way of creating a CSE is to manually enter the URLs of the sites you want to search over. Another is to use an annotations file that contains the URLs of sites you want to search over. These files can be hosted on your own site, or uploaded to Google (in the latter case, there is (small) limit on the size of file you can upload – 30KB.
The simplest annotations file is a two column (URL and Label) tab separated value file containing one row per site you want to include. Typically, sites are included using a URL pattern – onthewight.com/* for example, to say “index over all the pages on the onthewight.com domain.
The data file published by the LocalWebList includes a column containing the URL for the homepage for each hyperlocal site listed. We can download the datafile and then open it in the powerful data cleaning tool OpenRefine to inspect it:
If you skim through the URLs, you might notice that several sites have simple URLs (example.com), others are a bit more cluttered (example.com/index2.html), others point to sites like facebook. I’m going to make an arbitrary decision to ignore facebook sites and define patterns based on all the pages in a single domain.
To do that, I’m going to create a new column (url2) in OpenRefine from the URL column, that defines just such a pattern based on the original URL.
The following expression:
uses a regular expression to manage just such a transformation.
I can inspect the unique values generated by this transformation by looking at a text facet applied to the new url2 column:
If you sort by count in the text facet, you will see several of the hyperlocal sites have websites hosed on aboutmyarea, or facebook. (Click on one of those links in the text facet to show the sites associated with those domains.) I am going to discount those links from my CSE, so hover over the link and click on the “include” setting to toggle it to “exclude”. Then click on the “invert” option to show all the sites that aren’t the ones you’ve selected as excluded.
This leaves us with sites that are more likely unique:
Having got a filtered lists of sites, we can generate an annotations file containing the URL patterns we want to search over and the CSE label. The label identifies to Google which CSE the URLs in the annotations file apply to. We get that code by generating a CSE…
When creating a new CSE, along with giving it a name, you;ll also have to seed it with at least one URL. Simply enter a pattern for a URL you know you want to include in the search engine.
Hit create, and you’ll have a new CSE…
From the “Advanced” tab, go to the CSE annotations area and find the code for your CSE:
Now we’re in a position to add the CSE code to our annotations file – so copy the CSE label for your CSE… We can create the annotations file in OpenRefine from the “Export” menu, where we select “Templating”:
The templating option allows us to define a custom export template. The template is built up from a header, a row separator, a footer, and a row pattern that describes how to write out each row. I define a simple template as follows, and then export the file.
(Note – there are other ways I could have done this (indeed, there are often “other ways”!). For example, I could have created a new column containing just the CSE label value, and then done a custom table export, selecting the url2 column and label column, along with the TSV output format.)
Export the annotations file and then import it into the CSE – hit the “Add” button in the CSE annotations area.
Once uploaded (and remember, there is a 30KB file size limit on this route), go back to the Basics tab: you should find that your custom search engine now lists as sites to be searched over the sites you included in your annotations file, as well as being provided with a link to your CSE.
You can tweak with some of the styling for the CSE from the “Look and Feel” menu option in the CSE admin pages sidebar.
If you now click on your CSE URL you should find you have a minimal Google Custom Search engine that searches over several hundred UK hyperlocal websites.
To add in some of the sites we originally excluded, eg the ones on the aboutmyarea domain, we could add specific URL patterns in explicitly via the CSE control panel.
Google Custom search engines can be really quick to set up in a minimal form, but can also be customised further – for example, with tweaks to the ranking algorithm or with custom annotations (see for example Search Engine Powered Courses).
You can also generate lists of URLs from things like homepage links in Twitter bios grabbed from a Twitter list (eg Using Twitter Lists to Define Custom Search Engines – that code appears to have rotted slightly, but I have a fix…Let me know via the comments if you’re interested in generating CSEs from Twitter lists etc).
As I mentioned at the start, it’s been some years since I played with Google Custom Search Engines – I was really hopeful for them at one point, but Google never really seems to give them any love (not necessarily a bad thing – perhaps they are just enough over and under the radar for Google to cut them?), and I couldn’t seem to persuade anyone else (in the OU at least) that they were worth spending any time on.
I think a few librarians did pick up on them though! And if there is interest in the hyperlocal community for seeing what we might do with them, I’d be happy to put my thinking cap back on, work up some more tutorials or use cases, and run training workshops etc etc.
Over the last few days, I’ve been tinkering with OU Structured Authoring documents, XML docs from which OU course materials – both print and HTML – are generated (you can get an idea about what they look like from OpenLearn: find a course page with a URL of the form http://openlearn.open.ac.uk/mod/oucontent/view.php?id=397337&direct=1 and change direct to content: http://openlearn.open.ac.uk/mod/oucontent/view.php?id=397337&content=1; h/t to Colin Chambers for that one;-). I’ve been focussing in particular on the documents used to describe T151, an entry level online course I developed around all things gaming (culture, business, design and development), and the way in which we can automatically generate custom search engines based on these documents.
The course had a very particular structure – weekly topic explorations framed as a preamble, set of guiding questions, suggested resources (organised by type) and a commentary, along with a weekly practical session.
One XML doc was used per week, and was used to generate the separate HTML pages for each week’s study.
One of the experimental components of the course has been a Google Custom Search Engine, that supports searches over external resources that are linked to from the blog. The course also draws heavily on the Digital Worlds Uncourse blog, a site used to scope out the design of the course, as well as draft some of the materials used within it, and the CSE indexes both that site and the sites that are linked from it. (See eSTEeM Project: Custom Course Search Engines and Integrating Course Related Search and Bookmarking? for a little more context around this.)
Through using the course custom search engine myself, I have found several issues with it:
1) with a small index, it’s not really very satisfactory. If you only index exact pages that are linked to from the site, it can be quite hard getting any hits at all. A more relaxed approach might be to index the domains associated with resources, and also include the exact references explicitly with a boosted search rank. At the current time, I have code that scrapes external links from across the T151 course materials and dumps them into a single annotations file (the file that identifies which resources are included in the CSE) without further embellishment. I also have code that identifies the unique domains that are linked to from the course materials and which can also be added to the annotations file. On the to do list is to annotate the resources with labels that identify which topic they are associated with so we can filter results by topic.
2) the Google Custom Search Engines seem to behave very oddly indeed. In several of my experiments, one word queries often returned few results, more specific queries building on the original search term delivered more and different results. This gives a really odd search experience, and one that I suspect would put many users off.
3) I’ve been coming round more and more to the idea that the best way of highlighting course resources in a search context is through the use of Subscribed Links, that a user can subscribe to and that then appear in their Google search results if there is an exact query match. Unfortunately, Google pulled the Subscribed Links service in September (A fall spring-clean; for example of what’s been lost, see e.g. Stone Temple Consulting: Google Co-Op Subscribed Links).
4) The ability to feed promotions into the top of the CSE results listing is attractive (up to 3 promoted links can be displayed for any given query), but the automatic generation of query terms is problematic. Promotion definitions take the form:
<Promotion image_url="http://kmi.open.ac.uk/images/ou-logo.gif" title="Week 4" id="T151_Week4" queries="week 4,T151 week 4,t151 week 4" url="http://www.open.ac.uk" description="Topic Exploration 4A - An animated experience Topic exploration 4B - Flow in games "/>
There are several components we need to consider here:
- queries: these are the phrases that are used to trigger the display of the particular promotions links. Informal testing suggests that where multiple promotions are triggered by the same query, the order in which they are defined in the Promotions file determines the order in which they appear in the results. Note that the at most three (3) promotions can be displayed for any query. Queries may be based at least around either structural components (such as study week, topic number), subject matter terms (e.g. tags, keywords, or headings) and resource type (eg audio/video material, academic readings etc), although we might argue the resource type is not such a meaningful distinction (just because we can make it doesn’t mean we should!). In the T51 materials, presentation conventions provide us with a way of extracting structural components and using these to seed the promotions file. Identifying keywords or phrases is more challenging: students are unlikely to enter search phrases that exactly match section or subsection headings, so some element of term extraction would be required in order to generate query terms that are likely to be used.
- title: this is what catches the attention, so we need to put something sensible in here. There is a limit of 160 characters on the length of the title.
- description: the description allows us to elaborate on the title. There is a limit of 200 characters on the length of the description.
- url: the URL is required but not necessarily ‘used’ by our promotion. That is, if we are using the promotion for informational reasons, and not necessarily wanting to offer a click through, the link may be redundant. (However, the CSE still requires it to be defined.) Alternatively, we might argue that the a click through action should always be generated, in which case it might be worth considering whether we can generate a click through to a more refined query on the CSE itself?
Where multiple promotions are provided, we need to think about:
a) how they are ordered;
b) what other queries they are associated with (i.e. their specificity);
c) where they link to.
In picking apart the T151 structured authoring documents, I have started by focussing on the low hanging fruit when it comes to generating promotion links. Looking through the document format, it is possible to identify topics associated with separate weeks and questions associated with particular topics. This allows us to build up a set of informational promotions that allow the search engine to respond to queries of what we might term a navigational flavour. So for example, we can ask what topics are covered in a particular week (I also added the topic query as a query for questions related to a particular topic):
Or what a particular question is within a particular topic:
The promotion definitions are generated automatically and are all very procedural. For example, here’s a fragment from the definition of the promotion from question 4 in topic 4A:
<Promotion title="Topic Exploration 4A Question 4" queries="topic 4a q4,T151 topic 4a q4,t151 topic 4a q4,topic 4a,T151 topic 4a,t151 topic 4a" ... />
The queries this promotion will be displayed for are all based around navigational structural elements. This requires some knowledge of the navigational query syntax, and also provides an odd user experience, because the promotions only display on the main CSE tab, and the organic results from indexed sites turn up all manner of odd results for queries like “week 3” and “topic 1a q4″… (You can try out the CSE here.)
The promotions I have specified so far also lack several things:
1) queries based on the actual question description, so that a query related to the question might pull the corresponding promotion into the search results (would that be useful?)
2) a sensible link. At the moment, there is no obvious way in the SA document of identifying one or more resources that particularly relate to a specific question. If there was such a link, then we could use that information to automatically associate a link with a question in the corresponding promotions element. (The original design of the course imagined the Structured Authoring document itself being constructed automatically from component parts. In particular, it was envisioned that suggested links would be tagged on a social bookmarking service and then automatically pulled into the appropriate area of the Structured Authoring document. Resources could then be tagged in a way that associates them with one or more questions (or topics), either directly though a question ID, or indirectly through matching subject tags on a question and on a resource. The original model also considered the use of “suggested search queries” that would be used to populate suggested resources lists with results pulled in live from a (custom) search engine…)
At the moment, it is possible to exploit the T151 document structure to generate these canned navigational queries. The question now is: are promotional links a useful feature, and how might we go about automatically identifying subject meaningful queries?
At the limit, we might imagine the course custom search engine interface being akin to the command line in a text based adventure game, with the adventure itself being the learning journey, and the possible next step a combination of Promotions based guidance and actual search results…
[Code for the link scraping/CSE file generation and mindmap generator built around the T151 SA docs can be found at Github: Course Custom Search Engines]
PS as ever, I tend to focus on tinkering around a rapid prototype/demonstration at the technical systems overview level, with a passing nod to the usefulness of the service (which, as noted above, is a bit patchy where the searchengine index is sparse). What I haven’t done is spend much time thinking about the pedagogical aspects relating to how we might make most effective use of custom search engines in the course context. From a scoping point of view, I think there are a several things we need to unpick that relate to this: what is it that students are searching for, what context are they searching in, and what are they expecting to discover?
My original thinking around custom course search engines was that they would augment a search across course materials by providing a way of searching across the full text of resources* linked to from the course materials, and maybe also provide a context for searching over student suggested resources.
It strikes me that the course search engine is most likely to be relevant where there is active curation of the search engine that provides a search view over a reasonably sized set of resources discovered by folk taking the course and sharing resources related to it. “MOOCs” might be interesting in this respect, particularly where: 1) it is possible to include MOOC blog tag feeds in the CSE as a source of relevant content (both the course blog content and resources linked to from that content – the CSE can be configured to include resources that are linked to from a specified resource); 2) we can grab links that are tagged and shared with the MOOC code on social media and add those to the CSE annotations file. (Note that in this case, it would make sense to resolve shortened links to their ultimate destination URL before adding them to the CSE.) I’m not sure what role promotions might play in a MOOC though, or the extent to which they could be automatically generated?
*Full text search across linked to resources is actually quite problematic. Consider the following classes of online resources that we might expect to be linked to from course materials:
- academic papers, often behind a paywall: links are likely to be redirected through a library proxy service allowing for direct click-thru to the resource using institutional credentials (I assume the user is logged in to the VLE to see the link, and single sign on support allows direct access to any subscribed to resources via appropriate proxies. That is, the link to the resource leads directly to the full text, subscribed to version of the resource if the user is signed on to the institutional system and has appropriate credentials). There are several issues here: the link that is supplied to the CSE should be be the public link to the article homepage; the article homepage is likely to reveal little more than the paper abstract to the search engine. I’m not sure if Google Scholar does full-text indexing of articles, but even if it does, Scholar results are not available to the CSE. (There is also the issue of how we would limit the Scholar search to the articles we are linking to from the course materials.)
- news and magazine articles: again, these may be behind a paywall, but even if they are, they may have been indexed by Google. So they may be full text discoverable via a CSE, even if they aren’t accessible once you click through…
- video and audio resources: discovery in a large part will depend on the text on the web page the resources are hosted on. If the link is directly to an audio or video file, discoverability via the CSE may well be very limited!
- books: Google book search provides full text search, but this is not available via a CSE. Full text searchable collections of books are achievable using Google Books Library Shelves; there’s also an API available.
I guess the workaround to all this is not to use a Google Custom Search Engine as the basis for a course search engine. Instead, construct a repository that contains full text copies of all resources linked to from the course, and index that using a local search engine, providing aliased links to the original sources if required?
However, that wasn’t what this experiment was about!;-)
Course Resources as part of a larger connected graph
Another way of thinking about linked to course resources is that they are a gateway into a set of connected resources. Most obviously, for an academic paper it is part of a graph structure that includes:
– links to papers referenced in the article;
– links to papers that cite the article;
– links to other papers written by the same author;
– links to other papers in collections containing the article on services such as Mendeley;
– links into the social graph, such as the social connections of the author, or the discovery of people who have shared a link to the resource on a public social network.
For an informal resource such as a blog post, it might be other posts linked to from the post of interest, or other posts that link to it.
Thinking about resources as being part of one or more connected graphs may influence our thinking about the pedagogy. If the intention is that a learner is directed to a resource as a terminal, atomic resource, from which they are expected to satisfy a particular learning requirement, then we aren’t necessarily interested in the context surrounding the resource. If the intention is that the resource is gateway to a networked context around one or more ideas or concepts, then we need to select our resources so that they provide a springboard to other resources. This can be done directly (eg though following references contained within the work, or tracking down resources that cite it), or indirectly, for example by suggesting keywords or search phrases that might be used to discover related resources by independent means. Alternatively, we might link to a resource as an exemplar of the sort of resource students are expected to work with on a given activity, and then expect them to find similar sorts of, but alternative, resources for themselves.
How can we use customised search engines to support uncourses, or the course models used to support MOOC style offerings?
To set the scene, here’s what Stephen Downes wrote recently on the topic of How to partcipate in a MOOC:
You will notice quickly that there is far too much information being posted in the course for any one person to consume. We tried to start slowly with just a few resources, but it quickly turns into a deluge.
You will be provided with summaries and links to dozens, maybe hundreds, maybe even thousands of web posts, articles from journals and magazines, videos and lectures, audio recordings, live online sessions, discussion groups, and more. Very quickly, you may feel overwhelmed.
Don’t let it intimidate you. Think of it as being like a grocery store or marketplace. Nobody is expected to sample and try everything. Rather, the purpose is to provide a wide selection to allow you to pick and choose what’s of interest to you.
This is an important part of the connectivist model being used in this course. The idea is that there is no one central curriculum that every person follows. The learning takes place through the interaction with resources and course participants, not through memorizing content. By selecting your own materials, you create your own unique perspective on the subject matter.
It is the interaction between these unique perspectives that makes a connectivist course interesting. Each person brings something new to the conversation. So you learn by interacting rather than by mertely consuming.
When I put together the the OU course T151, the original vision revolved around a couple of principles:
1) the course would be built in part around materials produced in public as part of the Digital Worlds uncourse;
2) each week’s offering would follow a similar model: one or two topic explorations, plus an activity and forum discussion time.
In addition, the topic explorations would have a standard format: scene setting, and maybe a teaser question with answer reveal or call to action in the forums; a set of topic exploration questions to frame the topic exploration; a set of resources related to the topic at hand, organised by type (academic readings (via a libezproxy link for subscription content so no downstream logins are required to access the content), Digital Worlds resources, weblinks (industry or well informed blogs, news sites etc), audio and video resources); and a reflective essay by the instructor exploring some of the themes raised in the questions and referring to some of the resources. The aim of the reflective essay was to model the sort of exploration or investigation the student might engage in.
(I’d probably just have a mixed bag of resources listed now, along with a faceting option to focus in on readings, videos, etc.)
The idea behind designing the course in this way was that it would be componentised as much as possible, to allow flexibility in swapping resources or even topics in and out, as well as (though we never managed this), allowing the freedom to study the topics in an arbitrary order. Note: I realised today that to make the materials more easily maintainable, a set of ‘Recent links’ might be identified that weren’t referred to in the ‘My Reflections’ response. That is, they could be completely free standing, and would have no side effects if replaced.
As far as the provision of linked resources went, the original model was that the links should be fed into the course materials from an instructor maintained bookmark collection (for an early take on this, see Managing Bookmarks, with a proof of concept demo at CourseLinks Demo (Hmmm, everything except the dynamic link injection appears to have rotted:-().
The design of the questions/resources page was intended to have the scoping questions at the top of the page, and then the suggested resources presented in a style reminiscent of a search engine results listing, the idea being that we would present the students with too many resources for them to comfortably read in the allocated time, so that they would have to explore the resources from their own perspective (eg given their current level of understanding/knowledge, their personal interests, and so on). In one of my more radical moments, I suggested that the resources would actually be pulled in from a curated/custom search engine ‘live’, according to search terms specially selected around the current topic and framing questions, but I was overruled on that. However, the course does have a Google custom search engine associated with it which searches over materials that are linked to from the course.
So that’s the context…
Where I’m at now is pondering how we can use an enhanced custom search engine as a delivery platform for a resource based uncourse. So here’s my first thought: using a Google Custom Search Engine populated with curated resources in a particular area, can we use Google CSE Promotions to help scaffold a topic exploration?
Here’s my first promotions file:
<Promotions> <Promotion id="t151_1a" queries="topic 1a, Topic 1A, topic exploration 1a, topic exploration 1A, topic 1A, what is a game, game definition" title="T151 Topic Exploration 1A - So what is a game?" url="http://digitalworlds.wordpress.com/2008/03/05/so-what-is-a-game/" description="The aim of this topic is to think about what makes a game a game. Spend a minute or two to come up with your own definition. If you're stuck, read through the Digital Worlds post 'So what is a game?'" image_url="http://kmi.open.ac.uk/images/ou-logo.gif" /> </Promotions>
It’s running on the Digital Worlds Search Engine, so if you want to try it out, try entering the search phrase what is a game or game definition.
(This example suggests to me that it would also make sense to use result boosting to boost the key readings/suggested resources I proposed in the topic materials so that they appear nearer the top of the results (that’ll be the focus of a future post;-))
The promotion displays at the top of the results listing if the specified queries match the search terms the user enters. My initial feeling is that to bootstrap the process, we need to handle:
– queries that allow a user to call on a starting point for a topic exploration by specifically identifying that topic;
– “naive queries”: one reason for using the resource-search model is to try to help students develop effective information skills relating to search. Promotions (and result boosting) allow us to pick up on anticipated naive queries (or popular queries identified from search logs), and suggest a starting point for a sensible way in to the topic. Alternatively, they could be used to offer suggestions for improved or refined searches, or search strategy hints. (I’m reminded of Dave Pattern’s work with guided searches/keyword refinements in the University of Huddersfield Library catalogue in this context).
Here’s another example using the same promotion, but on a different search term:
Of course, we could also start to turn the search engine into something like an adventure game engine. So for example, if we type: start or about, we might get something like:
(The link I associated with start should really point to the course introduction page in the VLE…)
We can also use the search context to provide pastoral or study skills support:
These sort of promotions/enhancements might be produced centrally and rolled out across course search engines, leaving the course and discipline related customisations to the course team and associated subject librarians.
Just a final note: ignoring resource limitations on Google CSEs for a moment, we might imagine the following scenarios for their role out:
1) course wide: bespoke CSEs are commissioned for each course, although they may be supplemented by generic enhancements (eg relating to study skills);
2) qualification based: the CSE is defined at the qualification level, and students call on particular course enhancements by prefacing the search with the course code; it might be that students also see a personalised view of the qualification CSE that is tuned to their current year of study.
3) university wide: the CSE is defined at the university level, and students students call on particular course or qualification level enhancements by prefacing the search with the course or qualification code.
This is a note-to-self as much as anything, relating to the Course Detective custom search engine that searches over UK HE course prospectus web pages about the extent to which we might be able to use data such as the student satisfaction survey results (as made available via Unistats) to boost search results around particular subjects in line with student satisfaction ratings, or employment prospects, for particular universities?
It’s possible to tweak rankings in Google CSEs in a variety of ways. On the one hand, we can BOOST (improve the ranking), FILTER (limit results to members of a given set) or ELIMINATE (exclude) sites appearing in the search results listing. In the simplest case, we assign a BOOST, FILTER or ELIMINATE weight to a label, and then apply labels to annotations so that they benefit from the corresponding customisation. We can further refine the effect of the modification by applying a score to each annotation. The product of score and weight values determines the overall ranking modification that is applied to each result for a label applied to an annotation.
So here’s what I’m thinking:
– define labels for things like achievement or satisfaction that apply a boost to a result;
– allow uses to apply a label to a search;
– for each university annotation (that is, the listing that identifies the path to the pages for a particular university’s online prospectus), add a label with a score modifier determined by the achievement or satisfaction value, for example, for that institution;
– for refinement labels that tweak search rankings within a particular subject area, define labels corresponding to those subject areas and apply score modifiers to each institution based on, for example, the satisfaction level with that subject area. (Note: I’m not sure if the same path can have several different annotations provided to it with different scores?
For example, an annotation file typically contains a fragment that looks like:
<Annotations> <Annotation about="webcast.berkeley.edu/*" score="1"> <Label name="university_boost_highest"/> <Label name="lectures"/> </Annotation> <Annotation about="www.youtube.com/ucberkeley/*" score="1"> <Label name="university_boost_highest"/> <Label name="videos_boost_mid"/> <Label name="lectures"/> </Annotation> </Annotations>
I don’t know if this would work:
<Annotations> <Annotation about="example.com/prospectus/*" score="1"> <Label name="chemistry"/> </Annotation> <Annotations> <Annotation about="example.com/prospectus/*" score="0.5"> <Label name="physics"/> </Annotation> </Annotations>
That said, if the URLs are nicely structured, we might be able to do something like:
<Annotations> <Annotation about="example.com/prospectus/chemistry/*" score="1"> <Label name="chemistry"/> </Annotation> <Annotations> <Annotation about="example.com/prospectus/physics/*" score="0.5"> <Label name="physics"/> </Annotation> </Annotations>
albeit at the cost of having to do a lot more work in terms of identifying appropriate URI paths.
I also need to start thinking a bit more about how to apply refinements and ranking adjustments in course based CSEs.
If the desire for OU courses to make increased use of third party materials and open educational resources is realised, we are likely to see a shift in the pedagogy to one that is more resource based. This project seeks to explore the extent to which custom search engines tuned to particular courses may be used to support the discovery of appropriate resources published on the public web, and as indexed by Google, on any given course.
Many courses now include links to third party resources that have been published on the public web. Discovering appropriate resources in terms of relevance and quality can be a time consuming affair. The Google Custom Search Engine service allows users to define custom search engines (CSEs) that search over a limited set of domains or web pages, rather than the whole web.
(Topic based links can be discovered in a wide variety of places. For example, it is possible to create custom search engines based around the homepages of people added to a Twitter list, or the nominated blogs in annual award listings.)
The ranking of particular resources may also be boosted in the definition of the CSE via a custom ranking configuration. For example, open educational resources published in support of the course may be boosted in the search result rankings.
Alternatively, CSEs may be used to exclude results from particular domains, or return resources from the whole web with the ranking of results from specified pages or domains boosted as required. By opening up results to the whole of the web, if recent, relevant resources from an unspecified domain are identified in response to a particular search query, they stand a chance of being presented to the user in the results listing.
Synonyms for common terms may also be explicitly declared and refinement labels used to offer facet based search limits. This might be used to limit results to resources identified as particularly relevant for a particular unit, or block within a course, for example, or to particular topic areas spread across a course.
“Promoted” results may also be used to emphasise particular results in response to particular queries. A good example here might be to display promoted results relating to resources explicitly referenced in an exercise, assignment or activity.
If any of the indexed pages are marked up with structured data, it may be possible to expose this data using an rich snippet/enhanced search listing. Whilst there are few examples to date, enhanced listings that display document types or media types might be appropriate.
Examples of Google CSEs in action can be found here:
– faceted “HE CSE” metasearch engine over UK Higher Education Library websites, UK Parliamentary pages, OERs, video protocols for science experiments. This example demonstrates how the search engine may be embedded in a web page.
The project proposes the automated generation of custom search engines on a per course basis based on the resources linked to from any given course.
The deliverables will be:
1) an automated way of generating Google CSE definition files through link scraping of Structured Authoring/XML versions of online course materials. If necessary, additional scraping of non-SA, VLE published resources may be required.
2) a resource template page and/or widget in the VLE providing access to the customised course search engine
Success will be based on the extent to which:
1) students on pilot courses use the search engine;
2) a survey of students on courses using the search engine about how useful they found it
Search engine metrics will also form part of the reporting chain. If appropriate, we will also explore the extent to which search engine analytics can be used to enhance the performance of the search engine (for example, by tuning custom ranking configurations), as well offering “recent searches” information to students.
The placement of the search box for the CSE will be an important factor and any evaluation should take this into account, e.g. through A/B testing on course web pages.
Another variable relating to the extent to which a CSE is used by students is whether the CSE performs a whole web search with declared resources prioritised, or whether it just searches over declared resources. Again, an A/B test may be appropriate.
For activities that include a resource discovery component, it would be interesting to explore what effect embedding the search engine with the activity description page might have?
If course team members on any OU courses presenting over the next 9 months are interested in trying out a course based custom search engine, please get in touch. If academics on courses outside the OU would like to discuss the creation and use of course search engines for use on their own courses, I’d love to hear from you too:-)
eSTEeM is joint initiative between the Open University’s Faculty of Science and Faculty of Maths, Computing and Technology to develop new approaches to teaching and learning both within existing and new programmes.
Last week, Andrew Stott, Director of Digital Engagement in the Cabinet Office, announced his retirement date over Twitter:
At the time of writing, @dirdigeng follows slightly over two thousand folk on Twitter, so I thought I’d have a quick look at who the “players” are…
The network described is constructed as follows:
– nodes represent the people followed by @dirdigeng on Twitter;
– a directed edge from A to B means that A is following B.
In the first view (randomly layed out, using Gephi), we plot node size as linearly proportional to the number of dirdigeng’s friends who are following each of the other friends (that is, the in-degree of each node), and colour proportional to their total number of followers (including people not friended by @dirdigeng).
The colour mapping is non-linear – @Number10gov, @guardiantach and @mashable have significantly more followers that the other nodes – and is set via the spline control:
If we run the betweenness centrality statistic, and size nodes accordingly, we can see how the various parts of the network may be connected. (“Betweenness centrality is a measure based on the number of shortest paths between any two nodes that pass through a particular node. Nodes around the edge of the network would typically have a low betweenness centrality. A high betweenness centrality might suggest that the individual is connecting various different parts of the network together.”)
We can also run the modularity class statistic to try to partition the friends into small networks with a high degree of internal connections. Here’s what we get (click through on the image to see it in more detail):
Modularity groups help us understand the structure of the network in a bit more detail. I’ve started to think they might also be used to automatically generate a seeding set of people who form a highly interconnected community with an interest in a particular topic and from a particular stance.
As well as looking at the structure of the network, we can also create a search engine over the home pages declared in the Twitter bios of @dirdigeng’s friends. My thinking here is that this might provide a useful constrained search engine over sites engaged in social media and with an interest in “Digital Britain”.
The simplest custem search engine simply uses the URLs from the Twitter bios of folk followedd by @dirdigeng and adds them to a “Digital Britain” Google Custom search engine. However, one attractive feature of the Google CSEs is that you can also tweak the rankings by weighting results from different domains differently to give a “weighted” custom search engine.
As a quick experiment, I produced one weighted search engine where I set the score for each domain to be the normalised number of followers amongst @dirdigeng’s friends community. (That is, the domain score equalled the indegree of a node in the @dirdigEng friends network, divided by the total number of people in that network).
As you can see from the above, the results differ… Whether there is any improvement in the ranking of results is another thing. (There is also the question of how best to score, or boost, rankings based on networks stastics, and the extent to which rankings should be determined by friends network factors…)
It also strikes me that the modularity groups might also be used to inform the setup of a CSE. For example, separate modularity groups/classes may be used to define refinement label, allowing users to just search pages from members of a particular modularity class, or boost the results from those people.
And finally, I wonder whether we can mine the tweets of @dirdigeng’s friends, as well as those of @dirdigeng, to provide raw material for additional advice for searchers?