Tagged: eSTEeM

My ILI2012 Presentation – Derived Products from OpenLearn/OU XML Documents

FWIW, a copy of the slides I used in my ILI2012 presentation earlier this week – Making the most of structured content:data products from OpenLearn XML:

I guess this counts as a dissemination activity for my related eSTEeM project on course related custom search engines, since the work(?!) sort of evolved out of that idea…

The thesis is this:

  1. Course Units on OpenLearn are available as XML docs – a URL pointing to the XML version of a unit can be derived from the Moodle URL for the HTML version of the course; (the same is true of “closed” OU course materials). The OU machine uses the XML docs as a feedstock for a publication process that generates HTML views, ebook views, etc, etc of a course.
  2. We can treat XML docs as if they were database records; sets of structured XML elements can be viewed as if they define database tables; the values taken by the structured elements are like database table entries. Which is to say, we can treat each XML docs as a mini-database, or we we can trivially extract the data and pop it into a “proper”/”real” database.
  3. given a list of courses we can grab all the corresponding XML docs and build a big database of their contents; that is, a single database that contains records pulled from course XML docs.
  4. the sorts of things that we can pull out of a course include: links, images, glossary items, learning objectives, section and subsection headings;
  5. if we mine the (sub)section structure of a course from the XML, we can easily provide an interactive treemap version of the sections and subsections in a course; generating a Freemind mindmap document type, we can automatically generate course-section mindmap files that students can view – and annotate – in Freemind. We can also generate bespoke mindmaps, for example based on sections across OpenLearn courses that contain a particular search term.
  6. By disaggregating individual course units into “typed” elements or faceted components, and then reaggreating items of a similar class or type across all course units, we can provide faceted search across, as well as university wide “meta” view over, different classes of content. For example:
    • by aggregating learning objectives from across OpenLearn units, we can trivially create a search tool that provides a faceted search over just the learning objectives associated with each unit; the search returns learning outcomes associated with a search term and links to course units associated with those learning objectives; this might help in identifying reusable course elements based around reuse or extension of learning outcomes;
    • by aggregating glossary items from across OpenLearn units, we can trivially create a meta glossary for the whole of OpenLearn (or similarly across all OU courses). That is, we could produce a monolithic OpenLearn, or even OU wide, glossary; or maybe it’s useful to have redefine the same glossary terms using different definitions, rather than reuse the same definition(s) consistently across different courses? As with learning objectives, we can also create a search tool that provides a faceted search over just the glossary items associated with each unit; the search returns glossary items associated with a search term and links to course units associated with those glossary items;
    • by aggregating images from across OpenLearn units, we can trivially create a search tool that provides a faceted search over just the descriptions/captions of images associated with each unit; the search returns the images whose description/captions are associated with the search term and links to course units associated with those images. This disaggregation provides a direct way of search for images that have been published through OpenLearn. Rights information may also be available, allowing users to search for images that have been rights cleared, as well as openly licensed images.
  7. the original route in was the extraction of links from course units that could be used to seed custom search engines that search over resources referenced from a course. This could in principle also include books using Google book search.

I also briefly described an approach for appropriating Google custom search engine promotions as the basis for a search engine mediated course, something I think could be used in a sMoocH (search mediated MOOC hack). But then MOOCs as popularised have f**k all to do with innovation, don’t they, other than in a marketing sense for people with very little imagination.

During questions, @briankelly asked if any of the reported dabblings/demos (and there are several working demo) were just OUseful experiments or whether they could in principle be adopted within the OU, or even more widely across HE. The answers are ‘yes’ and ‘yes’ but in reality ‘yes’ and ‘no’. I haven’t even been able to get round to writing up (or persuading someone else to write up) any of my dabblings as ‘proper’ research, let alone fight the interminable rounds of lobbying and stakeholder acquisition it takes to get anything adopted as a rolled out as adopted innovation. If any of the ideas were/are useful, they’re Googleable and folk are free to run with them…but because they had no big budget holding champion associated with their creation, and hence no stake (even defensively) in seeing some sort of use from them, they unlikely to register anywhere.

Generating OpenLearn Navigation Mindmaps Automagically

I’ve posted before about using mindmaps as a navigation surface for course materials, or as way of bootstrapping the generation of user annotatable mindmaps around course topics or study weeks. The OU’s XML document format that underpins OU course materials, including the free course units that appear on OpenLearn, makes for easy automated generation of secondary publication products.

So here’s the next step in my exploration of this idea, a data sketch that generates a Freemind .mm format mindmap file for a range of OpenLearn offerings using metadata puled into Scraperwiki. The file can be downloaded to your desktop (save it with a .mm suffix), and then opened – and annotated – within Freemind.

You can find the code here: OpenLearn mindmaps.

By default, the mindmap will describe the learning outcomes associated with each course unit published on the Open University OpenLearn learning zone site.

By hacking the view URL, other mindmaps are possible. For example, we ca make the following additions to the actual mindmap file URL (reached by opening the Scraperwiki view) as follows:

  • ?unit=UNITCODE, where UNITCODE= something like T180_5 or K100_2 and you will get a view over section headings and learning outcomes that appear in the corresponding course unit.
  • ?unitset=UNITSET where UNITSET= something like T180 or K100 – ie the parent course code from which a specific unit was derived. This view will give a map showing headings and Learning Outcomes for all the units derived from a given UNITSET/course code.
  • ?keywordsearch=KEYWORD where KEYWORD= something like: physics This will identify all unit codes marked up with the keyword in the RSS version of the unit and generate a map showing headings and Learning Outcomes for all the units associated with the keyword. (This view is still a little buggy…)

In the first iteration, I haven’t added links to actual course units, so the mindmap doesn’t yet act as a clickable navigation surface, but that it is on the timeline…

It’s also worth noting that there is a flash browser available for simple Freemind mindmaps, which means we could have an online, in-browser service that displays the mindmap as such. (I seem to have a few permissions problems with getting new files onto ouseful.open.ac.uk at the moment – Mac side, I think? – so I haven’t yet been able to demo this. I suspect that browser security policies will require the .mm file to be served from the same server as the flash component, which means a proxy will be required if the data file is pulled from the Scraperwiki view.)

What would be really nice, of course, would be an HTML5 route to rendering a JSONified version of the .mm XML format… (I’m not sure how straightforward it would be to port the original Freemind flash browser Actionscript source code?)

Deconstructing OpenLearn Units – Glossary Items, Learning Outcomes and Image Search

It turns out that part of the grief I encountered here in trying to access OpenLearn XML content was easily resolved (check the comments: mechanise did the trick…), though I’ve still to try to sort out a workaround for accessing OpenLearn images (a problem described here)), but at least now I have another stepping stone: a database of some deconstructed OpenLearn content.

Using Scraperwiki to pull down and parse the OpenLearn XML files, I’ve created some database tables that contain the following elements scraped from across the OpenLearn units by this OpenLearn XML Processor:

  • glossary items;
  • learning objectives;
  • figure captions and descriptions.

You can download CSV data files corresponding to the tables, or the whole SQLite database. (Note that there is also an “errors” table that identifies units that threw an error when I tried to grab, or parse, the OpenLearn XML.)

Unfortunately, I haven’t had a chance yet to pop up a view over the data (I tried, briefly, but today was another of those days where something that’s probably very simple and obvious prevented me from getting the code I wanted to write working; if anyone has an example Scraperwiki view that chucks data into a sortable HTML table or a Simile Exhibit searchable table, please post a link below; or even better, add a view to the scraper:-)

So in the meantime, if ypu want to have a play, you need to make use of the Scraperwiki API wizard.

Here are some example queries:

  • a search for figure descriptions containing the word “communication” – select * from `figures` where desc like ‘%communication%’: try it
  • a search over learning outcomes that include the phrase how to followed at some point by the word dataselect * from `learningoutcomes` where lo like ‘%how to%data%’: try it
  • a search of glossary items for glossary terms that contain the word “period” or a definition that contains the word “ancient” – select * from `glossary` where definition like ‘%ancient%’ or term like ‘%period%’: try it
  • find figures with empty captions – select * from `figures` where caption==”: try it

I’ll try to add some more examples when I get a chance, as well as knocking up a more friendly search interface. Unless you want to try…?!;-)

Do We Need an OpenLearn Content Liberation Front?

For me, one of the defining attributes of openness relates to accessibility of the machine kind: if I can’t write a script to handle the repetitive stuff for me, or can’t automate the embedding of image and/or video resources, then whatever the content is, it’s not open enough in a practical sense for me to do what I want with it.

So here’s an, erm, how can I put this politely, little niggle I have with OpenLearn XML. (For those of you not keeping up, one of the many OpenLearn sites is the OU’s open course materials site; the materials published on the site as course unit contentful HTML pages are also available as structured XML documents. (When I say “structured”, I mean that certain elements of the materials are marked up in a semantically meaningful way; lots of elements aren’t, but we have to start somewhere ;-))

The context is this: following on from my presentation on Making More of Structured Course Materials at the eSTeEM conference last week, I left a chat with Jonathan Fine with the intention of seeing what sorts of secondary product I could easily generate from the OpenLearn content. I’m in the middle of building a scraper and structured content extractor at the moment, grabbing things like learning outcomes, glossary items, references and images, but I almost immediately hit a couple of problems, first with actually locating the OU XML docs, and secondly locating the images…

Getting hold of a machine readable list of OpenLearn units is easy enough via the OpenLearn OPML feed (much easier to work with than the “all units” HTML index page). Units are organised by topic and are listed using the following format:

<outline type="rss" text="Unit content for Water use and the water cycle" htmlUrl="http://openlearn.open.ac.uk/course/view.php?name=S278_12" xmlUrl="http://openlearn.open.ac.uk/rss/file.php/stdfeed/4307/S278_12_rss.xml"/>

URLs of the form http://openlearn.open.ac.uk/course/view.php?name=S278_12 link to a ‘homepage” for each unit, which then links to the first page of actual content, content which is also available in XML form. The content page URLs have the form http://openlearn.open.ac.uk/mod/oucontent/view.php?id=398820&direct=1, where the ID is one-one uniquely mapped to the course name identifier. The XML version of the page can then be accessed by changing direct=1 in the URL to content=1. Only, we don’t know the mapping from course unit name to page id. The easiest way I’ve found of doing that is to load in the RSS feed for each unit and grab the first link URL, which points the first HTML content page view of the unit.

I’ve popped a scraper up on Scraperwiki to build the lookup for XML URLs for OpenLearn units – OpenLearn XML Processor:

import scraperwiki

from lxml import etree

#via http://stackoverflow.com/questions/5757201/help-or-advice-me-get-started-with-lxml/5899005#5899005
def flatten(el):           
    result = [ (el.text or "") ]
    for sel in el:
        result.append(sel.tail or "")
    return "".join(result)

def getcontenturl(srcUrl):
    rss= etree.parse(srcUrl)
        contenturl= flatten(rssroot.find('./channel/item/link'))
    return contenturl

def getUnitLocations():
    #The OPML file lists all OpenLearn units by topic area
    tree = etree.parse(srcUrl)
    root = tree.getroot()
    #Handle each topic area separately?
    for topic in topics:
        tt = topic.get('text')
        print tt
        for item in topic.findall('./outline'):
            if it.startswith('Unit content for'):
                it=it.replace('Unit content for','')
                if len(cctmp)>1: ccpart=cctmp[1]
                else: ccpart=1
                print tt,it,slug,ccu,cc,ccpart,url,contenturl
                scraperwiki.sqlite.save(unique_keys=['ccu'], table_name='unitsHome', data={'ccu':ccu, 'uname':it,'topic':tt,'slug':slug,'cc':cc,'ccpart':ccpart,'url':url,'rssurl':rssurl,'ccurl':contenturl})


The next step in the plan (because I usually do have a plan; it’s hard to play effectively without some sort of direction in mind…) as far as images goes was to grab the figure elements out of the XML documents and generate an image gallery that allows you to search through OpenLearn images by title/caption and/or description, and preview them. Getting the caption and description from the XML is easy enough, but getting the image URLs is not

Here’s an example of a figure element from an OpenLearn XML document:

<Figure id="fig001">
<Image src="\\DCTM_FSS\content\Teaching and curriculum\Modules\Shared Resources\OpenLearn\S278_5\1.0\s278_5_f001hi.jpg" height="" webthumbnail="false" x_imagesrc="s278_5_f001hi.jpg" x_imagewidth="478" x_imageheight="522"/>
<Caption>Figure 1 The geothermal gradient beneath a continent, showing how temperature increases more rapidly with depth in the lithosphere than it does in the deep mantle.</Caption>
<Alternative>Figure 1</Alternative>
<Description>Figure 1</Description>

Looking at the HTML page for the corresponding unit on OpenLearn, we see it points to the image resource file at http://openlearn.open.ac.uk/file.php/4178/!via/oucontent/course/476/s278_5_f001hi.jpg:

So how can we generate that image URL from the resource link in the XML document? The filename is the same, but how can we generate what are presumably contextually relevant path elements: http://openlearn.open.ac.uk/file.php/4178/!via/oucontent/course/476/

If we look at the OpenLearn OPML file that lists all current OpenLearn units, we can find the first identifier in the path to the RSS file:

<outline type="rss" text="Unit content for Energy resources: Geothermal energy" htmlUrl="http://openlearn.open.ac.uk/course/view.php?name=S278_5" xmlUrl="http://openlearn.open.ac.uk/rss/file.php/stdfeed/4178/S278_5_rss.xml"/>

But I can’t seem to find a crib for the second identifier – 476 – anywhere? Which means I can’t mechanise the creation of links to actually OpenLearn image assets from the XML source. Also note that there are no credits, acknowledgements or license conditions associated with the image contained within the figure description. Which also makes it hard to reuse the image in a legal, rights recognising sense.

[Doh – I can surely just look at URL for an image in an OpenLearn unit RSS feed and pick the path up from there, can’t I? Only I can’t, because the image links in the RSS feeds are: a) relative links, without path information, and b) broken as a result…]

Reusing images on the basis of the OpenLearn XML “sourcecode” document is therefore: NOT OBVIOUSLY POSSIBLE.

What this suggests to me is that if you release “source code” documents, they may actually need some processing in terms of asset resolution that generates publicly resolvable locators to assets if they are encoded within the source code document as “private” assets/non-resolvable identifiers.

Where necessary, acknowledgements/credits are provided in the backmatter using elements of the form:

<Paragraph>Figure 7 Willes-Richards, J., et al. (1990) ; HDR Resource/Economics’ in Baria, R. (ed.) <i>Hot Dry Rock Geothermal Energy</i>, Copyright CSM Associates Limited</Paragraph>

Whilst OU-XML does support the ability to make a meaningful link to a resource within the XML document, using an element of the form:

<CrossRef idref="fig007">Figure 7</CrossRef>

(which presumably uses the Alternative label as the cross-referenced identifier, although not the figure element id (eg fig007) which is presumably unique within any particular XML document?), this identifier is not used to link the informally stated figure credit back to the uniquely identified figure element?

If the same image asset is used in several course units, there is presumably no way of telling from the element data (or even, necessarily, the credit data?) whether the images are in fact one and the same. That is, we can’t audit the OpenLearn materials in a text mechanised way to see whether or not particular images are reused across two or more OpenLearn units.

Just in passing, it’s maybe also worth noting that in the above case at least, a description for the image is missing. In actual OU course materials, the description element is used to capture a textual description of the image that explicates the image in the context of the surrounding text. This represents a partial fulfilment of accessibility requirements surrounding images and represents, even if not best, at least effective practice.

Where else might content need liberating within OpenLearn content? At the end of the course unit XML documents, in the “backmatter” element, there is often a list of references. References have the form:

<Reference>Sheldon, P. (2005) Earth’s Physical Resources: An Introduction (Book 1 of S278 Earth’s Physical Resources: Origin, Use and Environmental Impact), The Open University, Milton Keynes</Reference>

Hmmm… no structure there… so how easy would it be to reliably generate a link to an authoritative record for that item? (Note that other records occasionally use presentational markup such as italics (or emphasis) tags to presentationally style certain parts of some references (confusing presentation with semantics…).)

Finally, just a quick note on why I’m blogging this publicly rather than raising it, erm, quietly within the OU. My reasoning is similar to the reasoning we use when we tell students to not be afraid of asking questions, because it’s likely that others will also have the same question… I’m asking a question about the structure of an open educational resource, because I don’t quite understand it; by asking the question in public, it may be the case that others can use the same questioning strategy to review the way they present their materials, so when I find those, I don’t have to ask similar sorts of question again;-)

PS sort of related to this, see TechDis’ Terry McAndrew’s Accessible courses need and accessibilty-friendly schema standard.

PPS see also another take on ways of trying to reduce cognitive waste – Joss Winn’s latest bid in progress, which will examine how the OAuth 2.0 specification can be integrated into a single sign on environment alongside Microsoft’s Unified Access Gateway. If that’s an issue or matter of interest in your institution, why not fork the bid and work it up yourself, or maybe even fork it and contribute elements back?;-) (Hmm, if several institutions submitted what was essentially the same bid from multiple institutions, how would they cope during the marking process?!;-)

eSTeEM Conference Presentation – Making More of Structured Course Materials

A copy of the presentation I gave at the OU-eSTeEM conference (no event URL?) on generating custom course search engines and mining OU XML documents to generate course mindmaps (Making More of Structured Documents presentation; delicious stack/bookmark list of related resources):

Chatting to Jonathan Fine after the event, he gave me the phrase secondary products to describe things like course mindmaps that can be generated from XML source files of OU course materials. From what I can tell, there isn’t much if any work going on in the way of finding novel ways of exploiting the structure of OU structured course materials, other than using them simply as a way of generating different presentational views of the course materials as a whole (that is, HTML versions, maybe mobile friendly versions, PDF versions). (If that’s not the case, please feel free to put me right in the comments:-)

One thing Jonathan has been scouring the documents for is evidence of mathematical content across the courses; he also mentioned a couple of ideas relating to access audits over the content itself, such as extracting figure headings, or image captions. (This reminded me of the OpenLearn XML processor (and redux) I first played with 4 years ago (sigh… and nothing’s changed… sigh….), which stripped assets by type from the first generation of OU XML docs). So on my to do list is to have a deeper look at the structure of OU XML, have a peek at what sorts of things might meaningfully (and easily;-) extracted, and figure out two or three secondary products that can be generated as a result. Note that these products might be products for different audiences, at different times of the course lifecycle: tools for use by course team or LTS during production (such as accessibility checks), products to support maintenance (there is already a link checker, but maybe there is more that can be done here?), products for students (such as the mindmap), products for alumni, products for OpenLearn views over the content, products to support “learning analytics”, and so on. (If you have any ideas of what forms the secondary products might take, or what structures/elements/entities you’d like to see mined from OU XML, please let me know via the comments. For an example of an OU XML doc, see here.

Search Engine Powered Courses…

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:

   <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?" 
        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" />

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.

T151 CSE promotion - 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:

T151 CSE - topic 1a

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:

T151 CSE - start

(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:

T151 CSE - pastoral

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.

Tweaking Ranking Factors in the Course Detective Custom Search Engine

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:

  <Annotation about="webcast.berkeley.edu/*" score="1">
    <Label name="university_boost_highest"/>
    <Label name="lectures"/>

  <Annotation about="www.youtube.com/ucberkeley/*" score="1">
    <Label name="university_boost_highest"/>
    <Label name="videos_boost_mid"/>
    <Label name="lectures"/>

I don’t know if this would work:

  <Annotation about="example.com/prospectus/*" score="1">
    <Label name="chemistry"/>
  <Annotation about="example.com/prospectus/*" score="0.5">
    <Label name="physics"/>

That said, if the URLs are nicely structured, we might be able to do something like:

  <Annotation about="example.com/prospectus/chemistry/*" score="1">
    <Label name="chemistry"/>
  <Annotation about="example.com/prospectus/physics/*" score="0.5">
    <Label name="physics"/>

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.

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:

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

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:

Google subscribed link definition

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:

Google subscribed links

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…

eSTEeM Project: Library Website Tracking For VLE Referrals

Assuming my projects haven’t been cut out at the final acceptance stage because I haven’t yet submitted a revised project plan,

As OU courses are increasingly presented through the VLE, many of them opt to have one or more “Library Resources” pages that contain links to course related resources either hosted on the OU Library website or made available through a Library operated web service. Links to Library hosted or moderated resources may also appear inline in course content on the VLE. However, at the current time, it is difficult to get much idea about the extent to which any of these resources are ever accessed, or how students on a course make use of other Library resources.

With the state of the collection and reporting of activity data from the VLE still evolving, this project will explore the extent to which we can make use of data I do know exists, and to which I do have access, specifically Google Analytics data for the library.open.ac.uk domain.

The intention is to produce a three-way reporting framework using Google Analytics for visitors to the OU Library website and Library managed resources from the VLE. The reports will be targeted at: subject librarians who liaise with course teams; course teams; subscription managers.

Google Analytics (to which I have access) are already running on the library website and the matter just(?!) arises now of:

1) Identifying appropriate filters and segments to capture visits from different courses;

2) development of Google Analytics API wrapper calls to capture data by course or resource based segments and enable analysis, visualisation and reporting not supported within the Google Analytics environment.

3) Providing a meaningful reporting format for the three audience types. (note: we might also explore whether a view over the activity data may be appropriate for presenting back to students on a course.)

The Project
The OU Library has been running Google Analytics for several year, but to my knowledge has not started to exploit the data being collected as part of a reporting strategy on the usage of library resources resulting from referrals from the VLE. (Whenever a user clicks on a link in the VLE that leads to the Library website, the Google Analytics on the Library website can capture that fact.)

At the moment, we do not tend to work on optimising our online courses as websites so that they deliver the sorts of behaviour we want to encourage. If we were a web company, we would regularly analyse user behaviour on our course websites and modify them as a result.

This project represents the first step in a web analytics approach to understanding how our students access Library resources from the VLE: reporting. The project will then provide the basis for a follow on project that can look at how we can take insight from those reports and make them actionable, for example in the redesign of the way links to library resources are presented or used in the VLE, or how visitors from the VLE are handled when they hit the Library website.

The project complements work that has just started in the Library on a JISC funded project to making journal recommendations to students based on previous user actions.

The first outcome will be a set of Google Analytics filters and advanced segments tuned to the VLE visitor traffic and resource usage on the Library website. The second will be a set of Google analytics API wrappers that allow us to export this data and use it outside the Google Analytics environment.

The final deliverables are three report types in two possible flavours:

1) a report to subject librarians about the usage of library resources from visitors referred from the VLE for courses they look after

2) a report to librarians responsible for particular subscription databases showing how that resource is accessed by visitors referred from the VLE, broken down by course

3) a report to course teams showing how library resources linked to from the VLE for their course are used by visitors referred to those resources from the VLE.

The two flavours are:

a) Google analytics reports

b) custom dashboard with data accessed via the Google Analytics API

Recommendations will also be made based on the extent to which Library website usage by anonymous students on particular OU courses may be tracked by other means, such as affinity strings in the SAMS cookie, and the benefits that may accrue from this more comprehensive form of tracking.

If course team members on any OU courses presenting over the next 9 months are interested in how students are using the library website following a referral from the VLE, please get in touch. If academics on courses outside the OU would like to discuss the use of Google Analytics in an educational context, 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.

eSTEeM Project: Custom Course Search Engines

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:

Digital Worlds Cusotm Search Engine (created by hand; as used in T151).

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
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.