Picking up on a query I raised in Citation Positioning, here’s a quick summary of an online discussion featuring variously @edsu, @epoz, @ostephens and myself (I’m the one who knows absolutely nothing…!)
The context is: can I use the OAI-PMH interface on Citeseer to grab record level machine readable results from Citeseer. Note that I donlt really want to harvest all the Citeseer data, pop it into a database of my own, and then run queries on that; I just want a quick and dirty API to make a handful of calls to particular queries for a proof of concept hack;-)
Here’s what the Citeseer HTML page looks like:
It has a URL of the form: http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.122.728
The tabbed results pages have their own URLs:
– Active Bibliography, of the form http://citeseer.ist.psu.edu/viewdoc/similar?doi=10.1.1.122.7284&type=ab
– Co-Citation, of the form http://citeseer.ist.psu.edu/viewdoc/similar?doi=10.1.1.122.7284&type=cc
– Clustered Documents, of the form http://citeseer.ist.psu.edu/viewdoc/similar?doi=10.1.1.122.7284&type=sc
Here’s what I’m guessing:
– the ‘front page’ results are links to papers that reference/cite the target article, ordered by the number of times that they themselves have been cited; this is a subset of the total set of papes that cite the target article;
– the Active Bibliography is a subset of the articles that are referenced from/cited by the target article that have themselves been recently cited elsewhere (?! I’m guessing – the Citeseer site doesn’t seem to provide an explanation anywhere?)
– the co-citations are… I have no idea? Other papers that have been cited by papers that cite the target paper?
– Clustered Documents – these seem to be other Citeseer records relating to the same paper; do they all have the same citation info? I have no idea?????
As far as the OAI interface goes, it seems we can grab an individual record using a query of the form:
which returns a result of the form:
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"> <responseDate>2011-12-08T16:24:31+00:00</responseDate> <request identifier="oai:CiteSeerX.psu:10.1.1.122.7284" metadataPrefix="oai_dc" verb="GetRecord">http://citeseerx.ist.psu.edu/oai2</request> <GetRecord> <record> <header> <identifier>oai:CiteSeerX.psu:10.1.1.122.7284</identifier> <datestamp>2009-05-28</datestamp> </header> <metadata> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:title>The structure and function of complex networks</dc:title> <dc:creator>M. E. J. Newman</dc:creator> <dc:description> Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks. </dc:description> <dc:contributor> The Pennsylvania State University CiteSeerX Archives </dc:contributor> <dc:publisher/> <dc:date>2009-05-28</dc:date> <dc:date>2008-12-04</dc:date> <dc:date>2003</dc:date> <dc:format>application/pdf</dc:format> <dc:type>text</dc:type> <dc:identifier> http://citeseerx.ist.psu.edu/citeseerx/viewdoc/summary?doi=10.1.1.122.7284 </dc:identifier> <dc:source> http://www.cs.berkeley.edu/~christos/classics/graphsurvey.pdf </dc:source> <dc:language>en</dc:language> <dc:relation>10.1.1.109.4049</dc:relation> <dc:relation>10.1.1.120.3875</dc:relation> <dc:relation>10.1.1.31.1768</dc:relation> <dc:relation>10.1.1.153.5943</dc:relation> <dc:relation>10.1.1.37.234</dc:relation> <dc:relation>10.1.1.18.2720</dc:relation> <dc:relation>10.1.1.30.6583</dc:relation> <dc:relation>10.1.1.25.5619</dc:relation> <dc:relation>10.1.1.104.3739</dc:relation> <dc:relation>10.1.1.56.6742</dc:relation> <dc:relation>10.1.1.117.7097</dc:relation> <dc:relation>10.1.1.15.8793</dc:relation> <dc:relation>10.1.1.33.1635</dc:relation> <dc:relation>10.1.1.139.1580</dc:relation> <dc:relation>10.1.1.30.9552</dc:relation> <dc:relation>10.1.1.184.8874</dc:relation> <dc:relation>10.1.1.24.6195</dc:relation> <dc:relation>10.1.1.16.478</dc:relation> <dc:relation>10.1.1.31.3763</dc:relation> <dc:relation>10.1.1.25.7011</dc:relation> <dc:relation>10.1.1.37.5917</dc:relation> <dc:relation>10.1.1.84.9512</dc:relation> <dc:relation>10.1.1.7.1950</dc:relation> <dc:relation>10.1.1.129.6877</dc:relation> <dc:relation>10.1.1.25.1360</dc:relation> <dc:relation>10.1.1.16.1168</dc:relation> <dc:relation>10.1.1.115.8316</dc:relation> <dc:relation>10.1.1.143.1502</dc:relation> <dc:relation>10.1.1.130.1956</dc:relation> <dc:relation>10.1.1.20.814</dc:relation> <dc:relation>10.1.1.21.838</dc:relation> <dc:relation>10.1.1.16.2407</dc:relation> <dc:relation>10.1.1.23.9684</dc:relation> <dc:relation>10.1.1.62.7557</dc:relation> <dc:relation>10.1.1.16.6906</dc:relation> <dc:relation>10.1.1.2.4033</dc:relation> <dc:relation>10.1.1.43.7796</dc:relation> <dc:relation>10.1.1.25.1174</dc:relation> <dc:relation>10.1.1.10.4509</dc:relation> <dc:relation>10.1.1.27.3417</dc:relation> <dc:relation>10.1.1.120.9902</dc:relation> <dc:relation>10.1.1.20.5323</dc:relation> <dc:relation>10.1.1.86.8584</dc:relation> <dc:relation>10.1.1.3.3888</dc:relation> <dc:relation>10.1.1.1.9569</dc:relation> <dc:relation>10.1.1.78.4413</dc:relation> <dc:relation>10.1.1.142.7059</dc:relation> <dc:relation>10.1.1.161.114</dc:relation> <dc:relation>10.1.1.143.1242</dc:relation> <dc:relation>10.1.1.58.2706</dc:relation> <dc:relation>10.1.1.35.8293</dc:relation> <dc:relation>10.1.1.85.7061</dc:relation> <dc:relation>10.1.1.129.709</dc:relation> <dc:relation>10.1.1.16.5260</dc:relation> <dc:relation>10.1.1.7.4603</dc:relation> <dc:relation>10.1.1.37.2417</dc:relation> <dc:relation>10.1.1.37.2641</dc:relation> <dc:relation>10.1.1.117.3665</dc:relation> <dc:relation>10.1.1.122.6034</dc:relation> <dc:relation>10.1.1.11.7594</dc:relation> <dc:relation>10.1.1.20.9298</dc:relation> <dc:relation>10.1.1.27.4715</dc:relation> <dc:relation>10.1.1.94.2340</dc:relation> <dc:relation>10.1.1.196.2257</dc:relation> <dc:relation>10.1.1.1.2728</dc:relation> <dc:relation>10.1.1.58.3869</dc:relation> <dc:relation>10.1.1.33.6972</dc:relation> <dc:relation>10.1.1.35.4242</dc:relation> <dc:relation>10.1.1.28.9399</dc:relation> <dc:relation>10.1.1.12.2717</dc:relation> <dc:relation>10.1.1.6.61</dc:relation> <dc:relation>10.1.1.7.6756</dc:relation> <dc:relation>10.1.1.15.4857</dc:relation> <dc:relation>10.1.1.58.2087</dc:relation> <dc:relation>10.1.1.10.352</dc:relation> <dc:relation>10.1.1.110.6845</dc:relation> <dc:rights> Metadata may be used without restrictions as long as the oai identifier remains attached to it. </dc:rights> </oai_dc:dc> </metadata> </record> </GetRecord> </OAI-PMH>
I’m guessing the dc:relation elements refer to the papers listed on the ‘front page’ of the results for a given paper, that is, they are the most heavily cited papers that cite the target paper?
So a few questions that arise:
– what do the different results listings on the HTML pages actually refer to?
– what do the results in the OAI query above relate to?
– is it possible to get a list of all the papers cited/referenced by a target article? (Or failing that, is it possible to get hold of the Active Bibliography relations, which are presumably a subset of the complete set of bibliographic references contained within a paper?)
– is it possible to get a list of all the paper that cite/reference a particular target article?
If you can answer any or all of the above questions, please feel free to post the answer(s) in a comment below…:-)
A couple of months ago, when I started looking at the idea of emergent social positioning in online social networks, I was focussing on trying to model the positioning of certain brands and companies, in part with a view to trying to identify ones that were associated with innovation, or future thinking in some way.
Based on absolutely no evidence at all, I surmised that one useful signal in this regard might be the context in which companies or brands are mentioned in popular, MBA-related business books, the sort of thing that Harvard Business Review publish, for example.
Here’s how my thinking went then:
– generate a bipartite network graph that connects the book’s index terms with page numbers of the pages they appear on based on the index entries* in a given book. A bipartite graph is one that contains two sorts or classes of node (in this case, index term nodes and book page number nodes). The index terms are likely to include companies, brands, people and ideas/concepts. Sometimes, particular index terms may be identified as companies, names, etc, through presentational mark up – a bold font, or italics, for example. These presentational conventions can often be mapped onto semantic equivalents. Terms might also be passed through something like the Reuters’ Open Calais service, or TSO’s Data Enrichment Service.
– collapse the network graph by generating links between things that are connected to the same page number and remove the page number nodes from the graph. You now have a graph that connects brands, people and other index terms with each other, where edges represent the relation “is on the same page in the same book as”. If companies and other index terms appear on several pages together, we might reflect this by increasing the weight of the edge that connects them, for example by using edge weight to represent the number of pages where the two terms co-exist.
(*This will be obvious to some, but not to others. To a certain extent, a book index provides a faceted/search term limited search engine interface to a book, that returns certain pages as results to particular queries…)
Note that we can generate a network for a specific book, in which case we can render a graphical summary of the content, relations within and structure of that book, or we can generate more comprehensive networks that summarise the index term relations across several books.
My thinking then was that if we can grab the indexes of a set of business books, we could map which companies and brands were being associated either with each other or with particular concepts in MBA land.
Which is where the problem lays – because I haven’t found anywhere where I can readily get hold of the indexes of business books in a sensible machine readable format. Given an electronic cpy of a book, I guess I could run some text processing algorithms over it looking for word pairs in close association with each other and generating my own view over the book. But the reason for using an actual book index is at least twofold: firstly, because there has presumably been a a quality process that determines what terms are entered into the index; secondly, because the index, if used by a human reader, will be influencing which parts of the book (and hence which related terms) they will be exposed to.
(It’s maybe also worth noting that books also contain a lot of other structured metadata – tables of contents, lists of figures, titles, headings, subheadings, emphasis, lists, captions, and so on, all of which provide cues as to how the book is structured and how ideas and entities contained within it relate to each other.)
As to why I’m posting this now? I first floated this idea with @edchamberlain following a JISC bibliography data event, and he reminded me of it at the Arcadia Project review a couple of days ago ;-)
Related, sort of: Augmenting OU/BBC Co-Pro Programme Data With Semantic Tags, which looked at mapping corporate mentions in the BBC/OU co-pro business programme The Bottom Line:
Also Citation Positioning.
PS this is clever – and related – via @ostephens: http://www.eatyourbooks.com/ (“‘Tell us which books you own’ We have indexed the most popular cookbooks & magazines so recipes become instantly searchable.”).
It’s been years and years since I did either a formal literature review, or used a reference manager like EndNote or RefWorks in anger, but whilst at the Arcadia Project review in Cambridge a couple of days ago, I started wondering what sorts of ‘added value’ features I’d like to see, maybe even expect, from referencing software nowadays…
One of the ideas I’ve been playing with recently is the idea of emergent social positioning (ESP;-) in online social networks, which I’m defining in terms of where an individual or an expression of a particular interest group might be positioned in terms of the socially projected interests of people following that person or interest group.
For the case of an individual, the approach I’m taking is to look at who the followers of that individual follow to any great extent; for the case of an interest group, as evidenced by users of a particular hashtag, for example, it might be to look at who the followers of the users of the hashtag also follow in significant numbers.
A slightly more constrained approach might be to look at how the followers of the individual or the hashtag users follow each other (a depth 1.5 follower network about an indvidual or set of individals, in effect).
So for example, here’s a map I just grabbed of folk who are followed by 3 or more followers from a sampling of the followers recent users of the #gdslaunch (Government Digital Service launch) hashtag.
So what does this have to do with reference managers? Let’s start with a single academic paper (the ‘target’ paper), that contains a list of references to other works. If we can easily grab the reference lists from all those works, we can generate a depth 1.5 reference map that show how the works referenced in the first paper reference each other. Exploring the structural properties of this map may help us better understand the support basis for the ideas covered in our target paper.
By looking at the depth 2 reference network (that is, the network that shows references included in the target paper, and all their references), we may be able to discover additional (re)sources relevant to the target paper.
Unfortunately, getting free and and easy machine readable access to the lists of references contained within journal articles, conference papers and books is not trivial. There are patchy services such as CiteSeer, Citebase or opencitations.net, but I don’t think services like Mendeley, Zotero or CiteUlike are yet expressing this sort of data? Or maybe they are, and I’m missing a trick somewhere.
(Just by the by, presumably some of the commercial citation services have APIs that support at least accessing this data? If you know of any, could you add a link in the comments please?:-)
Another hack I’d like to try is to generate what more closely corresponds to the social positioning idea, which is to grab the references from a target paper, and then the papers that cite those references and see how they all link together. This would help position the target paper in the space of other papers referencing similar works. I think CiteSeer has this sort of functionality, though not in a graphical form?
PS on my to do list is seeing whether I can get reference lists for articles out of Citeseer using the Citeseer OAI-PMH endpoint. I’ve got as far as installing the pyoai Python library, but not had time to try it out yet. If anyone knows of a guide to OAI for complete novices, ideally with pyoai examples I can crib from, please post a link (or some examples) via the comments:-)
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.
What does it take for a digital scholar’s blog to become academically credible?
At a time when we know that folk go to Google for a lot of their search needs, the academic library argues it’s case, in part, as a place where you can go to get access to “good quality” (academically credible) and comprehensive information through what we might term academic search engines.
The library’s search offerings are presumably subscription based (?) and their results often link through to subscription content; but the academic life is a privileged one, and our institutions cover the access costs on our behalf. (I guess this could almost be considered one of the “+ benefits” you might imagine an enthusiastic copywriter assuming for an academic job ad!)
The library and information access privilege extends to students too, so we might imagine a well-intentioned, but perhaps naive, student thinking that if they run a search using the Library’s “academically certified” search engine, they will get the sort of result they can happily cite in an essay, without fear of criticism about the academic credibility of the source publication.
We might imagine, too, that academics and researchers also place an element of trust in the credibility of sources returned as results to search queries raised using library discovery services.
So here’s a claim (which is untested and may or may not be true): if you want your work to stand a chance of being referenced in a piece of scholarly work, you need it to be discoverable in the places that the scholar goes to discover supporting claims or related material for the work they’re doing. The assumption is that the scholar will use a library provided discovery service because it is less noisy than a general web search engine and is likely to return to resources from credible sources. The curation of sources – and what is not included in the index – is in part what the subscription discovery service offers.
What this means is that if digital scholars want their blogging activity to be discoverable in the academic context, they need to find some way of getting some of their blogposts at least into academic discovery service indices.
But this is not likely to happen, right? Wrong… Here’s what I noticed when I ran a search using the OU Library’s “one-stop” search earlier today:
A top two reference to a Mashable article (albeit identified as a news item) via the Newsbank database and a top ranked periodical article from Fast Company (via the UK/EIRE Reference Centre database). (Hmmm, I wonder how quickly this content is indexed? That is, how soon after posting on Mashable does an article become discoverable here?)
So maybe I need to start writing for Mashable?!
Or maybe not…?
One of the attractive features of WordPress as a publishing platform is that it provides feeds for everything, including category and tag level feeds. A handful of my category feeds are syndicated, for example to R-Bloggers, the Guardian Datablog blogroll and (I’m not sure if this still works?) the Online Journalism blog. Only posts tagged in a particular way are sent to the syndicated feeds.
So I’m wondering this: how much mileage would there be in setting up aggregation blogs around particular academic areas that not only syndicate content from publisher members, but also act as a focus for indexing by a service such as Newsbank? The content would be publisher-moderated (I don’t post content on non-R related matters to my R-bloggers syndication feed) and hopefully responsive to the norms of the aggregation community itself.
Precendents already exist of course; for example, Nature.com blogs aggregates blogs from a variety of working scientists. Is this content discoverable via the OU Library’s one stop/Ebsco search?
For an academic’s work to count in RAE terms, it needs to be cited. In order to be cited, it needs to be discoverable. Even if it isn’t citeable as a formal article, it can still make a contribution if it’s discoverable. To be academically discoverable, content needs to be discoverable via academic search engines. So why should Mashable count, but not personal academic blogs that are respected within their own communities?
PS I’m a bit out of touch with referencing converntions; I remember that pers. comm. used to be an acceptable way of crediting someone’s ideas they had personally communicated to you; is there a pub. comm. (that’s pub. comm. not just pub comm. ;-) equivalent that might be used to refer to online or offline public communications that might not otherwise be citeable?
Here’s a copy of the slides from my ILI2011 presentation on Appropriate IT:
One thing I wanted to explore was, if discovery happens elsewhere, and the role of the librarian is no longer focussed on discovery related issues, where can library folk help out? Here’s where I think we need to start placing some attention: sensemaking, and knowing what’s possible (aka helping redistribute the future that is already around us;-) Allied with this is the idea that we need to make more out of using appropriate IT for particular tasks, as well as appropriating IT where we can to make our lives easier.
In part, sensemaking is turning the wealth of relevant data out there into something meaningful for the question or issue at hand, or the choice we have to make. My own dabblings with social network analysis are approaches I’m working on that help me make sense of interest networks and social positioning within those networks so I can get a feel for how those communities are structured and who the major actors are within them.
As far as knowing what’s possible, I think we have a real issue with “folk IT” knowledge. Most of us have a reasonable grasp of folk physics and folk psychology. That is, we have a reasonable common-sense model of how the world works at the human scale (let go of an apple, it falls to the floor), and we can generally read other people from their behaviour; but how well developed is “folk IT” knowledge? Given that to most people the idea that you can search within a page in a wide variety of electronic documents using crtrl-F as a keyboard shortcut to a “search within page/document” feature is alien to them, I think our folk understanding of IT is limited to the principle of “if you switch it off and on again it should start working again”.
Folk IT is also tied up with computational thinking, but at a practical, “human scale”. So here are a few ideas I think the librarians need to start pushing:
– the idea of a graph; it’s what the web’s based around, after all, and it also helps us understand social networks. If you think of your website as a graph, with edges representing links that connect nodes/pages together, and realise that your on-site homepage is whatever page someone lands on from a search engine or third party link, you soon start to realise that maybe your website is not as usefully structured as you thought…
– some sort of common sense understanding of the role that URLs/URIs play in the browser, along with the idea that URIs are readable and hackable and also may say something about the way a website, or the resources it makes available, organised;
– the notion of “View Source”, that allows you to copy and crib the work of others when constructing your own applications, along with the very idea that you might be able to build web pages yourself out of free standing components.
– the idea of document types and applications that can work all sorts of magic given documents of that type; the knowledge that an MP3 file works well with an audio player or audio editor, for example, or that a PNG or JPG encodes an image, along with more esoteric formats such as KML (paste a URL to a KML file into the search box of a Google Maps search and see what happens, for example…). Knowledge of the filetype/document type gives you some sort of power over it, and helps you realise what sorts of thing you can do with it… (except for things like PDF, for example, which is to all intents and purposes a “can’t do anything with it” filetype;-)
I also think an understanding of pattern based string matching and what regular expressions allow you to do would go a long way towards helping folk who ever have to manipulate text or text-based data files, at least in terms of letting them know that there are often better ways of cleaning up a text file automagically rather than having to repeat the same operation over and over again on each separate row in file containing several thousand lines… They don’t need to know how to write the regular expression from the off, just that the sorts of operation regular expressions support are possible, and that someone will probably be able to show you how to do it…
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.