However, it is much easier for software to request a structured data version that does not contain all of the other HTML markup of the page. The best way to do this is through something called “content negotiation”. Basically it enables a requestor (that is, a software program) to send a request that also tells the web server which format is required. For example, if you want a representation of the data in the Javascript Standard Notation (JSON) format, which many software developers use, then you could issue a command such as this:

curl -L -H “Accept: application/ld+json” http://www.worldcat.org/oclc/41266045

And that is what you would get in return. Alternatively, you could simply request that format by using the appropriate filename extension:

http://www.worldcat.org/oclc/41266045.jsonld

Formats supported include RDF XML, JSON, and Turtle. Richard Wallis has written a more thorough description of this that can be very helpful in understanding how best to use this new service.

These changes make it much easier and faster to get the data a developer requires into their application in a highly usable way. We can’t wait to see what they do with it.

That began my lifelong relationship with computers, since I wrote my very first software program on that Commodore PET, stored on a cassette tape, in the early 1980s. It was a library orientation tutorial.

From there, I minored in computer science and majored in geography at Humboldt State University. As an assistant to a geography professor there I wrote statistical analysis programs in FORTRAN and took classes in COBOL and Pascal. These were batch systems, where you submitted your job and waited for it to be run at the whim of the operators. The largest computer I ever used at Humboldt in the 80s is now far eclipsed in both power and storage by the phone in my pocket.

At UC Berkeley, where I received my MLS in 1986 and worked for 15 years, I made the acquaintance of UNIX and online time sharing computing. What a breakthrough that was. You could write and run a program immediately and get instant feedback. For a lousy code jockey like me, it was heaven. I didn’t have to wait to find out I had left out a semicolon.

So fast forward to today. Earlier today I kicked off a computing job on the OCLC Research compute cluster that took 4 minutes and 19 seconds to complete. At first that sounds like a long time, until you find out what it did. The job located 129 MARC records out of nearly 300 million that had the text string “rdacontnet”. It did this without indexes or databases. 300 million MARC records are sitting out on spinning disks somewhere in Ohio and from California I searched every byte of those records for a specific text string and it completed in less than 5 minutes. Pinch me. I’m dreaming.

Photo courtesy of Don DeBold, Creative Commons License Attribution 2.0 Generic

It was remarkably quick and easy to find out, although I had to edit and re-run the code when I discovered a flaw in my logic. This is, sadly, all too frequently the case. But not too much later I had my result:

These are all of the occurrences of a 020 $a in WorldCat as of 1 May 2013 [Added for clarification: the prior sentence describes exactly what is being counted. That is, I am not (yet) examining ISBNs for 10-digit vs. 13-digit; therefore, many of the records with 2 ISBNs may in fact simply have both versions].  A few observations:

• Many items in WorldCat were published before the invention of the ISBN.
• Many items in WorldCat are not ISBN-appropriate (e.g., unpublished materials).
• ISBNs are therefore problematic as identifiers except for a narrow slice of materials (mainly printed books since the mid-60s).

A much better identifier for many purposes is, I assert, the OCLC number.

Currently the Wikidata Property for Sex states:

Sex for humans, should be one of male, female , intersex, or the special “unknown” value

Finding this to be a rather rigid view of the world I started discussing it on the Discussion Page as per protocol. Of note, on the other hand is how VIAF records “gender” not “sex.” The current VIAF data model similarly limits values to male, female, or unknown but a change to a more nuanced model is planned for June. Its worth reminding that VIAF is populated with data from the many authority files it aggregates. One underlying authority file, which has a more nuanced view on this recording, is the Library of Congress Control Number (LCCN). The LCCN will record many “sexes” for a specific person with accompanying dates of validity. This at least shows that there are better ways of recording sex – if its necessary to record it at all – which prompts me to invite your input on the Wikidata Discusison Page about better ways to record sex. With that said, lets dig into some graphs. (Click to see larger versions.)

Sex Ratios by Language

The method used to perform this visualization is to view all the Wikidata items with Property:Sex and then look at the inter-language link section of the item to see which languages have articles relating to this item. Dividing along the lines of language, we can find sex ratios per language. Below shows each language with more than 1,000 articles tagged with sex data, sorted by the percentage of Female values.

Wikidata Sex Ratios By Language, Minimum 1000 Items

If you’re not well versed in Wikidata’s use of language codes, you can look them up. And if you’ve never browsed the winning and losing htwiki and tlwiki, the Haitian and Tagalog Wikipedias, then you can peruse the list containing minimum 10,000 Items with Sex Data.

Wikidata Sex Ratios By Language, Minimum 10000 Items

Two notable things arise here. Firstly, Chinese Wikipedia is seemingly the most progressive. Secondly The Intersex category fails to score a single pixel of recognition. In fact the Wikipedia with the highest ratio of Intersex values – as determined by Wikidata – is Korean Wikipedia, but at just 0.0078%.

Data Caveats

Is this data reliable? A lot of it was imported from the German and other major Wikipedias. That can be a problem, because for any given Wikipedia Language there exists articles that have no linked equivalents in other languages. There may very well be Wikipedias with more or less skewed sex ratios, but they haven’t migrated their sex data to Wikidata, or they have no equivalent article in a language which has migrated its sex data. Lets see which languages have the most articles associated with sex data, of those above 1,000.

Total Number of Wikidata Items Tagged with Sex

Unsurprisingly we get a very Western view of the world. But wait, there are other data sources to corroborate against; that was one of the points of VIAFbot importing VIAF IDs into Wikidata. Let’s imagine an enhanced version of Wikidata, that uses VIAF sex data in addition to what’s currently tagged, using that VIAF ID bridge. I ran simulation of such enhanced version of Wikidata, but before we look at it, lets understand VIAF’s own biases.

Introducing VIAF

VIAF IDs have gender info derived from National Library files. There’s hope this may give us a different picture because VIAFs may be ever slightly less severe in its skew, although looking at its list of contributors reveals also a Western bias. Of ~24 Million VIAF records (not all about people)  1,299,396 have gender “male,” and 418,394 have gender “female.” This comes out to a percentage of 24.35% female.  (Unfortunately VIAF doesn’t note directly where LCCN has a more nuanced view, but it can be determined by crawling the RDF link to LCCN’s Marc XML which I explain later.) Now to compare the Wikidata and VIAF-enhanced-Wikidata sex ratios we overlay the two graphs. Here wherever you see light green that means that Wikidata’s data alone gave a higher female ratio, and where you see red, VIAF-enhanced-Wikidata data gives a higher female ratio.

Comparison of Wikidata Sex Ratios with and Without VIAF by Language, Minimum 1000 Items with sex

Reassuringly VIAF and Wikidata only disagreed on 0.0024% of 91,406 matches. There were seven cases where LCCN did have with multiple sexes and qualifying dates. Furthermore there are 52,407 cases where VIAF has Sex data but Wikidata does not. This might be a good juncture to import that data, if the Wikidata community wants.

Conclusions

There are articles in Wikidata which are not currently tagged with sex information, but whose sex information can be programmatically determined. There is some indication that tagging more articles would tend to produce more even sex ratios in Wikidata. If that were true, it would mean that “male” articles are more likely to be associated with sex data, though we cannot be positive about that claim. Finally recall that Wikidata’s data model for sex could also use some attention, and you the community are the instruments for that.

Software used

I wrote some simple scripts to crawl Wikidata and compare it VIAF and LCCN, its on  GitHub, and I also modified code from the Wikidata community for parsing dumps which I plan to contribute back.

Did you find anything confusing? Leave your comments below or find me online. On twitter I’m @notconfusing.

First was John Mark Ockerbloom’s Forward To Libraries which proposes “find in a Library” boxes in Wikipedia pages. The idea is compelling: facilitate automatic searches in your preferred library site on the topics of Wikipedia articles — one option utilizes VIAF IDs.

Similar look-up facilities were created by Owen Stephens and Thomas Meehan conducting pointed inquiry at the British Library site and other UK Academic resources. Stephens’ contemporaneous finds authors sharing their birth year with the Wikipedia page in question. Meanwhile Meehan’s bookmarklet will funnel you into relevant pages linked by VIAF at UCL’s Explore, and COPAC.

VIAF connections can also pave the way for new scholarly research. A team from Vienna University of Technology, released a paper that visualized Art History networks of Wikipedia, through VIAF IDs, and then ULAN. Here you can see the proportion Art History Subjects in Wikipedia, displayed on two dimensions derived from the ULAN connection: time and nationality.

Credit http://vsem.ec.tuwien.ac.at/wikiarthistory/

All of this is to say that VIAF data in English Wikipedia can as a very good ropebridge that allows for reuse, or recombination. The idea of a ropebridge is apt because the connection is somewhat shaky, at the moment it’s free text, semi-structured data that can be changed by anybody, but that doesn’t mean that the chasm isn’t being crossed.

Can you spot the weakness in all this collaboration though? We focused our first effort on English Language Wikipedia. The Germans, to their credit, have just as many VIAF IDs in their Wikipedia. The Italians copied the English Language data. However these separate efforts are not scalable to all 285 Wikipedias, nor does it allow all 285 Wikipedias to collaborate on the language-neutral VIAF Unique Identifiers.

Fortunately there is a solution, and that solution is Wikidata. Wikidata is first new Wikimedia Project since 2006, and will do three things. It will organize inter-language links into a central database (inter-language linking before was arduous and asymmetric). It will provide a central store of Semantic Data from the Wikipedia articles. And in the future it will be able to query that semantic data. Want to know more about Wikidata? Then look up Wikidata on Wikidata (obviously?!).

Now for a surprise – I’ve just finished migrating English Wikipedia’s VIAF data to Wikidata, and German, French, Italian, and Japanese datasets are in progress. (Code on Github). It takes about two weeks to inspect, clean, and copy the data over from each Wikipedia. I’ll post a full statistical breakdown once all the languages have finished. For now I’ll just say that the Wikidata VIAFbot is also migrating LCCN, GND, BNF, and SUDOC Identifiers as well as integrating for the first time ISNI IDs. At the time of this writing it records 750,000 edits and counting.

What does VIAF in Wikidata look like you ask? All pages about encyclopedic concepts are known as “Items” in Wikidata parlance, so lets inspect the item for Germaine Greer.

We first see all the Semantic Data Wikidata has about this topic. Each modicum of data is known as a “Claim” in Wikidata, is a triple,  and is structured as [this page] [property] [value]. You can see that [Germaine Greer] [GND (read: "is a " according to the German National Library)] [Person], and that [Germaine Greer] [is of sex] [female]. You can also see here that she’s got a lot of identifiers associated with her thanks to VIAFbot, which has sourced where it found the original VIAF ID. Now lets draw our attention to the bottom of the page to understand the impact.

This Wikidata page is associated with articles in 48 other languages. Each of those articles can capitalize on the semantic data stored above. That’s the beauty of Wikidata. Which now means that all of the data reuse cases that previously only worked for the English language Wikipedia, will now work for all of them. Austrian researchers can inspect Art History biases of not just English Wikipedia, but of dansk, Ελληνικά, हिन्दी, interlingua, Runa Simi, 中文, etc. etc.  That’s one of the starting reasons why it’s important to have Authority Control in Wikidata. There are of course more directions than one to travel across a ropebridge. Leading data-mules of bibliographic information across from VIAF into Wikidata is next.

Bi-lateral duplication of print books in Cascadia and NorCal

Now it may be the high degree of integration that is a primary characteristic of mega-regions is also a factor in the diffusion of library resources within those same regions.  Arguably, the strong networks of exchange and robust logistics infrastructure of mega-regions help to explain why we find such a low level of redundancy in library collections within those regions.  Even without coordinated collection development plans, it may be that the ease with which resources (including library books) flow within mega-regions exercises some influence on library acquisitions.  The incentive to acquire ‘just in case’ inventory will be less in a region where inter-lending networks are strong and it relatively easy to obtain copies from neighboring institutions, and this confidence in regional supply options may have a sort of invisible-hand effect that constrains redundant acquisitions.

This would suggest that the high degree of diffusion we see in regional collections – inventory distributed across a number of geographically distant institutions – is a characteristic of a ‘well-organized’ (though not deliberately engineered) library system.  By contrast, library collections in institutions located outside of mega-regions tends to exhibit a higher degree of redundancy.  This is partly a reflection of the large number of libraries that fall outside of the defined mega-regions – more than nine thousand individual OCLC institution symbols – but it seems likely that greater redundancy is needed to support demand in regions where ‘flows’ may be less efficient.   Compare the average library holdings per title for collections held outside of US mega-regions (about 14) to the ratio of holdings per title within mega-regions, which ranges from 2 to 9, for the Phoenix metro area and Chi-Pitts respectively.

Of course, there are other factors at play:  as a recent New York Times article showed, the geographic distribution of major research universities (and the libraries that serve them) is uneven – and regions with fewer research libraries will have a smaller concentration of rare or unique materials, compared to regions with many research intensive universities.  Not surprisingly, the BosWash region, which encompasses a substantial part of the ARL membership, has a relatively low level of redundancy in print book holdings (about 7 holdings per title) simply because the area is home to many institutions with large collections of rare or distinctive materials.

ARL Membership Map (2013)

Conversely, in areas with a high density of public libraries — which generally hold large collections of popular classics and best-selling titles — we see higher levels of overall redundancy in collections.  So one cannot infer that low levels of redundancy in library holdings across any region, whether organized against the mega-regions framework or anything else, are a reliable indicator of strong or weak flows.  Other regional factors, like the distribution of research universities and public libraries are clearly important.  It is interesting then to consider how the flow of library resources across regions may contribute to the organization of the library system as a whole.   Regional infrastructure will affect flows — but flows will also shape infrastructure.  Think, for example, of how the growth of rapid transit networks has transformed urban landscapes, encouraging the emergence of the sprawling metro areas that anchor mega-regions.

Lorcan Dempsey sometimes speaks of ‘library logistics’ and it is in this context that I have been thinking about the flow of library resources and more especially about the emergence of new hubs around which the library system is now being reconfigured.  Thom Hickey’s recent experiments in programmatically identifying concentrations of material related to a particular topic or identity — what we’ve referred to as ‘centers data’ – provide a new way to think about how the library system is organized and how it is changing.  It’s not clear if the existing centers reflect intentionally cultivated strengths in institutional holdings, or if they are merely accidents of history – an unsolicited donation of materials about a particular person, for instance.

In some cases, the association with known institutional centers of excellence seems evident:  it is not surprising, for example, to find that the University of Pittsburgh has the largest collection of material by or about Gonzalo Rojas, a celebrated Chilean poet.  Pitt has been a National Resource Center on Latin America for decades and it stands to reason they hold significant collections of Latin American literature.  Would an expert in the field have predicted that Pitt, rather than the University of Texas (a distinguished center of Latin American studies), has the most comprehensive collection related to Rojas?  Perhaps – I don’t have the domain knowledge to have an informed opinion about the likely location of the most comprehensive Latin American poetry collections in North America. Significantly, though, Pitt ranks within the top collections (by size) of works by related poets:

Gonzalo Rojas and related identities – University of Pittsburgh holdings ranked against other WorldCat libraries

As the figures here suggest, it is possible for a library to be a leading — or even the top-ranked–  ‘center’ of resources related to particular identity or topic without holding a vast number of titles.  This will obviously be true when the relevant oeuvre is limited:  to hold 100% of a small published record (a handful of titles, let’s say) is still significant.  What is more interesting is that the diffusion of library resources — the ‘scarcity’ that we find in institutional and within regional collections — effectively lowers the threshold for what constitutes excellence in institutional holdings.  In the example of Gonzalo Rojas, for example, Pitt’s 48 titles amount to less than 40% of the published works associated with the related VIAF heading.  Even so, this small collection is 75% larger (and 16% more comprehensive) than the related holdings at the Biblioteca Nacional de Chile — at least as they are reflected in WorldCat.  This is, at least to me, somewhat surprising.

This raises the question of how centers or hubs are revealed in the information environment.  Effective disclosure of collections that are distinctive not because of their rarity but because of their ‘excellence’ or completeness will be important if libraries are to be recognized as preferred hubs in the larger supply chain, where commercial providers are still dominant.  Ideally, one would like to have relevant library suppliers revealed in the network at the point of need, in the flow of the researcher’s work – which is increasingly likely to be outside the library discovery environment.  How could this be done?  Where do library fulfillment options fit in the knowledge graph? Somewhere in the Wikipedia infobox?   In Google’s info cards?  Happily, greater minds than mine are working on this problem.  Of one thing, at least, I’m certain:  understanding and representing the relationships between identities and topics in institutional and in regional collections – understanding how different ‘centers’ are related – will lead to new insights about how the library system is, and will be, organized.

We will pay the expenses of the selected individual to attend the conference where they will also be afforded a lightning talk slot to highlight their work for conference attendees. This is the first “Library Spotlight on Innovation” that we jointly developed with SemanticWeb.com, the producers of the conference. Richard Wallis, our Linked Data Evangelist, was instrumental in putting this together.

So are you doing something interesting with linked data? Or do you know of someone who is? If so, you can nominate yourself or someone else for this great opportunity. We want the broader world to know about how libraries are innovating with linked data.

For our MOOCs and Libraries event, it was important to come away with concrete of opportunities for librarians — hopefully now that we have a cohort of attendees (in person attendees, remote attendees, and those of you who have watched the videos, reviewed the Twitter stream, and read these summaries!) there are some positive and meaningful ways that librarians can engage with MOOCs. To help the end of the day on both Monday and Tuesday, my colleague Chrystie Hill led us in small group discussions. (We also tried to include the remote audience in the discussions, with mixed results).

The questions for discussion were:

On the last point, the small groups were asked to come up with their top three recommendations. Then as a whole, we heard all the “top three” from each table. Not surprisingly, there was quite a bit of overlap, and my colleague Dale Musselman nicely transcribed and organized the outcomes into 9 rough categories.

Of these, from my perspective, the things that every librarian can do is to take a MOOC, and contribute to the conversation by listening to others who have been invovled in MOOCs, and sharing information and experiences.

My thanks to Chrystie for structuring and facilitating this sessions, and to Dale for helping to organize the outcomes document.

You can take a look at the summary document and also the individual recommendations as contributed.

You can also watch the video for even more detail.

Approximate location of maximum subsidence in the United States.
Source: http://en.wikipedia.org/wiki/File:Gwsanjoaquin.jpg

There’s been a lot of attention to geologic subsidence of late, what with all the sinkholes opening up in Florida, Louisiana and other places. Here in California, we are more often concerned with the gradual change in ground level due to the draining of aquifers that support large-scale farming.  From year to year, the difference in ground level may be nearly imperceptible but over the space of a few decades the landscape has been radically transformed.

The subsidence metaphor was on my mind recently, as I was looking over some data compiled by my colleague Thom Hickey, documenting the usage of headings (subjects and names) in WorldCat. OCLC Research has done quite a lot of work exploring new approaches to managing subject and name authorities, notably in VIAF and FAST. I was interested to see how Thom’s data might be used to measure change — uplift and subsidence — in the library landscape. By computing the frequency with which FAST and VIAF headings occur in institutional collections cataloged in WorldCat, one can identify which libraries hold the most materials related to particular topics, places and people.  And this in turn provides a measure of the relative distinctiveness of library collections, judged not in terms of the ‘rarity’ of holdings but rather by the concentration of related content.

It  seemed to me that Thom’s data might have something interesting to say about how the emergence of large-scale digitized book aggregations – HathiTrust, Google Books, etc — is altering the library environment.  It stands to reason that as these large hubs begin to consolidate content sourced from libraries (and, in Google’s case, publishers), they will displace traditional library ‘centers of excellence’ in some subject areas.  Those who remember the DLF Aquifer project will recall that the initial prototype was designed to pool digitized resources in a given subject area (initially American History, later narrowed to Abraham Lincoln and the US Civil War).  In the very large aggregations of HathiTrust and GoogleBooks, subject specialization has emerged more gradually.  There has not been much public attention to measuring the scope of subject-based collections within those aggregations, nor to benchmarking them against existing institutional holdings.*

The FAST and VIAF centers data provide evidence of both subsidence and uplift in the current collections environment — that is, shifts in centers of excellence as measured by scope of subject based holdings.  The ‘re-leveling’ that has been wrought in just a few years of large-scale digitization is already significant.  Digital aggregations have, by design or accident, emerged as important subject repositories that rival and even outrank some of the largest institutional libraries in WorldCat.

For instance,  HathiTrust, an organization not yet five years old, already holds the greatest concentration of titles on the topic of marine biology, surpassing the Library of Congress as well as two major research universities with world-class oceanography programs.

In the case of Marine biology, the difference between the number of titles held by HathiTrust and the Library of Congress is not very large — fewer than 200 titles.  But in other instances, the relative subsidence of traditional centers of excellence is more dramatic.  For instance, Google Books substantially outranks several major research libraries in holdings related to Russian periodicals (journals, newspapers and the like).

This represents an important change in the library system, with monumental old hubs being progressively overshadowed by new collections that are produced not by the slow accretion of library acquisitions but by large-scale digitization and (re)aggregation.  It provides a compelling illustration of how Web-scale content aggregations are altering the library operating environment.  In the case of HathiTrust especially, this disruption can (and I think should) be seen as a positive change:  it enables libraries to rethink traditional, institution-scale collection management and stewardship — a topic we examined in our Cloud-sourcing Research Collections report some years ago.

Using Thom’s ‘centers’ data, we can identify hundreds of topics and identities for which HathiTrust offers better coverage than any other library in WorldCat.  Here a few topics in which the Digital Library distinguishes itself:

And a few of the personal names for which its coverage is unrivaled:

Interestingly, the other top-ranked collections (by size) for these same subjects and identities are not always the source of HathiTrust’s richness.  One might have anticipated that Hathi’s leadership was simply a by-product of aggregating content from existing centers of excellence, but in fact Hathi has developed unexpected strengths by aggregating at a very large scale from a diverse pool of contributors.  For example, Harvard University and the University of Michigan each hold sizable collections of works by the poet Jean Ingelow; yet, the richness of Hathi’s Ingelow collection is mostly due to contributions from campus libraries in the University of California system.

The FAST and VIAF ‘centers’ data provide a fascinating new vantage point on the changing collections landscape.  We’ll be looking at ways to integrate it into ongoing research projects, including the mega-regions work, where we hope it can help us detect regional collecting trends that might inform shared stewardship priorities.

*Note:  HathiTrust provides a nice visualizations and a list of subject areas in the Digital Library, based on Library of Congress classification numbers.  These provide a good overview of subject-based coverage but without reference to comparable coverage in other libraries. It is generally known that Google is selective with respect to identifying library partners, but I’m not aware of any public documentation related to a specific collection development strategy. Their aim, famously, is to provide comprehensive coverage of the world’s books, not to develop excellence in any given subject area.

MOOC Audiences by *s@lly*, on Flickr, cc-by-nc

We wrapped up the content portion of our meeting by reflecting on the audience for MOOCs (or what we know about participants), and also considering the audience through the lens of public libraries (which I admit, I don’t think about a lot of the time, except when I’m acting as a patron). I find the role (or potential role) for public libraries in MOOCs to be very exciting, and I think you’ll see why if you read the summary or watch the video of Margaret Todd’s talk.

We heard first from Howard Lurie (Vice President, Content Development, edX), who said he was from the “other platform.” (Although we tried to balance the program, many of the presenters were from “Coursera” institutions — I don’t think the platform matters all that much when talking about the library’s role in MOOCs, but there you have it.) According to Lurie, MOOCs provide an opportunity to look at learning/pedogogy through the lens of “big data” gathered during course implementation. Even with “low” completion rates the numbers are still quite high. Taking one edX class as an example (6.002x, Circuits and Electronics): 154,763 registered for the class; 26,349 tried the first problem set; 10,547 took the mid term; 9,318 passed the mid term; 8,240 took the final exam; 7,157 received certification. This is a lot of data to analyize that could help improve teaching — it would take many years of iterating a class in a traditional setting to get to those numbers (and of course, Audrey Watters would ask as she did in an excellent talk at WebWise, “Whose educational data is it?”) As with many presentations on MOOCs I’ve heard recently, Lurie highlighted “global stories” reflecting exposure to a topic for those who might not have had the opportunity otherwise (such as the Pakistani participant who said “this course was the most important experience in my life” One role for MOOCs might be to gather stories of learners around the world, and help universities identify talent.

Next we heard from Deirdre Woods (Interim Executive Director, Open Learning Initiative, University of Pennsylvania) who joked that in this environment, being around for a number of months makes you an old timer. Penn’s is in the open learning business because it’s a public good, and also because it’s good for Penn — online courses provides prospective students a taste of what they might expect from the college before they make a commitment. It’s also a good way to stay in touch with alumni. Woods shared that the faculty who have taught MOOCs acknowledge that it’s a huge undertaking but all say they would do it again. Part of the satisfaction? Faculty members reach more people in one course than in entire career. A little more about participants demographics: the majority of participants in Penn MOOCs were working in full time positions. 65% are male (they aren’t sure why). 30% of participants hang in through the duration of the course, but don’t do assignments.

Finally, we heard from Margaret Donnellan Todd (County Librarian, County of Los Angeles Public Library). Right now, LACoPL is loved and trusted by the community; as evidence of this, county residents recently voted to increase library funding. Not content to rest of their laurels, LACoPL has identified online relevance as an important component of their strategic plan. And the library has a challenge in terms of serving an educational shortfall in their community. Right now high school dropout rates are at about 40%, and community college graduation rates are low. Higher education in California (as elsewhere) is increasingly squeezed, and the option to go to community college in order to catch up, is no longer an option for all. All of these factors will lead to a decline in a local qualified workforce. With fewer and fewer options, LACoPL has begun to see itself as a center of learning, and positioning itself to support very practical and real educational educational needs. Public libraries excel at connecting people to services, partners, and peers. At an academic institution, MOOCs are an extension of existing online presence; in public libraries, MOOCs (and support for those taking MOOCs) may be an extension of their broad public education mission. Todd described how LACoPL has experimented with offering course through Ed2Go — even with little promotion, these courses have been very popular. What might be possible if public libraries extended their online courses, or worked with material being produced in MOOCs? One desire expressed by participants in MOOCs has been a need for a common space to come together with others taking the same class. Why not the public library as that space?

N.B. You may have noticed that in these postings, I purposefully am referring to those who take MOOCs as “participants” and not “students.” That’s a purposeful choice on my part. I don’t think we know enough about who is taking MOOCs and why to label them as students yet.