Asynchronous Microlearning and Microfranchising

Saturday, February 07, 2009

At Helioid, we will leverage the opportunity and advantages gleaned from our search solutions to support netroots humanitarianism, in addition to our primary goal of meeting the emerging need for a new sort of web search and academic/enterprise research. I posted an entry on this blog some time ago, entitled “Innovative Collaboration,” in which I discussed the promise of online innovative collaborative knowledge networks for effectively and efficiently aggregating the creative contributions from large user bases. I further discussed the contributions to be made to such networks by the sort of navigable, hierarchically structured representations of bodies of knowledge constructed by Helioid’s clustering algorithms. Information scientists like “Chaomei Chen”: have demonstrated that representations of published research in a given field – clustered by citation analysis – can so accurately characterize the field that changes in the representation can be used to predict the emergence of new research paradigms, and so these clustered representations can be used to help guide research. Others like Peter Pirolli argue that, since such representations illustrate areas in which relatively sparse research efforts have been undertaken, they can be used to optimally distribute the efforts of researchers over presently hot subject areas, and under-explored regions of the field. Moreover, there is a very clear opportunity presented by such structured knowledge representations for enabling any interested party to make some manner of contribution to such innovative networks, as such navigable representations allow users to assemble crash courses in a broad subject area, or pick and choose over specific topics in which their acumen might be lacking.

With Helioid’s clustering and search solutions, we intend to put into action all of these advantages for online innovative collaboration. Moreover, we believe that profound contributions can be made by such collaborative networks to the humanitarian sector in particular, and by leveraging the above advantages presented by our search and research services, we intend to launch a new kind of humanitarian organization. This humanitarian network will knit together the concepts of mass collaboration, asynchronous learning, microlearning, and microfranchising.* Asynchronous learning is a web-enabled student-centered method of learning, which does not require a set timeline or even a single instructor. The idea stresses self-study, combined with collaboration with other “students” on projects pertaining to the subject being studied. Asynchronous learning is actually one of the primary applications we’ve always envisioned for Helioid, as our research services allow users to navigate subject areas, arbitrarily focus or broaden their search, and explore the ways in which specific concepts within a subject relate to one another.

Last February, in Volume 12, Issue 1 of the Journal for Asynchronous learning, there was a fascinating paper on the merits of seeding microlearning centers in developing countries, by way of microfranchising strategies, entitled “Microfranchising Microlearning Centers: A Sustainable Model for Expanding the Right to Education in Developing Countries?” [“PDF”:] The author, Tiffany Ivins, Director of International Programs for the Center for Open Sustainable Learning, first outlines the strengths of microlearning in efficiently making available pre-packaged lessons, generally with immediately practical applications. She goes on to identify the weaknesses of microlearning: its inability to reach people in developing countries, as the lack of infrastructure development obviously limits access to the internet and other learning materials. The solution explored is then to seed microlearning centers in these communities, which provide any interested locals with a place to access the internet, receive lessons, interact with other students, and even obtain physical learning materials. Successful learning centers become such assets to their respective communities that they become able to charge a minimal fee for their job training or educational services, and thereby become sustainable. The centers are seeded by identifying educational center models that have met with success, and replicating those models in similar communities with small initial investments. The author used as an example for case study a youth-run learning center model in Nepal, which was replicated in various communities with genuine success.

These results are certainly exciting, but towards the end of the paper, the author notes a serious impediment to the overall success of this approach, and summarily suggests a possible solution. The problem is simply that there is so much work to be done, and not nearly enough organizations interested in doing something. The summarily alluded-to solution the author suggests is finding an “angel” to invest in each center. Well, it seems pretty clear that a massive collaborative network of individuals, each searching for a way to chip in, would meet this demand for “angels” pretty handily. The humanitarian network we envision will make use of Helioid’s clustering and search methods to allow users to navigate the space of ongoing humanitarian projects, and help connect them with the projects they’d be most interested in getting involved in. These users will help finance and seed microfranchises in developing countries, including microlearning centers. The asynchronous learning resources provided by Helioid will help broaden the base of knowledge available to students at the seeded microlearning centers, and may allow for students in the learning centers and/or investors in learning centers to design new lesson plans for other students. Furthermore, each successful learning center will undoubtedly produce individuals themselves interested in receiving investments for starting local businesses, providing further opportunities for economic development through microfranchising. So, by simply running with two of our core values (maximizing people’s breadth and depth of access to information, and enabling users to optimally collaborate with each other) we arrive at a fairly exciting opportunity to lead anyone interested in an ongoing effort to build up developing communities all over the world from the ground floor.

*Microlearning is also a form of online education, but has to do with pre-packaged lessons pertaining to very specific skill sets. Microfranchising is the financing of pre-packaged business plans with small initial investments, in order to efficiently repair and expand struggling economies from the local level up.

Innovative Collaboration

Tuesday, May 27, 2008

Last night I drifted off to sleep thinking about scientists and online collaboration. I’ve been thinking about these things as I drift off to sleep much more frequently than usual since I read the chapter on “social information foraging” in Peter Pirolli’s Information Foraging Theory. In said chapter, Pirolli describes a number of studies of trends in large groups of specialists working towards a common set of goals, and the degree to which such communities of specialists collectively aid their individual members in making contributions to meeting said goals. The subjects explored within a few of these studies that really caught my eye were the use of co-citation analysis to visualize a field of study or network of specialists, and, as Pirolli puts it, the “brokerage of structural holes” in these networks. The former of these I was familiar with, as the technique’s been pretty well explored from a variety of angles, but I had never seen the latter presented the way in which Pirolli does.

By ‘structural holes’, Pirolli means the areas within a field not being explored nearly as heavily by the specialists in the network as other areas. In any area of study, there will be a number of hot topics around which the majority of research within the field crystallizes. As a result, there will be perfectly viable topics for research that go unexplored, due to the community’s collective ignorance of their viability, and a visualization of the network will show clusters of closely related papers, separated by gaps, or ‘structural holes’. Members of the community rarely explore these gaps, because it is entirely unknown how fruitful such endeavors would be. So, disinclined to risk wasting their time looking for a chance breakthrough, they work to make incremental contributions to areas of ongoing study. However, when discoveries are made within these gaps, the benefit to the community is enormous, as it opens up a brand new area of study around which further research may develop. So, by way of a little cost/benefit analysis, it’s possible to work out the optimal number of people a research community should have exploring these underdeveloped areas at any given time.

This is all very intriguing, but what really makes me raise an eyebrow is the application of these ideas to a few online collaboration support projects that have caught my eye in the last couple months. Berkeley’s had a couple interesting projects along these lines, called BOINC and Bossa, which provide frameworks for distributed computing and what they call distributed thinking, respectively. BOINC provides a means for projects requiring a lot of processing power to recruit volunteers and form a computing cloud. Bossa similarly enables the cooperation of large numbers of individuals, in working toward some common research goal, like the cataloging of stars in a particular region of the sky. Similarly, MIT’s Center for Collective Intelligence is dedicated to fleshing out the ways in which a bunch of people and a bunch of computers can work together to “act more intelligently than any individuals, groups, or computers have ever done before.” To this end they’ve launched, which aims to use community visualizations to support the creation of “innovative collaborative knowledge networks.”

All this brings us back to why I’m so enamored with the idea of online collaboration between professional and amateur scientists alike. The whole idea behind Helioid is to leverage the benefits gained by visualizing web searches and online communities, in order to facilitate a more effective form of exploration of the information available on the web. Aside from the impression we’ll make on the general web search market, there’s a huge impact to be made in supporting these sorts of collaborative endeavors on a massive scale. I can’t help but dream of such massive collaborations, wherein groups of millions of professionals and amateurs are directed to unexplored research areas, in a collective effort to push every scientific field forward, and these efforts are supported by a core set of tools for visualizing the field of research and determining where the frontier lies.