eLearning is a Natural for Social Networking

eLearning is a natural for social networking but in any social network the path to success is not straight. Different kinds of feedback create a dynamic environment to raise the participation level.

Article sponsored by
Eedo Global Learning Services

David Shaw © 2009

eLearning is a natural for social networking but in any social network the path to success is not straight. Many organizations are implementing social networking in the blind hope that the network effect will kick in to drive up registrations; and then self-organization and collaboration, a feature of crowd-sourcing, will take care of the rest.

As if.

In general, the direct benefit of the network effect accrues to the owner of the network, not to the individual players. Small networks are different. A project wiki, for example, is owned by the project team, and the benefits of collaboration using the network accrue to the team. The team is the de facto owner of the network.

Larger networks are more complex. People join FaceBook en mass because the network effect has made it a social destination. It’s the place to be. But this didn’t just happen. FaceBook has context. FaceBook also has features that made it an attractor initially, and provided a continuing utility to users so that it has a good retention rate. However, the value of 250 million users accrues to FaceBook – it sells eyeballs to advertisers. (Nielsen Research says FaceBook has a 70% retention rate.)
Wikipedia is a different model. A small sub-network of active writers and editors create expert content for the benefit of the many. It’s an open-source publishing model.

LinkedIn, like FaceBook and MySpace, can connect you to friends (second tier) of your friends (first tier) and so on. But this has small value unless you “work your network” like the PowerBook woman in Starbucks working her Tweet Deck. This is so Social 1.0.

Jiggin’ for cod is a great past-time, but deep trawling with a bottom net is more profitable. But even with a deep net the fish still have to be sorted. Social 2.0 should jig automagically for you in the deep trawl by giving you significant feedback.

A large social network has to provide feedback on multiple dimensions so that you’re on the clue train. The dimensions must be ones that you value. And, again, the feedback has to be automated. This feedback amplifies the signal or signals that relate to you and your objectives. The rest is noise.

Without some means of detecting signals you will drown for days in the social swamp. If you have 100 friends in your network, and they each have 100, there will be 10,000 in your second level and so on. Nobody can manually work a network of 10,100 people so people typically join groups and try to work the groups. The effort to do this can be reduced if the system provides feedback to amplify the signal you want.
But how do you do this?

This series has explored social networking from different perspectives because there is no single design pattern. Previously in Engaging Citizens through Social Networking we showed how a public-policy issues-agenda site would work; and a future article will show how to apply social networking to a night club.

This article uses eLearning as a viewpoint to show how you might amplify a signal.

We will start with the light framework for mapping business objectives to social networking described in the previous article, The Secret Sauce of Social Success. Other relevant articles are:

• Developing Governance for Wikis, gives principles applicable to any type of social network.
• Taxonomies and their Cousins are Important , describes the importance of some kind of faceted taxonomy for navigation.

Engagement Strategy

The next step is to develop a strategy. This is the hard part. You can’t just buy a socially adept eLearning application and expect it to self-organize. Your strategy must be a synthesis of your business objectives, business culture, market and technology. If the strategy isn’t aligned, it may simply fragment your message. This is worse than doing nothing.

Two examples of eLearning engagement strategies are given here. The first one keeps it simple and small-scale. It assumes your market is internal. Essentially it’s a variant of an intranet. Note that there is no direct engagement with student prospects in the external marketplace. If your market is really external, an internally facing strategy such as this one will deliver limited marketing benefits. It can, of course, deliver other important benefits internally.

An internally focused strategy

A different strategy is required to engage both internally and externally. One approach that would suit some eLearning environments is to leverage an existing social destination such as FaceBook or LinkedIn. This is feasible. We can do it with either the FaceBook Connect or the LinkedIn OpenSocial application-programming interfaces. The next step is to develop some measurable objectives.

An externally focused strategy

Objectives

Every human activity has a social dimension. It’s the nature of the beast. Similarly, every software application has a social dimension. Some applications have a high-value social dimension, while others have a low one. But these dimensions are often dynamic and always contextual. A book-keeping application, for example, has a generally low-value social dimension. But it has a high one in the context of trying to get your travel expenses paid.

The purpose in incorporating social software in any application is to increase the participation level along some social dimension that maps well to a business objective.

Here are some objectives that could be considered for an eLearning application:

• Increase rate of success
• Reduce time to completion
• Increase take-up of courses
• Increase satisfaction with the process
• Get feedback to improve courses
• Develop courses collaboratively
• Contribute to team building
• Identify requirements for new courses

We say “some objectives” because one strong characteristic of a social network is that it can have outcomes that are not predicted. Social activity is an ongoing and ever-changing conversation, so the feedback changes dynamically.

The next steps in our framework map (a) objectives to functions and then (b) to Web 2.0 tools. But because we want to show how to amplify signals, we will instead use an information architecture.

Information Architecture

The below figure shows one view of a possible information architecture that integrates with FaceBook. Some functions like messaging have not been shown. Most components are optional with configurable access. The main ones are:

• Courses that students can elect. May be part of a curriculum. This is probably one facet of the navigational taxonomy.
• Blogs that instructors and students can write, either in general or about a course.
• Groups, typically a general group for the course led by the instructor and integrated with a group on FaceBook, and study groups set up by students.
• Pages that instructors and students can compose.
• Newsfeed reporting an individual’s activities in the portal.
• Friends to which individuals have linked.
• Notifications that individuals have elected to receive. These are delivered in several direct (email) and indirect ways (FaceBook notification, and maybe RSS or Twitter).
• Recommendations in which the system suggests connections that should have value for a specific individual.
• Synchronization with FaceBook (including single-sign-on).

Example of an eLearning information architecture

Feedback is provided in several ways. Out of the box most social applications give general feedback in the form of notifications of the most recent blog post, page, file, member sign-up and so forth. This is like a high-pass filter that scrapes the world to show you just the most recent mountain tops.

Personalized feedback is given through an individual’s newsfeed, which is shared with friends; and notifications of new and revised content in subscribed blogs, pages, groups, etc.

The FaceBook integration provides single-sign-on with FaceBook and our eLearning platform; synchronizes the course group with a members-only FaceBook group; and synchronizes a student’s profile, friends and activities between the two platforms. Note that this SSO is optional. Students can also log-on directly using the authentication service of the eLearning platform.

As mentioned before, you could do this with LinkedIn. We chose FaceBook for this example for several reasons. The strategic one is that if FaceBook introduces a professional category in its taxonomy, LinkedIn is dead overnight. At a recent and packed business seminar on LinkedIn the attendees overwhelmingly said, let’s talk about Twitter instead.

What is missing from most social applications is feedback about the activities down in the mountain valleys and on the social plain. To amplify these signals, we need some feedback filters.
For example, at the social level the system cannot know what the students are thinking about courses and related content It cannot know what they are saying to each other as represented by arrows in the strategy diagrams above. But these thoughts and convos lead to actions (e.g., subscribe to blog “A”) that are noted by the system and input back into the filters.

The more students, in a course, who subscribe to blog “A”, the greater the value of its social dimension in the context of the course. Its signal is being amplified. It may or may not be amplified in another context.

Connections

We need dynamic filters to amplify signals of interest.

These feedback filters should be designed to correspond to your business objectives. Most are based on an analysis of profiles attached to people, courses, blogs, page, groups and any other significant object. To give you an idea, two filters are shown below. Note these are just examples for illustration and are not meant to be definitive designs.

The first is a filter to suggest candidates that a student might want to connect with as a friend. A simple slot filter would just process the profiles of all students and look for matches. We could add a small amount of feedback and give matches a higher ranking based on friends in common, if any.
But this is still like cod jiggin’. What if the system suggested students who:

• Have profiles that match yours
• Attend the same courses and are active in course and study groups
• Follow your blog or pages by electing to receive notifications
• Have friends in common with you

If you look back at the information architecture you will see that including courses gives the capability to also find friend-prospects based on common membership in a course or study group. Similarly, some students may have elected to be notified of changes in your blog or pages. We call these followers or fans; and this makes them candidates to be friends.

Now we have a filter that is making some value assessments for us, reducing the amount of line time we have to spend in working the network. Some of the value in the network effect is starting to accrue to users.

We might do the same with objects in the system such as blogs and pages. For example, there are blogs:

• Related to your course
• Written by your friends
• Related to other courses your friends take
• Followed by friends
• Followed by students whose profiles match yours
• That simply have a profile that interests you

With this blog filter, the system will suggest to students blogs that meet some or all of the above criteria. It’s a bit like case-based searching.

The consequence of these feedback mechanisms is that they increase the participation level across the entire network. This increase in social interactions produces benefits aligned to your business objectives and, frequently, unexpected benefits that can re-invent and re-invigorate an organization’s purpose.