The buzz around badges is heating up in the educational environment. Whenever I hear badges, I immediately think of the old western movie where the hombre exclaims “We don’t need no stinkin’ badges.”

Cathy Davidson has a thoughtful post on “Can badging be the Zipcar of Testing and Assessment?” She relates her discovery of badges as a form of assessment:

“I first came across badging about four years ago.  I was researching the “How We Measure” chapter of Now You See It.  That chapter is a pointed, systematic (one reader called it “relentless”) critique of current forms of end-of-year compulsory grading and multiple choice bubble testing, especially the end-of-grade testing required by the 2002 No Child Left Behind national educational policy.  (Don’t get me started!)  It seemed unfair, though, to produce all of the historical, statistical, psychological, and cognitive evidence that shows how poor One Best Answer item-response testing is for motivating or even measuring learning without providing some alternatives.

“Badging is one of the alternative peer-evaluation systems I came across.  The more I talked with badging pioneers and practitioners, the more I began to leave my own misgivings aside.  This wasn’t just some expensive technology designed to line someone’s pockets.  Like Zipcars, badges proved to be an easy, flexible, customizable system that fit the needs of some organizations.  I’m especially interested in badging systems developed by the world-wide community of Web developers who engage in collaborations with people they may never meet face to face.  Companies such as TopCoder or networks like Stack Exchange have badges that allow one developer to recognize another’s skills at C++, for example, but also to give credit for a collaborator’s ability to come up with a brilliant idea getting everyone to agree on a workable solution to ship a product out the door on schedule.”

In the Atlantic article “Envisioning a Post-Campus America,” Megan McArdle provides 12 contemplations on what the future might hold for higher learning. Several of her speculations point to the shift to online learning.  One of the predictions looks at how students can signal their competence:

6.  “Young job-seekers will need new ways to signal diligence.  I’d expect to see a lot of free labor in the early years, something like what aspiring writers and visual artists already do with their blogs.  There will be more freelancing, more try-out employment, and more unpaid internships.”

Badges are one way to signal diligence. In the electronic text Learning, Freedom & The Web, the authors provide a comprehensive look at education in the future.  One of the sections in the book is on Badges. I was particularly intrigued with the Open Badge System Framework (which reminds me of Life-Wide Learning):

“Imagine… a world where your skills and competencies were captured more granularly across many different contexts, were collected and associated with your online identity and could be displayed to key stakeholders to demonstrate your capacities. In this ideal world, learning would not be limited to a formal classroom, but could come from open education courses, previous experience, discussion with peers, participation in a forum or that book you read…evidence of skills could be acquired automatically from your interactions with online content or peers, could be explicitly sought out through various assessments or could be based on nominations or endorsements from peers or colleagues. This would allow you to present a more complete picture of your skills and competencies to various audiences, from potential employers to yourself.

“This world is not purely fictional, but instead is the direction that we are moving. The next step is to support and acknowledge this learning so that these skills and competencies are available and become part of the conversation in hiring decisions, school acceptances, mentoring opportunities, and even self-evaluations. This is where badges come in.”

As I read through the rest of the chapter, it occurred to me that badges or more appropriately reputation could be the next big search organizer. In the beginning of the era of Google (and their fifty-seven signals), we have PageRank (link analysis) as a way to determine the priority of what should be displayed first given a search request:

“A PageRank results from a mathematical algorithm based on the graph, the webgraph, created by all World Wide Web pages as nodes and hyperlinks as edges, taking into consideration authority hubs such as cnn.com or usa.gov. The rank value indicates an importance of a particular page. A hyperlink to a page counts as a vote of support. The PageRank of a page is defined recursively and depends on the number and PageRank metric of all pages that link to it (“incoming links“). A page that is linked to by many pages with high PageRank receives a high rank itself. If there are no links to a web page there is no support for that page.”

Following the success of the graph analysis of Google’s PageRank, Facebook followed suit with their EdgeRank for prioritizing search requests and news items by your social graph of friends.

“EdgeRank is the Facebook algorithm that decides which stories appear in each user’s newsfeed. The algorithm hides boring stories, so if your story doesn’t score well, no one will see it.

“The first thing someone sees when they log into Facebook is the newsfeed. This is a summary of what’s been happening recently among their friends on Facebook.

“Every action their friends take is a potential newsfeed story. Facebook calls these actions ‘Edges.’ That means whenever a friend posts a status update, comments on another status update, tags a photo, joins a fan page, or RSVP’s to an event it generates an ‘Edge’, and a story about that Edge might show up in the user’s personal newsfeed.

“It’d be completely overwhelming if the newsfeed showed all of the possible stories from your friends. So Facebook created an algorithm to predict how interesting each story will be to each user. Facebook calls this algorithm ‘EdgeRank’ because it ranks the edges. Then they filter each user’s newsfeed to only show the top-ranked stories for that particular user.”

I wondered if you could rank searches based on the reputation of different web authors by the number of badges they had as indicators of their level of expertise. To explore this idea further I discussed this with a colleague, Ash Bhoopathy who is the founder of BetterAt. Since BetterAt, P2PU and the Open Badge System had both received funding from the MacArthur Foundation (along with Cathy Davidson), I figured Ash would be in position to help me understand this space. Ash shared that they were thinking along the lines of badges with a four stage level of certification:

• I say I did something
• Someone else confirms that I did that something
• An expert confirms that I did that something
• A relevant certifying organization confirms that I did that something

The above signals of increasing competence along with the texts associated with a given badge could provide “reputation scores” to prioritize which articles should be elevated in the returned search list depending on the reputation of the author(s).

To continue my research on the topic, I chatted with Professor Mark Zachry, UW HCDE Department, about his research work on reputation. Mark pointed me to several recent articles from his research group on reputation using Wikipedia as the tangible surrogate for their research. These articles include:

• “Building for Social Translucence: A Domain Analysis and Prototype System” (I love the term “social translucence“)
• “Finding Patterns in Behavioral Observations by Automatically Labelling Forms of Wikiwork in Barnstars” (Barnstars is like the notion of Badges)
• “Collaborative Sensemaking during Admin Permission Granting in Wikipedia“
• “Annotating Social Acts: Authority Claims and Alignment Moves in Wikipedia Talk Pages” (somebody in this team of authors has a great way with words – I can’t wait to work ‘alignment moves’ into my next conversation)
• “Wiki Architectures as Social Translucence Enablers“

I was delighted to get these pointers and start wading my way through these articles as I have always wondered why more researchers didn’t mine the data in the Wikipedia editing logs. I really liked the introduction to social translucence in the first article:

“Understanding and interpreting the behaviors of others in an online environment is hard. The cues and signals that we readily interpret in a face-to-face situation are not present or are at best attenuated. Lacking sufficient cues, users often misinterpret or misunderstand the actions and intentions of others. As the number of participants and the amount of interaction grows it becomes harder and harder for users to make sense of others’ actions, much less their own place in the community and the health of the community at large.

“Social translucence is a socio-technical term to describe how systems can facilitate understanding with regard to the actions of people in online environments. Social translucence includes three key design attributes: (a) mutual awareness of activities, (b) contextual propagation of socially salient cues (visibility), and (c) accountability for one’s actions. Through support for these three characteristics members in a community can better understand the types of activities that transpire, understand the norms of the community and the consequences for the actions that they may take.”

Now we have that small little matter of programming to extend these ideas of reputation for an environment like Wikipedia to the web as a whole. Can the Open Badge Project be a catalyst to get another part of the infrastructure needed for ReputationRank to go beyond PageRank and EdgeRank as the means for prioritizing what we search for on the web?