Learning Alliances

“Average response” vs. response of the “average person”. The point of using the middle response as a representation of the whole group is that you capture what the person in the middle of the distribution said, without being influenced by the tails (whether positive or negative):

Middle:

• Invigorating. Very interesting dialogue on 3 tensions
• Interesting, topical, relevant and well presented
• Interesting to get another perspective

People were engaged in Nancy’s presentation. They connected with her and with the topic. It’s interesting to to me that none of the responses commented on the fact that audience feedback was being collected in a wiki. Good to see the “3 tensions” from our tech study project mentioned. What people in “the middle” said is the main message.

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The data analysis strategy here is to chop the whole sorted list in half and then continue chopping the parts in half again. I picked out comments at the cut-points; similar to the median, here are the quartile and the “eighth” and the extreme:Positive Quartile:

• Fantastic � her delivery style and encouraging participation by audience was great
• Excellent entertaining presenter. It was good split up into 3 parts with food breaks which gave you a breather and time to refresh yourself

Positive Eighth:

• Comprehensive review of e-learning resources in community environment
• An inspiring speaker � I was a bit disappointed as I had registered my mobile beforehand (via an email) but I didn�t get messages � a very engaging and entertaining speaker

Positive Extreme:

• Fantastic � the best. Excellent speaker
• Absolutely excellent!! Inspiring, informative and innovative

There was a lot of enthusiasm for the session. Seems like quite a bit of variety, ranging from enthusiasm for the topic, for the process (e.g., SMS) and for Nancy herself as as a presenter.

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The negative comments were what first caught my eye because they were painful and so I picked out three comments at each of the cut-points in the distribution to provide a bit more to think about.Negative Quartile:

• Ok � food for thought
• Some thoughts and concepts that had not considered before. Great how split into 3 parts
• Food for thought. Good presenter

Negative Eighth:

• Too long � I can�t concentrate that long
• Started well but went on for too long, became boring
• Rather long � not particularly stimulating

Negative Extreme:

• Why do we need an American who is full of herself to tell us what we already dabbled in during our LearnScope projects?
• Long, elitist
• Hard to follow � esoteric. I didn�t warm to her and felt she presented poorly which immediately creates a barrier to an audience (read �me�). She was disjointed in my opinion. Not clear on outcomes.

The quartile comments are actually pretty positive, so you can say that three-fourths of the audience actually had a good experience. The “Negative Eighth” comments all seem to be about length, making me wonder why people stayed, particularly since it sounds like there were breaks where people could have left or stayed in the lobby. Were people being rewarded with “continuing education units” of some sort? The “Negative Extreme” comments stand out so that you might either pay a lot of attention to them or completely ignore them.

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One question that I’m left with is about how “personal” these kinds of evaluations are � and whether they should be.What do you see in these comments?

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Although Nancy White’s posting on “Feedback from Sydney LearnScope Event” was written at the end of her marathon in Australia, I’ve thought about it quite a few times since then, perhaps because of the strong feelings I had after our workshop at the Prato Conference during the same month. Capturing feedback in a wiki is an interesting example of how technology inherently changes our experience of face-to-face meetings just as it changes what’s possible. Reading what the more than 64 people said about the session with Nancy was a bit overwhelming so I made a note to myself to come back and take another look. One easy way to do it was to put the comments into a tool like http://tagcrowd.com/ (a tool that happened to hear of from Nancy). Here’s the output:

<!– #htmltagcloud{ font-family:\’lucida grande\’,trebuchet,\’trebuchet ms\’,verdana,arial,helvetica,sans-serif; line-height:2.4em; word-spacing:normal; letter-spacing:normal; text-decoration:none; text-transform:none; text-align:justify; text-indent:0ex; background-color:#fff; margin:0 0 2em 0; border:2px dotted #ddd; padding:2em}#htmltagcloud a:link{text-decoration:none}#htmltagcloud a:visited{text-decoration:none}#htmltagcloud a:hover{text-decoration:none;color:white;background-color:#05f}#htmltagcloud a:active{text-decoration:none;color:white;background-color:#03d}span.tagcloud0{font-size:1.0em;padding:0em;color:#ACC1F3;z-index:10;position:relative}span.tagcloud0 a{text-decoration:none; color:#ACC1F3}span.tagcloud1{font-size:1.4em;padding:0em;color:#ACC1F3;z-index:9;position:relative}span.tagcloud1 a{text-decoration:none;color:#ACC1F3}span.tagcloud2{font-size:1.8em;padding:0em;color:#86A0DC;z-index:8;position:relative}span.tagcloud2 a{text-decoration:none;color:#86A0DC}span.tagcloud3{font-size:2.2em;padding:0em;color:#86A0DC;z-index:7;position:relative}span.tagcloud3 a{text-decoration:none;color:#86A0DC}span.tagcloud4{font-size:2.6em;padding:0em;color:#607EC5;z-index:6;position:relative}span.tagcloud4 a{text-decoration:none;color:#607EC5}span.tagcloud5{font-size:3.0em;padding:0em;color:#607EC5;z-index:5;position:relative}span.tagcloud5 a{text-decoration:none;color:#607EC5}span.tagcloud6{font-size:3.3em;padding:0em;color:#4C6DB9;z-index:4;position:relative}span.tagcloud6 a{text-decoration:none;color:#4C6DB9}span.tagcloud7{font-size:3.6em;padding:0em;color:#395CAE;z-index:3;position:relative}span.tagcloud7 a{text-decoration:none;color:#395CAE}span.tagcloud8{font-size:3.9em;padding:0em;color:#264CA2;z-index:2;position:relative}span.tagcloud8 a{text-decoration:none;color:#264CA2}span.tagcloud9{font-size:4.2em;padding:0em;color:#133B97;z-index:1;position:relative}span.tagcloud9 a{text-decoration:none;color:#133B97}span.tagcloud10{font-size:4.5em;padding:0em;color:#002A8B;z-index:0;position:relative}span.tagcloud10 a{text-decoration:none;color:#002A8B}span.freq{font-size:10pt !important;color:#bbb}// –> audience bit boring clearly concepts delivery delved didn e-learners eb email engaging enjoyed entertaining esp excellent experiences feel felt flow food globally informative inspiring interaction interesting kept key learnscope loved messages mobile nancy ok parts practical presented presenter provoking really relevant resources sms speaker split stimulating techno tensions think via

The comments have an inherent linear structure that tagcrowd misses and which seems important. There’s an implicit “good to bad” Likert scale behind much of what people wrote. I thought that I could make up a kind of 5-number summary that systematically throws away some of the noise. This approach comes from John Tukey’s stem-and-leaf plots (I got interested in computing because of a long-standing interest in exploratory data analysis and statistics). So I did a rough sort of all the comments from the most positive to the most negative, and picked out the middle, the extremes, the quartiles, and so on.

(The text editor in WordPress seems to have trunkated some of the text, so I’m going to post the second part separately.)

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Lilia Efimova and Nancy White were talking about some of the ideas in our technology for communities project and Lilia comments on how “community” seems like a problematic term to some people because it’s ambiguous - it gets used in many different ways and at different scales.

I like the argument that King and Frost make that ambiguity has many uses in practice and that dis-ambiguation is a bit over-rated (John Leslie King and Robert L. Frost, “Managing Distance over Time: The Evolution of Technologies of Dis/Ambiguation” in Hinds, Pamela J. and Sara Kiesler (eds), Distributed Work (Cambridge, MA: MIT Press, 2002), pp. 3-26. One important thing that communities do is manage ambiguity. I think that managing the nuances and ambiguities involved in a term like “community” is an important thing to do. When a community uses a term to point to something ambiguous it’s often a flag for one of those “it depends” conversations. Among other things, looking for familiar but ambiguous characteristics of something like “community” in the “blogosphere,” for example, is a useful thing to do.

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In his article about “How to use del.icio.us to foster collaboration” Shawn Callahan talks about social bookmarking as a way to distribute work across a team, community or network. I do notice myself using the bookmarks that people in my community store in del.icio.us as he describes (in the sense of copying their bookmarks, referring to them later, using the content in some way). But I think that a more common use for me is just to notice what people are thinking about and noticing as they surf and bookmark.

A recent article titled “Eyeing up the collaboration” from the November 2nd, 2006 issue of The Economist remarks that “being able to identify what someone else is looking at is thought to have been so important to humans that people evolved to have eyes surrounded by brilliant whites to assist with the process.” Humans are different from other species, which tend to have sclera (the “whites” around our irises) that are not white.

So, fast forward a few million years, and we are evolving ways of being aware of what others are looking at — noticing in which direction someone else is gazing. Presence indicators on Skype or Instant Messengers give us information that’s useful in related ways. When I was at the CIRN conference in Italy a month ago, my wife would “see me online” and wave immediately, since we’re not used to a 9-hour time difference. It seems to me that del.icio.us provides an equivalent resource: it allows us to follow someone else’s gaze.

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