Serendipity 2.0

Arjen Vrielink and I write a monthly series titled: Parallax. We both agree on a title for the post and on some other arbitrary restrictions to induce our creative process. This time we decided to try and find out whether it is possible to engineer serendipity on the web. The post should start with a short (max. 200 words) reflection on what the Internet has meant for serendipity followed by three serendipitous discoveries including a description of how they were discovered. You can read Arjen’s post with the same title here.

There is an ongoing online argument over whether our increasing use of the Internet for information gathering and consumption has decreased our propensity for having serendipitous discoveries (see for example here, here or here). I have worried about this myself: my news consumption has become very focused on (educational) technology and has therefore become very silo-ed. No magazine has this level of specificity, so when I read a magazine I read more things I wasn’t really looking for than when I read my RSS feeds in Google Reader. This is a bit of red herring. Yes, the web creates incredibly focused channels and if all you are interested in is the history of the second world war, then you can make sure you only encounter information about that war; but at the same time the hyperlinked nature of the web as a network actually turns it into a serendipity machine. Who hasn’t stumbled upon wonderful new concepts, knowledge communities or silly memes while just surfing around? In the end it probably is just a matter of personal attitude: an open mind. In that spirit I would like to try and engineer serendipity (without addressing the obvious paradoxical nature of doing that).

Serendipity algorithm 1: Wikipedia
One way of finding serendipity in the Wikipedia is by looking at the categories of a particular article. Because of the many to many relationship between categories and articles these can often be very surprising (try it!). I have decided to take advantage of the many hyperlinks in Wikipedia and do the following:

• Start with the “Educational Technology” article
• Click on the first two links to other articles
• In these articles find two links that look interesting and promising to you
• In each of these four articles pick a link to a concept that you haven’t heard about yet or don’t understand very well
• Read these links and see what you learn

Instructional theory was the first link. From there I went to Bloom’s Taxonomy and to Paulo Freire. Bloom’s Taxonomy took me to DIKW, a great article on the “Knowledge Pyramid” explaining the data-to-information-to-knowledge-to-wisdom transformation. I loved the following Frank Zappa quote:

Information is not knowledge,
Knowledge is not wisdom,
Wisdom is not truth,
Truth is not beauty,
Beauty is not love,
Love is not music,
and Music is the BEST.

Paulo Freire took me to Liberation theology which is is a movement in Christian theology which interprets the teachings of Jesus Christ in terms of a liberation from unjust economic, political or social conditions. This began as a movement in the Roman Catholic church in Latin America in the 1950s-1960s. The paradigmatic expression of liberation theology came from Gutierrez from his book A Theology of Liberation in his which he coined the phrase “preferential option for the poor” meaning that God is revealed to have a preference for those people who are “insignificant”, “unimportant” and “marginalized”.

The second link was Learning theory (education). That led to Discovery learning and Philosophical anthropology. Discovery learning prompted me to read the The Grauer School. This link didn’t really work out. The Discovery learning article had alluded to the “Learn by Discovery” motto with which the school was founded, but the article about the school has no further information. A dead alley on the serendipity trail! Philosophical anthropology brought me to Hylomorphism which is a concept I hadn’t heard of before (or I had forgotten about: I used to study this stuff). It is a philosophical theory developed by Aristotle analyzing substance into matter and form. “Just as a wax object consists of wax with a certain shape, so a living organism consists of a body with the property of life, which is its soul.”

Conclusion: Wikipedia is excellent for serendipitous discovery.

Serendipity algorithm 2: the Accidental News Explorer (ANE)

The tagline of this iPhone application is “Look for something, find something else” and its information page has a quote by Lawrence Block: “One aspect of serendipity to bear in mind is that you have to be looking for something in order to find something else.” I have decided to do the following:

• Search for “Educational Technology”
• Choose an article that looks interesting
• Click on the “Related Topics” button
• Choose the most interesting looking topic
• Choose an article that looks interesting
• Click on the “Related Topics” button
• Choose the most interesting looking topic
• Read the most appealing article

The article that looked interesting was an article on Kurzweil educational Systems. The only related topic was “Dallas, Texas”. This brought me to an article on Nowitzki from where I chose “Joakim Noah” as a related topic. The most appealing article in that topic was titled: Who’s better: Al Horford or Joakim Noah?

Conclusion: An app like this could work, but it needs to be a little bit better in its algorithms and sources for finding related news. One thing I noticed about this particular news explorer is its complete US focus, you always seem to go to cities and then to sports or politics.

Serendipity algorithm 3: Twitter
Wikipedia allows you to make fortunate content discoveries, Twitter should allow the same but then in a social dimension. Let’s try and use Twitter to find interesting people. I have decided to do the following:

• Search for a the hashtag “#edtech”
• Look at the first three people who have used the hashtag and look at their first three @mentions
• Choose which of the nine people/organizations is the most to follow
• Follow this person and share/favourite a couple of tweets of this person

So the search brought me to @hakan_sentrk, @ShellTerrell and @briankotts. These three mentioned the following nine Twitter users/organizations:

1. @mike08, ESP teacher; ICT consultant; e-tutor
2. @MsBarkerED, Education Major, Michigan State University, Senior, Aspiring Urban Educator, enrolled in the course CEP 416
3. @jdthomas7, educational tech/math coach, former math, computer teacher. former director of technology at a local private school. specializing in tech/ed integration
4. @ozge, Teacher/trainer, preschool team leader, coordinator of an EFL DVD project, e-moderator, content & educational coordinator of Minigon reader series, edtech addict!
5. @ktenkely, Mac Evangelist, Apple Fanatic, Technology Teacher, classroom tech integration specialist, Den Star, instructional coach
6. @Parentella, Ever ask your child: What happened at school today? If so, join us.
7. @Chronicle, The leading news source for higher education.
8. @BusinessInsider, Business news and analysis in real time.
9. @techcrunch, Breaking Technology News And Opinions From TechCrunch

I decided to follow @ozge who seems to be a very active Twitter user posting mostly links that are relevant to education.

Conclusion: the way I set up this algorithm did not help in getting outside of my standard community of people. I was already following @ShellTerrell for example. I probably should have designed a slightly different experiment, maybe involving lists in some way (and choosing an a-typical list somebody is on). That might have allowed me to really jump communities, which I didn’t do in this case.

There are many other web services that could be used in a similar fashion as the above  for serendipitous discovery. Why don’t you try doing it with Delicious, with Facebook, with LinkedIn or with YouTube?