On June 6, 2007 Tom King and I gave a presentation on Search as a Mode of Learning at the AICC event in Sestri Levante, Italy. We covered two main areas, 'search as a mode of learning 'and the need for 'common approaches to search integration.' This post focuses on the former.
Key points,
- Learning can be understood as the development of effective search patterns
- New approaches to search are proliferating
- Search is being used for many new applications and has a growing role to play in learning
- Learning must become part of the larger ecology of information organization and exchange
Learning as search
There are a number of metaphors one can use to understand learning and how it enables effective action: play, navigation and process integration all offer compelling perspectives, models of action such as OODA loops (Observe Orient Decide Act) can put the focus more solidly on action, and approaches such as Cognitive Task Analysis help us understand how people build mental models and use them to guide decisions. But in this post I want to explore search as a metaphor and see how effective current search systems are in supporting learning by individuals and teams.
A couple of years ago I was at a presentation by Elliott Masie in which he recounted a conversation with Bill Gates. Gates noted, correctly, that the vast majority of eLearning is authored using PowerPoint and suggested that this made PowerPoint the most important eLeanring application. Elliott countered that the real killer app for learning is Google.
There is a lot to be said for Elliott's response. These days most people turn to Google or another search engine when they want to get quick access to information. And beyond this, search has become one of the first things we turn to when we need deeper knowledge as well. But there is a lot of frustration as well. There is the obvious issue that most searchers generate too many hits and sifting through these to find the most relevant is frustrating and time consuming. More importantly, few search applications, if any, provide explicit support the key activities involved in learning.
What are these activities? One way to think about learning is as a process of
- Defining a problem space
- Exploring this space to uncover its patterns and relationships
- Developing paths through the space
- Recognizing patterns in the space
Effective action takes place when one can (i) rapidly recognize patterns and (ii) navigate to solutions. And of course, one wants to get so good at recognizing patterns that one is sensitive to when the environment is changing and the old responses and paths are no longer taking you where you expect them to.
One way to approach this is to think about how people find ther way around a landscape or a city. Urban planner Kevin Lynch has thought deeply about this and in his 1960 book The Image of the City. In this book he identified five patterns that people use to navigate around cities. These patterns are more generally applicable, and my suggestion is that a good search system, one the supports learning, will support all five of them. The five patterns as applied to cities are as follows.
- Landmarks - prominent buildings or other features, visible from a distance, that allow you to orient yourself.
- Neighborhoods - areas that are self similar in some way, such as the type of building, the activities carried out such as shopping, entertainment, business or residential, and the kids of people that occupy them.
- Barriers - Many cities are divided by barriers, some natural such as rivers or cliffs, others artificial like highways, and some social.
- Paths - Neighborhoods are linked and barriers sometimes crossed by paths, the major thoroughfares that help one to travel around.
- Nodes - Places where many paths come together are nodes.
Understanding these patterns helps you to find your way around a city, to know where you are, and to explore your way to new places. How do these apply to search and learning?
When we search we want to be able quickly orient ourselves, using results that are easily remembered and that help us find our way, these are the landmarks. There are some sites that we go to because we know that it will be easier to find what we are looking for if we start there. Amazon is a landmark in the search for books. We also want to know the general category of the search results, the neighborhood so to speak, and then follow the links, or paths, from one resource to the next, dipping back into the search results when we get stuck or need elaboration. Some results will be very rich, leading to many other resources (hopefully relevant), these are the nodes. And of course there are some areas that are simply not linked and do not show whose search results are disjoint. There are barriers between the two areas. But there are times when forging ones own links between two areas, search and urban planning perhaps, may lead to a recognition of deeper patterns.
Search systems already support some of these patterns under the covers. Google's PageRank method of ranking search results makes use of linking patterns that are defined in terms of paths and nodes. It even has a very coarse notion of neighborhoods in that it divides the search universe up into scholarly articles, blogs, books and so on, not that these are the most meaningful categories for most search users. Some other search services do somewhat better, providing categorizations of search results that help users define neighborhoods. Mondosearch is one example of this. But I am not aware of any search engine that provides good explicit exposure of all of Lynch's patterns, or that supports the most common individual and team search behaviors.
Common Search Behaviors
Before returning to the theme of search as a mode of learning I would like to look at some common search behaviors as these are also the basis for learning.
I look first at the most common individual behaviors, then go on to the less well understood area of collaborative search.
Find
The most common use of search. Find comes in a number of flavors, sometimes one is looking for a specific piece of data, the population of Tashkent or the temperature forecast for Kitimat next Tuesday, or a combination of data, the closest theatre at which a specific film is playing this evening. Other times you may be looking for a specific resource, an organization's website or a book. You can also be searching for information about a more general theme and trying to find the resource(s) that best fit that theme. This is hardly a complete ontology of finding, but it gives the general sense.
Refind or Navigational Search
Almost as common as 'find' is to 'refind'. This is search as a form of navigation. Rather than bother to remember a URL, or in many cases even when we remember the URL, it is simplest just to go to search and enter an associated term, usually one that has successfully located the resource we are going to in the past. I recall that research suggests that this type of navigational search accounts for about 30% of searches on major search engines, I am looking for the reference, and I suspect that the same is true for organizations that have decent search engines.
Explore
With exploration we enter search modes that are directly related to learning. Exploratory search happens when one is not sure what one is looking for or how concepts relate. For some learning styles it is the best way to begin learning (for people with other styles it will just seem random though). One can see each search as a kind of probe into the design space, send in a probe, follow links, let the terms suggest new searches, and eventually a sort of concept map emerges.
Going back to the metaphor of search and navigating through a city, this is like getting to know a new place by going out and wandering around. If several different probes turn up the same basic website (on the WWW in 2007 Wikipedia comes to mind) one begins to find landmarks. Exploring the results for different search terms also gives a feel for neighborhoods, 'Jaguar' will take you to the 'Apple' neighborood, the 'luxury car' neighborhood and the 'big cats' neighborhood. 'Search' will take to an interesting group of neighborhoods with many paths between them, there is a 'learning' neighborhood, a 'query' neighborhood, a 'search engine' neighborhood, a 'search engine optimization or SEO' neighborhood and so on. This is a richly connected part of the world with many paths and landmarks.
Relate
Further exploration of the paths combined with querying around them helps the searcher build relations between different resources. Some search sites do this explicitly, especially those that support scientific research or patent claims, where explicit citations are an important part of the landscape and are used to organize search results. Building up sets of relationships, based on paths and often oriented around landmarks, is the first step towards pattern matching.
Match Patterns
To my mind pattern recognition, followed by pattern matching, is central to learning, and it is the role of search in finding, refinding and recognizing is key. This is why I believe search to be a primary mode of learning, and not just a way to locate learning. It is the experience of searching that strengthens one's ability to find and then recognize patterns, and it is the patterns that guide our actions.
Patterns come in many forms, and an exploration of this would turn an already long blog post into a book. But some key patterns include search patterns!, process patterns, recognition patterns, filter patterns, decision patterns, observation (situational awareness) patterns, and so on. The pattern of represent-replicate-vary-select is one very general pattern that can also be applied back on to learning and search.
A higher level skill then pattern recognition is pattern blending, taking two or more separate patterns and bringing them together to create a new mode of understanding and filtering the world. This is something to explore more in future posts, but a good introduction can be found in the book Designing With Blends by Manual Imaz and David Benyon.
Do current learning, knowledge and performance support systems provide good support for search as a mode of learning? Do they support search for patterns as a mode of performance? My sense is that the answer in both cases is no.
Social search
Individual search is only a small part of the possible world of search, just as individual learning is only a small part of learning. The next phase of both search and learning is likely to focus more on its social dimensions. Social search gets little explicit support from most search systems, Amazon with its recommendation engine is much better, and the same is true of most corporate on-line learning (the University systems, with their threaded discussion groups, are much better). Google is beginning to get this with Google Co-op, which combines personal search with social search.
A few approaches to social search are noted below.
Infer relevance from social structures
This is the key insight of Brin and Page in designing Google's PageRank algorithm - the sites that link to a site, and the sites that link to these, are an indicator of relevance. It would be nice if Google made this more explicit, though one can tease out some of the relationships by using various Google Hacks. As noted above, sites dedicated to searching patents and scientific journals are somewhat better than this.
Weight (filter)
Another thing that Google does implicitly and that Amazon or Digg do explicitly is to weight search results based on which resources a person selects after a search. The more often a certain resource is selected the higher its placement in search results.
Share and edit search results
A stronger social search mechanism is to allow people to save searches, edit the results, make comments and provide rankings, and then to pass these around among one and other. As noted above, Google Co-op provides a very partial implementation of this and other systems are emerging that provide better support. Indeed, this is an active area of interest at Monitor Group's LeveregePoint solutions team.
Share and edit search paths
Social search will not really begin to support social learning until it goes beyond sharing of results to the sharing of paths. It is by navigating (and building) search paths, and then sharing, commenting and editing these, that people come to recognize and share patterns, which is an essential part of learning. This was actually part of the inspiration for ThoughtShare, a company that I helped found back in the late 1990s with John Dill and Brian Fisher at Simon Fraser University. ThoughtShare later morphed into a blogging tool and then a contextual advertising service called Qumana, but the initial goal was to provide people with an easy way to record, share and edit paths though the World Wide Web.
Search as a General Paradigm
Over the past few years search has emerged as a general paradigm for how we interact with the world and for how we build information and business systems. The best treatment of this is Ambient Findability by Peter Morville. The ideas in this post about how search is an important mode of learning were influenced by Morville's more general treatment. Search is also absorbing important business functions such as business intelligence and information integration.
One can think of business intelligence as a primitive form of search, one that uses SQL to pull data from structured databases and then organize it into various reports and dashboards. Valuable yes, but not something that easily lets users bring in the many different kinds of information needed to recognize patterns and make decisions. As a result, the combination of search and business intelligence is beginning to attract attention. There is good coverage, including the results of a survey uncovering the business drivers, in a recent article in Intelligent Enterprise.
Search is also emerging as a new? paradigm for data and information integration. A search system that included good pattern recognition is an alternative to more programmatic approaches to data integration such as extract transform load (ETL) or web services. It requires that the data be open to search, and in some cases the acceptance of fuzzy, dare I say 'organic' results. Some will object that data integration requires precision and reliability. In some cases yes, but it also requires adaptability and the ability to recognize emerging patterns. Existing approaches to integration, based on explicit connections, will not achieve this as this assumes that the connections are known, can be known, in advance.
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For the mathematically inclined, Pentti Kanerva's book Sparse Distributed Memory provides a thought provoking treatment of how memory, search and learning fit together. See especially the final chapter The Organization of an Autonomous Learning System with its emphasis on pattern sequences.
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