While we could go through an exhaustive analysis of the top ten consumer apps of 2009 (Bing, Wolfram Alpha, Google Chrome, Posterous, Hulu, TweetDeck, Twitter, Aardvark, Google Voice, and Facebook), let's focus on WolframAlpha.com. The other applications are related to communication and social networking, and are having their own impact on learning and deserve separate treatment. This new approach to web search engines will continue changing the way we learn and present an opportunity to change the way we teach.
First, a little exploration. What is a web search engine? Is this the right category? Does the concept line up with what we're trying to do as learners? When a learner uses the web to solve a problem, she usually begins by selecting a search engine to develop a list of information resources. The assumption behind the approach is that there are one or more web pages that will have candidate answers. If we take "locating candidate solutions" as the function, search is included as one class of tools, but so are web directories, encyclopedias, and databases of articles. "Web search" is a limiting term that constrains our thinking; as problem solvers, we'd like to be able to generate candidate solutions so we can get on with the next step of exploring and evaluating solutions, applying them to our specific problem, and so on.
With this background, we can understand wolframalpha.com as both a search and a quantitative computational engine, combined, that helps problem solvers generate and explore candidate solutions. To quote directly from the site:
"As of now, Wolfram|Alpha contains 10+ trillion pieces of data, 50,000+ types of algorithms and models, and linguistic capabilities for 1000+ domains. Built with Mathematica—which is itself the result of more than 20 years of development at Wolfram Research—Wolfram|Alpha's core code base now exceeds 5 million lines of symbolic Mathematicacode. Running on supercomputer-class compute clusters, Wolfram|Alpha makes extensive use of the latest generation of web and parallel computing technologies, including webMathematica and gridMathematica.
Wolfram|Alpha's knowledge base and capabilities already span a great many domains, and its underlying framework has the power and flexibility to support ready extension to essentially any domain that is based on systematic knowledge."
Learners may explore math, chemistry, physics, statistics, and life sciences, among the thousand+ domains.
Adding the computational element greatly expands "search" because the learner is no longer limited to reviewing answers to asked questions, but may also ask new questions and use the powerful computation engines behind the site to help get answers.
What is the timeframe for the impact? To some extent, it has been happening for decades in mathematics and science. Thinking back to when calculators were introduced, teachers lamented that students would no longer practice their times tables and this important skill would disappear. Instead, we found that students needed to focus on problem solving and developing skill in performing machine aided calculations. The availability of mathematica and derive in the classroom has shifted learning in higher education from calculating to formulating better questions and being familiar with language of computation. The wolframalpha.com engine extends the impact from mathematics and science classes to a broad range of fields and will continue challenging how we teach and support learners and brings publicly available datasets into the engine directly.
What is the timeframe for impact in other fields? How fast can wolframalpha.com expand its domains? Faster, probably than teachers will change. This type of computational mindtool is well suited for problem based learning approaches and will have its biggest impact on formal learning when teachers adopt PBL approaches.
Learners, on the other hand, will change quickly as they find power in the answers. The frontier is upon us and moving quickly.
Posts
No comments:
Post a Comment