For more info and cool images check out the WISE project website.

Some of Technology for WISE was developed at Space Dynamics Laboratory here in Cache Valley where some of my neighbors work. Another fun connection for me is that the rocket that carried WISE into orbit was launched from Vandenberg Air Force base where my Dad was serving when I was born.

Here is some press that covered the award:

• Utah State Today – USU Researchers Awarded Governor’s Medals for Science and Technology
• USU College of Science – Three USU Scientists Receive Governor’s Science Medal
• Salt Lake Tribune – Herbert to honor recipients for contributions to science
• UtahPolicy.com – State Advisory Council For Science And Technology Announces Governor’s Science Medals For 2009

Here are some previous articles about the team as well:

• USU – Developed Virtual Math Tutor Aiding Students Worldwide
• USU – Winter Insights 2006

(1..100).each {|page| system("curl -o #{page}.html
   http://dmlcompetition.net/pligg/index.php?page=#{page}")}

Ruby rocks!

http://code.google.com/p/android/issues/detail?id=4784 (comment 35)

1) create a new gmail account on the computer.
2) call your cell phone with a different phone.
3) answer your cell phone then hit the back button and it will take you to the home
screen.
4) turn on wifi so it can do data and voice at the same time (remember that the phone
is still connected)
5) go to Settings -> Location & Security and disable lock pattern (you’ll need to
enter to correct pattern previously set)
6) go to Settings -> Accounts & Sync and click “Add Account” and add your newly
created Gmail account.
7) hang up the phone.
turn the phone back on, at the lockout screen, enter your new Gmail account info
and it should let you back in
9) once the phone is unlocked, you can go in setting and remove the newly added Gmail
account and keep the old one.

Google giveth, and Google taketh away, and then Google giveth again.

• Personal recommendations page – For each user, provide a personal recommendations page. Visually separate recommendations that they have already clicked on.
• Personal recommendations tool – Include a personal recommendations button on our folksemantic bar that when clicked on will display their recommendations in the right panel. Linking to the recommendations from that panel will refresh the content in the iframe (not do a full page refresh).
• Personal recommendations action link – Include a link to the user’s recommendations page on their dashboard.
• Inject recommendations into activity feeds – Whenever we generate new personal recommendations, inject them into a user’s activity feed that is displayed on their dashboard.
• Email personal recommendations – Email personal recommendations to users as often as they would like (controllable in their account settings).

• Identity feeds – RSS feeds that users register that represent their interests. For example, their blog or their delicious account.
• Clicks – The articles that the user clicks on.
• Shares – The articles that the user shares to others.
• Comments – Articles that the user comments on.
• Time on page – Amount of time that a user spends on an article before moving on.
• Searches – Searches the user executes.

Recommendation Assumptions

Some of our assumptions are:

• Semantic relatedness – The more semantically similar an article is to articles that a user has paid attention to, the more interesting to the user.
• Attention types – Different types of attention should be given different weights. For example, following a link to an article should not give it as much weight as writing the article.
• Attention details – The particulars of a given type of attention might make it more important than another attention of the same type. For example, if a person shares an article with 100 people, it might be reasonable to infer that it is more important than an article that they share with one person.
• Entry recency – The more recently an article has been added to the system, the more interesting to the user (they probably haven’t seen it before).
• Attention recency – The more recently a user has showed attention to an article, the more weight that should be given to it.
• Attention frequency – The more frequently a user has showed attention to an article, the more weight that should be given to it.

Stating these assumptions reminds me of the difference between relevance and certainty. So while an item that a user clicks on may be more relevant than an blog article they have written, it is harder to be certain of that. Our approach is to give the click less weight than the article.

Recommendation Score

Right now, we score articles using the formula:

(relevance)(attention type)(attention details)(attention recency)(article recency)

For all articles that a user has paid attention to, we score the 20 “related articles” using this algorithm; rank the scores and cache the top 20 (that the user hasn’t already clicked on) to recommend to the user. There are obvious weaknesses to this approach, but we are going to start there and see where to go next.

Possible Extensions / Improvements

We are considering:

• Collaborative Filtering, Bipartite Graph, and Discriminative Weight Algorithms – Putting into production, the algorithm that combines discriminative weights with a novel sparse matrix clustering method we conducted research on.
• Modeling user interests separately – Modeling users interests using multiple term vectors (one for each interest) by extracting vectors from closely related (clustered documents) that users have paid attention to.

David shared Evidence Based Design (not sure if this is what he was referring to) as a model. I shared Conditions of Learning – the idea that different types of learning outcomes should be taught differently, and Jim Cangelosi’s (forgive the flashing text) work on designing mathematics instruction for different types of learning outcomes. Interestingly he has advocated the idea of mini experiments as an approach for teachers to learn about and evolve learning.

ATE Central is an example of how a project in NSDL can influence other NSF programs. It brings all of the ATE resources in to one searchable portal. It builds the ATE brand and helps disseminate the projects. ATE is different than NSDL in that they focus on content development, industry connections, and the improvement of training and teaching for workforce development. ATE offers smaller grants and larger center grants. Example national, regional, and resource centers of excellence are geoTech, CARCAM, AgrowKnow. ATE Central has been funded for 1 year. They focus more on events than in other portals. This is partially because ATE focuses a lot on workshops including virtual. They create resource areas on ATE Central for each projects and centers. This has been a big deal to their projects to help them collaborate. ATE has a center that is funded just for evaluation. They send out a monthly update and are creating success stories. She showed videos of people that have found success of students that have benefited from ATE.

Linea Fletcher and Rachel is interested in the life of NSDL projects that continue beyond funding (are sustainable). They want to capture and share these stories. Another focus is on how to capture of evidence of impact across projects. They currently track 320 projects and aggregate and share it in interesting ways.

This work reminds me some of what Justin did with 51weeks – a platform for supporting communication around a conference and the other 51 weeks of the year.