PLEM: A Mashup-driven Long Tail Aggregator and Filter for Learning
I would like to share with you a new learning service called PLEM which can act as a mashup driven Long Tail aggregator and filter for learning.
PLEM as a Long Tail aggregator:
PLEM enables to collect a variety of niche learning elements (i.e. learning resources, learning services, learning experts, and learning communities) and make them available and easy to find. Everyone with an OpenID can log into PLEM and create a personalized space, where she can easily aggregate, manage, tag, rate, and share learning elements of interest.
Aggregation is supported by a federated search engine that enables to pull together learning resources from distributed sources, remix, and assemble them to form a new personalized learning resource collection (LRC). To achieve this, we utilize multiple social media search APIs to add open educational resources (via MIT OCW and OUNL OpenER), blogposts (via Google Blog Search and Technorati), videos (via Google Blog Search and YouTube), books (via Google Book Search), images (via Google Image Search and Flickr), and presentations (via Slideshare). The list of supported services will be extended to include e.g. other OER initiatives such as OUUK OpenLearn. The figure below shows the interface of this federated search engine. You can see here an example of an aggregated LRC.
The idea is not just to bookmark learning resources but to create a collection of the same, tag, rate, and share theses LRCs with others who can then save them to their spaces. The benefits of creating a LRC are twofold:
- Foster the concept of "The Learner as DJ". Scott Leslie and Harold Jarche talked about a similar concept: "The Open Educator as DJ".
- Finding a good LRC related to a topic we're interested in will be a huge time-saver. Recently, Firefox has adopted a similar approach with their new feature Add-on collections.
PLEM as a Long Tail Filter:
The primary aim of the PLEM filtering mechanism is to harness the collective intelligence to rank and recommend learning elements. The idea is quite simple, each filtering action on a learning element from the Web (e.g. comment, link, save, like, rate, vote, view, share) counts as one "vote" for that learning element. The mean value of all "votings" for a given learning element is then used to measure its popularity. As shown in the figure below, these "votes" currently include PLEM saves and ratings, Delicious saves, Friendfeed comments and likes, Yahoo inbound links, Digg votes, Google Trackbacks, and Technorati blog reactions. This list will be extended in the future to include other social media filtering services such as Twitter mentions and Diigo saves.
