Archive for the ‘Social Ratings’ Category
Aggregation and the Blogs at Penn State
It isn’t a big surprise that more and more faculty, staff, and students are taking advantage of the Blogs at Penn State for all sorts of things. We are seeing incredible examples of student blogs, faculty portfolios, staff spaces, and course sites popping up all over the PSU Personal Webspace. It also doesn’t come as a surprise that as more people start to create more digital content that discoverability becomes more of an issue. With this in mind, one of the things we are thinking a whole lot about is creating an easy solution for aggregating content into discoverable spaces.
In our world we think quite a bit about how to do this for teaching and learning purposes. One of the things we are looking at are social rating tools that also act as aggregators. I took the plunge this semester along with my co-instructor, Scott McDonald, and installed the open source tool Pligg for our C&I 597C course. The way it works is that students blog in their own PSU Blogs and their content is aggregated into our course Pligg site where it can be read, voted on, and commented on. Voting makes the top posts rise to the top. Commenting creates a vibrant community where students can share ideas in the open. So far it has proven to be a very interesting model.
This year we will be exploring more aggregation tools to help faculty and students create mash ups of the content that matters to them. We aren’t going to build the next iGoogle or Ning, but we will be spending a lot of energy in this space over the next year. If you have interest in this area, please contact us or leave a comment.
Social Ratings
Social rating systems are open systems that allow users to collectively evaluate the quality of nearly anything (e.g. books, blog posts, broadway shows, movies, news stories, hotels, etc…). In its simplest form, this may involve applying thumbs up/down or star ratings to a resource, and this can be extended to include reviews and discussions of the resources by multiple contributors. As more items are ranked, it is possible to utilize the rankings to generate sets of popular or important items, by sorting by applied relevancy ranking. In order to help maintain relevance, subsets of resources, and of people, may be required in order to rank items within the context of a course, semester, or group. This approach has huge implications in a distributed environment where courses are taking advantage of the Blogs at Penn State and faculty are looking to bring content into one location with ratings to help pull top posts to the surface. An example can be found here.
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