Aggregation of End User Data in Open Educational Resources

Aggregation of End User Data in Open Educational Resources

Mr. Subhasish KarakLibrarian, Panchmura Mahavidyalaya, Bankura, Formerly Kendriya Vidyalaya No#2, KalaikundaA US News Paper Headline

NSA collected US email records in bulk for more than two years under Obama

-the guardian

2The documents indicate that under the program, launched in 2001, a federal judge sitting on the secret surveillance panel called theFisa court would approve a bulk collection order for internet metadata "every 90 days". The collection of these records began under the Bush administration's wide-ranging warrantless surveillance program, collectively known by theNSA code name Stellar Wind.

The internet metadata of the sort NSA collected for at least a decade details the accounts to which Americans sent emails and from which they received emails. It also details the internet protocol addresses (IP) used by people inside the United States when sending emails information which can reflect their physical location. It did not include the content of emails.

"The calls you make can reveal a lot, but now that so much of our lives are mediated by the internet, your IP logs are really a real-time map of your brain: what are you reading about, what are you curious about, what personal ad are you responding to (with a dedicated email linked to that specific ad), what online discussions are you participating in, and how often?" said Julian Sanchez of the Cato Institute. "Seeing your IP logs and especially feeding them through sophisticated analytic tools is a way of getting inside your head that's in many ways on par with reading your diary," Sanchez added.

Open educational resources are learning content or tools that are offered free of charge under a copyright license granting permissions for users to engage in the "4R" activities: reuse, revise, remix, and redistribute. In essence, open educational resources are learning objects that use an open source license. Utility of OER:These digital materials have the potential to give people everywhere equal access to our collective knowledge and provide many more people around the world with access to quality education by making lectures, books and curricula widely available on the Internet for little or no cost. By enabling virtually anyone to tap into, translate and tailor educational materials previously reserved only for students at elite universities, OER has the potential to jump start careers and economic development in communities that lag behind. Millions worldwide have already opened this educational lockbox, but if OER is going to democratize learning and transform the classroom and teaching, then it must move from the periphery of education practice to center stage.User of OER interacts online with it and resulting creation of residual interaction data like web server logs e.g., users' browsers, users' IP addresses, location, duration and "social data" created during Web 2.0-style interactions with resources e.g., tags, comments, ratings, favorites, bookmark, forwards, downloads. These interactions data may be very useful if collected and analyzed.OER search platform:It collects metadata of OERs and compiles them into a searchable catalog that can be consulted by users. Search platforms have no way to access interaction data since it is always use external links which redirects to place where users interact with educational resources.

Aiming the K-12 students of India a website www.aikvta.in has added links to most useful schooling OER providers in the tab resources and it is being accessed by thousands of users.

Now the point pop up in mind - so many users access these OER, therefore can we know the activities of the users?If we provide a secure platform by using web 2.0 tools lot of interesting data can be aggregated. Aggregation of these end user data and analyses of it would be a unique feedback in OERs quality improvement & ranking.

Interaction Data Aggregation: Interaction data is data generated from an interaction between a user and an OER. Any interaction between a user and a resource (typically described in a metadata record) can be modeled as a interaction between the resource and its user. Instances of this interactions: "user X viewed resource Y on Fri. Mar. 22, 2015 at 2:54pm", "user Z tagged resource W with tag 'algebra' on Fri. March. 22, 2015 at 2:55pm". They can include more or less information about the context in which an interaction occurs.

Contextualized Attention Metadata (CAM)that can be used to represent atomic interaction data. The CAM specification was originally proposed for capturing behavioral information about learners in learning contexts. However, as shown in the examples above, atomic interaction data is personal data that requires some kind of identification of both the user and the resource. open specifications are available for modeling and exchanging interaction data. One approach isContextualized Attention Metadata atomic interaction data is personal data that requires some kind of identification of both the user and the resource.

14In 2010, as part of their Stem Exchange initiative, the U.S. National Science Digital Library (NSDL) released a "paradata" data model to "capture a user activity related to a resource that helps to elucidate its potential educational utility" (i.e., interaction data aggregated by resources). The paradata model comes with an XML binding. In a paradata record, a resource can be identified using either an identifier or its location, which allows for relating paradata and metadata records that refer to the same resource.Paradata :are data about the process of collecting survey data. They can include things like call record data, cai keystroke files and interviewer observations.15Social Data Aggregation:Portals connected to the OER if offer Web 2.0-like functionalities permitting users to personalize their information retrieval experience and interact with each other. For example, users bookmark and tag OER descriptions, review, rating, favorites, bookmark, and forwards counts, downloads counts and thus exchange their feedback with members of virtual communities.

Knowing that a collection is popular among target audiences can be of great help in ongoing curation and acquisition decision-making. There are many ways this kind of information can be used. For instance, interaction data makes it possible for content providers and repository owners to tailor the packaging and marketing of resources for particular regions. It makes it possible to track how current efforts to improve OERs uptake are succeeding and in what regions. Having interaction data for OERs makes it also possible to identify the most popular OERs (worldwide, continent, region, country, etc.) relying on crowdsourcing to sift out the best OERs from an ever-growing, global body of resourcesAggregation of these end user data and analyses of it would be a unique feedback in OERs quality improvement & ranking. Social data aggregation like definite reviews, ranking would attract the right users and make the service providers competitive, encourage to sustain, upgrade the OER. Any Suggestion Please?