Presenting Linked Data as (Learning) Objects to increase Research Efficiency

ICT can increase the efficiency of research by embedding and coordinating the search for information among a group of colleagues and other researchers in a discipline. This leads to an enormous time saving and acceleration of the research process. ICT has the potential to expose new possible research tracks and interesting data via channels which the researcher should not be familiar with. One type of promising ICT environments uses Learning objects for this purpose.

Linked Data and the Semantic Web Technology Stack provide the framework for the evolution of learning object repositories into a more flexible system of sharing learning resources and their metadata. Repositories containing this kind of objects are typically not providing a way to find other learning objects via related online resources, so it can be very difficult to navigate, and difficult to integrate several objects into a coherent view .

A map of (learning) objects would allow the researcher to get a well integrated and structured overview of all available data. At the same time researchers can obtain an achievement level for each object as they successfully assess them or use the entities contained in them: such as completing a tutorial, writing a positively reviewed summary or comment on document, attending a seminar or completing a test. It is also possible to gain a higher proficiency by reviewing and assessing the contributions of others. For more accurate views filtering of learning objects takes into account the previous contributions, assessments and reviews. The system identifies relations such as dependencies between the objects. This ensures that the objects presented are adapted, personalized and follow the progress and learning curve for each research target or learning objective.

The objects group entities that are related to the same learning or research goal. This ensures that the environment can clearly present resources part of a learning target. Researchers can immediately find out for each object what it is, how to get it (e.g. how to master) or simply get an overview:

  •   What is it?
    Each object contains the actual links to the original resources for verification. An explanation and positioning of the objects in several dimensions that are analyzed based on the metadata or the contents such as difficulty level, topics, dependencies and topicality for describing and positioning the object.
  •   How to get it?
    The links and actual content are ordered according to their dependencies and difficulty level, following a sorted sequence of linked objects creates a path like a literature list with relevant resources for the researcher. By providing links to the authors and allowing interaction with the objects it behaves as a forum to collaborate and converge with other research people to master various (sub)goals. This depends heavily on the researchers targets: setting a research target or learning goal can be seen as the equivalent of a search query.
  •   What is out there?
    Overview of where the community around them is focusing on, thereby using a centered view around their own contributions (for example publications) and a personal researcher profile. Identify where the own research can be improved using dependencies and links to research that is not covered by the researcher yet. Researchers can select new research targets and switch to new ‘tracks’ any time.

The paradox of a such a linked and interactive environment is that, when collecting research data such as contained digital libraries, the more embedded this data gets in information structures of universities (such as institutional repositories), communities, governments or other institutes, the less visible it becomes for their users, policy makers and investors . This is because all these repositories are structured in many different ways. The (meta)data differs strongly in quality and lay-out. For users it is complex to access the various repositories. The research data itself is made available in unstructured peer reviewed publications as a full text article with embedded tables and figures. This type of publication – archived, cleaned and retrievable – is a crucial facet in the academic research process. The exchange of the research data is stronlgy influenced by the social structure of scientists and their organizations because the effort to write, to review, to classify and administer research information keeps increasing.

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About laurensdv
Computer Science Student, interested in creating more innovating user experiences for information access. Fond of travelling around Europe!

One Response to Presenting Linked Data as (Learning) Objects to increase Research Efficiency

  1. Pingback: A Semantic Approach to Cross-Disciplinary Research Collaboration « Laurens goes semantic…

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