Linked data (LD) and the semantic web (SW) are an opportunity for learning analytics to streamline the analysis. It can be helpful to perform a more in-detail analysis of concepts and subjects dealt with in a coure. For example, you can extend beyond 'moodle posts' and find within them some instances of a concept that has to be treated in the course. If you have first the posts tagged/annotated or linked with meta-data, and then you do a SNAPP on the forum, you may find out that many of the interactions can be non-sense for the purpose of the forum, that people in those posts were actually greeting each other or writing about their holidays ;)
An old threat to the SW/LD opportunity is that first there must be someone/something annotating the posts with the concepts. If this has to be done manually, the opportunities of exploiting the SW an LD can decrease. A classical and critical text about that: http://bit.ly/1Dpk5. Fortunately, there exist great efforts to obtain metadata automatically, such as FreeBase or DBPedia.
Another issue is that we need to formalize the concepts and linkage relationships, i.e. the knowledge about educational concepts, so that they can be computer processed. We have to agree on the 'model' underlying that knowledge. But some people can be reluctant to an uniform, machine-understandable way of formalizing and expliciting the knowledge on education and educational research.
Re: How will (could) linked data and the semantic web impact learning?
by Juanma Dodero -
Number of replies: 2
In reply to Juanma Dodero
Re: How will (could) linked data and the semantic web impact learning?
by Thieme Hennis -
Juanma:
Another issue is that we need to formalize the concepts and linkage relationships, i.e. the knowledge about educational concepts, so that they can be computer processed. We have to agree on the 'model' underlying that knowledge. But some people can be reluctant to an uniform, machine-understandable way of formalizing and expliciting the knowledge on education and educational research.
Formalization requires agreement about definitions. Not only about education, but epistemology as a whole. What can we know? What is expertise? How should we define and structure knowledge? There is a bottom-up approach to this which becomes more feasible if the definitions and the structures are making sense and are already adopted by the community using them. An imposed ontology is less likely to become accepted than an emerged ontology. That leads to the question: how can we influence the emergence of ontologies? We have seen that folksonomies tend to expand and therefore more difficult to manage. If three different words in a folksonomy have the same meaning, how can we make people want to use only one?
My assumption is that reputation is an instrument that can be used for this matter: Suppose that people's reputation in a community is expressed using the keywords/definitions of the community ontology. Then these keywords all have a relative value that decreases with the number of keywords that mean the same. It is therefore wise for people who find their reputation important to agree upon a set of keywords that express their knowledge, and not use too many keywords for that.
PS. I am doing research in this direction, and wrote an algorithm that supports this reputation mechanism.
Another issue is that we need to formalize the concepts and linkage relationships, i.e. the knowledge about educational concepts, so that they can be computer processed. We have to agree on the 'model' underlying that knowledge. But some people can be reluctant to an uniform, machine-understandable way of formalizing and expliciting the knowledge on education and educational research.
Formalization requires agreement about definitions. Not only about education, but epistemology as a whole. What can we know? What is expertise? How should we define and structure knowledge? There is a bottom-up approach to this which becomes more feasible if the definitions and the structures are making sense and are already adopted by the community using them. An imposed ontology is less likely to become accepted than an emerged ontology. That leads to the question: how can we influence the emergence of ontologies? We have seen that folksonomies tend to expand and therefore more difficult to manage. If three different words in a folksonomy have the same meaning, how can we make people want to use only one?
My assumption is that reputation is an instrument that can be used for this matter: Suppose that people's reputation in a community is expressed using the keywords/definitions of the community ontology. Then these keywords all have a relative value that decreases with the number of keywords that mean the same. It is therefore wise for people who find their reputation important to agree upon a set of keywords that express their knowledge, and not use too many keywords for that.
PS. I am doing research in this direction, and wrote an algorithm that supports this reputation mechanism.
In reply to Thieme Hennis
Re: How will (could) linked data and the semantic web impact learning?
by Thieme Hennis -
the first paragraph (above) is a quote, but the it did not render it as such. anyway, I repeated the last paragraph of Juanma's response to reply to it.