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- W582376729 abstract "The goal of this thesis has been to investigate if it is possible to develop a knowledge structure, knowledge base, based on learning objects. In this connection a learning object is a digital unit which should, as a minimum, contain a picture and some text. Most likely a learning object would function as a container with anchors for video, animations and links to html-pages. For every learning object there exists a textual description. If we consider the learning object as an overhead presented in a classroom lecture, the text is a transcript of the explanation the lecturer would give, showing this overhead. This text should have a length of one half to a full A4 page. As part of this thesis I have developed a search engine based on the vector model from the field of Information Retrieval. The idea is that all of the learning material in a course should exist as learning objects collected in a repository. Based on the textual description tied to every learning object the contents of the repository will be represented as vectors in an n-dimensional vocabulary space. Using the search engine I will build an index for the collection of learning objects. Doing this, the engine will produce a similarity matrix that gives the similarity between all the learning objects. Based on the similarity matrix I will visualize the learning objects in a three dimensional knowledge structure. This visual structure will show all the learning objects and also how they are connected. The strength of the connection is determined from the similarity matrix. Two learning objects that have a large similarity will be close in the 3D-graph. This presentation of all the learning objects in a course can be used in many different ways. When a lecturer is planning a course he or she can query the repository of learning objects. The query will be represented as a vector and placed in the knowledge structure. The lecturer is looking for the objects close to the query vector, but in addition the neighbouring objects would be of interest. A student, solving a problem, can use the knowledge structure as a knowledge base. The advantage with this system is that it is built from statistic, calculating similarity between vectors. There is no need for manual treatment. This is also the weakness of the system, because there is no room for semantics. The experiments shows that the system has many interesting sides, but more research must bee done before it becomes a complete pedagogical system." @default.
- W582376729 created "2016-06-24" @default.
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- W582376729 date "2005-01-01" @default.
- W582376729 modified "2023-09-24" @default.
- W582376729 title "Building a Knowledge Base from Learning Objects" @default.
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