A review of annotation classification tools in the educational domain

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Abstract

An annotation consists of a portion of information that is associated with a piece of content in order to explain something about the content or to add more information. The use of annotations as a tool in the educational field has positive effects on the learning process. The usual way to use this instrument is to provide students with contents, usually textual, with which they must associate annotations. In most cases this task is performed in groups of students who work collaboratively. This process encourages analysis and understanding of the contents since they have to understand them in order to annotate them, and also encourages teamwork. To facilitate its use, computer applications have been developed in recent decades that implement the annotation process and offer a set of additional functionalities. One of these functionalities is the classification of the annotations made. This functionality can be exploited in various ways in the learning process, such as guiding the students in the annotation process, providing information to the student about how the annotation process is done and to the teacher about how the students write and how they understand the content, as well as implementing other innovative educational processes. In this sense, the classification of annotations plays a critical role in the application of the annotation in the educational field. There are many studies of annotations, but most of them consider the classification aspect marginally only. This paper presents an exploratory study of the classification mechanisms used in the annotation tools, identifying four types of cases: absence of classification mechanisms, classification based on controlled vocabularies, classification based on folksonomies, and classification based on ontologies.

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Gayoso-Cabada, J., Sarasa-Cabezuelo, A., & Sierra-Rodríguez, J. L. (2019, January 1). A review of annotation classification tools in the educational domain. Open Computer Science. Walter de Gruyter GmbH. https://doi.org/10.1515/comp-2019-0021

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