Abstract
In consideration of the growing availability of mobile devices for students, web-based and shared annotations of learning materials are becoming more popular. Annotating learning material is a method to promote engagement, understanding, and independence for all learners in a shared environment. Open educational resources have the potential to add valuable information and close the gap between learning materials by automatically linking them. However, current popular web-based text annotation tools for learners, such as Hypothesis and Diigo, do not support learners in discovering new learning resources based on the context, metadata and the content of the annotated resource. In this article, we present SALMON, a collaborative web-based annotation system, which dynamically links and recommends learning resources based on annotations, content and metadata. It facilitates methods of semantic analysis in order to automatically extract relevant content from lecture materials in the form of PDF web documents. SALMON categorizes documents automatically in a way that finding similar resources becomes faster for the learners and they can discover communities for interesting topics.
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Notes
- 1.
Hypothesis website, hypothesis project (2013), https://web.hypothes.is/, retrieved:2019-6-25.
- 2.
- 3.
Semantic knowledge extractor, Thomson Reuters (2018), http://www.opencalais.com/opencalais-api/, retrieved:2019-6-25.
- 4.
Microservice architecture, Martin Fowler (2012), https://martinfowler.com/, retrieved: 2019-6-25.
- 5.
SALMON GitHub repository, 02-06-2019, https://github.com/SALMON2PROJECT.
- 6.
Keyword extraction Framework, Machine Reading for the Semantic Web, STlab 2015 http://wit.istc.cnr.it/stlab-tools/fred/.
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Aprin, F., Manske, S., Hoppe, H.U. (2019). SALMON: Sharing, Annotating and Linking Learning Materials Online. In: Herzog, M., Kubincová, Z., Han, P., Temperini, M. (eds) Advances in Web-Based Learning – ICWL 2019. ICWL 2019. Lecture Notes in Computer Science(), vol 11841. Springer, Cham. https://doi.org/10.1007/978-3-030-35758-0_23
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