Facilitating Scientometrics in Learning Analytics and Educational Data Mining – the LAK Dataset

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Dietze, S.; Taibi, D.; d'Aquin, M.: Facilitating Scientometrics in Learning Analytics and Educational Data Mining – the LAK Dataset. In: Semantic Web 8 (2018), Nr. 3, S. 395-403. DOI: https://doi.org/10.3233/SW-150201

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10765

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Sum total of downloads: 150




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Abstract: 
The Learning Analytics and Knowledge (LAK) Dataset represents an unprecedented corpus which exposes a near complete collection of bibliographic resources for a specific research discipline, namely the connected areas of Learning Analytics and Educational Data Mining. Covering over five years of scientific literature from the most relevant conferences and journals, the dataset provides Linked Data about bibliographic metadata as well as full text of the paper body. The latter was enabled through special licensing agreements with ACM for publications not yet available through open access. The dataset has been designed following established Linked Data pattern, reusing established vocabularies and providing links to established schemas and entity coreferences in related datasets. Given the temporal and topic coverage of the dataset, being a near-complete corpus of research publications of a particular discipline, it facilitates scientometric investigations, for instance, about the evolution of a scientific field over time, or correlations with other disciplines, what is documented through its usage in a wide range of scientific studies and applications.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: Article
Publishing status: acceptedVersion
Issue Date: 2017
Appears in Collections:Forschungszentren

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of United States United States 49 32.67%
2 image of flag of Germany Germany 40 26.67%
3 image of flag of China China 9 6.00%
4 image of flag of United Kingdom United Kingdom 7 4.67%
5 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 5 3.33%
6 image of flag of Vietnam Vietnam 3 2.00%
7 image of flag of Taiwan Taiwan 3 2.00%
8 image of flag of Russian Federation Russian Federation 3 2.00%
9 image of flag of Ireland Ireland 3 2.00%
10 image of flag of Spain Spain 3 2.00%
    other countries 25 16.67%

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