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Integrating multiple data sources for learning analytics—review of literature

Jeanette Samuelsen,Weiqin Chen,B. Wasson

Published 2019 in Research and Practice in Technology Enhanced Learning

ABSTRACT

Learning analytics (LA) promises understanding and optimization of learning and learning environments. To enable richer insights regarding questions related to learning and education, LA solutions should be able to integrate data coming from many different data sources, which may be stored in different formats and have varying levels of structure. Data integration also plays a role for the scalability of LA, an important challenge in itself. The objective of this review is to assess the current state of LA in terms of data integration in the context of higher education. The initial search of six academic databases and common venues for publishing LA research resulted in 115 publications, out of which 20 were included in the final analysis. The results show that a few data sources (e.g., LMS) appear repeatedly in the research studies; the number of data sources used in LA studies in higher education tends to be limited; when data are integrated, similar data formats are often combined (a low-hanging fruit in terms of technical challenges); the research literature tends to lack details about data integration in the implemented systems; and, despite being a good starting point for data integration, educational data specifications (e.g., xAPI) seem to seldom be used. In addition, the results indicate a lack of stakeholder (e.g., teachers/instructors, technology vendors) involvement in the research studies. The review concludes by offering recommendations to address limitations and gaps in the research reported in the literature.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Research and Practice in Technology Enhanced Learning

  • Publication date

    2019-08-28

  • Fields of study

    Computer Science, Education

  • Identifiers
    DOI 10.1186/s41039-019-0105-4
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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