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An Analysis of Open Learner Models for Supporting Learning Analytics

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Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment

Abstract

Teaching and learning are increasingly being offered in distributed, online digital environments, often openly and at large-scale, traversing spatial and temporal boundaries. Within such environments, Learning Analytics technologies aim to provide the means for tracking and making sense of the multitude of educational data that is being generated, in order to inform educational and pedagogical decision making of different actors, such as learners, teachers, school leaders and parents. However, at the heart of Learning Analytics technologies in such distributed and open learning environments lies the Open Learner Model (OLM), that informs the data collection, processing and sense-making capabilities of the analytics technology. In this context the contribution of this chapter is to present a generic educational data-driven layered Open Learner Modelling framework, which can be used as a blueprint for the analysis (and design) of OLM instances. Furthermore, capitalizing on this framework, the chapter also performs a critical analysis of existing research in OLM works, in order to draw conclusions on the current status of this emerging field.

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Notes

  1. 1.

    It is mentioned that percentages in the Figures throughout this section might not always add to 100%, since in some cases there were overlaps in the characterization of OLM based on the analysis framework.

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Acknowledgements

The work presented in this paper has been partially funded by (a) the European Commission in the context of the OSOS project (Grant Agreement no. 741572) under the Horizon 2020 Framework Programme, Science with and for Society: Open Schooling and Collaboration on Science Education (H2020-SwafS-15-2016), and (b) the Greek General Secretariat for Research and Technology, under the Matching Funds 2014–2016 for the EU project “Inspiring Science: Large Scale Experimentation Scenarios to Mainstream eLearning in Science, Mathematics and Technology in Primary and Secondary Schools” (Project Number: 325123). This document does not represent the opinion of neither the European Commission nor the Greek General Secretariat for Research and Technology, and the European Commission and the Greek General Secretariat for Research and Technology are not responsible for any use that might be made of its content.

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Authors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Stylianos Sergis & Demetrios Sampson

  2. School of Education, Curtin University, Perth, WA, Australia

    Demetrios Sampson

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  1. Stylianos Sergis
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Editor information

Editors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Demetrios Sampson  & Stylianos Sergis  & 

  2. School of Education, Curtin University, Perth, WA, Australia

    Demetrios Sampson

  3. Department of Learning Technologies, University of North Texas, Denton, TX, USA

    J. Michael Spector

  4. Curtin University, Perth, WA, Australia

    Dirk Ifenthaler

  5. Economic and Business Education Learning, Design and Technology, University of Mannheim, Mannheim, Germany

    Dirk Ifenthaler

  6. Institute for Teaching & Learning Innovation (ITaLI), The University of Queensland, St. Lucia, QLD, Australia

    Pedro Isaías

Appendix 1

Appendix 1

Table 9.2 presents the detailed analysis of the OLM literature reported in Sect. 4.2.

Table 9.2 Analysis of OLM literature works
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Sergis, S., Sampson, D. (2019). An Analysis of Open Learner Models for Supporting Learning Analytics. In: Sampson, D., Spector, J.M., Ifenthaler, D., Isaías, P., Sergis, S. (eds) Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-030-15130-0_9

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