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School Analytics: A Framework for Supporting School Complexity Leadership

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Competencies in Teaching, Learning and Educational Leadership in the Digital Age

Abstract

Data-driven decision-making in education has received an increasing level of attention on a global scale, especially with the raising interest on big data. This trend has led to the development of two core analytics strands, namely Academic Analytics and Learning Analytics. The former focuses mainly on the macro layer of the organization and is addressed to higher education, while the latter focuses mainly on the micro/meso layers of the organization. Considering the diverse focal points and contexts of application of the two existing analytics strands, the ecosystemic nature of K-12 schools as social complex adaptive systems, as well as, the need for data-based evidence-driven school complexity leadership, we claim that a holistic decision support approach for addressing the full spectrum of school leaders’ tasks is required, beyond the existing analytics strands. Therefore, in this book chapter, we introduce the concept of School Analytics as a holistic, multilevel analytics framework aiming to integrate data collected from all micro-, meso- and macro- organizational layers. We analyze them in an intertwining manner towards providing continuous feedback loops and systemic decision support to K-12 school leaders.

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Notes

  1. 1.

    In terms of improving teaching practices, an emerging field that has been proposed is Teaching and Learning Analytics (Sergis & Sampson, 2016c), which refers to the process of data-driven reflective teaching practice, based on evidence collected from students’ performance indicators using learning analytics methods and tools.

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Acknowledgments

The work presented in this paper has been partially supported by (a) the Open Discovery Space: A socially powered and multilingual open learning infrastructure to boost the adoption of eLearning Resources Project that is funded by the European Commission’s CIP-ICT Policy Support Programme (Project Number: 297229) and (b) the Inspiring Science: Large-Scale Experimentation Scenarios to Mainstream eLearning in Science, Mathematics and Technology in Primary and Secondary Schools Project that is funded by the European Commission’s CIP-ICT Policy Support Programme (Project Number: 325123).

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  1. Department of Digital Systems, University of Piraeus & Information Technologies Institute, Centre for Research and Technology—Hellas, 150 Androutsou Street, Piraeus, GR-18532, Greece

    Stylianos Sergis

  2. School of Education, Curtin University, Bentley Campus, Building 501, Level 4, Kent Street, Perth, WA, 6102, Australia

    Demetrios G. Sampson

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  1. Stylianos Sergis
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Correspondence to Demetrios G. Sampson .

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  1. University of North Texas , DENTON, Texas, USA

    J. Michael Spector

  2. Deakin University, Melbourne, Victoria, Australia

    Dirk Ifenthaler

  3. Curtin University, School of Education Curtin University, Perth, West Australia, Australia

    Demetrios G. Sampson

  4. Universidade Aberta (Portuguese Open University), Lisbon, Portugal

    Pedro Isaias

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Sergis, S., Sampson, D.G. (2016). School Analytics: A Framework for Supporting School Complexity Leadership. In: Spector, J., Ifenthaler, D., Sampson, D., Isaias, P. (eds) Competencies in Teaching, Learning and Educational Leadership in the Digital Age. Springer, Cham. https://doi.org/10.1007/978-3-319-30295-9_6

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