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Implementing a data-driven framework for measuring digital readiness across European universities

11 pagesPublished: August 9, 2023

Abstract

This paper explores the feasibility of defining a data-driven framework for assessing digital readiness in Higher Education Institutions (HEIs). This is part of a project that aims at measuring digital readiness indicators with the ultimate objective to contribute to the quality assurance processes of the institutions. The project involves the design and demonstration of a prototype toolkit and an Application Programming Interface to enable self-reflection, using data sourced from the institutions' information systems, which are aligned with the indicators of the framework, to provide feedback to relevant stakeholders. The reported research examines information sources of the HEIs involved, auditing of proposed framework indicators, and proposals for further review on digital readiness frameworks and other data-driven approaches as well as the first architectural considerations on implementing the Digital Readiness tool.

Keyphrases: digital readiness, digitl transformation, institutional analytics

In: Jean-François Desnos and Martín López Nores (editors). Proceedings of European University Information Systems Congress 2023, vol 95, pages 129-139.

BibTeX entry
@inproceedings{EUNIS2023:Implementing_data_driven_framework,
  author    = {Konstantinos Tsimpanis and Nikolaos Avouris and Irene-Angelica Chounta and Sophia Daskalaki and Yannis Dimitriadis and Carlos Madrid Gari and Athanasia Kalouptsoglou and Alejandro Ortega-Arranz and Pantelis Balaouras},
  title     = {Implementing a data-driven framework for measuring digital readiness across European universities},
  booktitle = {Proceedings of European University Information Systems Congress 2023},
  editor    = {Jean-François Desnos and Martín López Nores},
  series    = {EPiC Series in Computing},
  volume    = {95},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/lpNd},
  doi       = {10.29007/ds3v},
  pages     = {129-139},
  year      = {2023}}
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