Cited by 103Online Learning and AnalyticsBig Data and Business IntelligenceEducational and Technological Research

How do we start? An approach to learning analytics adoption in higher education

Open access
Gasevic, Dragan, Tsai, Yi-Shan, Dawson, Shane, Pardo, Abelardo·2019·The International Journal of Information and Learning Technology

Abstract

Purpose: The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics? Design/methodology/approach: A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education. Findings: The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. Originality/value: The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1108/ijilt-02-2019-0024

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Professional development and training in LA can improve educators' capacity to use data-driven insights to support student learning.

    “Studies suggest that professional development and training in LA can improve educators' capacity to use data-driven insights to support student learning (Bergdahl et al., 2020;Bond et al., 2020;Gašević et al., 2019).”

    From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Gašević et al., 2019

    The discussion on p. 348 identifies developing data literacy and institutional capacity as milestones for learning analytics adoption. Section 2.1 (p. 343) also describes analytics helping instructors identify students who need support. Neither passage says that professional development or training improves educators’ capacity to use those insights. (LLM, conf=0.78)

    Experimental — accuracy not yet measured
  • LA must become more rigorous in adopting educational theory.

    “Gašević et al. (2019) emphasised that LA must become more rigorous in adopting educational theory.”

    From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Gašević et al. (2019)

  • There may be a gap between LA research and theory development.

    “Indicating that there may be a gap between LA research and theory development, Gašević et al. (2019) emphasised that LA must become more rigorous in adopting educational theory.”

    From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Gašević et al. (2019)

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Dawson, S., Bakharia, A. and Heathcote, E. (2010), "SNAPP: realising the affordances of real-time SNA within networked learning environments", Proceedings of the 7th International Conference on Networked Learning, pp. 125-133.
  2. Long, P.D., Siemens, G., Conole, G. and Gašević, D. (Eds) (2011), Proceedings of the 1st International Conference On Learning Analytics And Knowledge (LAK'11), ACM, New York, NY.
  3. Siemens, G., Gašević, D. and Dawson, S. (2015), Preparing for the Digital University: A Review of the History and Current State of Distance, Blended, and Online Learning, Gates Foundation through MOOC Research Initiative, Seattle, WA.

Cited by

Sorted by citations

Authors
Gasevic, D., Tsai, Y.-S., Dawson, S., & Pardo, A.
Affiliations
  • University of Edinburgh (GB)
  • University of South Australia (AU)
Venue
The International Journal of Information and Learning Technology
Publisher
Emerald
Published
2019-08-05
Volume
36
Issue
4
Pages
342-353
ISSN
2056-4880 (ISSN-L)
OpenAlex
W2950279021
Cited by
103
References
43
Citations by year
2026: 5 · 2025: 7 · 2024: 20 · 2023: 25 · 2022: 22 · 2021: 13 · 2020: 10 · 2019: 1
FWCI
12.45
Type
journal article
Language
en
Open access
Locations
  • International Journal of Information and Learning Technology (publishedVersion)
  • Monash University Research Portal (Monash University) (submittedVersion, other-oa)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
  • HAL (submittedVersion)
  • Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Physical Sciences › Computer Science › Information Systems › Educational and Technological Research
Keywords
learning analytics, institutional adoption, higher education, data-informed decision making, innovation adoption, institutional change, socio-technical systems, educational technology, learning and teaching practices

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review