Cited by 18Online Learning and AnalyticsData Visualization and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Peeking into the black box: visualising learning activities

Open access
García, Raquel M. Crespo, Pardo, Abelardo, Kloos, Carlos Delgado, Niemann, Katja, Scheffel, Maren, Wolpers, Martin·2012·International Journal of Technology Enhanced Learning

Abstract

Learning analytics has emerged as the discipline that fosters the learning process based on monitored data. As learning is a complex process that is not limited to a single environment, it benefits from a holistic approach where events in different contexts and settings are observed and combined. This work proposes an approach to increase this coverage. Detailed information is obtained by combining logs from a LMS and events recorded with a virtual machine given to the students. A set of visualisations is then derived from the collected events showing previously hidden aspects of an experience that can be shown to the teaching staff for their consideration. The visualisations presented focus on different learning outcomes, such as self learning, use of industrial tools, time management, information retrieval, collaboration, etc. Depending on the information to convey, different types of visualisations are considered, ranging from graphs to starbusts and from scatter plots to heatmaps.

Integrity signals

Registry

Crossref · 10.1504/ijtel.2012.048313

Cites

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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?

  • Students are easily accessible data subjects, and HEIs can avoid seeking student consent for data collection, retaining and analyzing student data with impunity because the data is generated as part of the educational process.

    “Students are easily accessible data subjects, and HEIs can avoid seeking student consent for data collection, retaining and analyzing student data with impunity because the data is generated as part of the educational process.17”

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Authors
García, R. M. C., Pardo, A., Kloos, C. D., Niemann, K., Scheffel, M., & Wolpers, M.
Affiliations
  • Universidad Carlos III de Madrid (ES)
  • Fraunhofer Institute for Applied Information Technology (DE)
Venue
International Journal of Technology Enhanced Learning
Published
2012
Volume
4
Issue
1/2
Pages
99
ISSN
  • 1753-5255 (ISSN-L)
  • 1753-5263
OpenAlex
W2160363881
Cited by
18
References
0
Citations by year
2023: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 6 · 2017: 1 · 2016: 4 · 2015: 1 · 2012: 1
FWCI
1.03
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • International Journal of Technology Enhanced Learning (publishedVersion)
  • e-Archivo (Carlos III University of Madrid) (acceptedVersion, other-oa)
  • Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
learning analytics, learning management system logs, self-directed learning, time management, information retrieval, collaboration, learning outcomes

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