Peeking into the black box: visualising learning activities
Open accessAbstract
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.
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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”
From Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads (2018) · cites this paper as [17]
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Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads
Jones, K., & Salo, D. (2018) · College & Research Libraries · doi:10.5860/crl.79.3.304 · Cited by 85 · see citing works
Ethical Challenges for Learning Analytics
Ferguson, R. (2019) · Journal of Learning Analytics · doi:10.18608/jla.2019.63.5 · Cited by 62 · see citing works
- 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
- Publisher
- Inderscience Publishers
- 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
- green
- hdl.handle.net
- 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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