Sustainability of Learning Analytics
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Education and Approaches".
Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 3548
Special Issue Editors
Interests: learning analytics; conceptual learning in mechanics; multiple-choice tests; study success of STEM students
Interests: learning analytics; recommender systems; intelligent user interfaces; data visualisation
Special Issue Information
Dear Colleagues,
Research about Learning Analytics has increasingly gained attention, as demonstrated by the geographic and substantive scope of several initiatives. Many approaches have been elaborated that use data of learners to inform different stakeholders about study progress, efficiency, and effectiveness. However, as the domain of Learning Analytics is maturing, the connection of research to long-term applicability is relatively underdeveloped. This may hinder further investment of policy makers and administrators.
While Learning Analytics (LA) is still a relatively young discipline, it is quickly expanding, both in substantive scope and geographic interest. Over the past decade, several promising results have been shared. However, in many cases LA tools demonstrate difficulties in making the transition from research artefacts into scalable solutions in real-life educational contexts. Research papers generally do not address the issues of scalability and sustainability of proposed solutions extensively, if at all, leaving practitioners with unclear guidelines to apply them in non-experimental settings.
This Special Issue hopes to invite fellow researchers studying scalability and sustainability of existing and proposed solutions, as well as frameworks available to researchers and practitioners to develop the needed strategies to achieve these objectives.
Authors are invited to submit work addressing one or more of the following topics:
- Generalizability: not uncommonly, LA research takes place in favorable settings, e.g. involving a researcher-teacher with detailed knowledge of the specific course, or other highly motivated stakeholders. We invite researchers to address this issue when presenting their own work, or to start from existing work to explore its reproducibility in challenging contexts.
- Return on investment: as LA projects are likely to end up competing for resources with other proposals, LA researchers need to include return-on-investment (ROI) in their reasoning. LA solutions that require only limited effort can be attractive, even if the expected impact is relatively low or even uncertain and vice versa. We welcome work that includes the concept of ROI in the description of LA solutions.
- Change management: chances of sustainable and scalable implementations are limited without acceptance of learners, teachers and other stakeholders. Many lessons learned in general change management should not be ignored by the LA community. Authors are invited to describe how change management relates to their LA solutions.
- Trust: even the best models and feedback tools are of little use if they are not acted upon due to a lack of trust by their users and policy makers. We welcome submissions that explore approaches of transparency and trust-building to LA.
Dr. Tinne De Laet
Dr. Katrien Verbert
Dr. Maren Scheffel
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- Learning analytics
- learning analytics dashboards
- scalability
- transparency
- sustainability
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