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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 2891

Special Issue Editors


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Guest Editor
Faculty of Engineering Science, KU Leuven, 3001 Leuven, Belgium
Interests: learning analytics; conceptual learning in mechanics; multiple-choice tests; study success of STEM students

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Guest Editor
Department of Computer Science, KU Leuven, 3001 Leuven, Belgium
Interests: learning analytics; recommender systems; intelligent user interfaces; data visualisation

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Guest Editor
Welten Institute, Open University of the Netherlands, 6419 AT Heerlen, the Netherlands
Interests: learning analytics; learning analytics evaluation; self-regulated learning; learning design

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Learning analytics
  • learning analytics dashboards
  • scalability
  • transparency
  • sustainability

Published Papers (1 paper)

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Research

19 pages, 553 KiB  
Article
Generalizing Predictive Models of Admission Test Success Based on Online Interactions
by Pedro Manuel Moreno-Marcos, Tinne De Laet, Pedro J. Muñoz-Merino, Carolien Van Soom, Tom Broos, Katrien Verbert and Carlos Delgado Kloos
Sustainability 2019, 11(18), 4940; https://doi.org/10.3390/su11184940 - 10 Sep 2019
Cited by 16 | Viewed by 2565
Abstract
To start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve [...] Read more.
To start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve into the learners’ interactions and to develop predictive models to forecast success in the test. Moreover, the use of different courses allows analyzing how models can generalize across courses. This article has the following objectives: (1) to develop and analyze predictive models to forecast who will pass the admission test, (2) to discover which variables have more effect on success in different courses, (3) to analyze to what extent models can be generalized to other courses and subsequent cohorts, and (4) to discuss the conditions to achieve generalizability. The results show that the average grade in SPOC exercises using only first attempts is the best predictor and that it is possible to transfer predictive models with enough reliability when some context-related conditions are met. The best performance is achieved when transferring within the same cohort to other SPOCs in a similar context. The performance is still acceptable in a consecutive edition of a course. These findings support the sustainability of predictive models. Full article
(This article belongs to the Special Issue Sustainability of Learning Analytics)
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