Sustainable Application of Educational Data Mining and 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 (5 July 2021) | Viewed by 3163
Special Issue Editors
Interests: educational data mining; application of artificial intelligence in education; learning technologies
Interests: pattern mining; application of data mining in education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Educational data mining (EDM) and learning analytics (LA) are two interdisciplinary communities of computer scientists, learning scientists, psychometricians, and researchers from other areas with the same objective of improving learning starting from data. They have grown quickly in the last two decades, and today there is a current increasing interest in moving from the lab to the general market so that EDM/LA results and models can be used by educational institutions and schools worldwide in real-life educational contexts. So, it is necessary to address some important issues about the sustainable application of EDM/LA results/models, such as transferability, scalability, generalizability, applicability, etc. In this way, we can close the EDM/LA loop by using the obtained results and models during the EDM/LA process. The final objective is to obtain useful feedback and recommendations from the different stakeholders (students, instructors, administrators, institutions, etc.) in order to conduct interventions in real educational contexts.
This Special Issue invites researchers interested in the sustainable application of educational data mining and learning analytics. Authors are invited to submit their works addressing one or more of the following topics:
- Transferability for storing knowledge gained while solving one problem and applying it to a different but related problem.
- Effectiveness and efficiency for producing the intended or expected result in the best possible manner with the least waste of time and effort.
- Interpretability and comprehensibility for helping us to understand how the model has learned to solve a given problem and explain the phenomena being modeled.
- Applicability for measuring how a solution is applicable to a problem.
- Generalizability or robustness of a model for measuring its successful application to data sets other than the one used for training and testing.
- Scalability for developing algorithms that are efficient when using very large real-world data sets.
Prof. Dr. Cristóbal Romero Morales
Dr. José María Luna
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
- educational data mining
- learning analytics
- educational data science
- sustainability
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.