The term Data Science (DS) refers to an interdisciplinary field that involves a series of methods, processes and systems, with the aim of extracting knowledge from data. DS, which is a discipline very related to Computing, has proved to be of great application in very different domains, particularly Education. In Educational environment, there are lots of learning-related processes involved, and great amounts of potential rich data are generated in educational institutions continuously. In order to extract knowledge from those data for a better understanding or learning-related processes, the use of DS approach seems to be useful and necessary.
The application of DS in the field of Education may result of great interests for involved stakeholders (students, instructors, institutions, …) since the extracted knowledge from educational data would be useful to deal with educational problems such as students’ performance improvement, high churning rates in educational institutions, learning delays, and so on.
There are a series of disciplines related to Educational Data Science, such as Educational Data Mining and Learning Analytics, and all of them are of importance for and can be considered in this special issue as long as educational processes are the core of the contributions. All contributions must contain an important background on pedagogical issues related to the educational processed addressed by DS techniques.
In particular, this special issue seeks original contributions of studies on the application of DS techniques in order to extract knowledge of interest for educational stakeholders as long as the analyzed data represent a particular educational process and the knowledge extracted is used to improve that process in some way. Papers that include discussions of the implementation of software and/or hardware approaches should focus on the implications for the improvement of any learning process. Priority will be given to papers that demonstrate a strong grounding in learning theory and/or rigorous educational research design. We will consider studies focused on tertiary and further education of any type (e-learning, blended and traditional education). Work should include an exhaustive validation in order to be considered, with position papers being occasionally admitted providing that they are very well written and include extraordinarily new ideas in the area. Survey papers may be also considered as long as they are clearly aligned with the special issue ideas and present interesting challenges and research opportunities in this area.
Suitable topics include, but are not limited to, the following:
• Educational data mining
• Learning analytics
• Educational data visualization
• Educational data preprocessing
• Educational data fusion/integration
• Educational time series analysis
• Andragogy data analysis
• Self-regulated learning data analysis
• Educational process mining
• Educational models generalization
• Ontologies for educational data science
• Big Data on educational data
• Educational open/linked data systems
• Gamification-related data analysis
Notes for Prospective Authors
This special issue is mainly intended (but not restricted) to contain extended versions of the best papers selected from the conferences DATA’20 and ICEMIS’20 with regard to Educational Data Science. In order to be considered for inclusion in this special issue, all articles sent for the DATA’20 and ICEMIS’20 conferences (contact the Guest Editors about the conferences) must include the following text in the Acknowledgement section: “This paper is intended for publication in the Special Issue on Data Science for analyzing and improving Educational processes”. Submitted papers should not have been previously published or be currently under consideration for publication elsewhere. Conference papers may be submitted to the special issue only if the paper has been completely rewritten and if appropriate written permissions have been obtained from any copyright holders of the original paper. All manuscripts should follow APA guidelines and will undergo blind peer review.
Manuscripts should be submitted to the special issue via the journal’s manuscript-submission and peer-review system at www.editorialmanager.com/jche and must indicate the submission is to the “SI: Edu-Data-Science” (Please note conference manuscripts must not be submitted to this site. Contact the Guest Editors for instructions on how to submit a paper to the DATA’20 or ICEMIS’20 conferences).
For further information on the special issue and submission procedures, please contact the Guest Editors. If you are considering submitting a manuscript to the conferences and a rewritten manuscript to the special issue, please contact the Guest Editors for specific procedure.
Submission deadline: 15 January 2021
Review notification: 15 April 2021
Submission of revised papers: 15 May 2021
Notification of final review results: 15 August 2021
– Prof Dr. Shadi A. Aljawarneh, Concorida University, Canada; firstname.lastname@example.org
– Dr. Juan Alfonso Lara Torralbo, Madrid Open University, UDIMA, Spain; email@example.com