8 de abril de 2019

Learning Analytics & Data-driven Education


Daniel Domínguez Figaredo (UNED, Spain), 
Justin Reich (MIT, USA) y 
José A. Ruipérez-Valiente (MIT, USA).


With the accelerated advance of data science and learning analytics, there has been a proliferation of research focused on the information generated by student activity in digital spaces. In distance education it is essential to use digital mediation systems in order to establish contact between students, teachers and resources, which makes this field very conducive to incorporating methods of learning analysis. Based on these evidences, this special issue aims to strengthen the connections between data-based educational research and the field of digital learning, with the aim of enriching knowledge about learning processes and the management of teaching in non-presential and digitally mediated spaces.

The phenomenon of data-driven education has led to different types of studies. There is a great deal of research using educational data mining that seeks to analyze student behavior patterns and to establish relationships between the variables involved in the learning process and learning outcomes. A second trend refers to studies with a pedagogical approach, which use the aggregated information resulting from the analysis of the data with the aim of improving instructional design, enriching didactic methods and better understanding the role of educational agents. Finally, there is also a significant amount of research that focuses on the institutional derivatives of the use of digital data and seeks to develop frameworks for improving strategic decision-making, organizational design, and curricular policies.

Contributions to this special issue may consist of both theoretical and applied approaches: they may be survey studies on the state of the art of data-driven education, or research papers presenting evidence of interest in this field of study. They may also employ quantitative or qualitative approaches from data science and educational data mining, as well as from the field of pedagogy and educational sciences. In the case of empirical research, contributions that go beyond documenting student activity in a course emphasizing the application in practice are particularly welcome. 

We invite case studies from traditional distance education settings (e.g. learning management systems) but also more specialized and contemporary environments like games for learning or intelligent tutoring systems. Research that provides a deeper insight into different learning processes, such as those that help to measure the skills that students acquire in online courses, how those skills change throughout a course or in lifelong learning situations, and, in general, that proposes causal reasoning to understand how the behaviour of students in digitally mediated spaces affects their learning, will also be welcome.

We propose to organize the special issue around the following themes:
  • Implications of analytics in the improvement of learning processes, teaching practices and instructional design.
  • New research on exploratory, predictive or causal analytics to improve learning success.
  • Pilot studies or policy frameworks for the implementation of learning analytics at an institutional level.
  • Analytics in open and connected learning spaces.
  • Innovative approaches to data-driven education.
Submission process

Articles should be submitted online via the RIED web-portal. We welcome full manuscripts of up to 7,000 words maximum, including abstract, notes and bibliography. Each paper will be reviewed by two referees. Papers may be published in English, Spanish and Portuguese. Publication and access are free and open. For more information on the submission process, see the authors guidelines section and especially consider the requirements and criteriademanded by RIED.


April, 2019: The call opens.
November, 15 - December 15 2019: Contributions submission open (upload the papers to the journal's website).
February-March, 2020: Decisions and comments sent to authors.
June, 2020: Expected publication.

Contact information

For questions regarding the special issue please contact the coordinators: ddominguez@edu.uned.es, jruipere@mit.edu or jreich@mit.edu