Special Issue "Supporting Student Learning and Engagement through Analytics"

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".

Deadline for manuscript submissions: 15 December 2022 | Viewed by 354

Special Issue Editors

Dr. Mohammad Khalil
E-Mail Website
Guest Editor
Centre for the Science of Learning & Technology (SLATE), University of Bergen, 5020 Bergen, Norway
Interests: learning analytics; technology-enhanced learning; self-regulated learning; privacy & ethics
Prof. Dr. Kairit Tammets
E-Mail Website
Guest Editor
Centre for Educational Technology, School of Digital Technologies, Tallinn University, 10120 Tallinn, Estonia
Interests: technology-enhanced learning; student-centered learning; cognitive processes; learning design and learning analytics
Dr. Terje Väljataga
E-Mail Website
Guest Editor
School of Educational Sciences, Tallinn University, 10120 Tallinn, Estonia
Interests: design and analytics of mobile outdoor learning; transformative experience; learner engagement

Special Issue Information

Dear Colleagues,

Learning Analytics is an emerging interdisciplinary research field that has received great attention. Learning Analytics is grounded in the research of computer and data science where students’ data can be used for gaining deeper insights on learning via data seeds. The field is also influenced by several other disciplines including, but not limited to, education, psychology, technology-enhanced learning, Artificial Intelligence, and statistics. However, the connection between these disciplines is often weak and the community of Learning Analytics has been trying to tackle a set of complex problems related to improving the student learning experience and the environments of their learning contexts.

Supporting students learning and engagement is an essential matter in Learning Analytics, also already widely addressed by the community. However, there is still a need for more comprehensive discussions on how Learning Analytics could empower the research on students’ learning and engagement and how to make clear connections between pedagogical, psychological and technological aspects of this complex line of research. Although creating and evaluating Learning Analytics interventions to help students retain and succeed (Wise, 2014), there is a lack of effective ways to supervise and measure students’ engagement in technology-enriched learning environment where the role of a teacher becomes crucial. Teachers, on the other hand, need a scaffold to design pedagogical interventions with learning technologies to support students’ higher-level cognitive processes (Mettis & Väljataga, 2020). To bridge the learning design and Learning Analytics, community has already acknowledged the need to develop solutions that help teachers to match the pedagogical concepts, learning designs and analytics. That kind of approach is not only meaningful for supporting students learning, but it can also support teacher professional learning, decision-making and improve student engagement (Khulbe & Tammets, 2021). One of the promising directions in the field to monitor and support students’ engagement is through using machine learning techniques, which could help understand student behaviour in online settings and further group and profile them to create personalized feedback and learning environment (Khalil & Ebner, 2017). Nevertheless, the community needs additional research to explore the pedagogical, methodological and technological aspects of research on students’ learning and engagement in the Learning Analytics-enriched learning environment.

This Special Issue intends to bring perspectives and approaches pertaining to supporting students learning and engagement using Learning Analytics to highlight both conceptual and empirical research. The Special Issue also intends to highlight and bring practices that feature the importance of supporting engagement and learning as well as valuing the broader research agenda of Learning Analytics.

We invite empirical, conceptual, and theoretical papers on a range of topics. Original research articles and reviews are welcome. Research areas may include (but are not limited) to the following:

  • Impact studies of Learning Analytics to students’ learning or engagement
  • Leveraging Learning Analytics to support students in virtual, physical, or hybrid learning settings
  • User experience studies of tools that support student learning or their engagement
  • Applications and practices of understanding student behaviour that can be further used to create interventions and provide feedback
  • Methodological and technological insights to use Learning Analytics to measure student engagement
  • Validating and evaluating Learning Analytics models and frameworks that are designed to support learners and their engagement
  • Highlighting the role of teachers to engage students in online and hybrid modes of learning
  • Implementations and practical studies that scale-up analytics to support students and their engagement in the context of both higher education and schools
  • Methods that contribute to supporting student learning and enhancing their engagement. These methods may refer to multimodal data and different logfiles data
  • Leveraging data streams and analytics to create personalised feedback.

We look forward to receiving your contributions.


Khalil, Mohammad, and Martin Ebner. 2017. Clustering patterns of engagement in Massive Open Online Courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education 29: 114–32.

Khulbe, Manisha, and Kairit Tammets. 2021. Scaffolding Teacher Learning During Professional Development with Theory-Driven Learning Analytics. In International Conference on Web-Based Learning. Cham: Springer, pp. 14–27.

Mettis, Kadri, and Terje Väljataga. 2020. Orchestrating Outdoor Location-Based Learning Activities. In Technology Supported Innovations in School Education. Cham: Springer, pp. 143–56.

Wise, Alyssa Friend. 2014. Designing pedagogical interventions to support student use of learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, IN, USA, March 24–28, pp. 203–11.

Dr. Mohammad Khalil
Prof. Dr. Kairit Tammets
Dr. Terje Väljataga
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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly 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 1400 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.


  • learning analytics
  • interventions
  • feedback
  • engagement
  • learning design
  • data science in education

Published Papers

This special issue is now open for submission.
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