Supporting Student Learning and Engagement through Analytics

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.

References

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

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