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: closed (15 March 2023) | Viewed by 6387

Special Issue Editors


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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

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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

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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.

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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Keywords

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

Published Papers (2 papers)

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12 pages, 939 KiB  
Article
Empirical Evidence to Support a Nudge Intervention for Increasing Online Engagement in Higher Education
by Alice Brown, Marita Basson, Megan Axelsen, Petrea Redmond and Jill Lawrence
Educ. Sci. 2023, 13(2), 145; https://doi.org/10.3390/educsci13020145 - 30 Jan 2023
Cited by 3 | Viewed by 3264
Abstract
Student engagement is recognised as being a critical factor linked to student success and learning outcomes. The same holds true for online learning and engagement in higher education, where the appetite for this mode of learning has escalated worldwide over several decades, and [...] Read more.
Student engagement is recognised as being a critical factor linked to student success and learning outcomes. The same holds true for online learning and engagement in higher education, where the appetite for this mode of learning has escalated worldwide over several decades, and as a result of COVID-19. At the same time teachers in higher education are increasingly able to access and utilise tools to identify and analyse student online behaviours, such as tracking evidence of engagement and non-engagement. However, even with significant headway being made in fields such as learning analytics, ways in which to make sense of this data, and to utilise data to inform interventions and refine teaching approaches, continue to be areas that would benefit from further insights and exploration. This paper reports on a project that sought to investigate whether low levels of student online engagement could be enhanced through a course specific intervention strategy designed to address student engagement with online materials in a regional university. The intervention used course learning analytics data (CLAD) in combination with the behavioral science concept of nudging as a strategy for increasing student engagement with online content. The study gathered qualitative and quantitative data to explore the impact of nudging on student engagement with 187 students across two disciplines, Education and Regional/Town Planning. The results not only revealed that the use of the nudge intervention was successful in increasing the levels of engagement in online courses but also revealed that the prerequisites for nudging were needed in order to increase success rates. The paper points to the value for the broader awareness, update, and use of learning analytics as well as nudging at a course, program, and institutional level to support student online engagement. Full article
(This article belongs to the Special Issue Supporting Student Learning and Engagement through Analytics)
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21 pages, 4824 KiB  
Systematic Review
Model-Based Learning Analytics for a Partnership of Teachers and Intelligent Systems: A Bibliometric Systematic Review
by Gerti Pishtari, Tobias Ley, Mohammad Khalil, Reet Kasepalu and Iiris Tuvi
Educ. Sci. 2023, 13(5), 498; https://doi.org/10.3390/educsci13050498 - 15 May 2023
Cited by 1 | Viewed by 1996
Abstract
This paper presents a bibliometric systematic review on model-based learning analytics (MbLA), which enable coupling between teachers and intelligent systems to support the learning process. This is achieved through systems that make their models of student learning and instruction transparent to teachers. We [...] Read more.
This paper presents a bibliometric systematic review on model-based learning analytics (MbLA), which enable coupling between teachers and intelligent systems to support the learning process. This is achieved through systems that make their models of student learning and instruction transparent to teachers. We use bibliometric network analysis and topic modelling to explore the synergies between the related research groups and the main research topics considered in the 42 reviewed papers. Network analysis depicts an early stage community, made up of several research groups, mainly from the fields of learning analytics and intelligent tutoring systems, which have had little explicit and implicit collaboration but do share a common core literature. Th resulting topics from the topic modelling can be grouped into the ones related to teacher practices, such as awareness and reflection, learning orchestration, or assessment frameworks, and the ones related to the technology used to open up the models to teachers, such as dashboards or adaptive learning architectures. Moreover, results show that research in MbLA has taken an individualistic approach to student learning and instruction, neglecting social aspects and elements of collaborative learning. To advance research in MbLA, future research should focus on hybrid teacher–AI approaches that foster the partnership between teachers and technology to support the learning process, involve teachers in the development cycle from an early stage, and follow an interdisciplinary approach. Full article
(This article belongs to the Special Issue Supporting Student Learning and Engagement through Analytics)
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