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Integrating multi-modal learning analytics dashboard in K-12 education: insights for enhancing orchestration and teacher decision-making

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Publicado en:Smart Learning Environments vol. 12, no. 1 (Dec 2025), p. 53
Autor principal: Possaghi, Isabella
Otros Autores: Vesin, Boban, Zhang, Feiran, Sharma, Kshitij, Knudsen, Cecilie, Bjørkum, Håkon, Papavlasopoulou, Sofia
Publicado:
Springer Nature B.V.
Materias:
Affect (Psychology)
Students
Usability
Collaboration
User experience
Heart rate
Knowledge acquisition
Educational technology
Decision making
Teachers
Dashboards
Classrooms
Learning
End users
Teaching methods
Design
Learning analytics
Real time
Focus Groups
Learning Activities
Teacher Response
Stakeholders
Influence of Technology
Learning Processes
Learning Experience
Literacy
Interviews
Student Experience
Cooperative Learning
Creative Activities
Feedback (Response)
Beliefs
Educational Objectives
Elementary Secondary Education
Learner Engagement
Constructivism (Learning)
Classroom Environment
Educational Researchers
Programming
Low Achievement
Acceso en línea:Citation/Abstract
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Resumen:Technological advancements are transforming teaching methods while offering wider windows into students’ learning journeys. Multi-modal Learning Analytics Dashboards (LADs) are tools that facilitate smart classroom orchestration by aggregating and analyzing students’ responses through sensors, such as facial expressions and heart rate, for real-time insights into student engagement and emotional states. In this study, we developed an LAD for open-ended activities in K-12 settings, where orchestration is non-linear and poses challenges for standardized evaluation methods. We engaged end users (e.g., educational researchers) in the process from the early design stages and investigated the feasibility of the LAD when used in the wild. The results show how affective data support greater awareness of students’ experiences, improving teachers’ orchestration through better decision-making and agency. Roadblocks were also identified regarding data interpretability, students’ privacy, and additional teacher workload, which can limit adoption and should be carefully addressed in future implementations. Further research should investigate students’ responses more closely and further develop strategies for the responsible, explainable, and unbiased use of student affective data in real classrooms.
ISSN:2196-7091
DOI:10.1186/s40561-025-00410-4
Fuente:Education Database