Abstract
The paper introduces a novel and extensible approach to generating labelled data called the Peer Data Labelling System (PDLS), suitable for training supervised Machine Learning algorithms for use in CCI research and development. The novelty is in classifying one child’s engagement using peer observation by another child, thus reducing the two-stage process of detection and inference common in emotion recognition to a single phase. In doing so, this technique preserves context at the point of inference, reducing the time and cost of labelling data retrospectively and stays true to the CCI principle of keeping child-participation central to the design process. We evaluate the approach using the usability metrics of effectiveness, efficiency, and satisfaction. PDLS is judged to be both efficient and satisfactory. Further work is required to judge its effectiveness, but initial indications are encouraging and indicate that the children were consistent in their perceptions of engagement and disengagement.
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Acknowledgments
We would like to thank the Head Teacher, staff and pupils of Ribblesdale High School and in particular the Head of Computer Science, Mr Steven Kay for their invaluable assistance and participation in this study.
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Parsonage, G., Horton, M., Read, J. (2023). The Peer Data Labelling System (PDLS). A Participatory Approach to Classifying Engagement in the Classroom. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14143. Springer, Cham. https://doi.org/10.1007/978-3-031-42283-6_13
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