The Peer Data Labelling System (PDLS). A participatory approach to classifying engagement in the classroom

Graham Parsonage*, Matthew Horton, Janet Read

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationHuman-Computer Interaction – INTERACT 2023
Subtitle of host publication19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part II
EditorsJosé Abdelnour Nocera, Marta Kristín Lárusdóttir, Helen Petrie, Antonio Piccinno, Marco Winckler
PublisherSpringer Cham
Number of pages10
ISBN (Electronic)9783031422836
ISBN (Print)9783031422829
Publication statusPublished - 25 Aug 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • data labelling
  • artificial intelligence
  • engagement


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