Raising Awareness of Data-ethics for PGCE Computing using Ballet and Biometrics as Narrative Tools: An AgileDBR Approach
type: CreativeWork
description: In contemporary society, educating individuals about the ethical implications of sharing personal biometric data is limited despite the omnipresence of data. This doctoral study explores the use of creative computing and arts practices, specifically biometrics and ballet, to raise awareness of data-ethics among Postgraduate Certificate in Education (PGCE) trainee computing teachers. This thesis explores the issue of how creative computing practices can effectively enhance data-ethics awareness among trainee computing teachers and contribute to widening participation in computing education. To address these research objectives, a novel methodology called AgileDBR, which combines Agile and Design-Based Research (DBR), is introduced. This methodology overcomes the limitations of traditional DBR by providing transparency, scaffolding, more flexible routines, and more fluid structures. Purposive sampling is employed to gather data from three distinct cohorts: Computing Educators, Dancers, and Technologists. The participants are organised into three cohorts: Computing Educators (including trainee computing teachers and PGCE leads) (n=17), Dancers (n=3), and Technologists (n=1). Data collection is conducted through three increments using interpretative phenomenological analysis (IPA) and computational methods such as Natural Language Processing (NLP). Biometric data capture involves skeleton and brainwave (electroencephalogram = EEG) recordings, using new tools to work in digital online settings. The captured data undergoes various processes, including dancer isolation, skeleton data tracking, pose estimation ML model, and joint mapping software. The findings of this study reveal that creative computing practices with biometrics and ballet by fostering personal connection to data thus effectively raises awareness of data-ethics and potentially broadens participation in computing education. Further research is still required. The integration of machine learning and creative…
abstract: In contemporary society, educating individuals about the ethical implications of sharing personal biometric data is limited despite the omnipresence of data. This doctoral study explores the use of creative computing and arts practices, specifically biometrics and ballet, to raise awareness of data-ethics among Postgraduate Certificate in Education (PGCE) trainee computing teachers. This thesis explores the issue of how creative computing practices can effectively enhance data-ethics awareness among trainee computing teachers and contribute to widening participation in computing education. To address these research objectives, a novel methodology called AgileDBR, which combines Agile and Design-Based Research (DBR), is introduced. This methodology overcomes the limitations of traditional DBR by providing transparency, scaffolding, more flexible routines, and more fluid structures. Purposive sampling is employed to gather data from three distinct cohorts: Computing Educators, Dancers, and Technologists. The participants are organised into three cohorts: Computing Educators (including trainee computing teachers and PGCE leads) (n=17), Dancers (n=3), and Technologists (n=1). Data collection is conducted through three increments using interpretative phenomenological analysis (IPA) and computational methods such as Natural Language Processing (NLP). Biometric data capture involves skeleton and brainwave (electroencephalogram = EEG) recordings, using new tools to work in digital online settings. The captured data undergoes various processes, including dancer isolation, skeleton data tracking, pose estimation ML model, and joint mapping software. The findings of this study reveal that creative computing practices with biometrics and ballet by fostering personal connection to data thus effectively raises awareness of data-ethics and potentially broadens participation in computing education. Further research is still required. The integration of machine learning and creative…
author: Smith-Nunes, Genevieve
language: eng
keywords: biometrics, classical ballet, data-ethics, education
license: https://www.repository.cam.ac.uk/bitstreams/fc31be0b-6261-467f-9595-177aef4b22b5/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2023-06-09
date modified: 2026-07-22T22:24:13Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.102086