{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/358427"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/358427","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Raising Awareness of Data-ethics for PGCE Computing using Ballet and Biometrics as Narrative Tools: An AgileDBR Approach","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 computing activities fosters interdisciplinary cross-curricular connections with subjects such as physical education, dance, and music, see appendix 11B. This research has significance and potential impact in aligning with Aim Four of England's Programme of Study for Computing, which emphasises the importance of creating ethical, responsible, competent, confident, and creative users of information and communication technology. The contribution to knowledge is in demonstrating that creative methods using dance and biometric data can be effective in achieving some of the objectives under this aim. Moreover, the new AgileDBR approach developed as part of this research is shown to provide a flexible and iterative research framework that enables efficient planning, evaluation, refinement, and adaptation, particularly crucial in addressing the challenges posed by the Covid-19 pandemic.","abstract_html":"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 computing activities fosters interdisciplinary cross-curricular connections with subjects such as physical education, dance, and music, see appendix 11B. This research has significance and potential impact in aligning with Aim Four of England's Programme of Study for Computing, which emphasises the importance of creating ethical, responsible, competent, confident, and creative users of information and communication technology. The contribution to knowledge is in demonstrating that creative methods using dance and biometric data can be effective in achieving some of the objectives under this aim. Moreover, the new AgileDBR approach developed as part of this research is shown to provide a flexible and iterative research framework that enables efficient planning, evaluation, refinement, and adaptation, particularly crucial in addressing the challenges posed by the Covid-19 pandemic.","abstract_has_math":false,"creators":["Smith-Nunes, Genevieve"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Wegerif, Rupert","Burnard, Pamela"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-06-09","date_published":"2023-06-09","updated_at":"2026-07-22T22:24:13Z","subjects":["biometrics","classical ballet","data-ethics","education"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/fc31be0b-6261-467f-9595-177aef4b22b5/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000196179575"],"render_values":[{"text":"0000-0001-9617-9575","href":"https://orcid.org/0000-0001-9617-9575","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.102086","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wegerif, Rupert","Burnard, Pamela"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Emotiv Arts Council England, Grants for the Arts"]},{"key":"dc:creator","label":"Author","values":["Smith-Nunes, Genevieve"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000196179575"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023-06-09"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/358427"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["biometrics","classical ballet","data-ethics","education"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/fc31be0b-6261-467f-9595-177aef4b22b5/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.102086"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/82ca9815-cf15-4f71-a90f-d5eb50fee96f/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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 computing activities fosters interdisciplinary cross-curricular connections with subjects such as physical education, dance, and music, see appendix 11B. This research has significance and potential impact in aligning with Aim Four of England's Programme of Study for Computing, which emphasises the importance of creating ethical, responsible, competent, confident, and creative users of information and communication technology. The contribution to knowledge is in demonstrating that creative methods using dance and biometric data can be effective in achieving some of the objectives under this aim. Moreover, the new AgileDBR approach developed as part of this research is shown to provide a flexible and iterative research framework that enables efficient planning, evaluation, refinement, and adaptation, particularly crucial in addressing the challenges posed by the Covid-19 pandemic."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["6a61a8ab45e763a6c86dd81a79fcbcf6","87eda9de84448d1f82354d60eee3eb5f"]},{"key":"dc:title","label":"Title","values":["Raising Awareness of Data-ethics for PGCE Computing using Ballet and Biometrics as Narrative Tools: An AgileDBR Approach"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wegerif, Rupert","Burnard, Pamela"],"dc:contributor.sponsor":["Emotiv Arts Council England, Grants for the Arts"],"dc:creator":["Smith-Nunes, Genevieve"],"dc:creator.authoridentifier":["0000000196179575"],"dc:date.issued":["2023-06-09"],"dc:description.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 computing activities fosters interdisciplinary cross-curricular connections with subjects such as physical education, dance, and music, see appendix 11B. This research has significance and potential impact in aligning with Aim Four of England's Programme of Study for Computing, which emphasises the importance of creating ethical, responsible, competent, confident, and creative users of information and communication technology. The contribution to knowledge is in demonstrating that creative methods using dance and biometric data can be effective in achieving some of the objectives under this aim. Moreover, the new AgileDBR approach developed as part of this research is shown to provide a flexible and iterative research framework that enables efficient planning, evaluation, refinement, and adaptation, particularly crucial in addressing the challenges posed by the Covid-19 pandemic."],"dc:format.checksum.md5":["6a61a8ab45e763a6c86dd81a79fcbcf6","87eda9de84448d1f82354d60eee3eb5f"],"dc:identifier.doi":["https://doi.org/10.17863/CAM.102086"],"dc:identifier.uri":["https://www.repository.cam.ac.uk/bitstreams/82ca9815-cf15-4f71-a90f-d5eb50fee96f/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/358427"],"dc:rights":["https://www.repository.cam.ac.uk/bitstreams/fc31be0b-6261-467f-9595-177aef4b22b5/download","http://purl.org/NET/rdflicense/allrightsreserved"],"dc:subject":["biometrics","classical ballet","data-ethics","education"],"dc:title":["Raising Awareness of Data-ethics for PGCE Computing using Ballet and Biometrics as Narrative Tools: An AgileDBR Approach"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-22T22:24:13Z"}