Code of practice for learning analytics
Resumen:
Learning analytics should be used for the benefit of students. This might be to assist them individually or through using aggregated and anonymised data to help other students. Learning analytics might also be used to improve the educational experience more generally. It is distinct from assessment and should be used for formative rather than summative purposes. The effective use of learning analytics will initially involve the deployment of new systems along with changes to institutional policies and processes. New data may be collected about individuals and their learning activities. The data will be analysed and interventions may take place as a result. This presents opportunities for positive engagements and impacts on learningas well as misunderstandings, misuse of data and adverse impacts on students. Complete transparency and clear institutional policies are therefore essential regarding the purposes of learning analytics, the data collected, the processes involved and how they will be used to enhance the educational experience. This code of practice aims to set out the responsibilities of educational institutions to ensure that learning analytics is carried out responsibly, appropriately and effectively, addressing the key legal, ethical and logistical issues which are likely to arise. Educational institutions in the UK already have information management practices and procedures in place and have extensive experience of handling sensitive and personal data in accordance with the Data Protection Act 1998. They must now also comply with the General Data Protection Regulation (GDPR). By transferring and adapting this expertise to regulate the processing of data for learning analytics, institutions should establish the practices and procedures necessary to process the data of individuals lawfully and fairly.
2015 | |
Learning Analytics Education Transparency Accountability Ciencias Sociales Ciencias de la Educación Análisis de datos Procesamiento de datos Ética Privacidad |
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Inglés | |
Fundación Ceibal | |
Ceibal en REDI | |
https://hdl.handle.net/20.500.12381/318 | |
Acceso abierto | |
Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional. (CC BY-NC-ND) |
Sumario: | Learning analytics should be used for the benefit of students. This might be to assist them individually or through using aggregated and anonymised data to help other students. Learning analytics might also be used to improve the educational experience more generally. It is distinct from assessment and should be used for formative rather than summative purposes. The effective use of learning analytics will initially involve the deployment of new systems along with changes to institutional policies and processes. New data may be collected about individuals and their learning activities. The data will be analysed and interventions may take place as a result. This presents opportunities for positive engagements and impacts on learningas well as misunderstandings, misuse of data and adverse impacts on students. Complete transparency and clear institutional policies are therefore essential regarding the purposes of learning analytics, the data collected, the processes involved and how they will be used to enhance the educational experience. This code of practice aims to set out the responsibilities of educational institutions to ensure that learning analytics is carried out responsibly, appropriately and effectively, addressing the key legal, ethical and logistical issues which are likely to arise. Educational institutions in the UK already have information management practices and procedures in place and have extensive experience of handling sensitive and personal data in accordance with the Data Protection Act 1998. They must now also comply with the General Data Protection Regulation (GDPR). By transferring and adapting this expertise to regulate the processing of data for learning analytics, institutions should establish the practices and procedures necessary to process the data of individuals lawfully and fairly. |
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