as they describe the flexible and independent learner who will succeed in a changing society. A clear understanding of deep learning is needed to explore the possible benefits to the learner and to the wider community. Although there is no single specific definition, Simms (2006) gives the following working definition: learning is “Deep secured when, through personalisation, the conditions for student learning are transformed.” This is useful as it highlights the importance of the conditions for deep learning and its trial association with personalisation. The emergence of the term ‘personalisation’ reflects the shift towards a much more learner-centred and inclusive education system. The focus on the individual found in deep learning makes this a potential source of personalisation. Simms (2006) also gives a description of a learner engaged in deep learning: “An articulate, autonomous but collaborative learner, with high meta-cognitive control and the generic skills of learning, gained through engaging educational experiences with enriched opportunities and challenges, and supported by various people, materials and ICT linked to general well-being but crucially focused on learning, in schools whose culture and structures sustain the continuous co-construction of education through shared leadership.” Structure of the report This report contains the findings from a small scale-study funded by Becta into deep learning experiences among 14- to 19-year-old learners. It was carried out by researchers from the Universities of Warwick and Bristol between October 2008 and April 2009. Although operating as a joint team, the researchers from the two universities utilised different research approaches. The Warwick group carried out a literature review of deep learning and co-ordinated a series of action research projects, which are available to view online in both and format. This report contains details of the various action research projects, key findings and recommendations. The summary of findings section outlines the work of the action researchers who, between them, report on a total of 13 projects related to deep learning and ICT. (For more information on the methodology of this project, please refer to Appendix 1.) Although the action research group is relatively small, these projects are best seen as a series of exploratory studies examining the implications of adopting strategies intending to enhance deep learning and the potential that ICT offers in achieving this with learners aged 14-19. Accordingly, the findings address the relevance of a notion of deep learning to the practitioners involved and the ways in which using ICT can achieve deep learning. Perspectives on the nature of deep learning
as they describe the flexible and independent learner who will succeed in a changing society. A clear understanding of deep learning is needed to explore the possible benefits to the learner and to the wider community. Although there is no single specific definition, Simms (2006) gives the following working definition: “Deep learning is secured when, through personalisation, the conditions for student learning are transformed.” This is useful as it highlights the importance of the conditions for deep learning and its association with personalisation. The emergence of the term ‘personalisation’ reflects the shift towards a much more learner-centred and inclusive education system. The focus on the individual found in deep learning makes this a potential source of personalisation. Simms (2006) also gives a description of a learner engaged in deep learning: “An articulate, autonomous but collaborative learner, with high meta-cognitive control and the generic skills of learning, gained through engaging educational experiences with enriched opportunities and challenges, and supported by various people, materials and ICT linked to general well-being but crucially focused on learning, in schools whose culture and structures sustain the continuous co-construction of education through shared leadership.” Structure of the report This report contains the findings from a small scale-study funded by Becta into deep learning experiences among 14- to 19-year-old learners. It was carried out by researchers from the Universities of Warwick and Bristol between October 2008 and April 2009. Although operating as a joint team, the researchers from the two universities utilised different research approaches. The Warwick group carried out a literature review of deep learning and co-ordinated a series of action research projects, which are available to view online in both and format. This report contains details of the various action research projects, key findings and recommendations. The summary of findings section outlines the work of the action researchers who, between them, report on a total of 13 projects related to deep learning and ICT. (For more information on the methodology of this project, please refer to Appendix 1.) Although the action research group is relatively small, these projects are best seen as a series of exploratory studies examining the implications of adopting strategies intending to enhance deep learning and the potential that ICT offers in achieving this with learners aged 14-19. Accordingly, the findings address the relevance of a notion of deep learning to the practitioners involved and the ways in which using ICT can achieve deep learning. Perspectives on the nature of deep learning trial
becta_2009_14-19deeplearningwarwick_report.docx