Learners reconceptualising education: Widening participation through creative engagement?

This paper argues that engaging imaginatively with ways in which statutory and further education is provided and expanding the repertoire of possible transitions into higher education, is necessary for providers both in higher education and in the contexts and phases which precede study at this level. Fostering dispositions for creativity in dynamic engagement with educational technology together with the consideration of pedagogy, learning objects, inclusion, policy and the management of change, requires innovative provision to span the spaces between school, home, work and higher education learning. Reporting on The Aspire Pilot, a NESTA-funded initiative at The Open University, the paper offers the beginning of a theoretical frame for considering learning, learners and learning systems in the information age prioritizing learner agency. It will report emergent empirical findings from this inter-disciplinary project, with a significant e-dimension, which seeks to foster the creativity of 13-19 year olds in considering future learning systems, developing provocations for others to explore creative but grounded possibilities. It explores implications arising from this project for approaches that may facilitate widening participation in higher education

Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks

Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct advantages, e.g., solving memory-dependent tasks and meta-learning. However, little effort has been spent on improving RNN architectures and on understanding the underlying neural mechanisms for performance gain. In this paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical results show that the network can autonomously learn to abstract sub-goals and can self-develop an action hierarchy using internal dynamics in a challenging continuous control task. Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch. We also found that improved performance can be achieved when neural activities are subject to stochastic rather than deterministic dynamics

Statutory Retirement Age and Lifelong Learning

The employability of an aging population in a world of continuous technical change is top of the political agenda. Due to endogenous human capital depreciation, the effective retirement age is often below statutory retirement age resulting in unemployment among older workers. We analyze this phenomenon in a putty-putty human capital vintage model and focus on education and the speed of human capital depreciation. Introducing a two-stage education system with initial schooling and lifelong learning, not even lifelong learning turns out to be capable of aligning economic and statutory retirement. However, lifelong learning can increase the number of people reaching statutory retirement age and hence reduce the problem of old age unemployment in an aging society.lifelong learning, retirement, unemployment, education system