Supervision For School Staff: What is Valuable about Solution Circles?

Group supervision is used for support, education and/or monitoring. Despite the potential value of these elements for school staff, it is rarely practised. This mixed methods research, from a critical realist perspective, explored the use of Solution Circles to structure staff supervision groups in three schools. Five circles were run in each school, involving thirty-one participants, eighteen of whom contributed data. Thirteen staff trained as facilitators. The self-efficacy, resilience and anxiety levels of the staff taking part were not found to be significantly different as a result of the intervention. However, a small effect size was noted for self-efficacy, perhaps worthy of further investigation in the context of the small sample size. Thematic analysis of participant feedback (gathered during the last circle, which ran as a Focus Group) indicated the following mechanisms as affecting the value of Solution Circles for staff supervision groups: the structure of the sessions; aspects linked to the groups meeting a ‘need to talk’; elements which helped participants to ‘feel like a team’; and, school context factors. Semi-structured interview data from six facilitators indicated that the structure of the circles, individual characteristics of facilitators, the provision of support for facilitators, and elements of the wider school context, were all mechanisms which affected the facilitation of the programme. Further research might implement elements of these mechanisms and measure their impact.

Spiking neural network model of sound localisation using the interaural intensity difference

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrateand-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived headrelated transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.