We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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Research into the relationship between innovative physical learning environments (PLEs) and innovative psychosocial learning environments (PSLEs) indicates that it must be understood as a network of relationships between multiple psychosocial and physical aspects. Actors shape this network by attaching meanings to these aspects and their relationships in a continuous process of gaining and exchanging experiences. This study used a psychosocial-physical, relational approach for exploring teachers’ and students’ experiences with six innovative PLEs in a higher educational institute, with the application of a psychosocial-physical relationship (PPR) framework. This framework, which brings together the multitude of PLE and PSLE aspects, was used to map and analyse teachers’ and students’ experiences that were gathered in focus group interviews. The PPR framework proved useful in analysing the results and comparing them with previous research. Previously-identified relationships were confirmed, clarified, and nuanced. The results underline the importance of the attunement of system aspects to pedagogical and spatial changes, and of a psychosocial-physical relational approach in designing and implementing new learning environments, including the involvement of actors in the discourse within and between the different system levels. Interventions can be less invasive, resistance to processes could be reduced, and innovative PLEs could be used more effectively.
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