2 resultados para reinforcement learning,cryptography,machine learning,deep learning,Deep Q-Learning (DQN),AES

em Repository Napier


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The present paper introduces a technology-enhanced teaching method that promotes deep learning. Four stages that correspond to four different student cohorts were used for its development and to analyse its effectiveness. The effectiveness of the method has been assessed in terms of examination results as well as results obtained from class response system software statistics. The evidence gathered indicates that the method developed is very effective and its implementation is straightforward. Furthermore, its success in achieving results seems to be independent of the skills and/or experience of the lecturer.

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The effectiveness and value of entrepreneurship education is much debated within academic literature. The individual’s experience is advocated as being key to shaping entrepreneurial education and design through a multiplicity of theoretical concepts. Latent, pre-nascent and nascent entrepreneurship (doing) studies within the accepted literature provide an exceptional richness in diversity of thought however, there is a paucity of research into latent entrepreneurship education. In addition, Tolman’s early work shows the existence of cases whereby a novel problem is solved without trial and error, and sees such previous learning situations and circumstances as “examples of latent learning and reasoning”, (Deutsch, 1956, pg115). Latent learning has historically been the cause of much academic debate however, Coon’s (2004, pg260) work refers to “latent (hidden) learning … (as being) … without obvious reinforcement and remains hidden until reinforcement is provided” and thus, forms the working definition for the purpose of this study.