Visual Approaches to Property Law Pedagogy

Purpose - To consider a more visual approach to property law teaching practices. This will be achieved by exploring the existence of ‘visual learners’ as a student body, evaluating the use of more visual teaching techniques in academic practice, recognising the historic dominance of text in legal education, and examining the potential for heightening visual teaching practices in the teaching of property law. Design/methodology/approach – The paper reviews and analyses some of the available literature on visual pedagogy, and visual approaches to legal education, but also introduces an amount of academic practitioner analysis. Findings – This paper evidences that, rather than focusing on the categorisation of ‘visual learner’, the modern academic practitioner should employ the customary use of more visual stimuli; consequently becoming a more ‘visual teacher’. This paper demonstrates that these practices, if performed effectively, can impact upon the information literacy of the whole student body: It also proffers a number of suggestions as to how this could be achieved within property law teaching practices. Practical implications – The paper will provide support for early-career academic practitioners, who are entering a teaching profession in a period of accelerated and continual change, by presenting an overview of pedagogic practices in the area. It will also provide a stimulus for those currently teaching on property law modules and support their transition to a more visual form of teaching practice. Originality/value – This paper provides a comprehensive overview of visual pedagogy in legal education, and specifically within that of property law, which has not been conducted elsewhere.

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).