Continuity in cognition

Designing for continuous interaction requires designers to consider the way in which human users can perceive and evaluate an artefact’s observable behaviour, in order to make inferences about its state and plan, and execute their own continuous behaviour. Understanding the human point of view in continuous interaction requires an understanding of human causal reasoning, of the way in which humans perceive and structure the world, and of human cognition. We present a framework for representing human cognition, and show briefly how it relates to the analysis of structure in continuous interaction, and the ways in which it may be applied in design

Improving the effectiveness of collaborative group work in primary schools: effect on Science attainment

This longitudinal research tests the effectiveness of the SPRinG programme which was developed through a collaboration between researchers and teachers and designed to provide teachers with strategies for enhancing pupil group work in ‘authentic’ classroom settings. An evaluation study involved comparing pupils in SPRinG classrooms and trained in group work skills with those who were not in terms of science attainment. There were 560 and 1027 pupils (8-10 years) in the experimental and control groups respectively. ‘Macro’ attainment data were collected at the start of the year. ‘Micro’ attainment data were collected in the spring and summer before and after science lessons involving either group work (intervention) or the control teachers’ usual approach. SPRinG pupils made greater academic progress than control pupils. Findings are discussed relative to enhancing the quantity and quality of group work in schools and a social pedagogic approach to classroom learning

On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis

Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system, with potential impact in its final performance. Despite its importance, text preprocessing has not received much attention in the deep learning literature. In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a standard neural text classifier. We perform an extensive evaluation on standard benchmarks from text categorization and sentiment analysis. While our experiments show that a simple tokenization of input text is generally adequate, they also highlight significant degrees of variability across preprocessing techniques. This reveals the importance of paying attention to this usually-overlooked step in the pipeline, particularly when comparing different models. Finally, our evaluation provides insights into the best preprocessing practices for training word embeddings.Comment: Blackbox EMNLP 2018. 7 page