A framework for the design of usable electronic text

This thesis examines the human issues underlying the design and usability of electronic text systems. In so doing it develops a framework for the conceptualisation of these issues that aims to guide designers of electronic texts in their attempts to produce usable systems. The thesis commences with a review of the traditional human factors literature on electronic text according to three basic themes: its concern with perceptual, manipulatory and structural issues. From this examination it is concluded that shortcomings in translating this work into design result from the adoption of overly narrow uni-disciplinary views of reading taken from cognitive psychology and information science which are inappropriate to serve the needs of electronic text designers. In an attempt to provide a more relevant description of the reading process a series of studies examining readers and their views as well as uses of texts is reported. In the first, a repertory grid based investigation revealed that all texts can be described in reader-relvant terms according to three criteria: why a text is read, what a text contains and how it is read. These criteria then form the basis of two investigations of reader-text interaction using academic journals and user manuals. The results of these studies highlighted the need to consider readers' models of a document's structure in discussing text usability. Subsequent experimental work on readers' models of academic articles demonstrated not only that such models are important aspects of reader-text interaction but that data of this form could usefully be employed in the design of an electronic text system. The proposed framework provides a broad, qualitative model of the important issues for designers to consider when developing a product It consists of four interactive elements that focus attention on aspects of reading that have been identified as central to usability. Simple tests of the utility and validity of the framework are reported and it is shown that the framework both supports reasoned analysis and subsequent prediction of reader behaviour as well as providing a parsimonious account of their verbal utterances while reading. The thesis concludes with an analysis of the likely uses of such a framework and the potential for electronic text systems in an increasingly information-hungry world

Journalistic practices of science popularization in the context of users’ agenda: A case study of „New Scientist”

The article includes a discussion of two models which describe contemporary communication processes in journalism: agenda-setting and news value, indicating the need to expand their research tools to include qualitative methods, and merging the analyses of the reception and the message. It also includes indications as to the possibility, or even the social relevance, of the methods for applying those research perspectives to analysing journalism popularising science. Later, I present the results of an analysis of the content of a sample of 500 most read popular science texts available on the New Scientist website. I demonstrate which thematic areas were valued by the readers, and what values are most commonly applied. Further, upon applying a filter in the form of surveys regarding reader preferences, I discuss the main linguistic devices utilised for controlling readers’ attention. The shaping of the hierarchy of importance of items of news is the result of a dynamic interaction between (1) the thematic priorities and discursive strategies of imposing elite representations of science within media agenda, and (2) the means of negotiating order and values of specific content, which are correlated with readers’ preferences, both in terms of the content and the form of providing popular scientific information

Modeling Task Effects in Human Reading with Neural Attention

Humans read by making a sequence of fixations and saccades. They often skip words, without apparent detriment to understanding. We offer a novel explanation for skipping: readers optimize a tradeoff between performing a language-related task and fixating as few words as possible. We propose a neural architecture that combines an attention module (deciding whether to skip words) and a task module (memorizing the input). We show that our model predicts human skipping behavior, while also modeling reading times well, even though it skips 40% of the input. A key prediction of our model is that different reading tasks should result in different skipping behaviors. We confirm this prediction in an eye-tracking experiment in which participants answers questions about a text. We are able to capture these experimental results using the our model, replacing the memorization module with a task module that performs neural question answering

Comprensión de textos como una situación de solución de problemas

La investigación en la comprensión de textos ha dado detalles de cómo las características del texto y los procesos cognitivos interactúan con el fin de consituir la comprensión y generar significado. Sin embargo, no existe un vínculo explícito entre los procesos cognitivos desplegados durante la comprensión de textos y su lugar en la cognición de orden superior, como en la resolución de problemas. El propósito de este trabajo es proponer un modelo cognitivo en el que la comprensión de textos se hace similar a una resolución de problemas y la situación que se basa en la investigación actual sobre los procesos cognitivos conocidos como la generación de la inferencia, la memoria y las simulaciones. La característica clave del modelo es que incluye explícitamente la formulación de las preguntas como un componente que aumenta la potencia de representación. Otras características del modelo se especifican y sus extensiones a la investigación básica y en la comprensión de textos y de orden superior los procesos cognitivos se describen aplican.Research in text comprehension has provided details as to how text features and cognitive processes interact in order to build comprehension and generate meaning. However, there is no explicit link between the cognitive processes deployed during text comprehension and their place in higher-order cognition, as in problem solving. The purpose of this paper is to propose a cognitive model in which text comprehension is made analogous to a problem solving situation and that relies on current research on well-known cognitive processes such as inference generation, memory, and simulations. The key characteristic of the model is that it explicitly includes the formulation of questions as a component that boosts representational power. Other characteristics of the model are specified and its extensions to basic and applied research in text comprehension and higher-order cognitive processes are outlined.Fil: Marmolejo Ramos, Fernando. University of Adelaide; AustraliaFil: Yomha Cevasco, Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

The dynamic model of writing and its implications for the FL classroom

The dynamic model of writing proposed by Matsuda (1997) is intended to visualize the complexity of interrelationships between the writer, text, and reader in second language writing instruction. Contrary to the static model, the dynamic model assumes an active role of the writer and acknowledges the writer‘s contribution not only to the text, but also to communication with the writer, and—as a consequence—accounts for intercultural interaction and negotiation of meaning. Because in the context of foreign language writing Matsuda‘s dynamic model is usually unrealistic, this paper proposes a model of foreign language writing which combines features of both the static and dynamic models to illustrate the unique complexity of foreign language writing

Analytic and constructive processes in the comprehension of text

This thesis explores the process of comprehension as a purposeful interaction between a reader and the information in a text. The review begins by discussing the difference between educational and psychological perspectives on comprehension. Approaches to the analysis of text structure are then described and models and theories of the representation of knowledge are evaluated. It is argued that these are limited in that they tend to focus either on the text or the reader: they either examine those procedures that are necessary for text analysis or the knowledge structures required for comprehension, storage and retrieval. Those that come nearest to examining the interaction between text and knowledge structures tend to be limited in terms of the texts they can deal with and they do not deal adequately with the predictive aspects of comprehension.Experiments are reported which look at the ongoing predictions made by readers, and how these are affected by factors such as text structure and ‘interestingness’. The experiments provided the opportunity for examining the potential of alternative methodologies (such as the content analysis of open-ended questions). It is felt that it is necessary to examine comprehension using methods which are direct but not intrusive. The studies reported demonstrate that it is possible to obtain reliable measures of a reader's predictions and that these are systematically affected by the structure and content of the text

Predicting the Quality of Short Narratives from Social Media

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc

THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes

Acquiring and Using Limited User Models in NLG

It is a truism of NLG that good knowledge of the reader can improve the quality of generated texts, and many NLG systems have been developed that exploit detailed user models when generating texts. Unfortunately, it is very difficult in practice to obtain detailed information about users. In this paper we describe our experiences in acquiring and using limited user models for NLG in four different systems, each of which took a different approach to this issue. One general conclusion is that it is useful if imperfect user models are understandable to users or domain experts, and indeed perhaps can be directly edited by them; this agrees with recent thinking about user models in other applications such as intelligent tutoring systems (Kay, 2001)