The progressive visualization, a new tool for analyzing the writing process

Writing is a complex process and it is difficult to know how to write well in order to make a good text. Many fields and techniques are combined to analyze how good writers operate compared to poor writers. Today it is possible to record a writing session on a computer and to have access to all the different operations that are made and when it happened. The problem with these files is that they contain a lot of data that is not well suited to be analyzed by humans without preprocessing. The goal of this research is to automatize that preprocessing step and help the searchers in their analyses of the writing process using visualization techniques. Résumé : Écrire un texte de qualité est un processus complexe. Plusieurs domaines d’études se penchent sur les possibilités d’analyse des pratiques d’écriture des bons scripteurs par rapport aux moins bons afin de comprendre les stratégies d’écriture gagnantes. Grâce à la technologie actuelle, il est possible d’enregistrer l’ensemble des opérations effectuées lors de la rédaction d’un texte sur ordinateur, ce qui en facilite l’étude. Par contre, les fichiers contenant l’information contiennent beaucoup de données et sans pré-traitement, elles ne sont pas appropriées pour l’analyse humaine. Nous proposons une nouvelle méthode de visualisation des données pour permettre de faciliter et d’automatiser une partie de l’analyse du processus d’écriture. Les Cahiers du GERAD G–2015–141 1 1 The challenges of understanding the writing process Writing a good text is not easy and requires a specific talent [24]. It has also been suggested that the skill of being a good writer improves with time and practice [29]. Nowadays, many softwares have been created to improve writing as an administrative task but none is oriented on literary creation [24]. Their use helps writing tasks in a business environment but they don’t transform their users into skilled or good writers. To understand the great authors’ writing creation mechanisms and strategies, psychologists, linguists and didacticians studied notorious authors such as Flaubert, Proust, Valéry and Zola [14]. The science of studying the writing process is called genetic criticism and its goal is to understand what kind are the different revisions made along the process that makes the text good at the end [3]. Many pattern assumptions have been studied already in the writing process. Starting from 1981, researchers suggested that the level of the writer’s proficiency influenced the nature and the frequency of revisions. It seems that less skilled writers don’t revise much and their modifications are mainly about syntax and punctuation [8] while good writers revise more and change the meaning of the text often over larger part of their written production [17]. We don’t know much about the text production practice in history and the writing process is far from being known in detail [24]. With the progress made in software technologies, it is possible to record each operation made by a writer and therefore to access detailed information about this persons’s writing process [27]. The files containing that information are called log. This data stored in those files is big and complex and is not appropriate for a human to analyze it without preprocessing [10]. Only a few tools exists to help analyzing the writing process. Two different aspects makes this process difficult to tackle [33]. First, the process can be modelled with many dimensions and secondly, each writing process is fundamentally different. Some authors already suggested that automatic analysis would be useful in linguistics. In 1997, machine learning and statistical pattern recognition, two data mining techniques, were used to automatically extract implicit or unknown facts from it [13]. 2 The role of visualizations for pattern recognition When tackling big amount of data, using visualizations is a great way to explore and analyze a given situation [38]. Visualizations can help identifying patterns, structures among data, irregularities and relationships over a specific timespan [30]. Their goal is to let the human eye find underlying structures [5]. After the data analyst understood and gained insight into the visualized data, he or she can draw conclusions from the discovered patterns [16]. This analysis method has its flaws: the human ability’s to process a lot of data is limited [28] and sometimes researchers tend to see relations between random and unrelated items, which is called apophenia [7]. In order to compensate those interpretation bias, “being able to derive scientific insight from data increasingly depends on having mathematical and perceptual models to provide the necessary foundation for effective data analysis and comprehension” [31]. Displaying data with graphic techniques may not be sufficient to help the user finding patterns [35]. The way people interact with the visualization tool and perceive the displayed data influences how the users understand the relationships and potential patterns [35]. Therefore, before creating a new visualization, it is important to think about the human factor and about ways to improve its effectiveness. A lot of research has been done to understand the cognitive support that visualizations give to assist the human brain while trying to visually understand data [35]. In order to create good visualizations, it is essential to consider two related concepts which are data representation and data presentation. When choosing the graph or chart type that illustrate the data’s variables, it’s considered to be the data representation step [21]. The delivery format, the overall appearance and design, including the choice of colour and the interactive features are part of the data presentation [2]. 2 G–2015–141 Les Cahiers du GERAD 2.1 Data representation The data representation is all about choosing the right physical way to represent data [21]. In order to create a visual version of the data, it is essential to think about what kind of variables will be represented. It is also important to think about the goal of the representation: whether it will be used for explorative analysis, confirmative analysis or simply the presentation of the results [2]. If time is a variable, the fact it is considered as an absolute number or if it is interpreted relatively changes the way it can be represented. When it’s absolute, position and lenght are used. In the case of a visualization in two dimensions such as what is displayed on a paper sheet, the time is usually mapped with a map or graph with two axis, time being displayed on one of them [2]. In the case where time can be interpreted, visual variables are used such as color and transparency to illustrate the duration between two events for example [2]. The other type of variables can either be represented on one of the axis or with shape or color to differenciate them [21]. Data visualisations are traditionaly static [21]. In order to properly illustrate how the data interacts, it is important to show change over time [4]. Because static visualizations are in two dimensions, it is complicated to make them represent efficiently problems that have more than two dimensions [30]. Consequently, one of the best ways to visualize spatiotemporal changes is to animate the data [38]. Spatiotemporal representations should be based on interactive sources that could allow the user to manipulate the data to allow a deeper understanding [32]. These functionalities help the user to be involved into a dynamic problem-solving methodology and go further in their analysis. Allowing the manipulation of variables and parameters is a way of showing many dimensions within a single display and to facilitate the combination of different variables when exploring visualized data [21]. To help the user investigate data, some authors suggested to include tasks that may be performed on the visualized data (Shneiderman 1996, as cited by [2]). Adjusting the view with the possibilities of having an overview and/or to zoom to change the degree of accuracy allows the user to see either the entire dataset or a more specific part of it and can be useful to use the data to achieve different goals. To filter and extract variables allows the user to remove unwanted information or to extract data with query parameters like sorting the data, selecting a specific temporal window, a selected spatial or geographic space or other statistical queries. Sometimes it is essential to gain specific insights to confirm or infirm a correlation, pattern or relationship within the data. When the data represents multidimensional information with a time-varying phenomena, it is essential to access the different structures and underlying layers. Even if the use of the different tasks is possible, it is necessary to have a mathematical proof of those patterns and relationships. Combining the visualization with mathematical data analytics is necessary for understanding the structures and relationships found [31]. 2.2 Data presentation That data representation is not the only aspect involved when creating visual support to describe and understand data. The overall ergonomics of the visualization is important to maximize the analysis potential. The presentation of data involves also the overall design of the visualization tool. While it is more important when the goal of the visualization is to present some results, some features are worth considering in an explorative-orientated model [36]. The most important aspect would be the choice of colors. According to Bartram [4], an appropriate combination of shape, symbols, colour, size and position should be chosen because that kind of visual information doesn’t require cognitive effort and is rather processed by the preattentive visual system. Displaying a complete combination of visual codes and dimensions increase the analyst’s efficiency and speed while looking at the representation. While colors can help