Understanding the temporal organization of writing is key to studying writing processes. Existing methods to segment writing into phases often rely on arbitrary rules, extensive manual annotation, or focus on numerous transitions. This study aimed to develop an automated segmentation method to detect distinctive transition in the dominant writing process, particularly the transition from first draft to revision. For this, keystroke data (source-based L1 writing (N = 80) and text simplification in L2 (N = 88)) were manually annotated. The BEAST algorithm was applied for Bayesian change point detection, based on five characteristics derived from the annotation criteria: (1) percentage of the final text written so far, (2) distance between typed and remaining characters, (3) relative cursor position, (4) source use, and (5) pause timings. The first three features proved most effective in identifying change points. A rule-based approach was further applied to select one final change point, which resulted in mediocre accuracy ranging from 31% exact agreement to 49% agreement within 60 seconds. To conclude, the BEAST algorithm is useful in detecting a variety of change points in writing processes, yet connecting them to meaningful phases is still quite complex.

Despite the growing interest in the dynamics of the writing process in writing research, publicly available large-scale corpora of keystroke logs have been rare. We introduce KLiCKe, a freely available collection of keystroke logs for around 5,000 argumentative texts written by adults in the United States. The KLiCKe corpus also includes human-rated holistic scores for the essays as well as writers' demographic details, their typing skills, and vocabulary knowledge. We describe our methods for constructing the corpus and present descriptives for different components of the corpus. To illustrate the use of the KLiCKe corpus, we report a study using a subset of the corpus to investigate whether keystroke features are associated with holistic writing quality for L1 and L2 writers. The study shows that higher writing scores are related to shorter pauses in general, shorter between-word pauses, lower proportion of deletions, higher proportion of insertions, and less process variance. The KLiCKe corpus provides a robust resource for researchers to study the dynamics of text production and revision that will help spur the development of process-oriented tools and methodologies in writing assessment and instruction.

Communicating clearly about their socially responsible activities is becoming increasingly important for companies, as a growing number of stakeholders with different goals, knowledge, and language skills seek information on corporate social responsibility (CSR). Furthermore, the ability to communicate clearly is particularly appreciated in the workplace. To fill a gap in CSR communication training, this article describes the development and preliminary evaluation of an interdisciplinary and multimodal online module whose goal is to train Dutch-speaking business students in the production of accessible CSR content in English. After presenting our module, we discuss its implications for future training and for corporate communication.

The study of revision has been a topic of interest in writing research over the past decades. Numerous studies have, for instance, shown that learning-to-revise is one of the key competences in writing development. Moreover, several models of revision have been developed, and a variety of taxonomies have been used to measure revision in empirical studies. Current advances in data collection and analysis have made it possible to study revision in increasingly precise detail. The present study aimed to combine previous models and current advances by providing a comprehensive product- and process-oriented tagset of revision. The presented tagset includes properties of external revisions: trigger, orientation, evaluation, action, linguistic domain, spatial location, temporal location, duration, and sequencing. We identified how keystroke logging, screen replays, and eye tracking can be used to extract both manually and automatically extract features related to these properties. As a proof of concept, we demonstrate how this tagset can be used to annotate revisions made by higher education students in various academic tasks. To conclude, we discuss how this tagset forms a scalable basis for studying revision in writing in depth.

To date, research into dynamic descriptions of text has focused mainly on the spoken mode; and while writing process research has examined language structures, it has largely ignored the functionality (meaning) inherent in them. Therefore, drawing on systemic functional linguistics (SFL) and keystroke logging software, this article takes a further step toward an interdisciplinary dialogue by outlining a new schematic for coding and analyzing revisions. More specifically, we show how revision activity can be tracked within functional components, across functional components, and across clauses in terms of forward and backward movements. By exploring three digitally constructed texts, which were produced and observed unobtrusively in a natural setting, we have attempted to illustrate how one writer’s revising process can be operationalized in terms of (a) chronological movement (sequence) and (b) spatial movement (location). Findings showed how activity was relatively consistent across datasets with regard to session management, revision frequency, and distribution of revision types. Moreover, results also showed how most revision activity occurred at, or ahead of, the point of inscription, particularly with regard to revising the end of clauses. However, findings also indicated that revising the start of clauses was equally important when considering the size of functional components.

This study aims to explore the process of reading during writing. More specifically, it investigates whether a combination of keystroke logging data and eye tracking data yields a better understanding of cognitive processes underlying fluent and nonfluent text production. First, a technical procedure describes how writing process data from the keystroke logging program Inputlog are merged with reading process data from the Tobii TX300 eye tracker. Next, a theoretical schema on reading during writing is presented, which served as a basis for the observation context we created for our experiment. This schema was tested by observing 24 university students in professional communication (skilled writers) who typed short sentences that were manipulated to elicit fluent or nonfluent writing. The experimental sentences were organized into four different conditions, aiming at (a) fluent writing, (b) reflection about correct spelling of homophone verbs, (c) local revision, and (d) global revision. Results showed that it is possible to manipulate degrees of nonfluent writing in terms of time on task and percentage of nonfluent key transitions. However, reading behavior was affected only for the conditions that explicitly required revision. This suggests that nonfluent writing does not always affect the reading behavior, supporting the parallel and cascading processing hypothesis.

Keystroke logging has become instrumental in identifying writing strategies and understanding cognitive processes. Recent technological advances have refined logging efficiency and analytical outputs. While keystroke logging allows for ecological data collection, it is often difficult to connect the fine grain of logging data to the underlying cognitive processes. Multiple methodologies are useful to offset these difficulties. In this article we explore the complementarity of the keystroke logging program Inputlog with other observational techniques: thinking aloud protocols and eyetracking data. In addition, we illustrate new graphic and statistical data analysis techniques, mainly adapted from network analysis and data mining. Data extracts are drawn from a study of writing from multiple sources. In conclusion, we consider future developments for keystroke logging, in particular letter- to word-level aggregation and logging standardization.

Moment to moment, a writer faces a host of potential problems. How does the writer’s mind coordinate this problem solving? In the original Hayes and Flower model, the authors posited a distinct process to manage this coordinating—that is, the “monitor.” The monitor became responsible for executive function in writing. In two experiments, the current authors investigated monitor function by examining the coordination of two common writing tasks—editing (i.e., correcting an error) and sentence composing—in the presence or absence of an error and with a low or high memory load for the writer. In the first experiment, participants could approach the editing and composing task in either order. On most trials (88%), they finished the sentence first, and less frequently (12%), they corrected the error first. The error-first approach occurred significantly more often under the low-load condition than the high-load condition. For the second experiment, participants were asked to adopt the less-used, error-first approach. Success in completing the assigned task order was affected by both memory load and error type. These results suggest that the monitor depends on the relative availability of working memory resources and coordinates subtasks to mitigate direct competition over those resources.

Error analysis involves detecting, diagnosing, and correcting discrepancies between the text produced so far (TPSF) and the writers mental representation of what the text should be. The use of different writing modes, like keyboard-based word processing and speech recognition, causes different type of errors during text production. While many factors determine the choice of error-correction strategy, cognitive effort is a major contributor to this choice. This research shows how cognitive effort during error analysis affects strategy choice and success as measured by a series of online text production measures. Text production is shown to be influenced most by error span, that is, whether the error spans more or less than two characters. Next, it is influenced by input mode, that is, whether the error has been generated by speech recognition or keyboard, and finally by lexicality, that is, whether the error comprises an existing word. Correction of larger error spans is more successful than that of smaller errors. Writers impose a wise speed accuracy trade-off during large error spans since correction is better, but preparation times (time to first action) and production times take longer, and interference reaction times are slower. During large error spans, there is a tendency to opt for error correction first, especially when errors occurred in the condition in which the TPSF is not preceded by an auditory prompt. In general, the addition of speech frees the cognitive demands of writing. Writers also opt more often to continue text production when the TPSF is presented auditorially first.