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Writing quality is dependent upon the organization and sequencing of cognitive processes during writing. College students need writing-strategy advice that is tailored to their individual needs and is cognizant of their already-established writing processes. However, there is an obstacle to providing such advice: Both writing instructors and the writers lack awareness of the moment-by-moment actions by which text was produced. This is because switching between the processes of defining the task, coming up with ideas, outputting text, evaluating, and revising is largely regulated implicitly. To address this shortcoming, the present study uses a design-based research approach to develop and evaluate a minimally viable prototype of a system called “ProWrite” that uses novel biometric technology (concurrent keystroke logging and eye tracking) for providing real-time, individualized, automated, process-focused feedback to writers. This feedback is grounded in the analysis of each writer's individual needs and is presented in the context of a learning cycle consisting of an initial diagnostic, an intervention assignment, and a final follow-up. In two iterations, eight students used the system. Effects on student behavior were determined through direct analysis of biometric writing-process data before and after remediation and through changes in writing-process and written-product measures. Semi-structured interviews revealed that students generally considered the system useful, and they would try to use the newly learned strategies in their future writing experiences. The study demonstrated that individualized, real-time feedback informed by biometric technology can effectively modify writers' processes when writing takes place. 

Revision plays an important role in writing, and as revisions break down the linearity of the writing process, they are crucial in describing writing process dynamics. Keystroke logging and analysis have been used to identify revisions made during writing. Previous approaches include the manual annotation of revisions, building nonlinear S-notations, and the automated extraction of backspace keypresses. However, these approaches are time-intensive, vulnerable to construct, or restricted. Therefore, this article presents a computational approach to the automatic extraction of full revision events from keystroke logs, including both insertions and deletions, as well as the characters typed to replace the deleted text. Within this approach, revision candidates are first automatically extracted, which allows for a simplified manual annotation of revision events. Second, machine learning is used to automatically detect revision events. For this, 7120 revision events were manually annotated in a dataset of keystrokes obtained from 65 students conducting a writing task. The results showed that revision events could be automatically predicted with a relatively high accuracy. In addition, a case study proved that this approach could be easily applied to a new dataset. To conclude, computational approaches can be beneficial in providing automated insights into revisions in writing. 

Recent advances in data and information technologies have enabled extensive digital datasets to be available to decision makers throughout the life cycle of a transportation project. However, most of these data are not yet fully reused due to the challenging and time-consuming process of extracting the desired data for a specific purpose. Digital datasets are presented only in computer-readable formats and they are mostly complicated. Extracting data from complex and large data sources is significantly time-consuming and requires considerable expertise. Thus, there is a need for a user-friendly data exploration framework that allows users to present their data interests in human language. To fulfill that demand, this study employs natural language processing (NLP) techniques to develop a natural language interface (NLI) which can understand users’ intent and automatically convert their inputs in the human language into formal queries. This paper presents the results of an important task of the development of such a NLI that is to establish a method for classifying the tokens of an ad-hoc query in accordance with their semantic contribution to the corresponding formal query. The method was validated on a small test set of 30 plain English questions manually annotated by an expert. The result shows an impressive accuracy of over 95%. The token classification presented in this paper is expected to provide a fundamental means for developing an effective NLI to transportation asset databases.