Early prediction of writing quality using keystroke logging

Feedback is important to improve writing quality; however, to provide timely and personalized feedback is a time-intensive task. Currently, most literature focuses on providing (human or machine) support on product characteristics, especially after a draft is submitted. However, this does not assist students who struggle during the writing process. Therefore, in this study, we investigate the use of keystroke analysis to predict writing quality throughout the writing process. Keystroke data were analyzed from 126 English as a second language learners performing a timed academic summarization task. Writing quality was measured using participants’ final grade. Based on previous literature, 54 keystroke features were extracted. Correlational analyses were conducted to identify the relationship between keystroke features and writing quality. Next, machine learning models (regression and classification) were used to predict final grade and classify students who might need support at several points during the writing process. The results show that, in contrast to previous work, the relationship between writing quality and keystroke data was rather limited. None of the regression models outperformed the baseline, and the classification models were only slightly better than the majority class baseline (highest AUC = 0.57). In addition, the relationship between keystroke features and writing quality changed throughout the course of the writing process. To conclude, the relationship between keystroke data and writing quality might be less clear than previously posited.

Continuous authentication using deep neural networks ensemble on keystroke dynamics

During the last years, several studies have been proposed about user identification by means of keystroke analysis. Keystroke dynamics has a lower cost when compared to other biometric-based methods since such a system does not require any additional specific sensor, apart from a traditional keyboard, and it allows the continuous identification of the users in the background as well. The research proposed in this paper concerns (i) the creation of a large integrated dataset of users typing on a traditional keyboard obtained through the integration of three real-world datasets coming from existing studies and (ii) the definition of an ensemble learning approach, made up of basic deep neural network classifiers, with the objective of distinguishing the different users of the considered dataset by exploiting a proper group of features able to capture their typing style. After an optimization phase, in order to find the best possible base classifier, we evaluated the ensemble super-classifier comparing different voting techniques, namely majority and Bayesian, as well as training allocation strategies, i.e., random and K-means. The approach we propose has been assessed using the created very large integrated dataset and the obtained results are very promising, achieving an accuracy of up to 0.997 under certain evaluation conditions.

Texting in motion: Keystroke logging and observing synchronous mobile discourse

This study explores the efficacy of keystroke logging as a method to qualitatively investigate the synchronous processes of discursive interaction through mobile devices as individuals go about their everyday lives. Heeding cautions from Boase (2013) concerning software variability across mobile technologies, as well as challenges from Ørmen and Thorhauge (2015) to use log data for qualitative research, our study offers one such methodological roadmap for observing—from the software side—the complex entanglements of humans and mobile technologies as they engage in mediated discourse. Our study draws upon keystroke analysis from the tradition of writing process research (Leijten & van Waes, 2013; Wengelin, 2006), as well as posthumanist methodologies for observing cybernetic interactions (Giddings, 2014), and extends Farman’s (2012) argument that asynchronous forms of mobile communication, such as text messaging, are performatively synchronous, since interlocutors are pulled toward embodied copresence via the mediated space. In doing so, we present a preliminary study of methods for directly observing how discursive processes manifest in-the-moment as a complex flow between human, machinic, and spatial components in a network assemblage.