Clustering students’ writing behaviors using keystroke logging: a learning analytic approach in EFL writing

Abstract

AbstractFeedback is an essential component of learning environments. However, providing feedback in populated classes can be challenging for teachers. On the one hand, it is unlikely that a single kind of feedback works for all students considering the heterogeneous nature of their needs. On the other hand, delivering personalized feedback is infeasible and time-consuming. Available automated feedback systems have helped solve the problem to some extent. However, they can provide personalized feedback only after a draft is submitted. To help struggling students during the writing process, we can use machine learning to cluster students who benefit the same from feedback using keystroke logs. We can apply the results in automated feedback systems that provide process feedback. In this study, we aim to find homogeneous student profiles based on their writing process indicators. We use fourteen process indicators to find clusters in the data set. We used these measures in a four-stage analysis, including (a) data preprocessing, (b) dimensionality reduction, (c) clustering, and (d) the analysis of the writing quality. Clustering techniques identified five different profiles: Strategic planners, Rapid writers, Emerging planners, Average writers, and Low-performing writers. We further validated the emerged profiles by comparing them concerning students' writing quality. The present work broadens our knowledge of how students interact with writing tasks and addresses how variations in writing behaviors lead to qualitatively different products. We discuss the theoretical underpinnings and potentials of finding profiles of students during writing in higher education.