Trace Data of Secondary Students and Linguistic Analysis to Predict Learner Performance in a Multi‐Text Writing Task

Abstract

Writing from multiple sources is a cognitively demanding task that requires students to integrate reading, planning, and composition processes. While prior research has demonstrated the importance of self‐regulated learning (SRL) and linguistic features in predicting writing performance, most studies have focused on university students, with limited research targeting secondary school learners, particularly across multilingual contexts. This study examined whether SRL processes derived from trace data and linguistic features from essays as learning products can be used to predict writing performance among secondary students from different countries and language backgrounds. A total of 257 secondary students from Australia, Colombia, Germany, and Finland completed a multi‐source essay task in their school language within a digital learning environment. SRL processes were inferred from interaction trace data, while essay features—topic coverage, cohesion, and word count—were extracted using language‐specific analysis models. These data sources were then used to train several machine learning models to predict students' performance on essay writing. Among the machine learning models tested, the Random Forest classifier achieved the highest accuracy (0.80) and recall (0.71). The most influential predictors were elaboration/organisation (process), topic coverage and cohesion (product), and country. Results revealed distinct engagement patterns across countries, suggesting that language and educational context were associated with differences in both SRL processes and writing quality. This study extends prior work by demonstrating that combining process and product features can support the prediction of writing performance across multilingual secondary education contexts. Findings emphasise the need for further research on younger learners and the role of linguistic background in SRL engagement.