Measuring secondary education students' self-regulated learning processes with digital trace data

Abstract

This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SRL in higher education and collected multi-trace data from 13-year-old students (N = 179) across three European countries during an essay-writing task. Hidden Markov modeling was employed to capture latent SRL processes. Four latent SRL processes emerged: orientation, first-reading, writing, and re-reading combined with monitoring. By clustering sequences of these latent SRL processes, we identified four sequential patterns of SRL processes at the task level: writing with metacognitive monitoring, writing intensively, reading first, writing next, and reading and writing simultaneously. Our findings highlight how AI and multi-trace data can be used to measure SRL during learning, providing a basis for enhancing personalized support.

Educational relevance and implications statement

Self-regulated learning (SRL) is vital in the digital world. In this study, we investigated secondary education students' SRL processes with digital trace data. We also demonstrated that the instrumentation of a digital learning environment and rule-based AI algorithm originally designed to measure SRL processes in higher education can be leveraged to measure SRL processes in secondary education students. The real-time measurement of covert SRL processes is important as this information can 1) raise students' awareness on their learning, 2) help teachers to support students' learning, and 3) form the basis for providing personalized support for these processes with the help of AI-enhanced learning technologies.