Self-Regulated Learning in Essay Writing: Consistency of Strategies and Impact on Outcomes

2026-05-13

AI Summary

This study addresses a critical gap in understanding how self-regulated learning (SRL) strategies dynamically evolve during online essay writing among secondary students and how these dynamics relate to academic outcomes. By analyzing behavioral logs from two online writing tasks administered one week apart, the research employs process mining and unsupervised machine learning to track SRL strategy shifts in an authentic digital setting. Three dominant strategy patterns were identified, with the less frequently used “intensive writing followed by selective reading” pattern emerging as a significant positive predictor of learning performance. Moreover, the findings reveal clear tendencies in the transfer of SRL strategies across tasks, offering novel empirical evidence on the implicit mechanisms through which SRL operates in digital learning environments.

Abstract

Background: Abilities for effective self-regulated learning (SRL) are critical for lifelong learning, particularly during adolescence when these skills consolidate and strongly influence future learning. Their importance has grown with the rise of online and blended education. Yet, little is known about how secondary school students self-regulate in online environments, how their SRL processes and strategies evolve, or how they affect outcomes. In secondary education, understanding these processes can reveal patterns and indicators of learning success, informing the design of online support mechanisms. Evidence from repeated-measures designs remains scarce. Objectives: This study aims to examine how secondary school students enact SRL strategies during online essay writing, how these strategies change over time, and how they relate to learning outcomes. Methods: We analysed metacognition-related trace data collected from secondary students during a two-wave online essay-writing task conducted one week apart in two Colombian schools (N = 93 for session 1, N = 95 for session 2) via a digital learning platform. Using a combination of process mining and unsupervised machine learning techniques, we identified dominant SRL strategies grounded in established SRL processes and examined their stability and association with learning outcomes. Results and conclusions: Three dominant SRL strategies were identified. Results showed variability: many students remained in or shifted to Read first, write next, while none used Write intensively, read selectively in session 2. Although less common, latter strategy was positively associated with learning outcomes.

Problem

• self-regulated learning
• online essay writing
• learning strategies
• secondary education
• learning outcomes

Innovation

• process mining
• unsupervised machine learning
• self-regulated learning
• trace data analysis
• online essay writing